{"build_at":"2026-10-15T18:16:37.761Z","entries":[{"type":"organization","slug":"abridge","href":"/companies/abridge/","name":"Abridge","summary":"Abridge develops AI software for health systems that records conversations between clinicians and patients and drafts clinical documentation from them for clinician review, with separate products for clinicians, nursing, and revenue-cycle teams. Its platform works inside electronic health record workflows.","keywords":"Abridge AI, Inc. United States (mailing address in Philadelphia, Pennsylvania) enterprise enterprise-software Abridge for clinicians Abridge for nursing"},{"type":"organization","slug":"adobe","href":"/companies/adobe/","name":"Adobe","summary":"Adobe makes creative, document, and marketing software, including Photoshop, Acrobat, and its enterprise content and marketing products. It develops Firefly, a family of generative AI models for images, video, and other media, which it offers in its apps and through APIs.","keywords":"Adobe Inc. San Jose, California enterprise enterprise-software consumer-products model-developer Adobe Firefly Adobe Firefly Services Acrobat AI Assistant"},{"type":"organization","slug":"agentic-ai-foundation","href":"/companies/agentic-ai-foundation/","name":"Agentic AI Foundation (AAIF)","summary":"The Agentic AI Foundation is a directed fund of The Linux Foundation that raises and spends funds to support open-source agentic AI projects approved by its Governing Board. The Linux Foundation announced it in December 2025 with three contributed projects: the Model Context Protocol from Anthropic, goose from Block, and AGENTS.md from OpenAI.","keywords":"agents open-source-steward The Linux Foundation"},{"type":"organization","slug":"agility-robotics","href":"/companies/agility-robotics/","name":"Agility Robotics","summary":"Agility Robotics builds Digit, a humanoid robot deployed commercially for warehouse, manufacturing, and distribution tasks such as tote handling, and Agility Arc, a cloud platform for managing Digit fleets. In September 2026 it announced Digit 5, a version designed to work close to people with less reliance on protective barriers.","keywords":"Agility Robotics, Inc. Salem, Oregon robotics robotics hardware-manufacturer Digit Agility Arc"},{"type":"organization","slug":"ai2","href":"/companies/ai2/","name":"Ai2 (Allen Institute for AI)","summary":"Ai2 is a non-profit AI research institute in Seattle, founded in 2014 by Paul Allen. Its work covers open models and training data, as well as robotics, conservation, and other applied research.","keywords":"The Allen Institute for Artificial Intelligence Seattle, Washington open-models research data model-developer research-lab nonprofit-research Ai2 Playground Asta Semantic Scholar"},{"type":"organization","slug":"all-hands-ai","href":"/companies/all-hands-ai/","name":"All Hands AI (OpenHands)","summary":"All Hands AI develops OpenHands, an open-source platform for building and running AI coding agents. It also offers a hosted service (OpenHands Cloud) and a commercial enterprise edition. The company now presents itself and its products under the OpenHands name.","keywords":"Massachusetts agents developer-platform enterprise-software OpenHands Cloud OpenHands Enterprise"},{"type":"organization","slug":"amazon","href":"/companies/amazon/","name":"Amazon (AWS)","summary":"Amazon runs retail, advertising, and cloud businesses and reports three segments: North America, International, and Amazon Web Services (AWS). Through AWS it offers AI services, including Amazon Bedrock for building generative AI applications and its own Amazon Nova models.","keywords":"Amazon.com, Inc. Seattle, Washington cloud frontier-models agents enterprise cloud-provider model-developer developer-platform consumer-products Amazon Bedrock Amazon Nova"},{"type":"organization","slug":"amd","href":"/companies/amd/","name":"AMD","summary":"AMD designs processors and accelerators, including EPYC server CPUs, AMD Instinct data center GPUs used for AI, adaptive SoCs and FPGAs, and networking products. It develops the ROCm open software stack for AI and HPC work on its GPUs.","keywords":"Advanced Micro Devices, Inc. Santa Clara, California chips chip-designer developer-platform AMD Instinct GPUs AMD ROCm"},{"type":"organization","slug":"anduril","href":"/companies/anduril/","name":"Anduril Industries","summary":"Anduril Industries describes itself as a defense technology company specializing in autonomous systems; it also develops the Lattice software platform. Its documented AI software includes Lattice for Command & Control, which it describes as an AI-enabled battle management platform, and Lattice for Mission Autonomy, which coordinates teams of uncrewed systems under a human operator.","keywords":"Anduril Industries, Inc. Costa Mesa, California defense robotics robotics hardware-manufacturer enterprise-software Lattice for Command & Control Lattice for Mission Autonomy Lattice SDK"},{"type":"organization","slug":"anthropic","href":"/companies/anthropic/","name":"Anthropic","summary":"Anthropic, which describes itself as an AI safety and research company, develops the Claude family of models. It offers Claude through web, desktop, and mobile apps, through the Claude Platform API, and through the Claude Code coding agent.","keywords":"Anthropic PBC San Francisco, California frontier-models agents research model-developer research-lab developer-platform consumer-products Claude Claude Platform (Claude API) Claude Code"},{"type":"organization","slug":"anyscale","href":"/companies/anyscale/","name":"Anyscale","summary":"Anyscale sells a managed platform for running Ray workloads (data processing, training, and online inference) across clouds and Kubernetes. Ray began at UC Berkeley's RISELab, and Anyscale contributed it to the PyTorch Foundation in 2025.","keywords":"Anyscale, Inc. San Francisco, California cloud developer-platform Anyscale Platform"},{"type":"organization","slug":"anysphere","href":"/companies/anysphere/","name":"Anysphere (Cursor)","summary":"Anysphere makes Cursor, an AI coding product with a desktop editor, coding agents, a command-line tool, and an automated code reviewer. Cursor offers models from several providers alongside its own Composer models. Since August 2026 Anysphere has been a wholly owned subsidiary of SpaceX.","keywords":"Anysphere, Inc. San Francisco, California agents developer-platform model-developer Cursor Cursor CLI Bugbot SpaceX"},{"type":"organization","slug":"apple","href":"/companies/apple/","name":"Apple","summary":"Apple makes and sells smartphones, personal computers, tablets, wearables, and accessories, and sells related services. Its AI features, called Apple Intelligence, run on-device and through Apple's Private Cloud Compute. Developers can reach Apple's on-device model through the Foundation Models framework, and Apple machine learning research publishes the open-source MLX array framework.","keywords":"Apple Inc. Cupertino, California research consumer-products hardware-manufacturer model-developer developer-platform Apple Intelligence Foundation Models framework"},{"type":"organization","slug":"apptronik","href":"/companies/apptronik/","name":"Apptronik","summary":"Apptronik develops the Apollo humanoid robot for manufacturing, logistics, and retail work. Its current model, Apollo 2, comes in bipedal and wheeled configurations; under a research partnership, data collected by Apollo 2 helps advance Google DeepMind's Gemini Robotics models.","keywords":"Apptronik, Inc. Austin, Texas robotics robotics hardware-manufacturer Apollo 2"},{"type":"organization","slug":"arc-institute","href":"/companies/arc-institute/","name":"Arc Institute","summary":"Arc Institute is an independent nonprofit research organization in Palo Alto, California, founded in 2021, that works in partnership with Stanford University, UC Berkeley, and UCSF. Its research spans experimental biology and computational models, including the Evo 2 DNA language model and virtual cell models.","keywords":"Arc Research Institute Palo Alto, California science research open-models research-lab nonprofit-research model-developer"},{"type":"organization","slug":"arc-prize-foundation","href":"/companies/arc-prize-foundation/","name":"ARC Prize Foundation","summary":"ARC Prize Foundation is a nonprofit that develops the ARC-AGI benchmarks, built from tasks that are simple for people but difficult for AI systems, and runs public competitions and model testing around them. It launched ARC-AGI-2 in March 2025 and the interactive ARC-AGI-3 in March 2026.","keywords":"ARC Prize Foundation (the name on its Form 990 for 2024); the website's terms of service and privacy policy name ARC Prize, Inc. at the same San Francisco address San Francisco, California research nonprofit-research open-source-steward ARC-AGI Toolkit (arc-agi Python package) ARC Prize 2026 (ARC-AGI-3 competition) ARC Prize Verified testing"},{"type":"organization","slug":"arcee-ai","href":"/companies/arcee-ai/","name":"Arcee AI","summary":"Arcee AI trains and publishes the Trinity family of open-weight language models, ranging from an on-device Nano model to a roughly 400-billion-parameter mixture-of-experts model. It also runs a hosted, OpenAI-compatible API.","keywords":"Arcee AI, Inc. San Francisco, California open-models research enterprise model-developer research-lab inference-provider Arcee Platform API Arcee chat"},{"type":"organization","slug":"arista-networks","href":"/companies/arista-networks/","name":"Arista Networks","summary":"Arista Networks makes network systems for data center, campus, and wide-area networks, built around its EOS network operating system. For AI workloads it sells the Etherlink family of Ethernet switches, including the 7800R AI spine, the 7060 AI leaf, and the 7700R4 Distributed Etherlink Switch, for networks that connect GPU and other accelerator clusters.","keywords":"Arista Networks, Inc. Santa Clara, California cloud enterprise hardware-manufacturer enterprise-software Arista Etherlink AI networking platforms Arista 7800R4 Series (AI Spine) Arista 7700R4 Distributed Etherlink Switch"},{"type":"organization","slug":"baseten","href":"/companies/baseten/","name":"Baseten","summary":"Baseten runs an inference platform for deploying open-source, custom, and fine-tuned models, offering per-token APIs for hosted open models, dedicated deployments in its cloud, a customer's cloud, or both, and managed training jobs. The company was started in 2019.","keywords":"Baseten Labs, Inc. San Francisco, California cloud inference-provider developer-platform Baseten Model APIs Baseten Dedicated Inference Baseten Training"},{"type":"organization","slug":"biohub","href":"/companies/biohub/","name":"Biohub (Chan Zuckerberg Biohub)","summary":"Biohub is a biomedical research organization, founded in 2016 and supported by the Chan Zuckerberg Initiative, that builds AI models of biology alongside data-generation, imaging, and laboratory technologies. After announcing in November 2025 that the EvolutionaryScale team would join it, Biohub now publishes the ESM protein models and runs the Biohub Platform for using them.","keywords":"Chan Zuckerberg Biohub, Inc. Redwood City, California science research research-lab nonprofit-research Biohub Platform"},{"type":"organization","slug":"block","href":"/companies/block/","name":"Block","summary":"Block is a publicly traded company that operates Square, a network for businesses, and Cash App, a consumer network, combining commerce, financial-services, software, and hardware products. Its Open Source Program Office created the goose AI agent, which Block contributed to the Agentic AI Foundation; it also describes AI features in its own products, including Managerbot for Square sellers and Moneybot in Cash App.","keywords":"Block, Inc. Oakland, California (principal executive office; no formal headquarters) agents consumer-products enterprise-software Managerbot Moneybot"},{"type":"organization","slug":"boltz","href":"/companies/boltz/","name":"Boltz","summary":"Boltz is a public benefit corporation that develops AI models for biomolecular structure prediction, binding-affinity estimation, and protein and small-molecule design. Its team started the Boltz models at MIT and founded Boltz PBC, announced in January 2026, which now presents Boltz-1, Boltz-2, and BoltzGen as its open-source models alongside a hosted platform and API.","keywords":"Boltz PBC Boston area, Massachusetts science open-models research model-developer research-lab developer-platform Boltz API Boltz Lab"},{"type":"organization","slug":"boston-dynamics","href":"/companies/boston-dynamics/","name":"Boston Dynamics","summary":"Boston Dynamics builds mobile robots: Spot, a quadruped used for industrial inspection; Stretch, a box-handling robot for warehouses; and Atlas, an electric humanoid being introduced with a select group of early adopters. Its Orbit software manages robot fleets and site inspection data. The company began in 1992 as a spin-off from the MIT Leg Lab and is controlled by Hyundai Motor Group.","keywords":"Boston Dynamics, Inc. Waltham, Massachusetts robotics robotics hardware-manufacturer Spot Atlas Stretch Orbit"},{"type":"organization","slug":"broadcom","href":"/companies/broadcom/","name":"Broadcom","summary":"Broadcom designs semiconductor products and sells infrastructure software, including VMware Cloud Foundation. Its AI-related semiconductor offerings include custom accelerators (XPUs), Ethernet switching and routing silicon, Ethernet NICs, and optical components, as well as racks and systems based on its XPUs.","keywords":"Broadcom Inc. Palo Alto, California chips enterprise chip-designer enterprise-software Tomahawk 6 (BCM78910 series) Ethernet switch VMware Private AI Foundation with NVIDIA"},{"type":"organization","slug":"carnegie-mellon-university","href":"/companies/carnegie-mellon-university/","name":"Carnegie Mellon University","summary":"Carnegie Mellon University is a university in Pittsburgh, Pennsylvania. It traces its origin to 1900, when Andrew Carnegie funded the Carnegie Technical Schools, and took its current form in 1967, when the Carnegie Institute of Technology merged with the Mellon Institute.","keywords":"Carnegie Mellon University Pittsburgh, Pennsylvania research university-lab research-lab"},{"type":"organization","slug":"cartesia","href":"/companies/cartesia/","name":"Cartesia","summary":"Cartesia develops speech models and offers them through an API, including the Sonic text-to-speech and Ink speech-to-text models, along with a platform for building and hosting voice agents. Its research centers on state space model (SSM) architectures, and it publishes some SSM language models on Hugging Face.","keywords":"Cartesia AI, Inc. San Francisco, California agents open-models model-developer developer-platform Sonic Ink Line"},{"type":"organization","slug":"center-for-ai-safety","href":"/companies/center-for-ai-safety/","name":"Center for AI Safety (CAIS)","summary":"The Center for AI Safety is a San Francisco-based research and field-building nonprofit whose stated mission is to reduce societal-scale risks from artificial intelligence. Its work covers technical safety research, including benchmarks, as well as field-building and advising industry and policymakers.","keywords":"Center for Artificial Intelligence Safety Inc San Francisco, California research nonprofit-research research-lab"},{"type":"organization","slug":"cerebras","href":"/companies/cerebras/","name":"Cerebras","summary":"Cerebras designs the Wafer-Scale Engine, a processor built from an entire silicon wafer, and rack-scale systems based on it for deployment in its own and customers' data centers. It also offers cloud inference and training services that run on its hardware.","keywords":"Cerebras Systems Inc. Sunnyvale, California chips cloud chip-designer hardware-manufacturer inference-provider compute-infrastructure Cerebras Inference Cerebras CS-4 system Cerebras Training Cloud"},{"type":"organization","slug":"chai-discovery","href":"/companies/chai-discovery/","name":"Chai Discovery","summary":"Chai Discovery develops AI models for predicting and designing molecular structures and interactions for drug discovery. It released Chai-1, a structure prediction model whose code and weights are now under Apache 2.0, and offers its later Chai-2 design models through an access program rather than as downloads.","keywords":"Chai Discovery, Inc. (as named in the original Chai-1 community license) San Francisco, California science open-models research model-developer research-lab Chai Lab web server Chai-2 access program"},{"type":"organization","slug":"character-ai","href":"/companies/character-ai/","name":"Character.AI","summary":"Character.AI runs a consumer chat app in which people talk by text or voice with AI characters created by users. It post-trains open models from other developers for its product, including CAI-Image, a family of image models post-trained from Qwen-Image.","keywords":"Character Technologies, Inc. Palo Alto, California consumer-products model-developer Character.AI"},{"type":"organization","slug":"chroma","href":"/companies/chroma/","name":"Chroma","summary":"Chroma develops the open-source Chroma database for vector, full-text, and metadata search, used for retrieval in AI applications, and sells Chroma Cloud, a hosted serverless version billed on usage.","keywords":"Chroma Inc. San Francisco, California data data-platform Chroma Cloud"},{"type":"organization","slug":"cisco","href":"/companies/cisco/","name":"Cisco","summary":"Cisco designs and sells networking, security, collaboration, and observability hardware and software. Its AI-related offerings include Silicon One networking silicon for AI data center networks, the Cisco Secure AI Factory with NVIDIA reference design, and Cisco AI Defense for securing AI applications and models.","keywords":"Cisco Systems, Inc. San Jose, California enterprise chips chip-designer enterprise-software Cisco Silicon One Cisco Secure AI Factory with NVIDIA Cisco AI Defense"},{"type":"organization","slug":"cline","href":"/companies/cline/","name":"Cline","summary":"Cline Bot Inc. develops Cline, an open-source AI coding agent offered as a VS Code extension, a command-line tool, an SDK, and a desktop app, with a separate JetBrains plugin. It also runs the Cline Provider, a hosted service that gives signed-in users credit-based access to models from other providers.","keywords":"Cline Bot Inc. United States (no headquarters city stated; the USPTO record gives a Wilmington, Delaware owner address, and the careers page lists on-site roles in San Francisco, California) agents developer-platform Cline for VS Code and JetBrains Cline CLI Cline Provider"},{"type":"organization","slug":"cloudflare","href":"/companies/cloudflare/","name":"Cloudflare","summary":"Cloudflare describes itself as a connectivity cloud company. Its AI offerings include Workers AI, which runs machine learning models on serverless GPUs across Cloudflare's global network, and AI Gateway, for monitoring and controlling an application's requests to AI model providers. It acquired the model-hosting platform Replicate in December 2025.","keywords":"Cloudflare, Inc. San Francisco, California cloud cloud-provider inference-provider developer-platform Workers AI AI Gateway Replicate"},{"type":"organization","slug":"cognition","href":"/companies/cognition/","name":"Cognition","summary":"Cognition makes Devin, an AI software engineering agent that runs in the cloud or on a developer's machine, together with Devin Desktop (formerly Windsurf) and Devin CLI. It also develops SWE-series coding models that are offered in these products.","keywords":"Cognition AI, Inc. San Francisco, California agents developer-platform model-developer Devin Devin Desktop (formerly Windsurf) Devin CLI"},{"type":"organization","slug":"common-crawl","href":"/companies/common-crawl/","name":"Common Crawl","summary":"Common Crawl is a nonprofit that crawls the web and makes its archives and derived datasets freely available. It has collected crawl data since 2008 and hosts it on Amazon Web Services through an AWS open data sponsorship program.","keywords":"The Common Crawl Foundation (listed by the IRS as Commoncrawl Foundation) Beverly Hills, California (address given in the Terms of Use and the IRS listing) data data-services"},{"type":"organization","slug":"contextual-ai","href":"/companies/contextual-ai/","name":"Contextual AI","summary":"Contextual AI builds an enterprise platform for creating AI agents and retrieval applications that work over an organization's own documents and data. It also sells component APIs for document parsing, instruction-following reranking, grounded generation, and natural-language unit tests for evaluating model outputs, and it has released open-weight reranker models.","keywords":"Contextual AI, Inc. Mountain View, California enterprise agents open-models model-developer enterprise-software developer-platform Contextual AI platform (Agent Composer) Contextual AI component APIs (Parse, Rerank, Generate, LMUnit)"},{"type":"organization","slug":"coreweave","href":"/companies/coreweave/","name":"CoreWeave","summary":"CoreWeave operates a cloud platform for AI workloads that combines GPU compute, managed Kubernetes on bare metal, storage, and networking for model training and inference. Its software offerings include the Weights & Biases developer platform.","keywords":"CoreWeave, Inc. Livingston, New Jersey cloud cloud-provider compute-infrastructure developer-platform CoreWeave GPU Compute CoreWeave Kubernetes Service (CKS) CoreWeave Forge (including Weights & Biases Models)"},{"type":"organization","slug":"cornell-university","href":"/companies/cornell-university/","name":"Cornell University","summary":"Cornell University is a privately endowed research university whose main campus is in Ithaca, New York; its charter was signed into law in 1865. Its AI-related research groups include the Kuleshov Group at Cornell Tech, which works on machine learning and generative models, and the Zhang Research Group in electrical and computer engineering, which studies hardware specialization for machine learning.","keywords":"Cornell University Ithaca, New York research university-lab research-lab"},{"type":"organization","slug":"crusoe","href":"/companies/crusoe/","name":"Crusoe","summary":"Crusoe builds and operates data centers for AI workloads and runs Crusoe Cloud, a GPU cloud platform with managed Kubernetes, Slurm, storage, and networking. It also offers managed inference and fine-tuning services for open models through Crusoe Intelligence Foundry.","keywords":"Denver, Colorado cloud cloud-provider compute-infrastructure inference-provider Crusoe Cloud Crusoe Managed Inference (Crusoe Intelligence Foundry) Crusoe AI data centers"},{"type":"organization","slug":"d-matrix","href":"/companies/d-matrix/","name":"d-Matrix","summary":"d-Matrix designs hardware and software for AI inference in data centers. Its products are the Corsair inference accelerator, the JetStream I/O accelerator for card-to-card communication, the Aviator software stack, and SquadRack rack-scale configurations that combine them.","keywords":"Santa Clara, California chips chip-designer hardware-manufacturer d-Matrix Corsair d-Matrix JetStream d-Matrix Aviator d-Matrix SquadRack"},{"type":"organization","slug":"databricks","href":"/companies/databricks/","name":"Databricks","summary":"Databricks sells a data and AI platform built on a lakehouse architecture that runs on AWS, Azure, and Google Cloud. The platform includes tools for building and serving AI agents and models, and Databricks-hosted access to open models through its Foundation Model APIs.","keywords":"San Francisco, California data enterprise agents data-platform enterprise-software inference-provider model-developer Databricks Data + AI Platform Agent Bricks Databricks Foundation Model APIs"},{"type":"organization","slug":"deep-cogito","href":"/companies/deep-cogito/","name":"Deep Cogito","summary":"Deep Cogito is a San Francisco AI company that post-trains open-weight language models, the Cogito series, on base models from other developers. It describes its training approach as Iterated Distillation and Amplification (IDA), a form of iterative self-improvement.","keywords":"Deep Cogito Inc. San Francisco, California open-models research model-developer research-lab"},{"type":"organization","slug":"dell-technologies","href":"/companies/dell-technologies/","name":"Dell Technologies","summary":"Dell Technologies sells PCs, servers, storage, and networking products and related services. Its Infrastructure Solutions Group includes a portfolio of AI-optimized servers, and it packages its AI infrastructure, data, and services offerings as the Dell AI Factory.","keywords":"Dell Technologies Inc. Round Rock, Texas enterprise hardware-manufacturer consumer-products Dell AI Factory Dell PowerEdge XE AI servers Dell AI Data Platform"},{"type":"organization","slug":"element-labs","href":"/companies/element-labs/","name":"Element Labs","summary":"Element Labs makes LM Studio, a desktop application for downloading and running language models on a local computer, with a chat interface and a local API server. It also publishes llmster, a headless version of LM Studio for servers, the lms command-line tool, and Python and TypeScript SDKs.","keywords":"Element Labs, Inc. New York, New York open-models developer-platform LM Studio llmster and the lms CLI LM Studio SDKs (lmstudio-python and lmstudio-js)"},{"type":"organization","slug":"eleutherai","href":"/companies/eleutherai/","name":"EleutherAI","summary":"EleutherAI is a non-profit AI research lab that trains and releases open models, datasets, and tools, and studies how models learn, how to evaluate them, and how to make open-weight systems safer. It began in July 2020 as a Discord community and incorporated as a non-profit research institute in early 2023.","keywords":"EleutherAI Institute (listed in the IRS exempt-organization extract as \"ELEUTHERAI INSTITUTE\") Washington, D.C. open-models research data research-lab nonprofit-research model-developer open-source-steward"},{"type":"organization","slug":"epoch-ai","href":"/companies/epoch-ai/","name":"Epoch AI","summary":"Epoch AI is an independent nonprofit that collects and analyzes data on AI development. It publishes data explorers and downloadable datasets on AI models, data centers, hardware, and related topics, runs benchmarks including FrontierMath, and publishes research reports, newsletters, and podcasts. It started in 2021 as a group of volunteers.","keywords":"Epoch Artificial Intelligence, Inc. San Francisco, California (address of record) research data research-lab nonprofit-research data-services Epoch AI data explorers and datasets"},{"type":"organization","slug":"essential-ai","href":"/companies/essential-ai/","name":"Essential AI","summary":"Essential AI is a San Francisco AI research company that publishes open models and data. Its releases include the Essential-Web v1.0 web dataset (June 2025) and the Rnj-1 language models for code and STEM tasks (December 2025).","keywords":"San Francisco, California open-models research data model-developer research-lab"},{"type":"organization","slug":"etched","href":"/companies/etched/","name":"Etched","summary":"Etched designs hardware for running AI model inference. Its website describes rack-scale \"frontier inference clusters\" for which it co-designs chips, packaging, interconnects, cooling, racks, and software, and says its first chip silicon was made on TSMC's N4P process.","keywords":"Etched, Inc. San Jose, California chips chip-designer hardware-manufacturer Etched rack-scale inference systems"},{"type":"organization","slug":"evolutionaryscale","href":"/companies/evolutionaryscale/","name":"EvolutionaryScale","summary":"EvolutionaryScale is a public benefit company that developed the ESM3 and ESM C (ESM Cambrian) protein language models. In November 2025 Biohub announced that the EvolutionaryScale team would join Biohub, and EvolutionaryScale's website now states that it is part of Biohub.","keywords":"EvolutionaryScale, PBC science open-models model-developer research-lab"},{"type":"organization","slug":"far-ai","href":"/companies/far-ai/","name":"FAR.AI","summary":"FAR.AI is a nonprofit that works on AI safety and security through in-house research, grantmaking, and events such as workshops and seminars. It also runs FAR.Labs, a coworking space in downtown Berkeley, California, for people and organizations working on trustworthy and secure AI.","keywords":"FAR AI, Inc. San Diego, California (address of record) research research-lab nonprofit-research"},{"type":"organization","slug":"figure","href":"/companies/figure/","name":"Figure","summary":"Figure builds general-purpose humanoid robots and Helix, its in-house vision-language-action model that controls them. Its current robot generation is Figure 03, designed for both home and commercial work.","keywords":"Figure AI Inc. San Jose, California robotics robotics hardware-manufacturer model-developer Figure 03 Helix"},{"type":"organization","slug":"fireworks-ai","href":"/companies/fireworks-ai/","name":"Fireworks AI","summary":"Fireworks AI operates a hosted platform for serving and training models, offering pay-per-token access to open models from other developers, dedicated deployments, and fine-tuning and reinforcement-learning training services.","keywords":"Fireworks.ai, Inc. San Mateo, California cloud inference-provider developer-platform Fireworks Inference Fireworks Training"},{"type":"organization","slug":"futurehouse","href":"/companies/futurehouse/","name":"FutureHouse","summary":"FutureHouse is a 501(c)(3) nonprofit research lab in San Francisco, founded in 2023, that builds AI systems intended to automate parts of scientific research, with a focus on biology. Its open releases include the PaperQA literature-agent package and the ether0 chemistry reasoning model.","keywords":"Future House Inc (as listed in the IRS exempt-organization extract) San Francisco, California science research agents open-models research-lab nonprofit-research model-developer open-source-steward"},{"type":"organization","slug":"genmo","href":"/companies/genmo/","name":"Genmo","summary":"Genmo is a San Francisco research company that builds video generation models, which it describes as world models. Its open release is Mochi 1, a text-to-video model published as a research preview in October 2024, and it runs a hosted playground for generating videos.","keywords":"Genmo Inc. San Francisco, California open-models research model-developer research-lab Genmo Playground"},{"type":"organization","slug":"georgia-institute-of-technology","href":"/companies/georgia-institute-of-technology/","name":"Georgia Institute of Technology","summary":"The Georgia Institute of Technology (Georgia Tech) is a public research university in Atlanta, Georgia, founded in 1885 and part of the University System of Georgia. Its AI research groups include the Polo Club of Data Science, which builds interactive tools for understanding machine learning models, among them Transformer Explainer.","keywords":"Atlanta, Georgia research university-lab research-lab"},{"type":"organization","slug":"github","href":"/companies/github/","name":"GitHub","summary":"GitHub runs a platform for hosting and collaborating on code, with cloud and on-premises offerings, and develops the GitHub Copilot AI coding assistant. It is owned by Microsoft, which reports GitHub within its Intelligent Cloud segment.","keywords":"GitHub, Inc. San Francisco, California agents developer-platform GitHub Copilot GitHub Copilot CLI Spec Kit Microsoft"},{"type":"organization","slug":"glean","href":"/companies/glean/","name":"Glean","summary":"Glean sells an enterprise AI platform that connects to a company's business applications and data sources to provide permission-aware search, an AI assistant, and AI agents. Its terms describe support for multiple third-party large language models.","keywords":"Glean Technologies, Inc. San Francisco, California enterprise agents enterprise-software developer-platform Glean Assistant Glean Agents Glean Developer Platform"},{"type":"organization","slug":"google","href":"/companies/google/","name":"Google (Alphabet)","summary":"Google is the largest of the businesses that make up Alphabet Inc. Alphabet reports Google through its Google Services and Google Cloud segments, and reports centralized research and development on frontier AI models as Alphabet-level activities. This record covers Google and its parent Alphabet together; Google DeepMind has its own record as a research unit.","keywords":"Alphabet Inc. (parent and SEC registrant); Google LLC (subsidiary) Mountain View, California frontier-models cloud open-models research model-developer research-lab cloud-provider developer-platform consumer-products Gemini API Gemini app Gemini Enterprise Agent Platform (formerly Vertex AI)"},{"type":"organization","slug":"google-deepmind","href":"/companies/google-deepmind/","name":"Google DeepMind","summary":"Google DeepMind is Google's AI research unit, formed in 2023 by combining DeepMind with the Brain team from Google Research. It is credited as the developer of the Gemma family of open-weight models.","keywords":"frontier-models open-models research research-lab model-developer Google (Alphabet)"},{"type":"organization","slug":"groq","href":"/companies/groq/","name":"Groq","summary":"Groq operates an inference cloud that runs AI models on LPU (Language Processing Unit) accelerators in data centers it deploys with partners. It developed the LPU and launched GroqCloud, and it offers developer access through its console as well as dedicated capacity tiers.","keywords":"United States (city not stated; legal mailing address is a P.O. box in Mountain View, California) cloud inference-provider compute-infrastructure GroqCloud"},{"type":"organization","slug":"harvard-university","href":"/companies/harvard-university/","name":"Harvard University","summary":"Harvard University is a university in Cambridge, Massachusetts, founded in 1636 by a vote of the Great and General Court of the Massachusetts Bay Colony. Its AI research includes the Kempner Institute for the Study of Natural and Artificial Intelligence, which studies the basis of intelligence in natural and artificial systems and publishes open-source software such as KempnerForge.","keywords":"President and Fellows of Harvard College Cambridge, Massachusetts research university-lab research-lab"},{"type":"organization","slug":"harvey","href":"/companies/harvey/","name":"Harvey","summary":"Harvey develops AI software for legal and professional services firms and in-house legal teams. Its platform includes agents that carry out multi-step legal tasks, Vault for storing and bulk-analyzing document collections, and Knowledge for legal, regulatory, and tax research across licensed and public sources.","keywords":"Harvey AI Corporation San Francisco, California enterprise agents enterprise-software Harvey agents Vault Knowledge"},{"type":"organization","slug":"hpe","href":"/companies/hpe/","name":"Hewlett Packard Enterprise (HPE)","summary":"HPE sells enterprise infrastructure and services spanning servers, hybrid cloud, and networking. Since November 2025 it has reported Cloud & AI and Networking as its main segments. Its AI offerings include HPE Private Cloud AI and other HPE AI Factory solutions, and HPE Cray supercomputers for HPC and AI workloads.","keywords":"Hewlett Packard Enterprise Company Spring, Texas enterprise hardware-manufacturer enterprise-software HPE Private Cloud AI HPE AI Factory HPE Cray Supercomputing"},{"type":"organization","slug":"hugging-face","href":"/companies/hugging-face/","name":"Hugging Face","summary":"Hugging Face runs the Hugging Face Hub, which hosts Git-based repositories of models, datasets, and demo apps (Spaces), and sells hosted inference and enterprise services. It also maintains open-source machine-learning libraries such as Transformers.","keywords":"Hugging Face, Inc. Brooklyn, New York open-models data developer-platform inference-provider open-source-steward Hugging Face Hub Inference Providers Inference Endpoints"},{"type":"organization","slug":"ibm","href":"/companies/ibm/","name":"IBM","summary":"IBM is a publicly traded technology company. In AI, it develops the Granite family of models and sells the watsonx products for building, running, and governing AI models and agents.","keywords":"International Business Machines Corporation Armonk, New York enterprise open-models model-developer enterprise-software IBM watsonx.ai IBM watsonx Orchestrate IBM watsonx.governance"},{"type":"organization","slug":"imbue","href":"/companies/imbue/","name":"Imbue","summary":"Imbue is a San Francisco company, founded in 2021, that builds software tools meant to keep AI agents under their users' control, with a focus on coding agents. Its products include Sculptor, a workspace for running coding agents in parallel; Vet, a tool that checks code changes and agent conversations for problems; and mngr, a command-line tool for managing coding agents. Most of its products are published as open source.","keywords":"Imbue, Inc. San Francisco, California agents research developer-platform research-lab Sculptor Vet mngr"},{"type":"organization","slug":"inception-labs","href":"/companies/inception-labs/","name":"Inception","summary":"Inception (Inception AI, Inc., at inceptionlabs.ai) develops Mercury, a family of diffusion language models that generate and refine many tokens in parallel rather than one at a time. It offers the models through an OpenAI-compatible API.","keywords":"Inception AI, Inc. Redwood City, California model-developer Inception API (Mercury models)"},{"type":"organization","slug":"inflection-ai","href":"/companies/inflection-ai/","name":"Inflection AI","summary":"Inflection AI develops its own foundation models and Pi, a conversational AI assistant it calls a personal intelligence partner. Its Inflection AI Labs group runs research and public product experiments, such as Pi Journeys, an assistant for life transitions like becoming a parent or changing careers.","keywords":"Inflection AI, Inc. San Francisco, California (mailing address) research model-developer consumer-products Pi"},{"type":"organization","slug":"intel","href":"/companies/intel/","name":"Intel","summary":"Intel designs and manufactures semiconductor products, including CPUs used in PCs and data centers. Its AI-related offerings include Intel Gaudi AI accelerators and the open-source OpenVINO inference toolkit.","keywords":"Intel Corporation Santa Clara, California chips chip-designer hardware-manufacturer developer-platform Intel Gaudi 3 AI accelerator OpenVINO toolkit"},{"type":"organization","slug":"lambda","href":"/companies/lambda/","name":"Lambda","summary":"Lambda is an AI cloud company that provides NVIDIA GPU infrastructure for training and inference, from on-demand instances and self-serve clusters to single-tenant superclusters. It was founded in 2012 by machine learning engineers.","keywords":"Lambda, Inc. San Jose, California cloud cloud-provider compute-infrastructure Lambda Instances 1-Click Clusters Lambda Superclusters"},{"type":"organization","slug":"lancedb","href":"/companies/lancedb/","name":"LanceDB","summary":"LanceDB builds data infrastructure for multimodal AI on the Lance columnar format: the open-source LanceDB embedded retrieval library and LanceDB Enterprise, a distributed multimodal lakehouse platform. In November 2025 LanceDB moved the Lance projects into a separate, community-governed lance-format GitHub organization.","keywords":"San Francisco, California data data-platform LanceDB Enterprise LanceDB OSS"},{"type":"organization","slug":"langchain","href":"/companies/langchain/","name":"LangChain","summary":"LangChain develops the open-source LangChain, LangGraph, and Deep Agents frameworks for building LLM agents. It also sells LangSmith, a commercial platform for tracing, evaluating, and deploying agents.","keywords":"LangChain Inc. San Francisco, California agents enterprise developer-platform enterprise-software LangSmith LangSmith Deployment LangSmith Fleet"},{"type":"organization","slug":"lawrence-berkeley-national-laboratory","href":"/companies/lawrence-berkeley-national-laboratory/","name":"Lawrence Berkeley National Laboratory","summary":"Lawrence Berkeley National Laboratory (Berkeley Lab) is a multiprogram national laboratory in Berkeley, California, managed by the University of California for the U.S. Department of Energy's Office of Science. It traces its founding to 1931. Berkeley Lab describes work on AI models and applications for science, AI-ready scientific datasets such as the Materials Project, and the computing and networking infrastructure used to train them.","keywords":"Berkeley, California research science research-lab"},{"type":"organization","slug":"lawrence-livermore-national-laboratory","href":"/companies/lawrence-livermore-national-laboratory/","name":"Lawrence Livermore National Laboratory","summary":"Lawrence Livermore National Laboratory (LLNL) is a U.S. Department of Energy national laboratory in Livermore, California, that began operations in 1952. It is operated by Lawrence Livermore National Security, LLC for DOE's National Nuclear Security Administration. LLNL describes AI as a core capability that it applies alongside experimental data, physics-based modeling, and high-performance computing, and it publishes the LBANN deep learning training toolkit as open-source software.","keywords":"Livermore, California research science defense research-lab"},{"type":"organization","slug":"lf-ai-data","href":"/companies/lf-ai-data/","name":"LF AI & Data Foundation","summary":"The LF AI & Data Foundation is a directed fund of The Linux Foundation that raises and spends funds to support open-source artificial intelligence, machine learning, and data projects. Its hosted projects include ONNX, which LF AI announced as a graduate-level project in November 2019.","keywords":"open-source-steward The Linux Foundation"},{"type":"organization","slug":"lightmatter","href":"/companies/lightmatter/","name":"Lightmatter","summary":"Lightmatter develops silicon photonics for AI infrastructure. Its Passage family covers optical interconnects in co-packaged, near-packaged, and on-board form factors, plus a photonic interposer reference platform, and its Guide light engine is a laser source for co-packaged optics. It says Passage and Guide are available to early-access partners.","keywords":"Lightmatter, Inc. Mountain View, California chips chip-designer hardware-manufacturer Passage L200 Passage L20 Passage M1000 evaluation kit Guide light engine"},{"type":"organization","slug":"lightning-ai","href":"/companies/lightning-ai/","name":"Lightning AI","summary":"Lightning AI created and maintains the PyTorch Lightning training framework and runs a cloud platform for building, training, and deploying AI models, including browser-based development environments (Studios) and on-demand GPUs. It merged with GPU infrastructure provider Voltage Park in January 2026 and operates under the Lightning AI name.","keywords":"San Francisco, California cloud cloud-provider compute-infrastructure developer-platform Lightning AI Studio Lightning GPU cloud"},{"type":"organization","slug":"linkedin","href":"/companies/linkedin/","name":"LinkedIn","summary":"LinkedIn is a professional networking service owned by Microsoft, which reports it within its Productivity and Business Processes segment. Its engineering team develops and maintains Liger Kernel, an open-source library of Triton GPU kernels for training large language models.","keywords":"LinkedIn Corporation Sunnyvale, California consumer-products enterprise-software Microsoft"},{"type":"organization","slug":"liquid-ai","href":"/companies/liquid-ai/","name":"Liquid AI","summary":"Liquid AI is a company spun out of MIT CSAIL in 2023 that develops Liquid Foundation Models (LFMs), hybrid models built for efficient deployment on phones, laptops, and other devices. It publishes LFM weights on Hugging Face and offers LEAP, a platform for deploying models on edge devices.","keywords":"Liquid AI, Inc. Cambridge, Massachusetts open-models research model-developer developer-platform LEAP (Liquid Edge AI Platform) Liquid Apollo"},{"type":"organization","slug":"llamaindex","href":"/companies/llamaindex/","name":"LlamaIndex","summary":"LlamaIndex develops the open-source LlamaIndex framework for building LLM agents and workflows over private data. It also sells LlamaParse, a hosted platform for document parsing, extraction, and indexing, which the company now describes as its main focus.","keywords":"LlamaIndex, Inc. San Francisco, California agents data enterprise developer-platform enterprise-software LlamaParse"},{"type":"organization","slug":"lmsys","href":"/companies/lmsys/","name":"LMSYS (Large Model Systems Organization)","summary":"LMSYS is a 501(c)(3) nonprofit that incubates open-source AI projects and research. It began in 2023 as a collaboration among UC Berkeley, Stanford, UC San Diego, Carnegie Mellon, and MBZUAI, and was incorporated as a nonprofit in September 2024. Its projects include the SGLang serving engine, FastChat, RouteLLM, and the Vicuna models.","keywords":"LMSYS Corp research open-models research-lab nonprofit-research open-source-steward"},{"type":"organization","slug":"luma-ai","href":"/companies/luma-ai/","name":"Luma AI","summary":"Luma AI develops generative models for video and images, including the Ray3.2 video model and the Uni-1.1 image model. It offers them through Luma Agents, a web-based creative workspace, and through a developer API.","keywords":"Luma AI, Inc. Redwood City, California model-developer consumer-products developer-platform Luma Agents Luma API"},{"type":"organization","slug":"marvell","href":"/companies/marvell/","name":"Marvell Technology","summary":"Marvell designs data infrastructure semiconductors for AI, cloud, carrier, and enterprise infrastructure. Its AI-related products include custom ASICs for cloud and AI data center customers, Teralynx Ethernet switch chips, optical interconnect DSPs, and the Photonic Fabric optical scale-up interconnect it gained by acquiring Celestial AI.","keywords":"Marvell Technology, Inc. Wilmington, Delaware chips chip-designer Marvell custom ASICs Teralynx Ethernet switches PAM4 optical DSPs Photonic Fabric technology"},{"type":"organization","slug":"mit","href":"/companies/mit/","name":"Massachusetts Institute of Technology","summary":"MIT is a university in Cambridge, Massachusetts, incorporated in 1861; its institutional policies describe it as independent and privately endowed. Its AI research groups include the MIT HAN Lab, which works on efficient AI methods such as model compression and quantization and maintains the AWQ code.","keywords":"Massachusetts Institute of Technology Cambridge, Massachusetts research university-lab research-lab"},{"type":"organization","slug":"meta","href":"/companies/meta/","name":"Meta","summary":"Meta operates social and messaging apps including Facebook, Instagram, WhatsApp, and Messenger, along with VR devices and AI glasses. Its Meta Superintelligence Labs builds the Muse model family, and Meta has released open-weight Llama and Muse Glimmer models. It offers the Meta AI assistant across its apps, the Muse personal agent, and the Meta Model API for developers.","keywords":"Meta Platforms, Inc. Menlo Park, California frontier-models open-models agents research model-developer research-lab consumer-products developer-platform Meta AI Meta Model API Muse (personal AI agent) Muse Code"},{"type":"organization","slug":"metr","href":"/companies/metr/","name":"METR (Model Evaluation & Threat Research)","summary":"METR is a research nonprofit that evaluates frontier AI models, measuring their autonomous capabilities and assessing whether and when AI systems could pose catastrophic risks. It publishes research, evaluation reports on specific models, and open-source tooling for running evaluations.","keywords":"Model Evaluation and Threat Research, Inc. Berkeley, California research research-lab nonprofit-research Inspect-Hawk"},{"type":"organization","slug":"micron","href":"/companies/micron/","name":"Micron Technology","summary":"Micron makes memory and storage products, including DRAM, NAND, and NOR. Its Cloud Memory Business Unit covers memory for large hyperscale cloud customers and high-bandwidth memory (HBM) for all data center customers, and the company markets HBM, low-power server memory modules, and data center SSDs for AI workloads.","keywords":"Micron Technology, Inc. Boise, Idaho chips chip-designer hardware-manufacturer Micron HBM (HBM3E and HBM4) Micron SOCAMM2 Micron data center SSDs"},{"type":"organization","slug":"microsoft","href":"/companies/microsoft/","name":"Microsoft","summary":"Microsoft develops and sells software, cloud services, and devices. Its AI offerings include the Microsoft Foundry platform on Azure, Copilot assistants, and models developed in-house, including the Phi family of small language models and MAI models.","keywords":"Microsoft Corporation Redmond, Washington cloud enterprise open-models frontier-models research model-developer research-lab cloud-provider enterprise-software developer-platform consumer-products Microsoft Foundry Microsoft Copilot"},{"type":"organization","slug":"midjourney","href":"/companies/midjourney/","name":"Midjourney","summary":"Midjourney develops image-generation models and an image-to-video model and offers them through its subscription web service. It also publishes niji・journey, an anime-style image app whose accounts and plans can be synced with Midjourney accounts.","keywords":"Midjourney, Inc. South San Francisco, California model-developer consumer-products Midjourney niji・journey"},{"type":"organization","slug":"mintplex-labs","href":"/companies/mintplex-labs/","name":"Mintplex Labs","summary":"Mintplex Labs develops AnythingLLM, an MIT-licensed application for chatting with documents and running AI agents with local or hosted language models. It distributes a free desktop app and a self-hosted Docker version and operates AnythingLLM Cloud, a paid hosted service.","keywords":"Mintplex Labs, Inc. California agents consumer-products enterprise-software AnythingLLM Desktop AnythingLLM Cloud"},{"type":"organization","slug":"mlcommons","href":"/companies/mlcommons/","name":"MLCommons","summary":"MLCommons is an AI engineering consortium whose members include companies, academics, and nonprofits. It develops benchmarks, including the MLPerf performance suites and the AILuminate safety and security benchmarks, as well as open datasets and tools such as Croissant and MLCube. It launched in December 2020.","keywords":"MLCommons Association Dover, Delaware (address listed on the About page) data standards-body open-source-steward"},{"type":"organization","slug":"modal","href":"/companies/modal/","name":"Modal","summary":"Modal operates a cloud platform for AI and data workloads, including model inference, training and fine-tuning, batch jobs, and sandboxes for running untrusted code. Developers define workloads and hardware in Python code, and usage is billed per second.","keywords":"Modal Labs, Inc. New York, New York cloud cloud-provider inference-provider developer-platform Modal Inference Modal Sandboxes"},{"type":"organization","slug":"modular","href":"/companies/modular/","name":"Modular","summary":"Modular develops the Mojo programming language and MAX, a framework for serving and building AI models on CPUs, GPUs, and other accelerators, and operates Modular Cloud, a hosted inference service. Qualcomm completed its acquisition of Modular in July 2026.","keywords":"Modular Inc Los Altos, California cloud developer-platform inference-provider Modular Cloud MAX Mojo Qualcomm"},{"type":"organization","slug":"mozilla-ai","href":"/companies/mozilla-ai/","name":"Mozilla.ai","summary":"Mozilla.ai is a company backed by the Mozilla Foundation that builds open-source AI tools and products. Its projects include any-llm, a Python library for calling different model providers through one interface, and llamafile, which packages a model and its runtime into a single executable that runs locally.","keywords":"MZL.AI, PBC San Francisco, California open-models agents developer-platform open-source-steward llamafile any-llm"},{"type":"organization","slug":"nist","href":"/companies/nist/","name":"National Institute of Standards and Technology","summary":"The National Institute of Standards and Technology (NIST) is a U.S. federal agency founded in 1901 and now part of the Department of Commerce. It works on measurement science, standards, and technology; its AI work includes the AI Risk Management Framework, the Dioptra test platform, and a center for AI evaluation and voluntary guidelines.","keywords":"Gaithersburg, Maryland research standards-body research-lab"},{"type":"organization","slug":"new-york-university","href":"/companies/new-york-university/","name":"New York University","summary":"New York University is a private research university founded in 1831. It is anchored in New York City and has degree-granting campuses in Abu Dhabi and Shanghai.","keywords":"New York University New York, New York research university-lab research-lab"},{"type":"organization","slug":"nomic-ai","href":"/companies/nomic-ai/","name":"Nomic AI","summary":"Nomic is a New York City company that offers what it calls a domain-specific AI platform for architecture, engineering, and construction firms, including agents and an Agent API. It has also released open-source AI software and models, including the GPT4All desktop application and the Nomic Embed embedding models.","keywords":"Nomic, Inc. New York, New York enterprise agents open-models data model-developer enterprise-software developer-platform Nomic Platform Nomic Agent API Nomic text embedding API"},{"type":"organization","slug":"nous-research","href":"/companies/nous-research/","name":"Nous Research","summary":"Nous Research is an AI research company that trains and releases open-weight language models, most prominently the Hermes series of post-trained models, and builds open-source agent and training software such as Hermes Agent and the Atropos reinforcement-learning environment framework.","keywords":"Nous Research, Inc. United States (city not stated; the store privacy policy gives a contact address in Austin, Texas) open-models agents research model-developer research-lab developer-platform Nous Portal Hermes Agent"},{"type":"organization","slug":"nvidia","href":"/companies/nvidia/","name":"NVIDIA","summary":"NVIDIA designs accelerated computing platforms, including data center GPUs, networking, and complete systems, along with software such as CUDA for building and running AI workloads. Its annual report describes two segments, Compute & Networking and Graphics.","keywords":"NVIDIA Corporation Santa Clara, California chips open-models chip-designer model-developer developer-platform NVIDIA Vera Rubin platform NVIDIA DGX platform NVIDIA NIM NVIDIA CUDA Toolkit"},{"type":"organization","slug":"oak-ridge-national-laboratory","href":"/companies/oak-ridge-national-laboratory/","name":"Oak Ridge National Laboratory","summary":"Oak Ridge National Laboratory (ORNL) is a U.S. Department of Energy Office of Science national laboratory in Oak Ridge, Tennessee, managed by UT-Battelle LLC for DOE. It began in 1943 as part of the Manhattan Project. ORNL describes AI research embedded in scientific workflows and AI security research, and it maintains open-source AI software such as HydraGNN.","keywords":"Oak Ridge, Tennessee research science research-lab"},{"type":"organization","slug":"openai","href":"/companies/openai/","name":"OpenAI","summary":"OpenAI develops AI models and offers them through ChatGPT, the OpenAI API, and developer tools such as Codex. It has also published open-weight models, including the gpt-oss series released under the Apache 2.0 license.","keywords":"OpenAI Group PBC San Francisco, California frontier-models open-models agents model-developer research-lab developer-platform consumer-products ChatGPT OpenAI API Codex CLI"},{"type":"organization","slug":"oracle","href":"/companies/oracle/","name":"Oracle","summary":"Oracle sells database and enterprise software and operates Oracle Cloud, made up of Oracle Cloud Applications and Oracle Cloud Infrastructure (OCI). OCI compute includes GPU-based and bare-metal offerings for AI model training and inference, and OCI Generative AI is a managed service for building generative AI applications.","keywords":"Oracle Corporation Austin, Texas cloud enterprise data cloud-provider enterprise-software data-platform OCI AI Infrastructure OCI Generative AI"},{"type":"organization","slug":"pacific-northwest-national-laboratory","href":"/companies/pacific-northwest-national-laboratory/","name":"Pacific Northwest National Laboratory","summary":"Pacific Northwest National Laboratory (PNNL) is a U.S. Department of Energy national laboratory in Richland, Washington, managed and operated by Battelle for DOE. It traces its start to January 4, 1965, when the Atomic Energy Commission awarded Battelle the contract to operate the Hanford Laboratories. PNNL applies AI across discovery science, applied energy, and national security, and it maintains the open-source NeuroMANCER library.","keywords":"Richland, Washington research science research-lab"},{"type":"organization","slug":"palantir","href":"/companies/palantir/","name":"Palantir","summary":"Palantir builds software platforms for data integration, analytics, and operational decision-making used by government and commercial customers. Its Artificial Intelligence Platform (AIP) connects large language models with customer data and workflows inside its Foundry and Gotham platforms.","keywords":"Palantir Technologies Inc. Aventura, Florida enterprise data defense agents enterprise-software data-platform Palantir AIP (Artificial Intelligence Platform) Palantir Foundry Palantir Gotham"},{"type":"organization","slug":"perplexity","href":"/companies/perplexity/","name":"Perplexity","summary":"Perplexity makes an AI search and answer assistant that responds to questions with cited web sources. It also offers a developer API platform with web-grounded agent, search, embeddings, and model-routing APIs.","keywords":"Perplexity AI, Inc. San Francisco, California agents consumer-products developer-platform model-developer Perplexity Perplexity API Platform"},{"type":"organization","slug":"physical-intelligence","href":"/companies/physical-intelligence/","name":"Physical Intelligence","summary":"Physical Intelligence develops foundation models for robot control, including the π0, π0-FAST, and π0.5 vision-language-action models. It publishes code and checkpoints for these models in its openpi repository.","keywords":"San Francisco, California robotics open-models research model-developer research-lab robotics"},{"type":"organization","slug":"poolside","href":"/companies/poolside/","name":"Poolside","summary":"Poolside trains foundation models for software engineering and publishes the Laguna family of open-weight agentic coding models. It offers the models through an OpenAI-compatible API, a terminal coding agent, and self-managed inference deployments.","keywords":"Poolside, Inc. San Francisco, California open-models agents enterprise model-developer research-lab developer-platform Poolside API Poolside Agent CLI (pool) Self-managed Poolside inference"},{"type":"organization","slug":"prime-intellect","href":"/companies/prime-intellect/","name":"Prime Intellect","summary":"Prime Intellect provides GPU compute, a hosted post-training platform (Lab) with an Environments Hub for reinforcement-learning environments, inference, and code sandboxes, and trains open-weight models such as the INTELLECT series. It maintains the open-source prime-rl training framework and the verifiers environment library.","keywords":"Prime Intellect, Inc. Dover, Delaware (principal place of business listed in its SEC Form D) open-models cloud research model-developer research-lab compute-infrastructure developer-platform Prime Intellect Compute Lab (hosted training and Environments Hub) Prime Intellect Inference"},{"type":"organization","slug":"princeton-university","href":"/companies/princeton-university/","name":"Princeton University","summary":"Princeton University is a university in Princeton, New Jersey. Researchers at Princeton University and Princeton Language and Intelligence created SWE-bench, a benchmark of real-world GitHub issues for evaluating language models on software engineering tasks.","keywords":"Princeton, New Jersey research university-lab research-lab"},{"type":"organization","slug":"profluent","href":"/companies/profluent/","name":"Profluent","summary":"Profluent is a company in Emeryville, California that trains protein language models and uses them to design proteins, starting with gene editors. Its public releases include the ProGen3 generative models under a non-commercial weights license, the Profluent-E1 encoder models under a permissive license with attribution requirements, and the AI-designed OpenCRISPR-1 gene editor.","keywords":"Profluent Bio Inc. Emeryville, California science open-models research model-developer research-lab"},{"type":"organization","slug":"pytorch-foundation","href":"/companies/pytorch-foundation/","name":"PyTorch Foundation","summary":"The PyTorch Foundation is a directed fund of The Linux Foundation. It supports the PyTorch framework and other hosted projects, including vLLM, DeepSpeed, Ray, Helion, and Safetensors. It was formed in September 2022, when Meta moved PyTorch to the Linux Foundation.","keywords":"open-source-steward The Linux Foundation"},{"type":"organization","slug":"qualcomm","href":"/companies/qualcomm/","name":"Qualcomm","summary":"Qualcomm develops semiconductors and licenses its patent portfolio. Its annual report names on-device AI among its foundational technologies, including the Hexagon NPU in Snapdragon platforms. It also sells data center products, including rack-scale AI inference accelerators under the Qualcomm Dragonfly brand.","keywords":"QUALCOMM Incorporated San Diego, California chips chip-designer developer-platform Qualcomm Dragonfly AI accelerators (AI200, AI250, AI300) Qualcomm Hexagon NPU Qualcomm AI Hub"},{"type":"organization","slug":"red-hat","href":"/companies/red-hat/","name":"Red Hat","summary":"Red Hat is an enterprise open-source software company, owned by IBM since 2019, whose products include Red Hat Enterprise Linux and Red Hat OpenShift. Its Red Hat AI portfolio covers inference software built on vLLM and llm-d, OpenShift AI, and Red Hat Enterprise Linux AI. In 2025 it launched llm-d, an open-source distributed inference project.","keywords":"Red Hat, Inc. Raleigh, North Carolina enterprise cloud enterprise-software open-source-steward Red Hat AI Inference Red Hat OpenShift AI Red Hat Enterprise Linux AI IBM"},{"type":"organization","slug":"reflection-ai","href":"/companies/reflection-ai/","name":"Reflection AI","summary":"Reflection AI is an AI company that says it is building open-weight frontier models, along with open-source software and infrastructure for customizing and running them. As of this review it had not published any model weights.","keywords":"Reflection AI, Inc. New York, New York research model-developer research-lab"},{"type":"organization","slug":"reka","href":"/companies/reka/","name":"Reka","summary":"Reka is an AI research lab that develops multimodal models (the Spark, Edge, Flash, and Core lines) that process video, images, audio, and text, and offers them through an API. It also runs Reka Cloud, a platform for serverless inference, dedicated GPU capacity, and model fine-tuning, and it has released some models, including Reka Flash 3 and 3.1, as open weights.","keywords":"Reka AI, Inc. San Francisco, California open-models cloud research model-developer research-lab inference-provider Reka API Reka Cloud"},{"type":"organization","slug":"replit","href":"/companies/replit/","name":"Replit","summary":"Replit offers a hosted platform, available on the web, desktop, and mobile, where people describe software in natural language and create, publish, and run apps with the help of Replit Agent. It has also published two small code-completion models on Hugging Face.","keywords":"Replit, Inc. Foster City, California agents developer-platform model-developer Replit Replit Agent"},{"type":"organization","slug":"runway","href":"/companies/runway/","name":"Runway","summary":"Runway develops generative AI models, including the Gen-4.5 video model and world models such as GWM-1. It offers its own and some third-party models for video, image, and audio work through a creative web and mobile app and through a developer API.","keywords":"Runway AI, Inc. New York, New York model-developer consumer-products developer-platform Runway Runway API (Runway Dev)"},{"type":"organization","slug":"ssi","href":"/companies/ssi/","name":"Safe Superintelligence Inc.","summary":"Safe Superintelligence Inc. (SSI) is an AI research company that describes a single goal and product: building safe superintelligence. It says it is an American company with offices in Palo Alto and Tel Aviv.","keywords":"Safe Superintelligence Inc. Palo Alto, California research research-lab"},{"type":"organization","slug":"salesforce","href":"/companies/salesforce/","name":"Salesforce","summary":"Salesforce sells customer relationship management software and related cloud applications, which it now organizes on its AI-based Agentforce 360 Platform for building and running AI agents. Its research group, Salesforce AI Research, publishes models and datasets on Hugging Face.","keywords":"Salesforce, Inc. San Francisco, California enterprise agents research enterprise-software model-developer research-lab Agentforce Data 360 (formerly Data Cloud) Slack AI"},{"type":"organization","slug":"sambanova","href":"/companies/sambanova/","name":"SambaNova","summary":"SambaNova builds AI inference infrastructure: Reconfigurable Dataflow Unit (RDU) chips, SambaRack systems, the SambaStack platform for dedicated deployments, and the SambaCloud inference service for open models.","keywords":"SambaNova Systems, Inc. San Jose, California chips cloud chip-designer hardware-manufacturer inference-provider SambaCloud SambaStack SambaRack"},{"type":"organization","slug":"sandia-national-laboratories","href":"/companies/sandia-national-laboratories/","name":"Sandia National Laboratories","summary":"Sandia National Laboratories is a multimission U.S. Department of Energy laboratory with its headquarters in Albuquerque, New Mexico, and a second principal laboratory in Livermore, California. It is managed and operated for DOE's National Nuclear Security Administration by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc. Sandia lists secure AI, computing co-design, and scientific machine learning among its AI research areas.","keywords":"Albuquerque, New Mexico research science defense research-lab"},{"type":"organization","slug":"scale-ai","href":"/companies/scale-ai/","name":"Scale AI","summary":"Scale AI provides training data, annotation, and human-feedback services for AI model developers, along with model evaluation work and software for building AI agents. It sells to enterprises and to government customers, including national security agencies.","keywords":"San Francisco, California data enterprise agents defense data-services enterprise-software Scale Data Engine Scale GenAI Portfolio Scale Donovan"},{"type":"organization","slug":"servicenow","href":"/companies/servicenow/","name":"ServiceNow","summary":"ServiceNow sells the ServiceNow AI Platform, enterprise software for running business workflows, with generative AI tools (Now Assist), AI agents, and AI governance features built in. Its research teams publish the open-weight Apriel language models, and ServiceNow co-stewards the BigCode project with Hugging Face.","keywords":"ServiceNow, Inc. Santa Clara, California enterprise agents open-models enterprise-software model-developer research-lab ServiceNow AI Platform AI Control Tower"},{"type":"organization","slug":"sierra","href":"/companies/sierra/","name":"Sierra","summary":"Sierra provides a platform on which businesses build and run AI agents for customer service and customer experience, deployed across chat, messaging, email, and voice. It also maintains τ-bench, an open-source benchmark for evaluating customer-service agents.","keywords":"Sierra Technologies, Inc. San Francisco, California enterprise agents enterprise-software Agent Studio Ghostwriter Channels"},{"type":"organization","slug":"sifive","href":"/companies/sifive/","name":"SiFive","summary":"SiFive designs and licenses processor IP based on the open RISC-V instruction set. Its AI-related products include the SiFive Intelligence family of cores with vector processing and, in the XM series, matrix processing, and it offers the BigSky development server for porting data center software to RISC-V.","keywords":"SiFive, Inc. Santa Clara, California chips chip-designer SiFive Intelligence family (X100, X200, X300 series) SiFive Intelligence XM Series BigSky SF-2U870 datacenter development platform"},{"type":"organization","slug":"skild-ai","href":"/companies/skild-ai/","name":"Skild AI","summary":"Skild AI develops the Skild Brain, a robotics foundation model that it describes as able to control many kinds of robots, including quadrupeds, humanoids, tabletop arms, and mobile manipulators. In August 2026 it introduced S1, a manipulation model that learns tasks from video demonstrations.","keywords":"Pittsburgh, Pennsylvania robotics robotics model-developer Skild Brain S1"},{"type":"organization","slug":"snowflake","href":"/companies/snowflake/","name":"Snowflake","summary":"Snowflake operates a cloud data platform, which it calls the AI Data Cloud, deployed across multiple public clouds and regions. Its Cortex AI services run large language models from several providers inside the platform, and its research team released the Arctic language model with open weights in 2024.","keywords":"Snowflake Inc. Menlo Park, California (principal executive offices) data enterprise agents open-models data-platform inference-provider model-developer Snowflake Cortex AI Snowflake CoWork Snowflake AI Data Cloud"},{"type":"organization","slug":"spacex","href":"/companies/spacex/","name":"SpaceX","summary":"SpaceX reports three business segments: Space (reusable launch vehicles), Connectivity (the Starlink satellite network), and AI. SpaceX acquired xAI in February 2026, and its AI segment covers the Grok models, AI products for consumers and businesses, the X platform, and AI computing infrastructure.","keywords":"Space Exploration Technologies Corp. Starbase, Texas frontier-models model-developer compute-infrastructure consumer-products"},{"type":"organization","slug":"stanford-university","href":"/companies/stanford-university/","name":"Stanford University","summary":"Stanford University is a university in Stanford, California, founded in 1885 by Leland and Jane Stanford; it admitted its first students in 1891. Its AI research groups include the Center for Research on Foundation Models (CRFM) and the Stanford NLP Group.","keywords":"The Board of Trustees of the Leland Stanford Junior University Stanford, California research university-lab research-lab"},{"type":"organization","slug":"supermicro","href":"/companies/supermicro/","name":"Supermicro","summary":"Supermicro designs and builds servers, storage systems, and switches, and sells rack-scale IT solutions. Its annual report describes rack-scale systems for AI and high-performance computing and liquid cooling, offered through its Data Center Building Block Solutions.","keywords":"Super Micro Computer, Inc. San Jose, California enterprise hardware-manufacturer Supermicro GPU servers Data Center Building Block Solutions (DCBBS)"},{"type":"organization","slug":"tesla","href":"/companies/tesla/","name":"Tesla","summary":"Tesla makes electric vehicles and energy generation and storage products. Its annual report says the company is focused on bringing AI into the real world through products and services such as Full Self-Driving (Supervised) and Robotaxi, and on developing AI robots, including Optimus, which it describes as a general-purpose autonomous humanoid robot.","keywords":"Tesla, Inc. Austin, Texas robotics consumer-products hardware-manufacturer robotics Full Self-Driving (Supervised) Robotaxi"},{"type":"organization","slug":"linux-foundation","href":"/companies/linux-foundation/","name":"The Linux Foundation","summary":"The Linux Foundation is a nonprofit that hosts and supports open-source software, hardware, standards, and data projects. Among them is the PyTorch Foundation, which operates as one of its directed funds.","keywords":"The Linux Foundation San Francisco, California (legal postal address) open-source-steward"},{"type":"organization","slug":"university-of-texas-at-austin","href":"/companies/university-of-texas-at-austin/","name":"The University of Texas at Austin","summary":"The University of Texas at Austin is a public university in Austin, Texas, founded in 1883 and one of the institutions of The University of Texas System. Its AI research groups include the Robot Perception and Learning (RPL) Lab, which works on robot learning and embodied AI and publishes research code in its GitHub organization.","keywords":"Austin, Texas research university-lab research-lab"},{"type":"organization","slug":"thinking-machines-lab","href":"/companies/thinking-machines-lab/","name":"Thinking Machines Lab","summary":"Thinking Machines Lab is an AI research and product company. It runs Tinker, a hosted API for fine-tuning open-weight models, and in July 2026 released the open-weight models Inkling and Inkling-Small.","keywords":"Thinking Machines Lab, Inc. San Francisco, California open-models research model-developer research-lab developer-platform Tinker"},{"type":"organization","slug":"tiny-corp","href":"/companies/tiny-corp/","name":"tiny corp","summary":"The tiny corp writes and maintains tinygrad, an MIT-licensed neural network framework, and sells the tinybox, a multi-GPU computer for deep learning.","keywords":"tinygrad, Corp. San Diego, California hardware-manufacturer open-source-steward tinybox"},{"type":"organization","slug":"together-ai","href":"/companies/together-ai/","name":"Together AI","summary":"Together AI runs a cloud platform for inference, fine-tuning, and training on open-weight models from many developers, and rents GPU clusters. Its website and APIs are operated by Together Computer, Inc.","keywords":"Together Computer, Inc. San Francisco, California cloud open-models inference-provider compute-infrastructure developer-platform Together Serverless Inference Together Fine-Tuning Together GPU Clusters"},{"type":"organization","slug":"typesafe-ai","href":"/companies/typesafe-ai/","name":"TypeSafe AI","summary":"TypeSafe AI is a San Francisco company that develops what it calls System One models, AI models that answer typed questions about supplied input with structured values and probabilities rather than generated text. Its first such model, Jev, launched in early access in September 2026 through a hosted API.","keywords":"TypeSafe AI, Inc. San Francisco, California research enterprise model-developer developer-platform System One API (Jev) TypeSafe client SDKs"},{"type":"organization","slug":"university-of-california-san-diego","href":"/companies/university-of-california-san-diego/","name":"University of California San Diego","summary":"UC San Diego is a public university campus of the University of California, located in La Jolla, California, and founded in 1960. Its AI research groups include the Hao AI Lab, which develops and maintains open-source models, evaluations, and systems, including the FastVideo framework for video generation.","keywords":"La Jolla, California research university-lab research-lab"},{"type":"organization","slug":"uc-berkeley","href":"/companies/uc-berkeley/","name":"University of California, Berkeley","summary":"UC Berkeley is a public university and a campus of the University of California, which was founded in 1868 and moved to its Berkeley campus in 1873. Its AI-related research includes the Sky Computing Lab and the Gorilla project, which runs the Berkeley Function Calling Leaderboard.","keywords":"Berkeley, California research university-lab research-lab"},{"type":"organization","slug":"university-of-illinois-urbana-champaign","href":"/companies/university-of-illinois-urbana-champaign/","name":"University of Illinois Urbana-Champaign","summary":"The University of Illinois Urbana-Champaign is a public research university with a land-grant mission, founded in 1867 and governed by the Board of Trustees of the University of Illinois. Its AI research groups include U Lab, which maintains LLMRouter, an open-source library for routing requests among large language models.","keywords":"Urbana-Champaign, Illinois research university-lab research-lab"},{"type":"organization","slug":"university-of-washington","href":"/companies/university-of-washington/","name":"University of Washington","summary":"The University of Washington is a public university founded in 1861, with campuses in Seattle, Bothell, and Tacoma. Washington state law designates it as the state university established in Seattle.","keywords":"University of Washington Seattle, Washington research university-lab research-lab"},{"type":"organization","slug":"unsloth","href":"/companies/unsloth/","name":"Unsloth","summary":"Unsloth develops open-source software for fine-tuning, reinforcement learning, and running language, image, audio, and embedding models on local hardware, offered as a Python library (Unsloth Core), a web interface (Unsloth Studio), and a desktop app (Unsloth Desktop).","keywords":"Unsloth AI Inc. San Francisco, California (owner address in the company's USPTO trademark registration) open-models developer-platform Unsloth Desktop"},{"type":"organization","slug":"vercel","href":"/companies/vercel/","name":"Vercel","summary":"Vercel operates a cloud platform for building and deploying web applications and AI agents. Its AI offerings include the AI Gateway for calling models from many providers, the v0 app builder, and the open-source AI SDK for TypeScript.","keywords":"Vercel Inc. San Francisco, California cloud agents developer-platform cloud-provider Vercel Vercel AI Gateway v0 AI SDK"},{"type":"organization","slug":"waymo","href":"/companies/waymo/","name":"Waymo","summary":"Waymo develops the Waymo Driver, an autonomous driving system, and runs a public, fully autonomous ride-hailing service in U.S. cities. It began as Google's self-driving car project in 2009, became a separate company under Alphabet in 2016, and is reported by Alphabet within its Other Bets.","keywords":"Waymo LLC Mountain View, California robotics research robotics consumer-products research-lab Waymo (ride-hailing service) Waymo Driver Waymo for Business Google (Alphabet)"},{"type":"organization","slug":"weights-and-biases","href":"/companies/weights-and-biases/","name":"Weights & Biases","summary":"Weights & Biases develops an AI developer platform for tracking and comparing machine learning experiments (W&B Models) and for tracing and evaluating LLM applications and agents (W&B Weave), and maintains the open-source wandb Python library. CoreWeave acquired it in May 2025, and its products are now offered within CoreWeave Forge.","keywords":"Weights and Biases, LLC San Francisco, California agents enterprise developer-platform Weights & Biases Models W&B Weave CoreWeave"},{"type":"organization","slug":"world-labs","href":"/companies/world-labs/","name":"World Labs","summary":"World Labs develops world models that generate and reconstruct 3D environments. Its Marble web app and World API create explorable 3D worlds from text, images, video, or panoramas, and it maintains Spark, an open-source renderer for 3D Gaussian splats.","keywords":"World Labs Technologies, Inc. San Francisco, California research model-developer research-lab developer-platform Marble World API Spark"},{"type":"organization","slug":"writer","href":"/companies/writer/","name":"Writer","summary":"Writer is a San Francisco enterprise AI company, founded in 2020, whose platform includes the WRITER Agent, AI Studio, and related tools for business teams. The platform uses Writer's own Palmyra X models by default, and Writer has also published some Palmyra models as downloadable weights on Hugging Face.","keywords":"Writer, Inc. San Francisco, California enterprise agents open-models model-developer enterprise-software Palmyra models via the Writer API WRITER Agent"},{"type":"organization","slug":"xai","href":"/companies/xai/","name":"xAI (SpaceXSI)","summary":"xAI develops the Grok family of AI models and offers them through the Grok assistant and a developer API. Since February 2026 it has been a wholly owned subsidiary of SpaceX, whose AI segment also includes the X platform and AI computing infrastructure.","keywords":"Palo Alto, California frontier-models open-models model-developer consumer-products developer-platform compute-infrastructure Grok xAI API Grok Build Grok Bot SpaceX"},{"type":"organization","slug":"zyphra","href":"/companies/zyphra/","name":"Zyphra","summary":"Zyphra is a San Francisco AI company that trains open foundation models, including the Zamba hybrid state-space language models and the ZAYA1 mixture-of-experts models. It also runs Zyphra Cloud, a hosted platform for model inference and GPU compute.","keywords":"Zyphra Technologies, Inc. San Francisco, California open-models research cloud model-developer research-lab inference-provider Zyphra Cloud Maia"},{"type":"software","slug":"agent-development-kit","href":"/open/agent-development-kit/","name":"Agent Development Kit (ADK)","summary":"Agent Development Kit (ADK) is an open-source framework from Google for building, evaluating, and deploying AI agents, from single agents to multi-agent systems and graph-based workflows. Google publishes implementations for Python, Java, Go, TypeScript, and Kotlin. The README says ADK is optimized for Gemini but model-agnostic.","keywords":"framework agents multi-agent Google Apache-2.0 Google (Alphabet)"},{"type":"software","slug":"a2a-protocol","href":"/open/a2a-protocol/","name":"Agent2Agent (A2A) Protocol","summary":"Agent2Agent (A2A) is an open protocol for communication between AI agents built on different frameworks and run by different parties. Agents publish Agent Cards describing their capabilities and endpoints, and exchange messages and long-running tasks over JSON-RPC, gRPC, or HTTP+JSON bindings without exposing internal state or tools. The project publishes the specification, SDKs, and samples.","keywords":"framework agents protocol interoperability Agent2Agent project (Linux Foundation; Agentic AI Foundation project) Apache-2.0 Agentic AI Foundation (AAIF) The Linux Foundation Google (Alphabet)"},{"type":"software","slug":"agents-md","href":"/open/agents-md/","name":"AGENTS.md","summary":"AGENTS.md is an open convention for a Markdown file, placed in a code repository, that gives AI coding agents project-specific context and instructions such as build steps, test commands, and code conventions. It is plain Markdown with no required fields; in a monorepo, nested files can apply to subprojects, with the file closest to the edited code taking precedence. OpenAI released it in August 2025 and contributed it to the Agentic AI Foundation in December 2025.","keywords":"framework agents coding-agent documentation AGENTS.md a Series of LF Projects, LLC (Agentic AI Foundation project) MIT Agentic AI Foundation (AAIF) The Linux Foundation OpenAI"},{"type":"software","slug":"ai-sdk","href":"/open/ai-sdk/","name":"AI SDK","summary":"The AI SDK is an open-source TypeScript toolkit from Vercel for building AI applications and agents. AI SDK Core gives one API for generating text and structured output, calling tools, and building agents across model providers, and AI SDK UI provides hooks for chat and generative interfaces in web frameworks.","keywords":"framework agents llm-applications developer-tools Vercel Apache-2.0 Vercel"},{"type":"evaluation-tool","slug":"ailuminate","href":"/open/ailuminate/","name":"AILuminate","summary":"AILuminate is MLCommons' family of safety and security benchmarks for generative AI systems, organized around 12 hazard categories. The v1.0 safety benchmark (December 2024) tests general-purpose chat systems with more than 24,000 human-written prompts and grades responses with an ensemble of evaluator models; a jailbreak benchmark measures how safety degrades under attack.","keywords":"Safety v1.0 and v1.1; Jailbreak v0.5 eval benchmark evaluation MLCommons (AI Risk & Reliability working group) CC-BY-4.0 Apache-2.0 MLCommons"},{"type":"model-family","slug":"alphaprotein-novo","href":"/open/alphaprotein-novo/","name":"AlphaProtein Novo","summary":"AlphaProtein Novo (AP Novo) is a Google DeepMind pipeline for designing new enzymes by scaffolding a structural catalytic motif. Its diffusion model, the AP Novo Generator, generates protein structures and sequences together, conditioned on the motif and ligand context; optional LigandMPNN sequence redesign, AlphaFold 3 structure prediction, and evaluation metrics complete the pipeline. The code and generator weights were published in October 2026 alongside a bioRxiv paper.","keywords":"model science protein-design Google DeepMind Google DeepMind"},{"type":"model-release","slug":"alphaprotein-novo-generator","href":"/open/alphaprotein-novo-generator/","name":"AlphaProtein Novo Generator","summary":"The pretrained AP Novo Generator weights, a single generator.bin.zst file that the alphaprotein-novo package (JAX) loads to co-generate protein structures and amino acid sequences conditioned on a catalytic motif and ligand context. The weights are not in the repository; the README gives a direct download from Google Cloud Storage.","keywords":"AP Novo Generator (generator.bin.zst) model science protein-design Google DeepMind AlphaProtein Novo Generator Model Parameters Terms of Use Apache-2.0 Google DeepMind AlphaProtein Novo"},{"type":"software","slug":"anythingllm","href":"/open/anythingllm/","name":"AnythingLLM","summary":"AnythingLLM is an application for chatting with documents and building AI agents on top of local or hosted language models. It is available as a desktop app for macOS, Windows, and Linux and as a multi-user Docker version, with connectors to many model providers and vector databases.","keywords":"runtime local-inference agents retrieval Mintplex Labs MIT Mintplex Labs"},{"type":"model-family","slug":"apriel","href":"/open/apriel/","name":"Apriel","summary":"Apriel is ServiceNow's series of small open-weight language models, built by its SLAM (ServiceNow Language Models) lab. Apriel-5B was the first release. It was followed by 15B-parameter reasoning models: Apriel-Nemotron-15b-Thinker, the multimodal Apriel-1.5-15b-Thinker, and Apriel-1.6-15b-Thinker (December 2025). In April 2026 ServiceNow released Super Apriel, a 15B model derived from Apriel 1.6 whose layers can switch between attention and linear-time mixers at inference time.","keywords":"model language-model reasoning multimodal ServiceNow (SLAM lab) ServiceNow"},{"type":"model-release","slug":"apriel-1-6-15b-thinker","href":"/open/apriel-1-6-15b-thinker/","name":"Apriel-1.6-15B-Thinker","summary":"A multimodal reasoning model of about 15B parameters that accepts text and images and writes its reasoning steps before a marked final answer. ServiceNow released it in December 2025 as an update to Apriel-1.5-15b-Thinker, with further continual pretraining, supervised fine-tuning, and reinforcement learning aimed partly at shorter reasoning.","keywords":"Apriel-1.6-15b-Thinker model language-model reasoning multimodal vision-language ServiceNow (SLAM lab) MIT ServiceNow Apriel"},{"type":"evaluation-tool","slug":"arc-agi","href":"/open/arc-agi/","name":"ARC-AGI","summary":"ARC-AGI is a series of benchmarks that measure how efficiently a system acquires new skills on unfamiliar tasks, using tasks that rely only on basic \"core knowledge\" priors and are easy for people. ARC-AGI-1 (introduced in 2019) and ARC-AGI-2 (March 2025) are grid puzzles in which the solver infers a transformation from a few input-output examples; ARC-AGI-3 (March 2026) consists of interactive, turn-based game environments in which agents must explore and infer goals without explicit instructions. Public tasks are released for each version, while semi-private and private evaluation sets used for official scoring are held back.","keywords":"eval evaluation benchmark reasoning ARC Prize Foundation Apache-2.0 Apache-2.0 MIT ARC Prize Foundation"},{"type":"software","slug":"autogen","href":"/open/autogen/","name":"AutoGen","summary":"AutoGen is Microsoft's open-source Python framework for building multi-agent AI applications that act autonomously or alongside people. It comprises an event-driven Core, the higher-level AgentChat API, Extensions for external services such as MCP servers and code execution, and the no-code AutoGen Studio. The project is now in maintenance mode, and Microsoft directs new users to its successor, Microsoft Agent Framework.","keywords":"framework agents Microsoft MIT CC-BY-4.0 Microsoft"},{"type":"software","slug":"awq","href":"/open/awq/","name":"AWQ (Activation-aware Weight Quantization)","summary":"AWQ is a post-training method and codebase for low-bit (INT3/INT4) weight-only quantization of large language models, including instruction-tuned and multimodal models. It uses activation statistics to find the most important weight channels and scales them before quantization, without backpropagation. The repository also includes 4-bit CUDA kernels and TinyChat, an inference interface for running quantized models on desktop and edge GPUs.","keywords":"framework quantization inference compression MIT HAN Lab MIT Massachusetts Institute of Technology"},{"type":"evaluation-tool","slug":"berkeley-function-calling-leaderboard","href":"/open/berkeley-function-calling-leaderboard/","name":"Berkeley Function Calling Leaderboard (BFCL)","summary":"The Berkeley Function Calling Leaderboard (BFCL) is a benchmark and live leaderboard from UC Berkeley's Gorilla project for evaluating how well language models call functions and tools. It checks serial and parallel function calls in several programming languages using abstract syntax tree (AST) evaluation. Later versions add multi-turn and multi-step scenarios and, in V4, agentic tasks such as web search, memory management, and format sensitivity.","keywords":"2026.3.23 eval evaluation benchmark agents Gorilla project, UC Berkeley Apache-2.0 Apache-2.0 University of California, Berkeley"},{"type":"evaluation-tool","slug":"bigcodebench","href":"/open/bigcodebench/","name":"BigCodeBench","summary":"BigCodeBench is a Python code-generation benchmark from the BigCode project with 1,140 function-level tasks that require calls to 139 libraries across 7 domains. It has two variants, Complete (prompts with structured docstrings) and Instruct (natural-language instructions for chat models), plus a 148-task Hard subset, and it is scored by executing unit tests. The GitHub repository was archived and made read-only on July 20, 2026.","keywords":"eval evaluation benchmark code-model BigCode project (open scientific collaboration) Hugging Face (co-leads the BigCode steering committee) ServiceNow (co-leads the BigCode steering committee) Apache-2.0 Apache-2.0 Hugging Face ServiceNow"},{"type":"software","slug":"bitnet","href":"/open/bitnet/","name":"bitnet.cpp (BitNet inference framework)","summary":"bitnet.cpp is Microsoft's official inference framework for 1-bit large language models, such as BitNet b1.58, whose weights are ternary (1.58-bit). It provides optimized kernels for running these models on x86 and ARM CPUs, with a separate GPU inference kernel, and is based on llama.cpp.","keywords":"runtime inference local-inference quantization Microsoft MIT Microsoft"},{"type":"model-family","slug":"boltz-2","href":"/open/boltz-2/","name":"Boltz-2","summary":"Boltz-2 is a biomolecular foundation model that predicts the 3D structures of complexes of proteins, nucleic acids, and small molecules together with protein-ligand binding affinity. It was released in June 2025 by the Boltz team at MIT Jameel Clinic with Recursion and is now presented by Boltz PBC as one of its open-source models. It ships as a structure and confidence checkpoint plus a separate affinity checkpoint.","keywords":"model science structure-prediction Boltz (Boltz PBC) Boltz Massachusetts Institute of Technology"},{"type":"model-release","slug":"boltz-2-v2","href":"/open/boltz-2-v2/","name":"Boltz-2 (boltz2_conf and boltz2_aff checkpoints)","summary":"The Boltz-2 weights published on June 6, 2025: a structure and confidence checkpoint (boltz2_conf.ckpt) and a separate binding-affinity checkpoint (boltz2_aff.ckpt), released together in one Hugging Face repository and loaded by the boltz package, which runs the latest model by default. The two checkpoints are assessed together because the affinity prediction runs as part of the same Boltz-2 pipeline.","keywords":"boltz-community/boltz-2: boltz2_conf.ckpt + boltz2_aff.ckpt model science structure-prediction Boltz (Boltz PBC) MIT Boltz Massachusetts Institute of Technology Boltz-2"},{"type":"model-family","slug":"chai-1","href":"/open/chai-1/","name":"Chai-1","summary":"Chai-1 is a multi-modal foundation model from Chai Discovery for predicting molecular structures, covering proteins, small molecules, DNA, RNA, glycosylations, and other modifications. It was first released in September 2024, and its license changed in November 2024 (see the release record). The chai_lab package (latest PyPI release 0.6.1, March 2025) runs it locally.","keywords":"model science structure-prediction Chai Discovery Chai Discovery"},{"type":"model-release","slug":"chai-1-v0-6","href":"/open/chai-1-v0-6/","name":"Chai-1 (weights used by chai_lab 0.6.1)","summary":"The Chai-1 weights used by the current chai_lab package (version 0.6.1, published March 18, 2025). They are distributed as six exported PyTorch module files (feature embedding, bond projection, token embedder, trunk, diffusion module, and confidence head). The package fetches them from Chai's asset server, and the same six files are in the Hugging Face repository, last updated February 18, 2025. The sources reviewed do not say which earlier package versions used these same files.","keywords":"chai_lab 0.6.1 (models_v2 components) model science structure-prediction Chai Discovery Apache-2.0 Chai Discovery Chai-1"},{"type":"software","slug":"chroma-db","href":"/open/chroma-db/","name":"Chroma (open-source database)","summary":"Chroma is an open-source database for AI applications that stores documents, embeddings, and metadata and supports vector, full-text, regex, and metadata search. It can run in memory inside a program, persist to local disk, or run as a server, with client libraries for Python, JavaScript/TypeScript, and Rust.","keywords":"runtime vector-database retrieval embedding Chroma Apache-2.0 Chroma"},{"type":"model-family","slug":"chronos","href":"/open/chronos/","name":"Chronos (time series models)","summary":"Chronos is Amazon's family of pretrained time-series forecasting models. It includes the original T5-based Chronos models (March 2024, 8M to 710M parameters), the patch-based Chronos-Bolt models (November 2024), and Chronos-2 (October 2025), a 120M-parameter encoder-only model that adds multivariate and covariate-informed forecasting.","keywords":"model time-series forecasting Amazon (Amazon Web Services) Amazon (AWS)"},{"type":"model-release","slug":"chronos-2","href":"/open/chronos-2/","name":"Chronos-2","summary":"Chronos-2 is a 120M-parameter, encoder-only time-series foundation model from Amazon, released on October 20, 2025. Inspired by the T5 encoder, it produces multi-step quantile forecasts and uses group attention to learn in context across related series and covariates, supporting univariate, multivariate, and covariate-informed forecasting with contexts up to 8,192 steps and horizons up to 1,024 steps.","keywords":"Chronos-2 (amazon/chronos-2, 120M) model time-series forecasting Amazon (Amazon Web Services) Apache-2.0 Apache-2.0 Amazon (AWS) Chronos (time series models)"},{"type":"software","slug":"cline-extension","href":"/open/cline-extension/","name":"Cline (open-source coding agent)","summary":"Cline is an open-source AI coding agent that reads a project, edits files, and runs terminal commands, asking for approval at each step unless auto-approve is turned on. Its public repository holds a shared agent SDK, a command-line tool, the VS Code extension, and a desktop app for macOS and Windows; the JetBrains plugin is not open-sourced.","keywords":"runtime agents coding-agent developer-tools Cline Bot Inc. Apache-2.0 Cline"},{"type":"model-family","slug":"clip","href":"/open/clip/","name":"CLIP","summary":"CLIP (Contrastive Language-Image Pre-Training) is an OpenAI family of paired image and text encoders trained with a contrastive objective on 400 million image-text pairs, which lets the model classify images zero-shot from natural-language labels. OpenAI released the models in stages from January 2021 to April 2022, from ResNet-50 and ViT-B/32 up to ViT-L/14 and ViT-L/14 at 336-pixel resolution.","keywords":"model multimodal vision-language embedding OpenAI OpenAI"},{"type":"model-release","slug":"clip-vit-large-patch14","href":"/open/clip-vit-large-patch14/","name":"CLIP ViT-L/14","summary":"CLIP ViT-L/14 pairs a ViT-L/14 Vision Transformer image encoder with a masked self-attention Transformer text encoder, trained contrastively to match images with their captions. OpenAI released it in January 2022; a variant further trained at 336-pixel resolution (ViT-L/14@336px) followed in April 2022.","keywords":"ViT-L/14 model multimodal vision-language embedding OpenAI MIT OpenAI CLIP"},{"type":"software","slug":"codex-cli","href":"/open/codex-cli/","name":"Codex CLI","summary":"Codex CLI is OpenAI's open-source coding agent that runs locally in a terminal, where it can inspect, edit, and run code in a local repository. The README points to separate Codex experiences for code editors, a desktop app, and Codex Web, OpenAI's cloud-based agent.","keywords":"runtime agents coding-agent developer-tools OpenAI Apache-2.0 OpenAI"},{"type":"model-family","slug":"cogito","href":"/open/cogito/","name":"Cogito","summary":"Cogito is Deep Cogito's series of open-weight language models; the v2 and v2.1 models are hybrid reasoning models that can answer directly or reason first. Cogito v1 Preview (April 2025) covered 3B to 70B models on Llama and Qwen bases; Cogito v2 Preview (July 2025) added 70B dense, 109B MoE, 405B dense, and 671B MoE models on Llama and DeepSeek bases; and Cogito v2.1 (November 2025) is a 671B MoE model with 37B active parameters on a DeepSeek base.","keywords":"model language-model reasoning Deep Cogito Deep Cogito"},{"type":"model-release","slug":"cogito-v2-preview-llama-405b","href":"/open/cogito-v2-preview-llama-405b/","name":"Cogito v2 Preview Llama 405B","summary":"A 405B-parameter dense hybrid reasoning model that Deep Cogito post-trained from Meta's Llama 3.1 405B, released in July 2025 as one of four Cogito v2 Preview models. It can answer directly or reason first, supports a 128k-token context, and was trained in over 30 languages using Deep Cogito's Iterated Distillation and Amplification approach.","keywords":"cogito-v2-preview-llama-405B model language-model reasoning Deep Cogito Llama 3.1 Community License Agreement Deep Cogito Cogito"},{"type":"model-release","slug":"cogito-v2-1-671b","href":"/open/cogito-v2-1-671b/","name":"Cogito v2.1 671B","summary":"A 671B-parameter mixture-of-experts model with 37B active parameters that Deep Cogito post-trained in-house from the DeepSeek-V3 base model. It is a hybrid reasoning model that can answer directly or reason first, supports a 128k-token context, and was trained in over 30 languages; Deep Cogito says process supervision of reasoning chains lets it reach answers with shorter reasoning.","keywords":"cogito-671b-v2.1 model language-model reasoning mixture-of-experts Deep Cogito MIT Deep Cogito Cogito"},{"type":"dataset","slug":"common-crawl-corpus","href":"/open/common-crawl-corpus/","name":"Common Crawl corpus","summary":"An archive of web crawl data collected by Common Crawl's crawlers since 2008, released as periodic crawls. Each crawl is published as WARC files (raw HTTP responses, requests, and metadata), WAT files (computed metadata as JSON), and WET files (extracted plain text), together with URL indexes.","keywords":"CC-MAIN-2026-39 (latest crawl at review) dataset pretraining-data Common Crawl Foundation Common Crawl Terms of Use (limited, non-exclusive license) Common Crawl"},{"type":"model-family","slug":"contextual-reranker","href":"/open/contextual-reranker/","name":"Contextual AI Reranker","summary":"Contextual AI's reranker models score how relevant retrieved documents are to a query and can follow a natural-language instruction about how to rank them, for example to prefer recent sources. Reranker v2, released in August 2025, comes in 1B, 2B, and 6B sizes with quantized versions, supports more than 100 languages and inputs of up to 32K tokens, and is published as open weights for noncommercial use; managed versions are served through Contextual AI's Rerank API.","keywords":"model retrieval reranker Contextual AI Contextual AI"},{"type":"model-release","slug":"contextual-reranker-v2-2b","href":"/open/contextual-reranker-v2-2b/","name":"Contextual AI Reranker v2 2B","summary":"The 2B-parameter size of Contextual AI Reranker v2, an instruction-following multilingual reranker released in August 2025. Given a query, an optional natural-language instruction, and a document, it outputs a relevance score; the card lists support for more than 100 languages and inputs of up to 32K tokens.","keywords":"ctxl-rerank-v2-instruct-multilingual-2b model retrieval reranker Contextual AI CC-BY-NC-SA-4.0 Contextual AI Contextual AI Reranker"},{"type":"software","slug":"coremltools","href":"/open/coremltools/","name":"Core ML Tools (coremltools)","summary":"Core ML Tools (coremltools) is Apple's Python package for converting machine learning models from libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost, and LibSVM into the Core ML format, and for reading, writing, and optimizing Core ML models. On macOS it can also check a conversion by running predictions through Core ML.","keywords":"framework model-conversion inference local-inference Apple BSD-3-Clause Apple"},{"type":"model-release","slug":"liquid-d1-3b","href":"/open/liquid-d1-3b/","name":"d1-3B","summary":"d1-3B is a 3.12-billion-parameter multimodal decision model from Liquid AI, post-trained from its LFM2.5-VL-3B vision-language model. It accepts text, JSON, and images with a 32,768-token context and answers named questions in one forward pass without generating output tokens; the card says it is not a chat model.","keywords":"d1-3B model multimodal vision-language edge Liquid AI LFM Open License v1.0 Liquid AI Liquid AI d1"},{"type":"software","slug":"deepspeed","href":"/open/deepspeed/","name":"DeepSpeed","summary":"DeepSpeed is an open-source PyTorch library for distributed training and inference of large models; its features include the ZeRO memory optimizations, 3D parallelism, Ulysses sequence parallelism, and mixture-of-experts support. Microsoft contributed it to LF AI & Data as an incubation project in February 2025, and in May 2025 it became a hosted project of the PyTorch Foundation, which names Microsoft as the contributor.","keywords":"framework training distributed-computing inference PyTorch Foundation (hosted project) DeepSpeed Technical Steering Committee (project committers) Apache-2.0 CC-BY-4.0 PyTorch Foundation The Linux Foundation Microsoft"},{"type":"software","slug":"diffusers","href":"/open/diffusers/","name":"Diffusers","summary":"Diffusers is Hugging Face's open-source PyTorch library for pretrained diffusion models that generate images, video, and audio. It is organized around the DiffusionPipeline API, with interchangeable noise schedulers and model components, and supports adapters such as LoRA. Version 0.40.0 (August 2026) removed JAX/Flax support.","keywords":"0.40.0 framework inference training Hugging Face Apache-2.0 Hugging Face"},{"type":"model-family","slug":"dinov3","href":"/open/dinov3/","name":"DINOv3","summary":"DINOv3 is Meta's third generation of self-supervised vision backbones, announced in August 2025. The suite includes a 6.7B-parameter ViT-7B/16, distilled Vision Transformers from 21M to 840M parameters, ConvNeXt models, and two backbones trained on satellite imagery, plus task heads for classification, depth, detection, and segmentation. The web-image models were trained on LVD-1689M, a curated set of about 1.7 billion images.","keywords":"model computer-vision self-supervised Meta (FAIR) Meta"},{"type":"model-release","slug":"dinov3-vit7b16","href":"/open/dinov3-vit7b16/","name":"DINOv3 ViT-7B/16 (LVD-1689M)","summary":"The largest DINOv3 backbone: a 6.7-billion-parameter Vision Transformer with 16-pixel patches, trained by Meta with self-supervised learning (a DINO self-distillation loss and an iBOT masked-image-modeling loss) on LVD-1689M. Training ran in three stages: pretraining, Gram anchoring, and high-resolution adaptation.","keywords":"ViT-7B/16, pretrain LVD-1689M model computer-vision self-supervised Meta (FAIR) DINOv3 License Meta DINOv3"},{"type":"software","slug":"dioptra","href":"/open/dioptra/","name":"Dioptra","summary":"Dioptra is an open-source software test platform for assessing trustworthy characteristics of AI models, built by NIST's National Cybersecurity Center of Excellence and announced by NIST in July 2024. It runs as a set of containerized services with a REST API that can be used through a web interface or a Python client to design, run, and track experiments, and its README says it supports the Measure function of the NIST AI Risk Management Framework.","keywords":"framework evaluation security National Institute of Standards and Technology (NIST) CC-BY-4.0 National Institute of Standards and Technology"},{"type":"software","slug":"docling","href":"/open/docling/","name":"Docling","summary":"Docling is an open-source Python toolkit that converts documents such as PDF, DOCX, PPTX, XLSX, HTML, images, audio, and other formats into a structured DoclingDocument that can be exported as Markdown, HTML, or JSON for generative AI applications. It includes PDF layout, reading-order, and table-structure analysis, OCR, and optional vision-language model pipelines. The project began at IBM Research in Zurich and has been hosted by the LF AI & Data Foundation since 2025.","keywords":"2.131.0 framework document-processing retrieval-augmented-generation LF AI & Data Foundation (hosted project) IBM Research (originating team) MIT LF AI & Data Foundation The Linux Foundation IBM"},{"type":"dataset","slug":"dolma","href":"/open/dolma/","name":"Dolma","summary":"Dolma is Ai2's family of English pretraining corpora built from web pages, academic publications, code, and encyclopedic text, used to train the OLMo models. The first release (v1, August 2023) was updated through v1.7 (April 2024); Dolma 3, used for Olmo 3, consists of a pool of about 9.3 trillion tokens and curated pretraining, mid-training, and long-context mixes. A Dolma 3.5 pool with additional sources and filtering has since been published.","keywords":"dataset pretraining-data Ai2 (Allen Institute for AI) ODC-By-1.0 Apache-2.0 Ai2 (Allen Institute for AI)"},{"type":"software","slug":"dspy","href":"/open/dspy/","name":"DSPy","summary":"DSPy is an open-source Python framework for building language-model programs from compositional Python modules instead of hand-written prompts. It includes optimizer algorithms that tune the prompts and weights of those programs. The project started at the Stanford NLP group.","keywords":"3.4.0 framework prompt-optimization agents retrieval-augmented-generation Stanford NLP Group MIT Stanford University"},{"type":"model-release","slug":"embeddinggemma-2","href":"/open/embeddinggemma-2/","name":"EmbeddingGemma 2","summary":"EmbeddingGemma 2 is a multimodal embedding model from Google DeepMind that maps text (including code), images, video, and audio, alone or combined in one input, into a single 768-dimensional vector space. The model card gives 740M parameters in total: a 270M-parameter text model plus a 170M vision encoder and a 300M audio encoder that can be loaded separately. All inputs share an 8,192-token context, and outputs can be shortened to 512, 256, or 128 dimensions. Google's release page dates it to October 6, 2026.","keywords":"embeddinggemma-2 model embedding multimodal multilingual local-inference Google DeepMind Apache-2.0 Google DeepMind Google (Alphabet) Gemma"},{"type":"model-release","slug":"embeddinggemma-300m","href":"/open/embeddinggemma-300m/","name":"EmbeddingGemma 300M","summary":"EmbeddingGemma is a multilingual text embedding model in the Gemma family, built from Gemma 3 with T5Gemma initialization. Google's documentation gives its size as 308M parameters; the Hugging Face card calls it a 300M-parameter model. It accepts up to 2,048 input tokens and outputs 768-dimensional vectors, which can be truncated to 512, 256, or 128 dimensions through Matryoshka Representation Learning. Google's release page dates its release to September 4, 2025.","keywords":"embeddinggemma-300m model embedding multilingual local-inference Google DeepMind Gemma Terms of Use Google DeepMind Google (Alphabet) Gemma"},{"type":"dataset","slug":"epoch-ai-models","href":"/open/epoch-ai-models/","name":"Epoch AI Data on AI Models","summary":"A public database of machine learning models maintained by Epoch AI, recording for each model estimates such as training compute, parameter count, training dataset size, training cost, training duration, and power draw. It is published as an interactive explorer and as CSV files covering notable models, large-scale models (trained with at least 10^23 FLOP), frontier models (top five in training compute when released), and all models.","keywords":"dataset training-compute ai-trends Epoch AI CC-BY-4.0 Epoch AI"},{"type":"model-family","slug":"esm","href":"/open/esm/","name":"ESM (EvolutionaryScale and Biohub protein models)","summary":"Protein language models developed by EvolutionaryScale and, since its team joined Biohub in November 2025, released by Biohub. The line includes ESM3 (June 2024), a generative model that reasons over protein sequence, structure, and function, with a largest version of 98B parameters and a small open model; ESM C (ESM Cambrian, December 2024), representation models at 300M, 600M, and 6B parameters; and, from May 2026, ESMFold2 structure prediction models and sparse autoencoders built on ESM C.","keywords":"model language-model multimodal Biohub (Chan Zuckerberg Biohub, Inc.) EvolutionaryScale (original developer; now part of Biohub) Biohub (Chan Zuckerberg Biohub) EvolutionaryScale"},{"type":"model-release","slug":"esm3-sm-open-v1","href":"/open/esm3-sm-open-v1/","name":"ESM3 open small (esm3-sm-open-v1)","summary":"The openly released small ESM3 model, which EvolutionaryScale describes as a 1.4B-parameter open model. ESM3 is a generative masked language model that takes partial prompts across protein sequence, structure, and function tracks and predicts all three. EvolutionaryScale released it in June 2024; Biohub now distributes it under the MIT license.","keywords":"esm3-sm-open-v1 model language-model multimodal Biohub (Chan Zuckerberg Biohub, Inc.) EvolutionaryScale (original developer; now part of Biohub) MIT Biohub (Chan Zuckerberg Biohub) EvolutionaryScale ESM (EvolutionaryScale and Biohub protein models)"},{"type":"model-release","slug":"esmc-6b","href":"/open/esmc-6b/","name":"ESMC 6B (ESM Cambrian)","summary":"The largest ESM C (ESM Cambrian) protein language model: 6 billion parameters, 80 transformer layers, and 2.37e23 training FLOPs. EvolutionaryScale introduced ESM C in December 2024; Biohub's May 2026 release of ESMC, ESMFold2, and ESM Atlas included the 6B weights, and ESMFold2 is trained on top of a frozen ESMC 6B.","keywords":"ESMC-6B (esmc-6b-2024-12 on the Biohub Platform API) model language-model embedding Biohub (Chan Zuckerberg Biohub, Inc.) EvolutionaryScale (original developer; now part of Biohub) MIT Biohub (Chan Zuckerberg Biohub) EvolutionaryScale ESM (EvolutionaryScale and Biohub protein models)"},{"type":"dataset","slug":"essential-web","href":"/open/essential-web/","name":"Essential-Web v1.0","summary":"Essential-Web v1.0 is a web text dataset of about 24 trillion tokens in 23.6 billion documents, built by Essential AI from 101 Common Crawl snapshots. Every document carries metadata from a 12-category taxonomy covering subject (Free Decimal Correspondence, a Dewey Decimal-inspired scheme), page type, reasoning depth, education level, and quality, so subsets can be selected with metadata filters.","keywords":"v1.0 dataset pretraining-data Essential AI ODC-By-1.0 Essential AI"},{"type":"model-family","slug":"ether0","href":"/open/ether0/","name":"ether0","summary":"ether0 is a 24B-parameter chemistry reasoning model from FutureHouse, released in June 2025. It reasons in English and answers with molecular structures written as SMILES. It was fine-tuned and trained with reinforcement learning from Mistral AI's Mistral-Small-24B-Instruct-2501.","keywords":"model language-model reasoning science FutureHouse FutureHouse"},{"type":"model-release","slug":"ether0-24b","href":"/open/ether0-24b/","name":"ether0 (24B)","summary":"The only published ether0 checkpoint: a 24B-parameter language model that reasons in English and outputs molecules as SMILES. It is fine-tuned and reinforcement-learning trained from Mistral-Small-24B-Instruct-2501, followed by safety post-training. It was posted to Hugging Face on June 4, 2025.","keywords":"futurehouse/ether0 model language-model reasoning science FutureHouse Apache-2.0 Apache-2.0 FutureHouse ether0"},{"type":"model-family","slug":"evo-2","href":"/open/evo-2/","name":"Evo 2","summary":"Evo 2 is a family of DNA language models from Arc Institute, developed with NVIDIA and collaborators at institutions including Stanford University, UCSF, UC Berkeley, Goodfire, and the University of Washington. The models use the StripedHyena 2 architecture, work at single-nucleotide resolution with context of up to 1 million base pairs, and were trained on the OpenGenome2 dataset. Checkpoints range from 1B to 40B parameters; a 20B checkpoint derived from the 40B model was added in February 2026.","keywords":"model language-model Arc Institute Arc Institute NVIDIA"},{"type":"model-release","slug":"evo-2-20b","href":"/open/evo-2-20b/","name":"Evo 2 20B","summary":"A 20-billion-parameter Evo 2 checkpoint with 1 million base-pair context, released in February 2026. Arc created it from Evo 2 40B without additional training by removing layers that logit-lens analysis showed could be dropped with little effect on loss, while keeping the unembedding layer.","keywords":"evo2_20b model language-model Arc Institute Apache-2.0 Apache-2.0 Arc Institute NVIDIA Evo 2"},{"type":"model-release","slug":"evo-2-40b","href":"/open/evo-2-40b/","name":"Evo 2 40B","summary":"The 40-billion-parameter Evo 2 checkpoint, with 50 layers and a context length of up to 1 million base pairs. It was trained autoregressively on OpenGenome2 on 2,048 GPUs, per the Savanna README; a separate evo2_40b_base checkpoint trained at 8,192-token context is also published.","keywords":"evo2_40b model language-model Arc Institute Apache-2.0 Apache-2.0 Apache-2.0 Arc Institute NVIDIA Evo 2"},{"type":"software","slug":"executorch","href":"/open/executorch/","name":"ExecuTorch","summary":"ExecuTorch is PyTorch's open-source stack for running AI models on-device, from phones and laptops to embedded systems and microcontrollers. A model is captured with torch.export, lowered for selected hardware backends into a .pte program, and run through C++, Python, Swift/Objective-C, Kotlin/Java, or JavaScript APIs.","keywords":"runtime inference local-inference on-device Meta BSD-3-Clause Meta PyTorch Foundation"},{"type":"software","slug":"faiss","href":"/open/faiss/","name":"Faiss","summary":"Faiss is a library for similarity search and clustering of dense vectors, written in C++ with Python wrappers. It includes exact and approximate nearest-neighbor indexes, among them compressed (quantization-based) and graph-based (HNSW, NSG) indexes, and GPU implementations of several algorithms. It is developed mainly at Meta's Fundamental AI Research (FAIR) group, which released it in March 2017.","keywords":"framework vector-search numerical-computing retrieval Meta (Fundamental AI Research) MIT Meta"},{"type":"software","slug":"fastvideo","href":"/open/fastvideo/","name":"FastVideo","summary":"FastVideo is a post-training and inference framework for accelerated video generation, maintained by the Hao AI Lab at UC San Diego. Its README lists full and LoRA fine-tuning of open video diffusion transformers, Distribution Matching Distillation (DMD2), Video Sparse Attention and sparse distillation, causal distillation through Self-Forcing, and sequence-parallel distributed training and inference.","keywords":"framework video-generation training inference diffusion Hao AI Lab, UC San Diego Apache-2.0 University of California San Diego"},{"type":"model-family","slug":"fastvlm","href":"/open/fastvlm/","name":"FastVLM","summary":"FastVLM is a family of vision-language models from Apple, described in a CVPR 2025 paper, built around FastViTHD, a hybrid convolutional-transformer vision encoder that outputs fewer visual tokens to reduce encoding time for high-resolution images. Apple released 0.5B, 1.5B, and 7B variants that pair the encoder with Qwen2 language models, with PyTorch checkpoints and versions exported for Apple silicon.","keywords":"model vision-language multimodal Apple Apple"},{"type":"model-release","slug":"fastvlm-7b","href":"/open/fastvlm-7b/","name":"FastVLM 7B","summary":"FastVLM 7B is the 7B variant of Apple's FastVLM release. It combines Apple's FastViTHD vision encoder with the Qwen2-7B language model in a LLaVA-style architecture and accepts an image plus a text prompt to generate text.","keywords":"FastVLM-7B model vision-language multimodal Apple Apple Machine Learning Research Model License Agreement Apple software license (ml-fastvlm code) Apple FastVLM"},{"type":"dataset","slug":"fineweb","href":"/open/fineweb/","name":"FineWeb","summary":"FineWeb is Hugging Face's English web-text pretraining dataset, built by extracting, filtering, and deduplicating pages from Common Crawl snapshots dating back to 2013. The card describes it as more than 18.5 trillion tokens (GPT-2 tokenizer), up from about 15 trillion at first release in April 2024; version 1.4.0 (July 2025) added the Common Crawl snapshots from January to June 2025. Hugging Face also published FineWeb-Edu, an educational subset.","keywords":"v1.4.0 dataset pretraining-data Hugging Face (FineData, HuggingFaceFW) ODC-By-1.0 Apache-2.0 Hugging Face"},{"type":"model-release","slug":"florence-2-large","href":"/open/florence-2-large/","name":"Florence-2-large","summary":"Florence-2-large is the larger pretrained Florence-2 vision model, listed at 0.77B parameters on its model card. It takes an image and a task prompt and returns text, boxes, or polygons for tasks such as captioning, detection, grounding, and OCR. Since a December 2024 update, the Hugging Face repository holds a version continued-pretrained for a 4k context length on 0.1B samples, which the card says might not be trained well; the same update changed OCR output to include line separators.","keywords":"Florence-2-large model vision-language multimodal computer-vision Microsoft (Azure AI) MIT Microsoft Microsoft Florence-2"},{"type":"software","slug":"gemini-cli","href":"/open/gemini-cli/","name":"Gemini CLI","summary":"Gemini CLI is an open-source AI agent from Google that runs in a terminal and uses Google's Gemini models. It has built-in tools for file operations, shell commands, web fetching, and Google Search grounding, supports MCP servers and extensions, and can run non-interactively in scripts.","keywords":"runtime agents coding-agent developer-tools Google Apache-2.0 Google (Alphabet)"},{"type":"model-family","slug":"gemma","href":"/open/gemma/","name":"Gemma","summary":"Gemma is a family of open-weight models credited to Google DeepMind. Google's release page lists general-purpose generations including Gemma 3 (2025) and Gemma 4 (2026), along with specialized variants such as EmbeddingGemma, MedGemma, ShieldGemma, and FunctionGemma.","keywords":"model language-model multimodal Google DeepMind Google DeepMind Google (Alphabet)"},{"type":"model-release","slug":"gemma-4-12b","href":"/open/gemma-4-12b/","name":"Gemma 4 12B Unified","summary":"Gemma 4 12B Unified is an 11.95-billion-parameter Gemma 4 model with an encoder-free design: image patches and audio waveforms are projected directly into the language model instead of passing through separate encoders. It accepts text, image, audio, and video (as frames) input and generates text, with a 256K-token context window. Google's release page dates its release to June 3, 2026, after the other Gemma 4 sizes.","keywords":"4 (12B Unified) model language-model multimodal vision-language speech reasoning Google DeepMind Apache-2.0 Google DeepMind Google (Alphabet) Gemma"},{"type":"model-release","slug":"gemma-4-26b-a4b","href":"/open/gemma-4-26b-a4b/","name":"Gemma 4 26B A4B","summary":"Gemma 4 26B A4B is the mixture-of-experts model in the Gemma 4 generation, with 25.2 billion total and 3.8 billion active parameters (8 of 128 experts active, plus one shared expert). It accepts text and image input, can process video as frames, and generates text, with a 256K-token context window. Google's release page dates the initial Gemma 4 release, which included this size, to March 31, 2026.","keywords":"4 (26B A4B) model language-model multimodal vision-language reasoning mixture-of-experts Google DeepMind Apache-2.0 Google DeepMind Google (Alphabet) Gemma"},{"type":"model-release","slug":"gemma-4-31b","href":"/open/gemma-4-31b/","name":"Gemma 4 31B","summary":"Gemma 4 31B is the dense model in the Gemma 4 generation, with 30.7 billion parameters. It accepts text and image input, can process video as frames, and generates text, with a 256K-token context window. It is published as pre-trained and instruction-tuned checkpoints; Google's release page dates the initial Gemma 4 release, which included this size, to March 31, 2026.","keywords":"4 (31B) model language-model multimodal vision-language reasoning Google DeepMind Apache-2.0 Google DeepMind Google (Alphabet) Gemma"},{"type":"software","slug":"goose","href":"/open/goose/","name":"goose","summary":"goose is an open-source, general-purpose AI agent that runs on the user's machine, with a desktop app, a command-line interface, and an API, and is written in Rust. It works with multiple hosted and local model providers and connects to extensions through the Model Context Protocol. Block created goose and contributed it to the Agentic AI Foundation.","keywords":"runtime agents coding-agent automation goose, a Series of LF Projects, LLC (Agentic AI Foundation project) Apache-2.0 Agentic AI Foundation (AAIF) The Linux Foundation Block"},{"type":"software","slug":"gpt-neox","href":"/open/gpt-neox/","name":"GPT-NeoX","summary":"GPT-NeoX is EleutherAI's library for training large autoregressive language models on GPUs. It builds on NVIDIA's Megatron-LM and on DeepSpeed, and supports distributed training with ZeRO and 3D parallelism, several positional-embedding and attention options, mixture of experts, and predefined configurations for architectures such as Pythia and Llama. EleutherAI used it to train GPT-NeoX-20B and the Pythia suite.","keywords":"research-stack training distributed-computing EleutherAI Apache-2.0 EleutherAI"},{"type":"model-family","slug":"gpt-oss","href":"/open/gpt-oss/","name":"gpt-oss","summary":"gpt-oss is OpenAI's series of open-weight, text-only reasoning models, released in August 2025 in two sizes: gpt-oss-120b and gpt-oss-20b. Both are mixture-of-experts transformers trained for OpenAI's harmony response format. OpenAI later published gpt-oss-safeguard models fine-tuned from them for safety classification.","keywords":"model language-model reasoning mixture-of-experts OpenAI OpenAI"},{"type":"model-release","slug":"gpt-oss-120b","href":"/open/gpt-oss-120b/","name":"gpt-oss-120b","summary":"gpt-oss-120b is the larger gpt-oss model: a 36-layer mixture-of-experts transformer with 116.8B total and about 5.1B active parameters per token, using 128 experts with the top 4 selected per token. It is text-only, supports context up to 131,072 tokens, and ships with its MoE weights quantized to MXFP4.","keywords":"gpt-oss-120b model language-model reasoning mixture-of-experts OpenAI Apache-2.0 Apache-2.0 OpenAI gpt-oss"},{"type":"model-release","slug":"gpt-oss-20b","href":"/open/gpt-oss-20b/","name":"gpt-oss-20b","summary":"gpt-oss-20b is the smaller gpt-oss model: a 24-layer mixture-of-experts transformer with 20.9B total and about 3.6B active parameters per token, using 32 experts with the top 4 selected per token. It is text-only, supports context up to 131,072 tokens, and ships with its MoE weights quantized to MXFP4.","keywords":"gpt-oss-20b model language-model reasoning mixture-of-experts OpenAI Apache-2.0 Apache-2.0 OpenAI gpt-oss"},{"type":"model-family","slug":"gpt-oss-safeguard","href":"/open/gpt-oss-safeguard/","name":"gpt-oss-safeguard","summary":"gpt-oss-safeguard is a pair of open-weight, text-only reasoning models from OpenAI, gpt-oss-safeguard-120b and gpt-oss-safeguard-20b, post-trained from the corresponding gpt-oss models to classify content against a policy that the user supplies. OpenAI's technical report is dated October 29, 2025. OpenAI lists the models as a model partner of the Robust Open Online Safety Tools (ROOST) Model Community, a group of safety practitioners working with open models.","keywords":"model language-model reasoning mixture-of-experts safety classification OpenAI OpenAI"},{"type":"model-release","slug":"gpt-oss-safeguard-120b","href":"/open/gpt-oss-safeguard-120b/","name":"gpt-oss-safeguard-120b","summary":"gpt-oss-safeguard-120b is the larger gpt-oss-safeguard model, a text-only safety reasoning model fine-tuned from gpt-oss-120b. The model card gives 117B total parameters with 5.1B active. Its configuration lists 36 layers, 128 experts with 4 used per token, and a maximum of 131,072 position embeddings. The mixture-of-experts weights are quantized to MXFP4; attention, router, embedding, and output layers are not.","keywords":"gpt-oss-safeguard-120b model language-model reasoning mixture-of-experts safety classification OpenAI Apache-2.0 Apache-2.0 OpenAI gpt-oss-safeguard"},{"type":"model-release","slug":"gpt-oss-safeguard-20b","href":"/open/gpt-oss-safeguard-20b/","name":"gpt-oss-safeguard-20b","summary":"gpt-oss-safeguard-20b is the smaller gpt-oss-safeguard model, a text-only safety reasoning model fine-tuned from gpt-oss-20b. The model card gives 21B total parameters with 3.6B active. Its configuration lists 24 layers, 32 experts with 4 used per token, and a maximum of 131,072 position embeddings. The mixture-of-experts weights are quantized to MXFP4; attention, router, embedding, and output layers are not.","keywords":"gpt-oss-safeguard-20b model language-model reasoning mixture-of-experts safety classification OpenAI Apache-2.0 Apache-2.0 OpenAI gpt-oss-safeguard"},{"type":"software","slug":"gpt4all","href":"/open/gpt4all/","name":"GPT4All","summary":"GPT4All is Nomic's open-source desktop application for running large language models locally on Windows, macOS, and Linux. It includes LocalDocs for chatting with local files, a Python SDK, and an API server, and its Python client wraps llama.cpp.","keywords":"runtime inference local-inference Nomic AI MIT Nomic AI"},{"type":"model-release","slug":"granite-4-2-30b","href":"/open/granite-4-2-30b/","name":"Granite 4.2 30B","summary":"Granite 4.2 30B is a 30-billion-parameter dense decoder-only language model from IBM with built-in reasoning, selectable thinking, non-thinking, and low-effort modes, and tool calling. The model card states native 128K-token context support with long-context extension to 512K, while the configuration file shipped with the weights sets a maximum of 131,072 positions. Per the model card, the 30B model received a second supervised fine-tuning phase with up-sampled agentic data.","keywords":"4.2 (30B) model language-model reasoning IBM (Granite Team) Apache-2.0 Apache-2.0 CDLA-Permissive-2.0 IBM IBM Granite"},{"type":"model-release","slug":"granite-4-2-3b","href":"/open/granite-4-2-3b/","name":"Granite 4.2 3B","summary":"Granite 4.2 3B is a 3-billion-parameter dense decoder-only language model from IBM with built- in reasoning, selectable thinking, non-thinking, and low-effort modes, and tool calling. The model card states native 128K-token context support with long-context extension to 512K, while the configuration file shipped with the weights sets a maximum of 131,072 positions.","keywords":"4.2 (3B) model language-model reasoning IBM (Granite Team) Apache-2.0 Apache-2.0 CDLA-Permissive-2.0 IBM IBM Granite"},{"type":"model-release","slug":"granite-4-2-8b","href":"/open/granite-4-2-8b/","name":"Granite 4.2 8B","summary":"Granite 4.2 8B is an 8-billion-parameter dense decoder-only language model from IBM with built-in reasoning, selectable thinking, non-thinking, and low-effort modes, and tool calling. The model card states native 128K-token context support with long-context extension to 512K, while the configuration file shipped with the weights sets a maximum of 131,072 positions.","keywords":"4.2 (8B) model language-model reasoning IBM (Granite Team) Apache-2.0 Apache-2.0 CDLA-Permissive-2.0 IBM IBM Granite"},{"type":"model-family","slug":"grok","href":"/open/grok/","name":"Grok","summary":"Grok is xAI's family of large language models, first launched in November 2023 and offered mainly through the Grok assistant and the xAI API. xAI has published weights for two earlier models: Grok-1 in March 2024 and Grok 2 in August 2025. SpaceX's prospectus describes current Grok models as proprietary.","keywords":"model language-model mixture-of-experts xAI (SpaceXSI) xAI (SpaceXSI)"},{"type":"model-release","slug":"grok-2","href":"/open/grok-2/","name":"Grok 2","summary":"Grok 2 is a post-trained language model that xAI trained and used in 2024; its weights, about 500 GB in 42 files, were published on Hugging Face in August 2025. Press coverage at the time reported the release as Grok 2.5.","keywords":"Grok 2 model language-model xAI (SpaceXSI) xAI Community License Agreement xAI (SpaceXSI) Grok"},{"type":"model-release","slug":"grok-1","href":"/open/grok-1/","name":"Grok-1","summary":"Grok-1 is a 314-billion-parameter mixture-of-experts language model (8 experts, 2 used per token) that xAI trained from scratch. The published checkpoint is the raw base model from pretraining that ended in October 2023 and is not fine-tuned for dialogue or other applications.","keywords":"Grok-1 model language-model mixture-of-experts xAI (SpaceXSI) Apache-2.0 xAI (SpaceXSI) Grok"},{"type":"evaluation-tool","slug":"harvey-lab","href":"/open/harvey-lab/","name":"Harvey LAB (Legal Agent Benchmark)","summary":"LAB is an open-source benchmark from Harvey for evaluating AI agents on legal work. It consists of a task set, in which each task gives an agent instructions and a matter file of documents and defines a rubric of pass/fail criteria, and an execution harness for running agents against the tasks and grading their work product. The tasks span transactional, advisory, regulatory, and litigation work across many legal practice areas.","keywords":"eval evaluation benchmark agents legal Harvey MIT Harvey"},{"type":"evaluation-tool","slug":"helm","href":"/open/helm/","name":"HELM (Holistic Evaluation of Language Models)","summary":"HELM is an open-source Python framework from Stanford's Center for Research on Foundation Models for evaluating foundation models, including language and multimodal models. It provides benchmarks in a standardized format, one interface to models from several providers, metrics beyond accuracy such as efficiency, bias, and toxicity, and a web UI and leaderboards for inspecting results. HELM entered maintenance mode on June 1, 2026.","keywords":"0.5.16 eval evaluation benchmark Stanford Center for Research on Foundation Models (CRFM) Apache-2.0 Stanford University"},{"type":"model-family","slug":"hermes","href":"/open/hermes/","name":"Hermes","summary":"Hermes is Nous Research's series of post-trained language models, each built on a base model released by another developer. Releases listed by Nous include Nous-Hermes Llama 2 13B (July 2023), Hermes 2 models on Mistral, Mixtral, Yi, and Llama 3 bases (2023 to 2024), Hermes 3 on Llama 3.1 (August 2024), Hermes 4 at 405B, 70B, and 14B (August 2025), and Hermes 4.3 36B (December 2025). Hermes 4 and 4.3 are hybrid reasoning models that can answer directly or reason in think tags first.","keywords":"model language-model reasoning Nous Research Nous Research"},{"type":"model-release","slug":"hermes-4-70b","href":"/open/hermes-4-70b/","name":"Hermes 4 70B","summary":"A 70B-parameter hybrid reasoning model that Nous Research post-trained from Meta's Llama 3.1 70B base model, released in August 2025 with Hermes 4 405B and 14B. Nous trained it on a newly synthesized dataset of about 5 million reasoning and non-reasoning samples, and it can either answer directly or reason inside think tags first.","keywords":"Hermes-4-70B model language-model reasoning Nous Research Meta Llama 3 Community License (card license field \"llama3\") Nous Research Hermes"},{"type":"model-release","slug":"hermes-4-3-36b","href":"/open/hermes-4-3-36b/","name":"Hermes 4.3 36B","summary":"A 36B-parameter hybrid reasoning model that Nous Research post-trained from ByteDance Seed's Seed-OSS-36B-Base. It was Nous's first Hermes model trained in a decentralized way over the internet on the Psyche network, with context extended up to 512K tokens. Nous also released a centrally trained version of the same model as a research comparison.","keywords":"Hermes-4.3-36B model language-model reasoning Nous Research Apache-2.0 Nous Research Hermes"},{"type":"software","slug":"hermes-agent","href":"/open/hermes-agent/","name":"Hermes Agent","summary":"Hermes Agent is an agent runtime from Nous Research with command-line and messaging interfaces, persistent memory, reusable skills, and scheduled tasks. It can connect to different model providers and local or remote execution backends. It is separate from the Hermes family of language models.","keywords":"runtime agents automation Nous Research MIT Nous Research"},{"type":"evaluation-tool","slug":"humaneval","href":"/open/humaneval/","name":"HumanEval","summary":"HumanEval is a code-generation benchmark released by OpenAI with the Codex paper in July 2021. It contains 164 hand-written Python programming problems, each with a function signature, docstring, reference solution, and unit tests, and measures functional correctness of generated code with the pass@k metric. The GitHub repository provides the problem file and the evaluation harness.","keywords":"eval evaluation benchmark code-model OpenAI MIT OpenAI"},{"type":"evaluation-tool","slug":"humanitys-last-exam","href":"/open/humanitys-last-exam/","name":"Humanity's Last Exam (HLE)","summary":"A multimodal benchmark of 2,500 expert-written, closed-ended academic questions across more than a hundred subjects, including mathematics, the humanities, and the natural sciences, in multiple-choice and short-answer formats suited to automated grading. It was organized by teams at the Center for AI Safety and Scale AI with questions from nearly 1,000 subject-matter contributors, and was published in Nature in January 2026.","keywords":"HLE (2,500 questions, finalized April 2025); HLE-Rolling; HLE-Diamond (September 2026) eval benchmark evaluation reasoning multimodal Center for AI Safety Scale AI MIT MIT Center for AI Safety (CAIS) Scale AI"},{"type":"software","slug":"hydragnn","href":"/open/hydragnn/","name":"HydraGNN","summary":"HydraGNN is a PyTorch implementation of multi-headed graph neural networks from Oak Ridge National Laboratory, with separate output heads for graph-level and node-level properties. Its README lists distributed training with DDP, FSDP, and DeepSpeed; equivariant layers such as EGNN, PaiNN, MACE, and DimeNet; heterogeneous graph learning; global attention through GPS; and training of machine-learned interatomic potentials with energy-conserving forces.","keywords":"framework graph-neural-network training distributed-computing science Oak Ridge National Laboratory BSD-3-Clause Oak Ridge National Laboratory"},{"type":"model-family","slug":"granite","href":"/open/granite/","name":"IBM Granite","summary":"Granite is IBM's family of models. IBM lists language, speech, vision, guardian (risk detection), embedding, and time-series lines. The most recent language-model generation on IBM's Granite page is Granite 4.2: dense reasoning models in 3B, 8B, and 30B sizes, released in August 2026 and post-trained from Granite 4.1 base models.","keywords":"model language-model reasoning speech vision-language embedding IBM IBM"},{"type":"model-family","slug":"inkling","href":"/open/inkling/","name":"Inkling","summary":"Inkling is Thinking Machines Lab's family of open-weight multimodal mixture-of-experts models that accept text, image, and audio input and produce text. The first release, Inkling (975B total and 41B active parameters), was published on July 15, 2026, followed by Inkling-Small (276B total and 12B active) on July 30, 2026.","keywords":"model language-model multimodal mixture-of-experts reasoning Thinking Machines Lab Thinking Machines Lab"},{"type":"model-release","slug":"inkling-975b","href":"/open/inkling-975b/","name":"Inkling (975B)","summary":"Inkling is a 66-layer decoder-only mixture-of-experts transformer with 975B total and 41B active parameters, routing each token to 6 of 256 experts plus 2 shared experts. It accepts text, image, and audio input, outputs text, and supports a context window of up to 1M tokens. Thinking Machines Lab says it trained the model from scratch on 45 trillion tokens of text, images, audio, and video.","keywords":"Inkling model language-model multimodal mixture-of-experts reasoning Thinking Machines Lab Apache-2.0 Thinking Machines Lab Inkling"},{"type":"model-release","slug":"inkling-small","href":"/open/inkling-small/","name":"Inkling-Small","summary":"Inkling-Small is a 42-layer decoder-only mixture-of-experts transformer with 276B total and 12B active parameters, routing each token to 6 of 256 experts plus 2 shared experts. Like Inkling, it accepts text, image, and audio input, outputs text, and supports a context window of up to 1M tokens.","keywords":"Inkling-Small model language-model multimodal mixture-of-experts reasoning Thinking Machines Lab Apache-2.0 Thinking Machines Lab Inkling"},{"type":"software","slug":"hawk","href":"/open/hawk/","name":"Inspect-Hawk (Hawk)","summary":"Inspect-Hawk is METR's open-source platform for running Inspect AI evaluations on cloud infrastructure. Users define tasks, agents, and models in a YAML file; Hawk runs each evaluation in an isolated Kubernetes pod, manages model API credentials through a proxy, streams logs, stores results in a PostgreSQL warehouse, and serves a web interface for browsing them. It is built on Inspect AI, the evaluation framework created by the UK AI Security Institute.","keywords":"framework evaluation agents distributed-computing METR (Model Evaluation & Threat Research) MIT METR (Model Evaluation & Threat Research)"},{"type":"model-family","slug":"intellect","href":"/open/intellect/","name":"INTELLECT","summary":"INTELLECT is Prime Intellect's series of open-weight models. INTELLECT-1 is a 10B model trained from scratch on 1 trillion tokens with compute from distributed community contributors; INTELLECT-2 is a 32B model produced by a distributed reinforcement-learning run on QwQ-32B; INTELLECT-3 (November 2025) is a 106B mixture-of-experts model (12B active) post-trained from GLM-4.5-Air-Base with supervised fine-tuning and large-scale RL; and INTELLECT-3.1 continues INTELLECT-3 with further RL.","keywords":"model language-model reasoning mixture-of-experts Prime Intellect Prime Intellect"},{"type":"model-release","slug":"intellect-3","href":"/open/intellect-3/","name":"INTELLECT-3","summary":"A 106B-parameter mixture-of-experts reasoning model with 12B active parameters, post-trained by Prime Intellect from Z.ai's GLM-4.5-Air-Base. Training used two supervised fine-tuning stages (general reasoning, then agentic) followed by large-scale reinforcement learning on math, code, science, logic, deep-research, and software-engineering environments, run with prime-rl on a 512-GPU H200 cluster over about two months.","keywords":"INTELLECT-3 model language-model reasoning mixture-of-experts Prime Intellect MIT Apache-2.0 MIT Prime Intellect INTELLECT"},{"type":"model-release","slug":"intellect-3-1","href":"/open/intellect-3-1/","name":"INTELLECT-3.1","summary":"A 106B-parameter mixture-of-experts reasoning model (12B active) that continues the training of INTELLECT-3 with additional reinforcement learning on math, coding, software-engineering, and agentic tasks, using prime-rl and environments built with the verifiers library.","keywords":"INTELLECT-3.1 model language-model reasoning mixture-of-experts Prime Intellect MIT Apache-2.0 MIT Prime Intellect INTELLECT"},{"type":"software","slug":"jax","href":"/open/jax/","name":"JAX","summary":"JAX is a Python library for array computation and program transformation, including automatic differentiation, just-in-time compilation, and vectorization of NumPy-style code. It compiles programs with the XLA compiler to run on CPUs, GPUs, TPUs, and other accelerators.","keywords":"framework numerical-computing training compiler distributed-computing JAX core team (Google open-source project) Apache-2.0 Google (Alphabet)"},{"type":"software","slug":"kempnerforge","href":"/open/kempnerforge/","name":"KempnerForge","summary":"KempnerForge is a PyTorch-native framework from Harvard's Kempner Institute for fault-tolerant distributed training of foundation models on AI clusters. It trains decoder-only Transformers and mixture-of-experts models with FSDP2, tensor, expert, and pipeline parallelism and FP8 mixed precision, and it includes asynchronous checkpointing with auto-resume, SLURM preemption handling, activation-extraction hooks for interpretability, and vision-language model training.","keywords":"framework training distributed-computing interpretability mixture-of-experts Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University MIT Harvard University"},{"type":"software","slug":"keras","href":"/open/keras/","name":"Keras","summary":"Keras is an open-source deep learning API. Keras 3 is multi-backend: models can run on JAX, TensorFlow, or PyTorch, and an OpenVINO backend supports inference only. The keras.io site also documents KerasHub, which provides Keras 3 implementations of model architectures paired with pretrained checkpoints.","keywords":"framework training Keras team at Google Apache-2.0 Google (Alphabet)"},{"type":"model-family","slug":"laguna","href":"/open/laguna/","name":"Laguna","summary":"Laguna is Poolside's family of mixture-of-experts language models for agentic coding. Poolside's release notes and announcements list Laguna XS.2 and Laguna M.1 (April 2026), Laguna XS 2.1 (announced 2 July 2026), and Laguna S 2.1 (21 July 2026), with sizes from 33B total (3B active) to 225B total (23B active) parameters.","keywords":"model language-model code-model mixture-of-experts reasoning Poolside Poolside"},{"type":"model-release","slug":"laguna-s-2-1","href":"/open/laguna-s-2-1/","name":"Laguna S 2.1","summary":"Laguna S 2.1 is a mixture-of-experts model with 118B total and about 8B active parameters per token, built for agentic coding. It has 48 layers mixing global and sliding-window attention, 256 routed experts plus one shared expert, and a 1,048,576-token context window, with reasoning that can be switched on or off per request.","keywords":"S 2.1 model language-model code-model mixture-of-experts reasoning Poolside OpenMDW License Agreement, version 1.1 Poolside Laguna"},{"type":"model-release","slug":"laguna-xs-2-1","href":"/open/laguna-xs-2-1/","name":"Laguna XS 2.1","summary":"Laguna XS 2.1 is a mixture-of-experts model with 33B total and 3B active parameters per token, built for agentic coding on a local machine. Poolside describes it as an upgraded version of Laguna XS.2 with the same architecture. It supports interleaved reasoning between tool calls and has a 262,144-token context window.","keywords":"XS 2.1 model language-model code-model mixture-of-experts reasoning local-inference Poolside OpenMDW License Agreement, version 1.1 Poolside Laguna"},{"type":"software","slug":"langchain-framework","href":"/open/langchain-framework/","name":"LangChain (framework)","summary":"LangChain is an open-source Python framework for building agents and LLM applications. It gives a standard interface across model providers and many third-party integrations. Its agent API is built on LangGraph, and a JavaScript/TypeScript version, LangChain.js, lives in a separate repository.","keywords":"framework agents llm-applications LangChain Inc. MIT LangChain"},{"type":"software","slug":"langgraph","href":"/open/langgraph/","name":"LangGraph","summary":"LangGraph is an open-source, low-level orchestration framework and runtime for long-running, stateful agents. It is built by LangChain Inc. but can be used without the LangChain library, and a JavaScript/TypeScript version, LangGraph.js, lives in a separate repository. The README credits Pregel and Apache Beam as inspirations.","keywords":"framework agents orchestration LangChain Inc. MIT LangChain"},{"type":"software","slug":"lbann","href":"/open/lbann/","name":"LBANN (Livermore Big Artificial Neural Network Toolkit)","summary":"LBANN is an open-source deep learning training framework from Lawrence Livermore National Laboratory, built for high-performance computing systems. Its documentation describes combining model parallelism through domain decomposition with data parallelism and ensemble training, and support for supervised, self-supervised, unsupervised, and adversarial (GAN) training. The repository's main branch now holds LBANNv2, a pre-alpha Python package described as LBANN's core integration with PyTorch; the earlier toolkit remains on the v1.x branches.","keywords":"framework training distributed-computing hpc Lawrence Livermore National Laboratory (LBANN project) Apache-2.0 Lawrence Livermore National Laboratory"},{"type":"software","slug":"lemonade","href":"/open/lemonade/","name":"Lemonade","summary":"Lemonade is a local AI server that runs language, speech, and image-generation models on a PC's CPU, GPU, or NPU and exposes them through OpenAI-, Anthropic-, and Ollama-compatible APIs. It is also offered as an embeddable binary for applications, and it supports several inference engines, including llama.cpp and engines for AMD Ryzen AI NPUs.","keywords":"runtime inference local-inference serving Lemonade project (sponsored and backed by AMD, with community maintainers) Apache-2.0 AMD"},{"type":"model-family","slug":"lfm","href":"/open/lfm/","name":"LFM (Liquid Foundation Models)","summary":"LFM is Liquid AI's series of hybrid language and multimodal models for on-device use, built from gated short-convolution blocks and a small number of grouped-query attention blocks. LFM2 (from July 2025) introduced open-weight dense models from 350M parameters upward and later an 8.3B mixture-of-experts model. LFM2.5 (from January 2026) extended pretraining and scaled up reinforcement-learning post-training; 2026 releases include LFM2.5-8B-A1B (May), LFM2.5-2.6B (August), and LFM2.5-VL-3B (August), alongside vision, audio, and encoder variants.","keywords":"model language-model multimodal local-inference Liquid AI Liquid AI"},{"type":"model-release","slug":"lfm2-5-2-6b","href":"/open/lfm2-5-2-6b/","name":"LFM2.5-2.6B","summary":"A 2.69B-parameter text-only hybrid model with 30 layers (22 double-gated short-convolution blocks and 8 grouped-query attention blocks), a 131,072-token context window, and support for 16 languages. Liquid AI pretrained it on about 34 trillion tokens and post-trained it for agentic use; it always reasons before answering.","keywords":"LFM2.5-2.6B model language-model reasoning local-inference Liquid AI LFM Open License v1.0 Liquid AI LFM (Liquid Foundation Models)"},{"type":"model-release","slug":"lfm2-5-8b-a1b","href":"/open/lfm2-5-8b-a1b/","name":"LFM2.5-8B-A1B","summary":"A text-only mixture-of-experts model with 8.3B total and 1.5B active parameters, built from 18 double-gated convolution blocks and 6 grouped-query attention blocks. Liquid AI pretrained it on 38 trillion tokens, extended its context to 128,000 tokens, and tuned it for reasoning; it writes a chain of thought before its final answer. It succeeds LFM2-8B-A1B.","keywords":"LFM2.5-8B-A1B model language-model reasoning mixture-of-experts local-inference Liquid AI LFM Open License v1.0 Liquid AI LFM (Liquid Foundation Models)"},{"type":"software","slug":"liger-kernel","href":"/open/liger-kernel/","name":"Liger Kernel","summary":"Liger Kernel is an open-source collection of Triton GPU kernels for training large language models, developed at LinkedIn. It provides Hugging Face-compatible implementations of layers such as RMSNorm, RoPE, SwiGLU, and cross-entropy (including a fused linear cross-entropy), plus memory-efficient losses for post-training methods such as DPO, ORPO, and KTO.","keywords":"framework training LinkedIn BSD-2-Clause LinkedIn"},{"type":"model-family","slug":"liquid-d1","href":"/open/liquid-d1/","name":"Liquid AI d1","summary":"d1 is Liquid AI's family of decision models. Instead of generating text, a d1 model takes a state (text, JSON, images, or for some checkpoints audio) and a set of named questions and returns typed answers (yes/no, a choice, or a score) in a single forward pass. Liquid AI released the first open-weight d1 models, d1-3B and the experimental d1-omni-600M, on October 7, 2026.","keywords":"model multimodal vision-language edge Liquid AI Liquid AI"},{"type":"software","slug":"litert","href":"/open/litert/","name":"LiteRT","summary":"LiteRT is Google's on-device runtime for machine learning and generative AI models on Android, iOS, desktop, web, and IoT platforms, with CPU, GPU, and NPU acceleration. LiteRT is the new name for TensorFlow Lite; it runs models converted from PyTorch, TensorFlow, and JAX.","keywords":"runtime inference local-inference on-device Google (Google AI Edge) Apache-2.0 Google (Alphabet)"},{"type":"model-family","slug":"llama","href":"/open/llama/","name":"Llama","summary":"Llama is Meta's family of large language models. Meta's Llama models repository lists generations from Llama 2 (July 2023) through Llama 4 (April 2025), each with its own license file and acceptable use policy. Llama 4 is distributed under the custom Llama 4 Community License Agreement rather than a standard open-source license.","keywords":"model language-model multimodal mixture-of-experts Meta Meta"},{"type":"model-release","slug":"llama-3-1-8b","href":"/open/llama-3-1-8b/","name":"Llama 3.1 8B","summary":"Llama 3.1 8B is the smallest model in Meta's Llama 3.1 collection, released on July 23, 2024 in pretrained and instruction-tuned versions. It is a text-only, auto-regressive transformer with grouped-query attention and a 128K context length. The model card lists eight supported languages and says the collection was pretrained on about 15 trillion tokens with a December 2023 cutoff.","keywords":"3.1 8B model language-model Meta Llama 3.1 Community License Agreement Meta Llama"},{"type":"model-release","slug":"llama-3-1-tulu-3-1-8b","href":"/open/llama-3-1-tulu-3-1-8b/","name":"Llama 3.1 Tülu 3.1 8B","summary":"An 8B instruction-following model from Ai2's Tülu 3 line, post-trained from Meta's Llama 3.1 8B base model through supervised fine-tuning and DPO, then reinforcement learning with verifiable rewards. Version 3.1 differs from the original Tülu 3 8B only in the final RL stage, which switched from PPO to GRPO (without a reward model) with further hyperparameter tuning.","keywords":"Llama-3.1-Tulu-3.1-8B model language-model Ai2 (Allen Institute for AI) Llama 3.1 Community License Agreement Apache-2.0 Ai2 (Allen Institute for AI) Tülu"},{"type":"model-release","slug":"llama-4-maverick-17b-128e","href":"/open/llama-4-maverick-17b-128e/","name":"Llama 4 Maverick (17Bx128E)","summary":"Llama 4 Maverick is a natively multimodal mixture-of-experts model from Meta with 17 billion active and 400 billion total parameters across 128 experts. It accepts multilingual text and images and produces text and code; the model card lists a 1M-token context length and an August 2024 knowledge cutoff. Pretrained and instruction-tuned versions were released.","keywords":"4 Maverick 17B-128E model language-model multimodal mixture-of-experts Meta Llama 4 Community License Agreement Meta Llama"},{"type":"model-release","slug":"llama-4-scout-17b-16e","href":"/open/llama-4-scout-17b-16e/","name":"Llama 4 Scout (17Bx16E)","summary":"Llama 4 Scout is a natively multimodal mixture-of-experts model from Meta with 17 billion active and 109 billion total parameters across 16 experts. It accepts multilingual text and images and produces text and code; the model card lists a 10M-token context length and an August 2024 knowledge cutoff. Pretrained and instruction-tuned versions were released.","keywords":"4 Scout 17B-16E model language-model multimodal mixture-of-experts Meta Llama 4 Community License Agreement Meta Llama"},{"type":"model-release","slug":"llama-guard-4-12b","href":"/open/llama-guard-4-12b/","name":"Llama Guard 4 12B","summary":"Llama Guard 4 is a 12-billion-parameter safety classifier from Meta for text and images. Given a prompt sent to a language model, or a model's response, it generates text saying whether the content is safe or unsafe and, if unsafe, which categories it violates: 13 categories based on the MLCommons hazards taxonomy plus a text-only category for code interpreter abuse. It is a dense model pruned from the pre-trained Llama 4 Scout and fine-tuned for classification, and it accepts multiple images in one prompt. Meta released it on April 29, 2025, as one of its Llama protection tools.","keywords":"Llama-Guard-4-12B model safety classification multimodal vision-language Meta Llama 4 Community License Agreement Meta Llama"},{"type":"software","slug":"llama-cpp","href":"/open/llama-cpp/","name":"llama.cpp","summary":"llama.cpp is a C/C++ library and set of tools for running LLM and vision-language model inference locally or in the cloud, built on the ggml tensor library. It supports integer quantization and many hardware backends. It includes a command-line interface and an OpenAI-compatible server.","keywords":"runtime inference local-inference ggml team at Hugging Face (ggml.ai, acquired by Hugging Face in 2026), with the ggml-org community MIT Hugging Face"},{"type":"software","slug":"llamafile","href":"/open/llamafile/","name":"llamafile","summary":"llamafile packages an open model's weights and a llama.cpp-based inference runtime into a single executable that runs locally on several operating systems without installation. It combines llama.cpp with Cosmopolitan Libc and includes whisperfile, a single-file speech-to-text tool built on whisper.cpp.","keywords":"runtime inference local-inference Mozilla.ai Apache-2.0 Mozilla.ai"},{"type":"software","slug":"llamaindex-framework","href":"/open/llamaindex-framework/","name":"LlamaIndex (framework)","summary":"LlamaIndex is an open-source Python framework for building LLM agents, event-driven workflows, and retrieval-augmented generation (RAG) over private data. It is split into a core package and hundreds of integration packages for models, embeddings, and vector stores; the company also offers a TypeScript SDK. The README says the company now focuses mainly on its LlamaParse platform but keeps the framework available as an open toolkit.","keywords":"framework agents rag llm-applications LlamaIndex, Inc. MIT LlamaIndex"},{"type":"software","slug":"llm-d","href":"/open/llm-d/","name":"llm-d","summary":"llm-d is an open-source distributed inference serving stack for running large language models on Kubernetes. It sits above model servers such as vLLM and adds request routing that is aware of prefix caches and load, KV-cache offloading, prefill/decode disaggregation, expert parallelism for large models, autoscaling, and batch processing. Red Hat launched it in May 2025, and it became a Cloud Native Computing Foundation Sandbox project in March 2026.","keywords":"runtime inference serving distributed-computing Cloud Native Computing Foundation (Linux Foundation), Sandbox project Red Hat (launched the project; MAINTAINERS.md lists Red Hat staff in project leadership and as community managers) Google (MAINTAINERS.md lists Google staff in project leadership) IBM (IBM Research is a founding contributor; MAINTAINERS.md lists IBM staff in project leadership and as a community manager) Apache-2.0 The Linux Foundation Red Hat Google (Alphabet) IBM CoreWeave NVIDIA"},{"type":"software","slug":"llmrouter","href":"/open/llmrouter/","name":"LLMRouter","summary":"LLMRouter is an open-source Python library for routing each query to a suitable large language model, balancing response quality against inference cost. It implements more than 16 routing methods across single-round, multi-round, multimodal, agentic, and personalized routers, and adds a command-line interface, a Gradio chat interface, and a pipeline for generating routing training data. The accompanying paper introduces xRouteBench, a routing benchmark.","keywords":"framework routing inference evaluation language-model U Lab, University of Illinois Urbana-Champaign MIT University of Illinois Urbana-Champaign"},{"type":"evaluation-tool","slug":"lm-evaluation-harness","href":"/open/lm-evaluation-harness/","name":"LM Evaluation Harness","summary":"An open-source framework from EleutherAI for evaluating language models on many benchmark tasks through one interface. It supports local models (for example through Hugging Face Transformers or vLLM) and hosted model APIs, and its README lists more than 60 standard academic benchmarks with hundreds of subtasks and variants.","keywords":"0.4.13 eval evaluation benchmark EleutherAI MIT EleutherAI"},{"type":"software","slug":"marin","href":"/open/marin/","name":"Marin","summary":"Marin is an open lab and software platform for developing foundation models, covering data curation, filtering, tokenization, pretraining, post-training, and evaluation, with experiments, code, and training logs published as the work happens. It began at Stanford in 2024, originating in Stanford CRFM and HAI, and is now primarily developed by the nonprofit Open Athena with Stanford CRFM as a core collaborator. Models trained with it include Marin 8B and Marin 32B.","keywords":"research-stack training language-model pretraining-data Open Athena (Open Athena AI Foundation Inc.) Stanford Center for Research on Foundation Models (CRFM) Apache-2.0 Stanford University"},{"type":"software","slug":"markitdown","href":"/open/markitdown/","name":"MarkItDown","summary":"MarkItDown is a Microsoft Python library and command-line tool that converts files such as PDF, Word, PowerPoint, Excel, HTML, images, audio, EPUB, and ZIP archives into Markdown. It aims to keep document structure (headings, lists, tables, links) for use by language models and text analysis pipelines rather than for high-fidelity human-readable conversion.","keywords":"framework document-processing retrieval-augmented-generation Microsoft MIT Microsoft"},{"type":"software","slug":"max","href":"/open/max/","name":"MAX (Modular)","summary":"MAX is Modular's framework for serving and building AI models. It serves models through an OpenAI-compatible API, offers PyTorch-like Python APIs for writing model pipelines, and includes a GPU kernel library for NVIDIA, AMD, and Apple hardware. Its Python API, model pipelines, and GPU kernels are open source; the MAX package as a whole is distributed under the Modular Community License.","keywords":"runtime inference serving compiler Modular Apache-2.0 WITH LLVM-exception Modular Community License (MAX package usage and distribution) Modular"},{"type":"software","slug":"maxtext","href":"/open/maxtext/","name":"MaxText","summary":"MaxText is an open-source LLM library and reference implementation written in Python and JAX for training on Google Cloud TPUs and GPUs. It provides implementations of Gemma, Llama, DeepSeek, Qwen, Mistral, and other open model architectures, with support for pre-training and for post-training methods such as supervised fine-tuning and reinforcement learning.","keywords":"research-stack training distributed-computing language-model Google (AI Hypercomputer GitHub organization) Apache-2.0 Google (Alphabet)"},{"type":"model-family","slug":"medgemma","href":"/open/medgemma/","name":"MedGemma","summary":"MedGemma is a collection of Gemma 3 variants trained by Google on medical text and images, published as part of Health AI Developer Foundations (HAI-DEF). MedGemma 1 was released in May 2025 (4B multimodal and 27B text-only) with a 27B multimodal variant in July 2025, and MedGemma 1.5 4B followed in January 2026.","keywords":"model multimodal vision-language language-model medical Google (Health AI Developer Foundations) Google (Alphabet)"},{"type":"model-release","slug":"medgemma-1-5-4b-it","href":"/open/medgemma-1-5-4b-it/","name":"MedGemma 1.5 4B","summary":"MedGemma 1.5 4B is a multimodal, instruction-tuned update to MedGemma 1 4B, released by Google on January 13, 2026. It accepts text and images and produces text, adding support for CT and MRI volumes, whole-slide histopathology, longitudinal chest X-rays, anatomical localization, lab-report extraction, and text-based EHR interpretation.","keywords":"1.5.0 (4B multimodal, instruction-tuned) model multimodal vision-language medical Google (Health AI Developer Foundations) Health AI Developer Foundations Terms of Use Apache-2.0 Google (Alphabet) MedGemma"},{"type":"model-release","slug":"medgemma-27b-it","href":"/open/medgemma-27b-it/","name":"MedGemma 27B (multimodal)","summary":"MedGemma 27B multimodal is the 27B multimodal variant of MedGemma 1, released by Google on July 9, 2025. It accepts text and images and produces text, and unlike the 27B text-only variant it was also trained on medical images and FHIR-based electronic health record data. It is published only as an instruction-tuned model.","keywords":"1.0.0 (27B multimodal, instruction-tuned) model multimodal vision-language medical Google (Health AI Developer Foundations) Health AI Developer Foundations Terms of Use Apache-2.0 Google (Alphabet) MedGemma"},{"type":"software","slug":"megatron-lm","href":"/open/megatron-lm/","name":"Megatron-LM and Megatron Core","summary":"NVIDIA's Megatron-LM repository contains two components: Megatron Core, a library of GPU-optimized building blocks for training transformer models at scale (tensor, pipeline, data, expert, and context parallelism; FP16, BF16, FP8, and FP4 mixed precision), and Megatron-LM, a reference training setup with pre-configured scripts built on Megatron Core. The README says Megatron Core development moved to GitHub in December 2025, with all development and CI now happening in the open.","keywords":"research-stack training distributed-computing NVIDIA BSD-3-Clause NVIDIA"},{"type":"model-family","slug":"florence-2","href":"/open/florence-2/","name":"Microsoft Florence-2","summary":"Florence-2 is a family of vision foundation models from Microsoft's Azure AI group that handle captioning, object detection, visual grounding, segmentation, and OCR through text prompts, using a sequence-to-sequence design with a DaViT vision encoder. The models were trained on FLD-5B, a set of 5.4 billion visual annotations on 126 million images that the team built with an iterative automated annotation process. Microsoft published four checkpoints on Hugging Face in June 2024: Florence-2-base (0.23B parameters) and Florence-2-large (0.77B), plus versions of each fine-tuned on a collection of downstream tasks (base-ft and large-ft).","keywords":"model vision-language multimodal computer-vision Microsoft (Azure AI) Microsoft"},{"type":"software","slug":"mlc-llm","href":"/open/mlc-llm/","name":"MLC LLM","summary":"MLC LLM is an open-source machine learning compiler and deployment engine for large language models. Compiled models run on MLCEngine, which offers an OpenAI-compatible API through a REST server and Python, JavaScript, iOS, and Android interfaces, with GPU backends including CUDA, ROCm, Vulkan, Metal, WebGPU, and OpenCL.","keywords":"runtime inference compiler local-inference MLC team (MLC open community) CMU Catalyst (lists MLC LLM among its own research projects) Apache-2.0 Carnegie Mellon University"},{"type":"software","slug":"mlflow","href":"/open/mlflow/","name":"MLflow","summary":"MLflow is an open-source platform for managing machine learning and generative AI development. For LLM applications and agents it provides tracing and observability (with OpenTelemetry integration), evaluation, prompt management and optimization, and an AI Gateway; for model training it provides experiment tracking, model evaluation, a model registry, and deployment tools. Databricks created MLflow, which joined the Linux Foundation in June 2020.","keywords":"3.16.1 framework training evaluation llm-applications MLflow Project, a Series of LF Projects, LLC (Linux Foundation) Databricks (creator) Apache-2.0 The Linux Foundation Databricks"},{"type":"evaluation-tool","slug":"mlperf","href":"/open/mlperf/","name":"MLPerf","summary":"MLPerf is MLCommons' family of system performance benchmarks. Its suites cover training (time to train a model to a target quality), inference in datacenter, edge, mobile, and tiny settings, and also client PCs, storage, automotive, HPC training, and inference endpoints. Results are submitted in rounds, reviewed by the submitting organizations, and published together by MLCommons.","keywords":"eval benchmark evaluation training inference MLCommons Apache-2.0 Apache-2.0 Apache-2.0 MLCommons"},{"type":"software","slug":"mlx","href":"/open/mlx/","name":"MLX","summary":"MLX is an array framework for machine learning on Apple silicon from Apple machine learning research. It has a NumPy-like Python API plus C++, C, and Swift APIs, composable function transformations, lazy computation, and a unified memory model that shares arrays between the CPU and GPU.","keywords":"framework training inference numerical-computing local-inference Apple (machine learning research) MIT Apple"},{"type":"model-family","slug":"mochi","href":"/open/mochi/","name":"Mochi","summary":"Mochi is Genmo's line of text-to-video diffusion models. Its only open-weight release so far is Mochi 1 preview (October 2024), a 10B-parameter model published with its video VAE under Apache 2.0. Genmo's hosted playground refers to a Mochi 1.1 model, for which this catalog found no open-weight release.","keywords":"model multimodal Genmo Genmo"},{"type":"model-release","slug":"mochi-1-preview","href":"/open/mochi-1-preview/","name":"Mochi 1 preview","summary":"A 10B-parameter text-to-video diffusion model built on Genmo's Asymmetric Diffusion Transformer (AsymmDiT) and released as a research preview in October 2024. It is paired with AsymmVAE, a 362M-parameter video autoencoder released alongside it, and encodes prompts with a single T5-XXL model. The initial release generates 480p video.","keywords":"mochi-1-preview model multimodal Genmo Apache-2.0 Apache-2.0 Genmo Mochi"},{"type":"software","slug":"model-context-protocol","href":"/open/model-context-protocol/","name":"Model Context Protocol (MCP)","summary":"The Model Context Protocol is an open protocol for connecting AI applications to external data sources, tools, and workflows through MCP servers and clients. The project publishes the specification, a TypeScript schema with a generated JSON Schema, documentation, and official SDKs in ten programming languages. Anthropic introduced it in November 2024 and donated it to the Agentic AI Foundation in December 2025.","keywords":"framework agents protocol tool-use Model Context Protocol a Series of LF Projects, LLC (Agentic AI Foundation project) Apache-2.0 MIT CC-BY-4.0 Agentic AI Foundation (AAIF) The Linux Foundation Anthropic"},{"type":"software","slug":"mojo","href":"/open/mojo/","name":"Mojo","summary":"Mojo is a programming language developed by Modular that pairs Python-style syntax with systems programming and metaprogramming features, for writing code that runs on CPUs and GPUs. Its compiler, standard library, and tooling are published in the Mojo directory of Modular's modular repository; Mojo 1.0 was released in August 2026.","keywords":"framework compiler programming-language numerical-computing Modular Apache-2.0 WITH LLVM-exception Modular"},{"type":"model-family","slug":"molmo","href":"/open/molmo/","name":"Molmo","summary":"Molmo is Ai2's family of open vision-language models. The first release (September 2024) comprised MolmoE-1B, Molmo-7B-O, Molmo-7B-D, and Molmo-72B, trained with Ai2's PixMo data. Molmo 2 (December 2025) added video and multi-image understanding and grounding in 4B and 8B models built on Qwen3 and a 7B model (Molmo2-O-7B) built on Ai2's own Olmo 3. Ai2 has since built specialized variants on the line, including MolmoAct 2 for robot manipulation (May 2026).","keywords":"model vision-language multimodal Ai2 (Allen Institute for AI) Ai2 (Allen Institute for AI)"},{"type":"model-release","slug":"molmo2-o-7b","href":"/open/molmo2-o-7b/","name":"Molmo2-O 7B","summary":"Molmo2-O-7B is the Molmo 2 vision-language model built on Ai2's Olmo 3 7B Instruct language model with a SigLIP 2 vision encoder, released in December 2025. It accepts images, multiple images, and video with text, and can answer questions, caption, and point to or track objects. Ai2 describes it as the variant whose full model flow is open, since its language model is also Ai2's.","keywords":"Molmo2-O-7B model vision-language multimodal Ai2 (Allen Institute for AI) Apache-2.0 Apache-2.0 Ai2 (Allen Institute for AI) Molmo"},{"type":"model-family","slug":"meta-muse","href":"/open/meta-muse/","name":"Muse (Meta)","summary":"Muse is a model family from Meta Superintelligence Labs, introduced with Muse Spark in April 2026. Muse Spark versions are offered through Meta AI and the Meta Model API without public weights. Muse Glimmer, released in August 2026, is an open-weight model distilled from Muse Spark. Meta's developer site also lists Muse Image and Muse Voice Transcribe.","keywords":"model language-model multimodal reasoning Meta (Meta Superintelligence Labs) Meta"},{"type":"model-family","slug":"microsoft-muse","href":"/open/microsoft-muse/","name":"Muse (Microsoft Research WHAM)","summary":"Muse is Microsoft Research's name for its World and Human Action Model (WHAM), a generative model of gameplay trained on human play data from the Xbox game Bleeding Edge. It can generate game visuals, controller actions, or both. It was developed by Microsoft Research's Game Intelligence and Teachable AI Experiences teams with Ninja Theory.","keywords":"model multimodal Microsoft Research Microsoft"},{"type":"model-release","slug":"muse-glimmer-30b","href":"/open/muse-glimmer-30b/","name":"Muse Glimmer 30B","summary":"Muse Glimmer is an open-weight model from Meta Superintelligence Labs with about 29.6 billion parameters, including a roughly 1.8-billion-parameter perception encoder. It is a dense transformer that takes text and images as input and produces text, and was distilled from Muse Spark for agentic tasks on consumer hardware. The model card lists a context length of 131,072+ tokens and a January 4, 2026 knowledge cutoff.","keywords":"Glimmer-30B model language-model multimodal vision-language reasoning local-inference Meta (Meta Superintelligence Labs) Apache-2.0 Meta Muse (Meta)"},{"type":"dataset","slug":"nemotron-cc","href":"/open/nemotron-cc/","name":"Nemotron-CC","summary":"Nemotron-CC is NVIDIA's English pretraining dataset built from Common Crawl. The original release (December 2024) has 6.3T tokens, 4.4T globally deduplicated original tokens and 1.9T synthetic tokens, drawn from 99 Common Crawl snapshots (CC-MAIN-2013-20 through CC-MAIN-2024-30) and split into five quality buckets. Nemotron-CC-v2 (August 2025) and Nemotron-CC-v2.1 (December 2025), published on Hugging Face, add newer snapshots, further synthetic rephrasings, translated question-answer data, and other subsets.","keywords":"dataset pretraining-data NVIDIA Common Crawl Terms of Use (original Nemotron-CC release) NVIDIA Data Agreement for Model Training (Nemotron-CC-v2 and v2.1) NVIDIA"},{"type":"software","slug":"neuromancer","href":"/open/neuromancer/","name":"NeuroMANCER","summary":"NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations) is an open-source differentiable programming library written in PyTorch and published by Pacific Northwest National Laboratory. It is built for learning to solve parametric constrained optimization problems, physics-informed system identification, and model-based optimal control, and it provides a symbolic interface for adding physics equations, domain knowledge, and constraints to learned models.","keywords":"framework scientific-machine-learning optimization control training Pacific Northwest National Laboratory BSD-style license (Battelle Memorial Institute) Pacific Northwest National Laboratory"},{"type":"model-family","slug":"nomic-embed","href":"/open/nomic-embed/","name":"Nomic Embed","summary":"Nomic Embed is Nomic's series of open embedding models. It began with nomic-embed-text-v1 (February 2024), a text embedder with an 8,192-token context, and continued with v1.5 and its resizable Matryoshka embeddings (February 2024), vision models aligned to the v1.5 space (June 2024), the multilingual mixture-of-experts nomic-embed-text-v2-moe (February 2025), Nomic Embed Code (March 2025), and Nomic Embed Multimodal (April 2025).","keywords":"model embedding Nomic AI Nomic AI"},{"type":"model-release","slug":"nomic-embed-text-v1-5","href":"/open/nomic-embed-text-v1-5/","name":"nomic-embed-text-v1.5","summary":"A text embedding model that updates nomic-embed-text-v1 with Matryoshka representation learning, so its 768-dimension embeddings can be shortened to as few as 64 dimensions, or binarized, with a small loss in quality. Released in February 2024, it follows v1's long-context design, which Nomic describes as supporting 8,192 tokens.","keywords":"nomic-embed-text-v1.5 model embedding Nomic AI Apache-2.0 Apache-2.0 Nomic AI Nomic Embed"},{"type":"model-release","slug":"nomic-embed-text-v2-moe","href":"/open/nomic-embed-text-v2-moe/","name":"nomic-embed-text-v2-moe","summary":"A multilingual text embedding model that uses a mixture-of-experts design (8 experts, top-2 routing), with 475M total and 305M active parameters. It covers about 100 languages, produces 768-dimensional embeddings that can be truncated to 256 through Matryoshka training, and accepts inputs of up to 512 tokens. Nomic released it in February 2025.","keywords":"nomic-embed-text-v2-moe model embedding mixture-of-experts Nomic AI Apache-2.0 Apache-2.0 Nomic AI Nomic Embed"},{"type":"model-family","slug":"cosmos","href":"/open/cosmos/","name":"NVIDIA Cosmos","summary":"Cosmos is NVIDIA's family of world foundation models, datasets, and tools for physical AI such as robots, autonomous vehicles, and smart infrastructure. Earlier generations were released as separate Predict, Transfer, and Reason models (for example Cosmos-Predict2.5 and Cosmos-Reason2). Cosmos 3 merges understanding and generation of text, images, video, audio, and actions in one mixture-of-transformers model family; its Super (64B) and Nano (16B) models were released on May 31, 2026, and the Edge (4B) model followed in July 2026.","keywords":"model world-model multimodal robotics video-generation NVIDIA NVIDIA"},{"type":"model-release","slug":"cosmos3-nano","href":"/open/cosmos3-nano/","name":"NVIDIA Cosmos3-Nano","summary":"Cosmos3-Nano is the 16B-parameter model in NVIDIA's Cosmos 3 family, released on May 31, 2026. It shares the family's mixture-of-transformers design, generating text autoregressively and images, video, audio, and robot actions by diffusion, from combinations of text, image, video, audio, and action-trajectory inputs.","keywords":"Cosmos3-Nano (16B) model world-model multimodal robotics video-generation NVIDIA OpenMDW License Agreement, version 1.1 (OpenMDW-1.1) OpenMDW License Agreement, version 1.1 (Cosmos-Framework code) NVIDIA NVIDIA Cosmos"},{"type":"model-release","slug":"cosmos3-super","href":"/open/cosmos3-super/","name":"NVIDIA Cosmos3-Super","summary":"Cosmos3-Super is the 64B-parameter model in NVIDIA's Cosmos 3 family, released on May 31, 2026. It uses a mixture-of-transformers design with an autoregressive tower that generates text and a diffusion tower that generates images, video, audio, and robot actions, and it takes combinations of text, image, video, audio, and action-trajectory inputs.","keywords":"Cosmos3-Super (64B) model world-model multimodal robotics video-generation NVIDIA OpenMDW License Agreement, version 1.1 (OpenMDW-1.1) OpenMDW License Agreement, version 1.1 (Cosmos-Framework code) NVIDIA NVIDIA Cosmos"},{"type":"software","slug":"cuopt","href":"/open/cuopt/","name":"NVIDIA cuOpt","summary":"cuOpt is NVIDIA's GPU-accelerated optimization engine for linear programming, quadratic programming, and vehicle routing, with beta support for mixed integer, quadratically constrained quadratic, and second-order cone programming. Its core is written in C++ with C, Python, and server APIs. NVIDIA announced in March 2025 that it would open-source cuOpt, and an NVIDIA post in June 2025 said it was available under Apache 2.0.","keywords":"framework optimization numerical-computing NVIDIA Apache-2.0 NVIDIA"},{"type":"software","slug":"nvidia-dynamo","href":"/open/nvidia-dynamo/","name":"NVIDIA Dynamo","summary":"Dynamo is NVIDIA's open-source framework for serving generative AI models across many GPUs and nodes. It sits above inference engines such as vLLM, SGLang, and TensorRT-LLM and adds disaggregated prefill and decode, KV-cache-aware request routing, KV cache offloading, and SLA-based autoscaling behind an OpenAI-compatible frontend. It is written in Rust and Python.","keywords":"runtime inference serving distributed-computing NVIDIA Apache-2.0 NVIDIA"},{"type":"model-family","slug":"gr00t","href":"/open/gr00t/","name":"NVIDIA Isaac GR00T N","summary":"Isaac GR00T N is NVIDIA's family of open vision-language-action models for humanoid and other robots. The models take camera images, language instructions, and robot state and output continuous actions, using a vision-language backbone with a diffusion-transformer action head. The repository describes N1.7 as the latest version of GR00T N1 and lists N1.5 and N1.6 as previous releases.","keywords":"model robotics vision-language-action NVIDIA NVIDIA"},{"type":"model-release","slug":"gr00t-n1-7-3b","href":"/open/gr00t-n1-7-3b/","name":"NVIDIA Isaac GR00T N1.7 3B","summary":"GR00T N1.7 is a 3B-parameter vision-language-action model from NVIDIA that maps camera images, language instructions, and robot proprioception to continuous robot actions. It replaces the earlier Eagle backbone with Cosmos-Reason2-2B and uses a flow-matching diffusion transformer as its action head. NVIDIA first released it in early access in April 2026; the repository now describes it as a general availability release.","keywords":"N1.7 (GR00T-N1.7-3B) model robotics vision-language-action NVIDIA NVIDIA Open Model License Agreement Apache-2.0 NVIDIA NVIDIA Isaac GR00T N"},{"type":"software","slug":"isaac-lab","href":"/open/isaac-lab/","name":"NVIDIA Isaac Lab","summary":"Isaac Lab is an open-source, GPU-accelerated framework for robot learning built on NVIDIA Isaac Sim. It provides robot models, ready-to-train environments, sensor simulation, and workflows for reinforcement learning, imitation learning, and motion planning. Its technical report describes it as the successor to Isaac Gym; its development started from the Orbit framework.","keywords":"framework robotics simulation training NVIDIA BSD-3-Clause Apache-2.0 NVIDIA"},{"type":"software","slug":"nemo-framework","href":"/open/nemo-framework/","name":"NVIDIA NeMo Framework","summary":"NeMo Framework is NVIDIA's open-source Python framework for pretraining, post-training, and reinforcement learning of generative AI models, organized as separate libraries in the NVIDIA-NeMo GitHub organization, including Megatron Bridge, AutoModel, NeMo RL, Curator, Evaluator, Export-Deploy, Run, and NeMo Speech. In 2026 the original NeMo repository was split and now focuses on audio, speech, and multimodal LLMs as NeMo Speech (version 3.0), and for the framework container from 26.02 onward NVIDIA points to Megatron Bridge's documentation and release notes.","keywords":"framework training distributed-computing speech NVIDIA Apache-2.0 Apache-2.0 Apache-2.0 Apache-2.0 NVIDIA"},{"type":"model-family","slug":"nemotron","href":"/open/nemotron/","name":"NVIDIA Nemotron","summary":"Nemotron is NVIDIA's family of models for building AI agents. The current generation is organized into Nano, Lightning, Super, and Ultra tiers; NVIDIA publishes the weights on Hugging Face and, for several releases, training datasets and training recipes.","keywords":"model language-model reasoning mixture-of-experts NVIDIA NVIDIA"},{"type":"model-release","slug":"nemotron-3-super-120b-a12b","href":"/open/nemotron-3-super-120b-a12b/","name":"NVIDIA Nemotron 3 Super 120B-A12B","summary":"Nemotron 3 Super is an NVIDIA language model with 120B total and 12B active parameters, using a hybrid Mamba-2 and attention architecture with latent mixture-of-experts layers and multi-token prediction, and configurable reasoning. NVIDIA released BF16, FP8, and NVFP4 checkpoints and a separate base checkpoint on Hugging Face.","keywords":"3 Super (120B-A12B), v1.0 model language-model reasoning mixture-of-experts NVIDIA NVIDIA Nemotron Open Model License Apache-2.0 NVIDIA NVIDIA Nemotron"},{"type":"model-release","slug":"nemotron-3-ultra-550b-a55b","href":"/open/nemotron-3-ultra-550b-a55b/","name":"NVIDIA Nemotron 3 Ultra 550B-A55B","summary":"Nemotron 3 Ultra is an NVIDIA language model with 550B total and 55B active parameters, built on a hybrid Mamba-2 and attention architecture with latent mixture-of-experts layers and multi-token prediction. Reasoning can be switched on or off through the chat template. NVIDIA released BF16 and NVFP4 checkpoints and a separate base checkpoint on Hugging Face.","keywords":"3 Ultra (550B-A55B), v1.0 model language-model reasoning mixture-of-experts NVIDIA OpenMDW License Agreement, version 1.1 Apache-2.0 NVIDIA NVIDIA Nemotron"},{"type":"model-release","slug":"nemotron-3-5-lightning-30b-a3b","href":"/open/nemotron-3-5-lightning-30b-a3b/","name":"NVIDIA Nemotron 3.5 Lightning 30B-A3B","summary":"Nemotron 3.5 Lightning is an NVIDIA language model with 30B total and 3B active parameters, using a hybrid Mamba-2 and attention mixture-of-experts architecture with multi-token prediction and configurable reasoning. NVIDIA released full-precision BF16 weights, an NVFP4 checkpoint for deployment, draft models for speculative decoding, and a separate base checkpoint.","keywords":"3.5 Lightning (30B-A3B), GA model language-model reasoning mixture-of-experts NVIDIA OpenMDW License Agreement, version 1.1 Apache-2.0 NVIDIA NVIDIA Nemotron"},{"type":"model-family","slug":"parakeet","href":"/open/parakeet/","name":"NVIDIA Parakeet","summary":"Parakeet is NVIDIA's family of automatic speech recognition models built on the FastConformer encoder in the NeMo toolkit, released with CTC, RNN-T, TDT, and hybrid TDT-CTC decoders. The first checkpoints were published on Hugging Face in December 2023; the card for one of them, parakeet-rnnt-1.1b, says it was developed jointly by the NVIDIA NeMo and Suno.ai teams. Later releases include the English parakeet-tdt-0.6b-v2 (April 2025), the 25-language parakeet-tdt-0.6b-v3 (August 2025), and parakeet-unified-en-0.6b (April 2026), which handles offline and streaming transcription in one model; there are also language-specific checkpoints such as Japanese, Danish, and Vietnamese.","keywords":"model speech NVIDIA (NeMo) NVIDIA"},{"type":"model-release","slug":"parakeet-tdt-0-6b-v3","href":"/open/parakeet-tdt-0-6b-v3/","name":"NVIDIA parakeet-tdt-0.6b-v3","summary":"parakeet-tdt-0.6b-v3 is a 600-million-parameter multilingual speech recognition model with a FastConformer encoder and a TDT decoder, which predicts tokens and their durations jointly. It extends parakeet-tdt-0.6b-v2 from English to 25 European languages, detects the spoken language automatically, and outputs punctuated, capitalized text with word- and segment-level timestamps. NVIDIA released it on Hugging Face on August 14, 2025.","keywords":"parakeet-tdt-0.6b-v3 model speech NVIDIA (NeMo) CC-BY-4.0 Apache-2.0 NVIDIA NVIDIA Parakeet"},{"type":"model-release","slug":"parakeet-unified-en-0-6b","href":"/open/parakeet-unified-en-0-6b/","name":"NVIDIA parakeet-unified-en-0.6b","summary":"parakeet-unified-en-0.6b is a 600M-parameter English speech recognition model that serves both offline and streaming transcription from one checkpoint. It pairs a 24-layer FastConformer encoder, trained jointly in offline and streaming modes with a mode-consistency regularization loss, with an RNN-T decoder; streaming latency can be set from 2,080 ms down to 160 ms. Output includes punctuation and capitalization. NVIDIA released it on Hugging Face on April 7, 2026.","keywords":"parakeet-unified-en-0.6b model speech NVIDIA (NeMo) NVIDIA Open Model License Agreement Apache-2.0 NVIDIA NVIDIA Parakeet"},{"type":"software","slug":"ollama","href":"/open/ollama/","name":"Ollama","summary":"Ollama is an open-source application and runtime for downloading and running open models on macOS, Windows, and Linux. It has an API, Python and JavaScript client libraries, and integrations with coding agents and other tools. Its company, Ollama Inc., also offers cloud-hosted models.","keywords":"runtime inference local-inference Ollama Inc. MIT"},{"type":"model-family","slug":"olmo","href":"/open/olmo/","name":"OLMo","summary":"OLMo (styled Olmo in recent releases) is Ai2's series of open language models. Releases have included the first OLMo 1B and 7B models (February 2024), OLMoE (September 2024), OLMo 2 (November 2024 to May 2025), and Olmo 3 at 7B and 32B (November 2025), with Olmo 3.1 32B checkpoints added in December 2025. In March 2026 Ai2 also released Olmo Hybrid, a 7B model that mixes attention layers with linear RNN layers.","keywords":"model language-model reasoning Ai2 (Allen Institute for AI) Ai2 (Allen Institute for AI)"},{"type":"model-release","slug":"olmo-3-1025-7b","href":"/open/olmo-3-1025-7b/","name":"Olmo 3 7B (base)","summary":"The pretrained base model at the 7B scale in Ai2's Olmo 3 release, trained in three stages (pretraining, mid-training, and long-context extension) on Dolma 3 data. The model card lists 5.93 trillion training tokens, 32 layers, and a 65,536-token context length.","keywords":"Olmo-3-1025-7B model language-model Ai2 (Allen Institute for AI) Apache-2.0 Apache-2.0 Ai2 (Allen Institute for AI) OLMo"},{"type":"model-release","slug":"olmo-3-7b-instruct","href":"/open/olmo-3-7b-instruct/","name":"Olmo 3 7B Instruct","summary":"A 7B chat model in Ai2's Olmo 3 release, post-trained from the Olmo 3 7B base model with supervised fine-tuning, direct preference optimization, and reinforcement learning with verifiable rewards on the Dolci datasets. It responds without a separate reasoning trace, unlike the Olmo 3 Think models.","keywords":"Olmo-3-7B-Instruct model language-model Ai2 (Allen Institute for AI) Apache-2.0 Apache-2.0 Apache-2.0 Ai2 (Allen Institute for AI) OLMo"},{"type":"model-release","slug":"olmo-3-1-32b-think","href":"/open/olmo-3-1-32b-think/","name":"Olmo 3.1 32B Think","summary":"A 32B reasoning model from Ai2's December 2025 Olmo 3.1 update. It was produced by continuing the reinforcement learning run behind Olmo 3 32B Think for about three more weeks with additional passes over the Dolci-Think-RL data, and it writes a reasoning trace before its final answer.","keywords":"Olmo-3.1-32B-Think model language-model reasoning Ai2 (Allen Institute for AI) Apache-2.0 Apache-2.0 Apache-2.0 Ai2 (Allen Institute for AI) OLMo"},{"type":"software","slug":"olmo-core","href":"/open/olmo-core/","name":"OLMo-core","summary":"OLMo-core is Ai2's Python library of building blocks for large-scale distributed training with PyTorch, used for its OLMo language models. The repository also holds the official training scripts for released OLMo 2 and Olmo 3 models and has beta support for text generation, including a chat demo. Version 3.0.0 was released on October 1, 2026.","keywords":"framework training distributed-computing Ai2 Apache-2.0 Ai2 (Allen Institute for AI)"},{"type":"software","slug":"onnx","href":"/open/onnx/","name":"ONNX (Open Neural Network Exchange)","summary":"ONNX is an open format for representing machine learning models, covering both deep learning and traditional ML. It defines an extensible computation graph model, built-in operators, and standard data types, with a current focus on inference. Facebook and Microsoft started the ONNX community in 2017, and LF AI announced it as a graduate-level project in November 2019.","keywords":"1.23.1 framework inference compiler ONNX project (hosted by the LF AI & Data Foundation) Apache-2.0 LF AI & Data Foundation The Linux Foundation Microsoft Meta"},{"type":"software","slug":"onnx-runtime","href":"/open/onnx-runtime/","name":"ONNX Runtime","summary":"ONNX Runtime is Microsoft's open-source, cross-platform engine for running and accelerating machine-learning models in the ONNX format. It runs models exported from frameworks such as PyTorch and TensorFlow/Keras and from classical libraries such as scikit-learn, LightGBM, and XGBoost, applying graph optimizations and hardware-specific execution providers; it also offers accelerated training for transformer models on NVIDIA GPUs.","keywords":"runtime inference local-inference Microsoft MIT Microsoft"},{"type":"software","slug":"open-webui","href":"/open/open-webui/","name":"Open WebUI","summary":"Open WebUI is a self-hosted web interface for AI models that is built to run entirely offline. It connects to Ollama and OpenAI-compatible APIs and includes retrieval over local documents, role-based user and group permissions, plugins, and voice features.","keywords":"runtime local-inference chat-interface Open WebUI, Inc. Open WebUI License (BSD 3-Clause terms plus a branding clause) BSD-3-Clause MIT"},{"type":"software","slug":"openai-agents-sdk","href":"/open/openai-agents-sdk/","name":"OpenAI Agents SDK","summary":"The OpenAI Agents SDK is an open-source framework from OpenAI for building multi-agent workflows from agents, tools, handoffs, guardrails, and sessions, with built-in tracing. OpenAI describes it as a production-ready upgrade of its earlier Swarm experiment. The Python SDK has a separate JavaScript/TypeScript counterpart.","keywords":"framework agents multi-agent OpenAI MIT MIT OpenAI"},{"type":"evaluation-tool","slug":"openai-evals","href":"/open/openai-evals/","name":"OpenAI Evals","summary":"OpenAI Evals is an open-source framework from OpenAI for evaluating language models and systems built on them, together with a registry of existing evals. Evals are defined in YAML and run from the oaieval and oaievalset command-line tools against \"completion functions\", which by default call models through the OpenAI API. The repository is not archived, but activity since mid-2024 has been limited to maintenance, and its README now points users to OpenAI's hosted Evals in the OpenAI dashboard.","keywords":"eval evaluation benchmark OpenAI MIT OpenAI"},{"type":"software","slug":"openai-mcp-extensions","href":"/open/openai-mcp-extensions/","name":"OpenAI MCP Extensions","summary":"OpenAI MCP Extensions is a specification and pair of SDKs from OpenAI that add ChatGPT-specific capabilities on top of the Model Context Protocol (MCP) and MCP Apps, so that plugins can appear in ChatGPT's interface. Documented extensions include sidebar and thread entrypoints, file-type handlers, structured settings, composer at-mentions, and extended form elicitation. The TypeScript SDK extends the official MCP TypeScript SDKs for servers and apps; the Python SDK extends the official MCP Python SDK for servers.","keywords":"framework agents protocol developer-tools OpenAI Apache-2.0 OpenAI"},{"type":"software","slug":"openclaw","href":"/open/openclaw/","name":"OpenClaw","summary":"OpenClaw is a self-hosted AI assistant with a gateway connecting model providers, tools, and messaging channels. It provides command-line and browser interfaces and supports both hosted and local model providers.","keywords":"runtime agents automation OpenClaw Foundation MIT"},{"type":"model-family","slug":"openelm","href":"/open/openelm/","name":"OpenELM","summary":"OpenELM is a family of small language models released by Apple in April 2024 in 270M, 450M, 1.1B, and 3B parameter sizes, each as a pretrained and an instruction-tuned model. The models use layer-wise scaling to allocate parameters across transformer layers, and Apple published them together with its CoreNet training library, pre-training configurations, training logs, and evaluation instructions.","keywords":"model language-model Apple Apple"},{"type":"model-release","slug":"openelm-3b-instruct","href":"/open/openelm-3b-instruct/","name":"OpenELM 3B Instruct","summary":"OpenELM 3B Instruct is the 3B-parameter instruction-tuned model in Apple's April 2024 OpenELM release. It was pretrained with Apple's CoreNet library on about 1.8 trillion tokens of public data and then instruction-tuned on the UltraFeedback dataset using the Hugging Face Alignment Handbook.","keywords":"OpenELM-3B-Instruct model language-model Apple Apple Machine Learning Research Model License Agreement Apple software license (CoreNet) Apple OpenELM"},{"type":"software","slug":"openhands","href":"/open/openhands/","name":"OpenHands","summary":"OpenHands is an open-source platform for building and running AI coding agents. Its main repository now holds Agent Canvas, a self-hosted browser control center. Agent Canvas runs the OpenHands agent, or other agents that support the Agent Client Protocol, on local, remote, or cloud backends. The agent itself, the Python SDK, and the Agent Server are developed in the separate Software Agent SDK repository.","keywords":"framework agents coding-agent All Hands AI (OpenHands) MIT MIT All Hands AI (OpenHands)"},{"type":"software","slug":"openpi","href":"/open/openpi/","name":"openpi","summary":"openpi is Physical Intelligence's repository of code and model checkpoints for its π0, π0-FAST, and π0.5 vision-language-action models for robot control. It provides base checkpoints pre-trained on more than 10,000 hours of robot data, fine-tuned checkpoints for the ALOHA, DROID, and LIBERO setups, and JAX and PyTorch code for inference and fine-tuning.","keywords":"research-stack robotics vision-language training inference Physical Intelligence Apache-2.0 Physical Intelligence"},{"type":"software","slug":"openvino","href":"/open/openvino/","name":"OpenVINO","summary":"OpenVINO is Intel's open-source toolkit for converting, optimizing, and running deep learning models, including language and generative models. It converts models from PyTorch, TensorFlow, ONNX, PaddlePaddle, JAX, and other frameworks and runs inference on CPUs (x86 and ARM), Intel GPUs, and Intel NPUs, with APIs in C++, Python, C, and Node.js.","keywords":"runtime inference local-inference compiler Intel Apache-2.0 Intel"},{"type":"software","slug":"openxla","href":"/open/openxla/","name":"OpenXLA","summary":"OpenXLA is an open-source project that develops machine learning compiler and infrastructure components linking frameworks such as JAX, PyTorch, and TensorFlow to hardware backends. Its website lists XLA (a compiler for GPUs, CPUs, and ML accelerators), StableHLO (a portable operation set), Shardy (an MLIR-based tensor partitioner), PJRT (a device and compiler interface), XProf (profiling tools), and Tokamax.","keywords":"framework compiler training inference distributed-computing OpenXLA project maintainers (Google-led technical leadership) Apache-2.0 Apache-2.0 Apache-2.0 Google (Alphabet)"},{"type":"model-family","slug":"palmyra","href":"/open/palmyra/","name":"Palmyra","summary":"Palmyra is Writer's family of language models. The Palmyra X models (currently X4, X5, and X6) are offered through Writer's platform and API. Writer has also published Palmyra models on Hugging Face: Palmyra-X-4.3-73B and domain models for medicine and finance under its non-commercial open model license, and the small Palmyra-mini models (September 2025) under Apache 2.0.","keywords":"model language-model Writer Writer"},{"type":"model-release","slug":"palmyra-mini","href":"/open/palmyra-mini/","name":"Palmyra-mini","summary":"A small text model from Writer, fine-tuned from Qwen2.5-1.5B, that the model card lists at 1.7 billion parameters with a 131,072-token context window. It is the non-reasoning member of the three-model Palmyra-mini family that Writer released in September 2025.","keywords":"palmyra-mini model language-model reasoning Writer Apache-2.0 Writer Palmyra"},{"type":"model-release","slug":"palmyra-mini-thinking-b","href":"/open/palmyra-mini-thinking-b/","name":"Palmyra-mini-thinking-b","summary":"A small reasoning model in Writer's Palmyra-mini family, released in September 2025. Writer built it on NVIDIA's OpenReasoning-Nemotron-1.5B and applied reinforcement-learning fine-tuning; the model card lists a 131,072-token context window.","keywords":"palmyra-mini-thinking-b model language-model reasoning Writer Apache-2.0 Writer Palmyra"},{"type":"software","slug":"paper-qa","href":"/open/paper-qa/","name":"PaperQA (paper-qa)","summary":"PaperQA is a Python package and command-line tool (pqa) from FutureHouse for retrieval-augmented question answering over PDFs, text, Office documents, and source code, focused on scientific literature. Versions 5 and later, called PaperQA2, add an agent that searches papers, gathers evidence, and writes answers with in-text citations; the package moved to calendar versioning in December 2025.","keywords":"framework agents retrieval science FutureHouse Apache-2.0 FutureHouse"},{"type":"evaluation-tool","slug":"petri","href":"/open/petri/","name":"Petri (Inspect Petri)","summary":"Petri (Parallel Exploration Tool for Risky Interactions) is an open-source tool for automated alignment audits of language models. An auditor model drives multi-turn conversations with a target model from seed instructions, simulating tools and rolling back turns, and a judge model scores the transcripts. Anthropic released it in October 2025 and handed its development to Meridian Labs, an AI evaluation nonprofit, in May 2026 with the release of Petri 3.0. The repository moved from safety-research/petri to meridianlabs-ai/inspect_petri.","keywords":"eval evaluation alignment safety agents Meridian Labs MIT Anthropic"},{"type":"model-family","slug":"phi","href":"/open/phi/","name":"Phi","summary":"Phi is Microsoft's family of small language models. Generations have included Phi-3, Phi-3.5, and Phi-4, with instruction, reasoning, multimodal, and vision-language variants published on Hugging Face and Microsoft Foundry. Recent Phi-4 releases include Phi-4-mini-flash-reasoning (July 2025) and Phi-4-reasoning-vision-15B (March 2026).","keywords":"model language-model reasoning multimodal Microsoft Microsoft"},{"type":"model-release","slug":"phi-4-mini-flash-reasoning","href":"/open/phi-4-mini-flash-reasoning/","name":"Phi-4-mini-flash-reasoning","summary":"Phi-4-mini-flash-reasoning is a 3.8-billion-parameter, English, text-only Microsoft model fine-tuned for mathematical reasoning. It uses a hybrid \"SambaY\" decoder-hybrid-decoder architecture that mixes state space model layers with attention and Differential Attention, and supports a 64K-token context.","keywords":"4-mini-flash-reasoning model language-model reasoning Microsoft MIT Microsoft Phi"},{"type":"model-release","slug":"phi-4-reasoning-vision-15b","href":"/open/phi-4-reasoning-vision-15b/","name":"Phi-4-reasoning-vision-15B","summary":"Phi-4-reasoning-vision-15B is a 15-billion-parameter multimodal reasoning model from Microsoft. It combines the Phi-4-Reasoning language model with a SigLIP-2 vision encoder, takes text and images as input, produces text, and has a 16,384-token context length. It can either reason step by step or answer directly, depending on the task.","keywords":"4-reasoning-vision-15B model vision-language multimodal reasoning Microsoft MIT MIT Microsoft Phi"},{"type":"software","slug":"prime-rl","href":"/open/prime-rl/","name":"prime-rl","summary":"prime-rl is Prime Intellect's open-source framework for large-scale, asynchronous reinforcement learning and supervised fine-tuning of language models. It separates an FSDP2-based trainer, a vLLM inference service, and an orchestrator that gathers rollouts from verifiers environments, and it supports multi-node deployment on Slurm and Kubernetes.","keywords":"research-stack training distributed-computing Prime Intellect Apache-2.0 Prime Intellect"},{"type":"model-family","slug":"profluent-e1","href":"/open/profluent-e1/","name":"Profluent-E1","summary":"Profluent-E1 is a family of protein encoder language models from Profluent, announced in November 2025 in 150M, 300M, and 600M parameter sizes. The models can take homologous sequences alongside a query sequence (retrieval augmentation) or run on a single sequence, and Profluent presents them as drop-in replacements for ESM-family encoders.","keywords":"model science embedding protein-language-model Profluent Profluent"},{"type":"model-release","slug":"e1-150m","href":"/open/e1-150m/","name":"Profluent-E1 150M (E1-150m)","summary":"The 150M-parameter member of the Profluent-E1 family of retrieval-augmented protein encoder models, released in November 2025 alongside E1-600m and E1-300m. The Hugging Face safetensors metadata counts 154,423,330 parameters stored in BF16. It can encode a single protein sequence or a query preceded by homologous sequences.","keywords":"Profluent-Bio/E1-150m model science embedding protein-language-model Profluent Profluent-E1 Clickthrough License Agreement Apache-2.0 Profluent Profluent-E1"},{"type":"model-release","slug":"e1-300m","href":"/open/e1-300m/","name":"Profluent-E1 300M (E1-300m)","summary":"The 300M-parameter member of the Profluent-E1 family of retrieval-augmented protein encoder models, released in November 2025 alongside E1-600m and E1-150m. The Hugging Face safetensors metadata counts 274,317,346 parameters stored in BF16. It can encode a single protein sequence or a query preceded by homologous sequences.","keywords":"Profluent-Bio/E1-300m model science embedding protein-language-model Profluent Profluent-E1 Clickthrough License Agreement Apache-2.0 Profluent Profluent-E1"},{"type":"model-release","slug":"e1-600m","href":"/open/e1-600m/","name":"Profluent-E1 600M (E1-600m)","summary":"The 600M-parameter member of the Profluent-E1 family of retrieval-augmented protein encoder models, released in November 2025 alongside E1-300m and E1-150m. The Hugging Face safetensors metadata counts 641,438,754 parameters stored in BF16. It can encode a single protein sequence or a query preceded by homologous sequences.","keywords":"Profluent-Bio/E1-600m model science embedding protein-language-model Profluent Profluent-E1 Clickthrough License Agreement Apache-2.0 Profluent Profluent-E1"},{"type":"model-family","slug":"pythia","href":"/open/pythia/","name":"Pythia","summary":"Pythia is EleutherAI's suite of language models built for research on how models learn during training and change with scale. The main suite has eight sizes from 70M to 12B parameters, each trained once on the Pile and once on a deduplicated Pile, on the same data in the same order, with 154 checkpoints per model. The current suite was retrained and re-released in April 2023; 14M and 31M models and additional random-seed runs were added later.","keywords":"model language-model EleutherAI EleutherAI"},{"type":"model-release","slug":"pythia-12b","href":"/open/pythia-12b/","name":"Pythia 12B","summary":"The largest model in EleutherAI's Pythia suite: an English decoder-only language model with about 11.8 billion total parameters, trained on the Pile (not deduplicated) for about 300 billion tokens. EleutherAI publishes 154 checkpoints from initialization to the end of training as Hugging Face branches.","keywords":"pythia-12b model language-model EleutherAI Apache-2.0 Apache-2.0 EleutherAI Pythia"},{"type":"software","slug":"pytorch","href":"/open/pytorch/","name":"PyTorch","summary":"PyTorch is an open-source machine learning framework for tensors and dynamic neural networks in Python, with GPU acceleration and a C++ distribution (LibTorch). Meta originally developed it and released it in 2016. Since September 2022 it has been hosted by the PyTorch Foundation under the Linux Foundation.","keywords":"framework training numerical-computing distributed-computing PyTorch Foundation BSD-3-Clause PyTorch Foundation The Linux Foundation Meta"},{"type":"software","slug":"pytorch-lightning","href":"/open/pytorch-lightning/","name":"PyTorch Lightning","summary":"PyTorch Lightning is an open-source Python framework that structures PyTorch training code and handles engineering tasks such as multi-GPU and multi-node distributed training and mixed precision. The same repository contains Lightning Fabric, a lighter layer for scaling custom PyTorch training loops, and both ship in the lightning package. It was open-sourced in 2019 and is maintained by Lightning AI.","keywords":"framework training distributed-computing Lightning AI Apache-2.0 Lightning AI"},{"type":"software","slug":"qualcomm-ai-hub-models","href":"/open/qualcomm-ai-hub-models/","name":"Qualcomm AI Hub Models","summary":"A Python package and GitHub repository of machine learning models prepared for deployment on Qualcomm devices. It provides export scripts that compile, quantize where applicable, profile, and run models through Qualcomm AI Hub Workbench, end-to-end demos for most models, and sample application code. Many of the packaged models come from other developers, such as YOLO, Llama, Mistral, Phi, and Qwen variants.","keywords":"framework inference local-inference Qualcomm Technologies, Inc. BSD-3-Clause Qualcomm"},{"type":"software","slug":"ray","href":"/open/ray/","name":"Ray","summary":"Ray is an open-source distributed computing framework for AI workloads, with a core distributed runtime and libraries for data processing, training, and serving. It was developed at UC Berkeley's RISELab and became a PyTorch Foundation-hosted project in October 2025, contributed by Anyscale.","keywords":"framework distributed-computing training serving PyTorch Foundation (hosted project) Apache-2.0 PyTorch Foundation The Linux Foundation Anyscale"},{"type":"dataset","slug":"redpajama","href":"/open/redpajama/","name":"RedPajama","summary":"RedPajama is Together AI's open pretraining dataset project. RedPajama-V1 (RedPajama-Data-1T, 2023) is a 1.2-trillion-token reproduction of the LLaMA training-data recipe drawn from Common Crawl, C4, GitHub, arXiv, Wikipedia, and Stack Exchange. The current version, RedPajama-V2 (October 2023), contains over 100 billion documents from 84 Common Crawl snapshots in English, German, French, Spanish, and Italian, with more than 40 precomputed quality signals and duplicate markers; its deduplicated, annotated portion is about 30 trillion tokens.","keywords":"V2 dataset pretraining-data Together AI (Together Computer, Inc.) Apache-2.0 Together AI"},{"type":"model-family","slug":"reka-flash","href":"/open/reka-flash/","name":"Reka Flash","summary":"Reka Flash is a line of Reka language models; this record covers its open-weight releases. Reka Flash 3 (March 2025) is a 21B-parameter reasoning model that Reka says was pretrained from scratch on publicly accessible and synthetic data, then instruction-tuned and trained with reinforcement learning; it was released as open weights under the Apache License 2.0. Reka Flash 3.1 (July 2025) updated it with further reinforcement learning aimed at coding and agentic tasks, and was released with a 3.5-bit quantized version and the Reka Quant quantization library.","keywords":"model language-model reasoning Reka Reka"},{"type":"model-release","slug":"reka-flash-3-1","href":"/open/reka-flash-3-1/","name":"Reka Flash 3.1","summary":"A 21B-parameter language model released by Reka in July 2025 as an update to Reka Flash 3. Reka post-trained it with supervised fine-tuning on synthetic and public datasets followed by large-scale reinforcement learning with verifiable rewards, with a focus on coding and on serving as a base for fine-tuning on agentic tasks. It writes a reasoning trace before its answer.","keywords":"reka-flash-3.1 model language-model reasoning code-model Reka Apache-2.0 Reka Reka Flash"},{"type":"model-family","slug":"rnj","href":"/open/rnj/","name":"Rnj","summary":"Rnj is Essential AI's line of 8B-parameter dense language models trained from scratch and aimed at code and STEM work. Rnj-1 (a base model and an instruction-tuned model) was released in December 2025, followed in 2026 by Rnj-1.5 Instruct, which extends the context window from 32K to 160K tokens.","keywords":"model language-model code-model Essential AI Essential AI"},{"type":"model-release","slug":"rnj-1-instruct","href":"/open/rnj-1-instruct/","name":"Rnj-1 Instruct","summary":"The instruction-tuned version of Essential AI's Rnj-1, an 8.3B-parameter dense model trained from scratch. The base model was pretrained on 8.4T tokens at an 8K context, extended to a 32K context in a 380B-token mid-training stage, and then given a 150B-token supervised fine-tuning stage to produce this model.","keywords":"rnj-1-instruct model language-model code-model Essential AI Apache-2.0 Essential AI Rnj"},{"type":"model-release","slug":"rnj-1-5-instruct","href":"/open/rnj-1-5-instruct/","name":"Rnj-1.5 Instruct","summary":"A long-context follow-up to Rnj-1 Instruct that extends the context window from 32K to 160K tokens. Essential AI built it from the Rnj-1 base model, switching most attention layers to block-local attention with a group of global layers in the middle, and added long-context mid-training data and more software-engineering training data.","keywords":"rnj-1.5-instruct model language-model code-model Essential AI Apache-2.0 Essential AI Rnj"},{"type":"software","slug":"rocm","href":"/open/rocm/","name":"ROCm","summary":"ROCm is AMD's open software stack for GPU-accelerated computing on AMD GPUs. Its Core SDK includes the HIP runtime and programming interface, compilers including LLVM, math and deep learning libraries such as rocBLAS and MIOpen, the RCCL communication library, and profiling, debugging, and monitoring tools. Since version 7.14 it has been built and released through TheRock, AMD's open build and release system; version 10.1.0 was released on October 5, 2026.","keywords":"framework compiler numerical-computing training inference AMD MIT Apache-2.0 Apache-2.0 WITH LLVM-exception NCSA BSD-2-Clause GNU General Public License v3.0 (ROCgdb) GPL 2.0 with Linux-syscall-note (ROCK-Kernel-Driver) Custom licenses (hipCUB, RCCL) MIT AMD"},{"type":"model-release","slug":"sam-2-1-hiera-large","href":"/open/sam-2-1-hiera-large/","name":"SAM 2.1 Hiera-Large","summary":"SAM 2.1 Hiera-Large is the largest checkpoint (224.4M parameters, per the README) in Meta's SAM 2.1 suite, an improved set of SAM 2 checkpoints released in September 2024. SAM 2 is a transformer with streaming memory that segments objects in images and tracks them through video from point, box, or mask prompts.","keywords":"2.1 (sam2.1_hiera_large) model computer-vision segmentation Meta (FAIR) Apache-2.0 Meta Segment Anything (SAM)"},{"type":"model-release","slug":"sam-3-1","href":"/open/sam-3-1/","name":"SAM 3.1","summary":"SAM 3.1 is a March 2026 update of Meta's SAM 3, a model that detects, segments, and tracks objects in images and video from text phrases, image exemplars, or visual prompts such as points, boxes, and masks. The update adds Object Multiplex, which groups tracked objects into shared-memory buckets and processes them jointly instead of one at a time, together with new checkpoints and inference optimizations.","keywords":"3.1 (Object Multiplex) model computer-vision segmentation Meta (Meta Superintelligence Labs) SAM License Meta Segment Anything (SAM)"},{"type":"model-family","slug":"segment-anything","href":"/open/segment-anything/","name":"Segment Anything (SAM)","summary":"Segment Anything is Meta's family of promptable segmentation models. The original SAM (April 2023) produces object masks in images from prompts such as points or boxes; SAM 2 (July 2024, updated as SAM 2.1 in September 2024) extends this to video with streaming memory; SAM 3 (November 2025, updated as SAM 3.1 in March 2026) adds detection, segmentation, and tracking of every instance of a concept named by a short text phrase or image exemplar. Licenses differ by generation: Apache 2.0 for SAM and SAM 2, and Meta's custom SAM License for SAM 3.","keywords":"model computer-vision segmentation Meta Meta"},{"type":"software","slug":"sentence-transformers","href":"/open/sentence-transformers/","name":"Sentence Transformers","summary":"Sentence Transformers is a Python framework for using and training embedding and reranker models. It computes dense embeddings with Sentence Transformer models, scores text pairs with Cross-Encoder (reranker) models, and also supports sparse encoders and multi-vector models for ColBERT-style late-interaction retrieval, with pretrained models published on Hugging Face. Version 6.1.0 was released on September 18, 2026.","keywords":"framework embedding retrieval training inference Hugging Face Apache-2.0 Hugging Face"},{"type":"software","slug":"sglang","href":"/open/sglang/","name":"SGLang","summary":"SGLang is an open-source serving framework for large language models and multimodal models, designed for low-latency, high-throughput inference on anything from a single GPU to large distributed clusters. Its runtime features include RadixAttention prefix caching, prefill-decode disaggregation, speculative decoding, continuous batching, several forms of parallelism, structured outputs, and quantization.","keywords":"0.5.20 runtime inference serving LMSYS (hosting organization) RadixArk (company described as a maintainer in a post on the LMSYS blog) Apache-2.0 LMSYS (Large Model Systems Organization)"},{"type":"software","slug":"smolagents","href":"/open/smolagents/","name":"smolagents","summary":"smolagents is an open-source Python library from Hugging Face for building agents with few abstractions. Its CodeAgent writes its actions as Python code, a ToolCallingAgent uses conventional tool calling, and agents and tools can be shared through the Hugging Face Hub.","keywords":"framework agents multi-agent Hugging Face Apache-2.0 Hugging Face"},{"type":"model-family","slug":"smollm","href":"/open/smollm/","name":"SmolLM","summary":"SmolLM is Hugging Face's series of small language models. The first SmolLM release (July 2024) came in 135M, 360M, and 1.7B sizes; SmolLM2 kept those three sizes, with the 1.7B model trained on about 11 trillion tokens (paper February 2025); and SmolLM3 (July 2025) is a 3B model released as base and instruct checkpoints, with a dual-mode reasoning instruct model. The SmolVLM vision-language models are maintained in the same repository.","keywords":"model language-model reasoning local-inference Hugging Face (Smol Models Research, HuggingFaceTB) Hugging Face"},{"type":"model-release","slug":"smollm3-3b","href":"/open/smollm3-3b/","name":"SmolLM3 3B","summary":"SmolLM3-3B is the instruct model of Hugging Face's SmolLM3 release (July 2025), a 3B-parameter decoder-only transformer pretrained on about 11 trillion tokens of web, code, math, and reasoning data in stages, then mid-trained on reasoning data and aligned with supervised fine-tuning and Anchored Preference Optimization. It can answer with or without an extended reasoning trace, supports tool calling, and natively covers six European languages.","keywords":"SmolLM3-3B model language-model reasoning local-inference Hugging Face (Smol Models Research, HuggingFaceTB) Apache-2.0 Apache-2.0 Apache-2.0 Hugging Face SmolLM"},{"type":"model-family","slug":"snowflake-arctic","href":"/open/snowflake-arctic/","name":"Snowflake Arctic","summary":"Arctic is a text-in, text-and-code-out language model pretrained from scratch by the Snowflake AI Research Team. It uses a dense-MoE hybrid transformer that combines a 10B dense model with a residual 128x3.66B mixture-of-experts MLP, for 480B total and 17B active parameters with top-2 gating. Base and instruct-tuned checkpoints were released on April 24, 2024 with weights and code under Apache-2.0.","keywords":"model language-model mixture-of-experts Snowflake AI Research Snowflake"},{"type":"model-release","slug":"snowflake-arctic-base","href":"/open/snowflake-arctic-base/","name":"Snowflake Arctic Base","summary":"Arctic Base is the pretrained checkpoint of Snowflake Arctic, a dense-MoE hybrid transformer (a 10B dense model plus a residual 128x3.66B MoE MLP) with 480B total and 17B active parameters, pretrained from scratch by the Snowflake AI Research Team. It takes text input and generates text and code.","keywords":"Base model language-model mixture-of-experts Snowflake AI Research Apache-2.0 Apache-2.0 Snowflake Snowflake Arctic"},{"type":"model-release","slug":"snowflake-arctic-instruct","href":"/open/snowflake-arctic-instruct/","name":"Snowflake Arctic Instruct","summary":"Arctic Instruct is the instruct-tuned checkpoint of Snowflake Arctic, a dense-MoE hybrid transformer (a 10B dense model plus a residual 128x3.66B MoE MLP) with 480B total and 17B active parameters, developed by the Snowflake AI Research Team. It takes text input and generates text and code.","keywords":"Instruct model language-model mixture-of-experts Snowflake AI Research Apache-2.0 Apache-2.0 Snowflake Snowflake Arctic"},{"type":"software","slug":"sparkjs","href":"/open/sparkjs/","name":"Spark (sparkjs)","summary":"Spark is an open-source JavaScript renderer for 3D Gaussian splats built on THREE.js, made by World Labs. It renders splats together with ordinary meshes, loads common splat formats such as PLY, SPZ, and SPLAT, and lets splats be edited and animated on the GPU. Spark 2.0 added a level-of-detail system for streaming large splat worlds.","keywords":"framework 3d-rendering gaussian-splatting computer-vision World Labs MIT World Labs"},{"type":"software","slug":"spec-kit","href":"/open/spec-kit/","name":"Spec Kit","summary":"Spec Kit is an open-source toolkit from GitHub that gives AI coding agents structured processes and reusable templates. Its core process, spec-driven development, turns requirements into a specification, a technical plan, and a task list before implementation; bug-fixing and idea-assessment processes are available as bundled extensions.","keywords":"framework agents coding-agent developer-tools GitHub MIT GitHub"},{"type":"model-family","slug":"starcoder2","href":"/open/starcoder2/","name":"StarCoder2","summary":"StarCoder2 is a family of code generation models from the BigCode project, released on February 28, 2024 in 3B, 7B, and 15B sizes and trained on The Stack v2. Per Hugging Face's announcement, ServiceNow trained the 3B model, Hugging Face the 7B model, and NVIDIA the 15B model. The bigcode organization on Hugging Face also hosts an instruction-tuned variant, StarCoder2-15B-Instruct-v0.1.","keywords":"model language-model code-model BigCode project (open scientific collaboration) Hugging Face (BigCode co-steward) ServiceNow (BigCode co-steward) Hugging Face ServiceNow NVIDIA"},{"type":"model-release","slug":"starcoder2-15b","href":"/open/starcoder2-15b/","name":"StarCoder2-15B","summary":"The largest StarCoder2 base model, with 15B parameters, trained on more than 4 trillion tokens covering 600+ programming languages from The Stack v2. It uses grouped-query attention, a 16,384-token context window with 4,096-token sliding-window attention, and a fill-in-the-middle training objective. NVIDIA trained it with the NeMo framework on its Eos supercomputer.","keywords":"starcoder2-15b model language-model code-model BigCode project (open scientific collaboration) Hugging Face (BigCode co-steward) ServiceNow (BigCode co-steward) BigCode OpenRAIL-M v1 License Agreement Apache-2.0 Hugging Face ServiceNow NVIDIA StarCoder2"},{"type":"model-release","slug":"starcoder2-3b","href":"/open/starcoder2-3b/","name":"StarCoder2-3B","summary":"The smallest StarCoder2 base model, with about 3B parameters, trained on more than 3 trillion tokens from The Stack v2 and other sources. Its model card lists 17 programming languages, grouped-query attention, a 16,384-token context window with 4,096-token sliding-window attention, and a fill-in-the-middle objective. ServiceNow trained it for the BigCode project.","keywords":"starcoder2-3b model language-model code-model BigCode project (open scientific collaboration) ServiceNow (BigCode co-steward; trained this model) Hugging Face (BigCode co-steward) BigCode OpenRAIL-M v1 License Agreement Apache-2.0 ServiceNow Hugging Face StarCoder2"},{"type":"software","slug":"strands-agents","href":"/open/strands-agents/","name":"Strands Agents","summary":"Strands Agents is an open-source SDK from AWS for building and running AI agents in Python and TypeScript. It runs a model-driven agent loop in the developer's own process, with tools, MCP support, multi-agent patterns, memory and sessions, and tracing. Its repository also holds Strands harness, a preassembled agent, along with a CLI and the documentation site.","keywords":"framework agents multi-agent Amazon Web Services (AWS) Apache-2.0 Amazon (AWS)"},{"type":"model-release","slug":"superapriel-15b-instruct","href":"/open/superapriel-15b-instruct/","name":"SuperApriel-15B-Instruct","summary":"A 15B-parameter instruction-tuned \"supernet\" in which each of 48 decoder layers carries four token-mixer variants: full attention, sliding-window attention, Gated DeltaNet, and Kimi Delta Attention. Choosing one mixer per layer gives deployment presets that trade output quality for decoding speed from a single checkpoint. It was derived from Apriel-1.6-15b-Thinker by distillation followed by supervised fine-tuning.","keywords":"SuperApriel-15B-Instruct model language-model reasoning ServiceNow (SLAM Labs) MIT Apache-2.0 ServiceNow Apriel"},{"type":"evaluation-tool","slug":"swe-sweep","href":"/open/swe-sweep/","name":"SWE-sweep","summary":"SWE-sweep is a software-engineering benchmark in which a coding agent is given a real repository containing many known bugs, with no hint about their type or location, and must find and fix as many as it can. Each task is built from real GitHub issue and pull-request pairs at a commit where many of those bugs are present at once, and repairs are graded with hidden tests from the fixing pull requests plus the existing test suite to catch regressions. The paper describes 100 repositories across 22 programming languages.","keywords":"eval evaluation benchmark coding-agent Meta MIT Meta"},{"type":"software","slug":"synthid-bio","href":"/open/synthid-bio/","name":"SynthID Bio","summary":"SynthID Bio is a set of methods from Google DeepMind for watermarking AI-generated protein sequences and biomolecular structures so that their AI origin can later be detected. SynthID Bio-sequence adds watermarked sampling to the ProteinMPNN inverse-folding model, and SynthID Bio-structure is a fine-tuned AlphaFold 3 model that embeds a watermark in generated structures. The public repository contains the sequence watermarking code, a standalone detection script, and data supporting the paper.","keywords":"framework science safety protein-language-model Google DeepMind Apache-2.0 MIT CC-BY-4.0 Google DeepMind"},{"type":"software","slug":"synthid-text","href":"/open/synthid-text/","name":"SynthID Text","summary":"SynthID Text is Google DeepMind's reference implementation of the text watermarking method described in its 2024 Nature paper. The method changes only how a language model samples tokens, using a configuration built around a sequence of integer keys, and detects the watermark from the text without running the model. The repository provides mean, weighted mean, and Bayesian scoring functions for detection, a mix-in that adds watermarking to Gemma and GPT-2 classes from Hugging Face Transformers, a Colab notebook, and human evaluation data.","keywords":"framework safety watermarking provenance language-model Google DeepMind Apache-2.0 CC-BY-4.0 Google DeepMind"},{"type":"software","slug":"tensorflow","href":"/open/tensorflow/","name":"TensorFlow","summary":"TensorFlow is an open-source, end-to-end machine learning platform with stable Python and C++ APIs, originally developed within Google Brain. With the 2.21 release in March 2026, Google said it would limit TensorFlow work to security and bug fixes, dependency updates, and community contributions, and pointed new generative AI work to Keras 3, JAX, and PyTorch.","keywords":"framework training numerical-computing distributed-computing Google (TensorFlow team) Apache-2.0 Google (Alphabet)"},{"type":"software","slug":"tensorrt-llm","href":"/open/tensorrt-llm/","name":"TensorRT-LLM","summary":"TensorRT-LLM is NVIDIA's open-source library for running large language model inference on NVIDIA GPUs. It provides a Python LLM API, optimized kernels, Python and C++ runtimes, and an online serving command (trtllm-serve).","keywords":"runtime inference serving NVIDIA Apache-2.0 NVIDIA"},{"type":"evaluation-tool","slug":"terminal-bench","href":"/open/terminal-bench/","name":"Terminal-Bench","summary":"Terminal-Bench is a benchmark of hard tasks that AI agents must complete in command-line environments; each task has its own container environment, a human-written reference solution, and verification tests. It was first released in May 2025; Terminal-Bench 2.0 (November 2025, 89 tasks) introduced the Harbor framework for running tasks, and since July 2026 it has been maintained as a \"continuous benchmark\" with versioned releases, the latest being 4.0 (August 2026).","keywords":"eval evaluation benchmark Stanford University Laude Institute Harbor (harbor-framework project) Apache-2.0 Stanford University"},{"type":"dataset","slug":"the-pile","href":"/open/the-pile/","name":"The Pile","summary":"The Pile is an 825 GiB English text dataset for language-model pretraining, assembled by EleutherAI from 22 component datasets that include web text (Pile-CC, from Common Crawl), academic and professional sources such as PubMed Central, arXiv, and FreeLaw, books, GitHub code, and dialogue. It was described in a December 2020 paper and a 2022 datasheet and is the training corpus of EleutherAI's Pythia models. Its current availability is limited; see access conditions.","keywords":"dataset pretraining-data EleutherAI MIT EleutherAI"},{"type":"software","slug":"tiktoken","href":"/open/tiktoken/","name":"tiktoken","summary":"tiktoken is OpenAI's open-source byte pair encoding (BPE) tokenizer for use with OpenAI's models. It converts text to the token numbers a model sees and back, using named encodings such as o200k_base and cl100k_base, and can return the encoding that corresponds to a given OpenAI model name. It also includes an educational submodule for training and visualizing a simple BPE tokenizer, and a plugin mechanism for registering other encodings.","keywords":"framework tokenizer language-model OpenAI MIT OpenAI"},{"type":"model-family","slug":"timesfm","href":"/open/timesfm/","name":"TimesFM","summary":"TimesFM (Time Series Foundation Model) is a family of pretrained time-series forecasting models developed by Google Research, first described in an ICML 2024 paper. Open checkpoints have been released as versions 1.0, 2.0, 2.5 (September 2025), and 3.0 (August 2026); version 3.0 adds native multivariate forecasting and native support for past-only and past-and-future covariates.","keywords":"model time-series forecasting Google Research Google (Alphabet)"},{"type":"model-release","slug":"timesfm-2-5-200m","href":"/open/timesfm-2-5-200m/","name":"TimesFM 2.5 200M","summary":"TimesFM 2.5 is a 200M-parameter time-series forecasting model from Google Research, released in September 2025. Compared with TimesFM 2.0 it has fewer parameters (200M, down from 500M), a context length of up to 16k points, an optional 30M quantile head for continuous quantile forecasts up to a 1k horizon, and no frequency indicator input.","keywords":"2.5 (timesfm-2.5-200m) model time-series forecasting Google Research Apache-2.0 Apache-2.0 Google (Alphabet) TimesFM"},{"type":"model-release","slug":"timesfm-3-0","href":"/open/timesfm-3-0/","name":"TimesFM 3.0","summary":"TimesFM 3.0 is a Google Research time-series forecasting model released in August 2026. Google's announcement gives it 330 million parameters and describes native multivariate forecasting, support for past-only and past-and-future covariates, and probabilistic forecasts with nine quantiles. The model card lists a 20-layer transformer architecture.","keywords":"3.0 (google/timesfm-3.0-pytorch) model time-series forecasting Google Research TimesFM Non-Commercial License v1.0 Apache-2.0 Google (Alphabet) TimesFM"},{"type":"software","slug":"tinygrad","href":"/open/tinygrad/","name":"tinygrad","summary":"tinygrad is a small end-to-end deep learning framework with a tensor library and autograd, an IR and compiler that fuse and lower kernels, a JIT, and modules for neural networks, optimizers, and datasets. Its API is similar to PyTorch's, and it supports both training and inference across several accelerator backends.","keywords":"framework training inference compiler tiny corp MIT tiny corp"},{"type":"software","slug":"transformer-explainer","href":"/open/transformer-explainer/","name":"Transformer Explainer","summary":"Transformer Explainer is an open-source interactive visualization that runs the GPT-2 (small) model, which has 124 million parameters, in the web browser. Users enter their own text and follow how the Transformer's components and operations combine to predict the next token. It was created by the Polo Club of Data Science at Georgia Tech and is described in a CHI 2026 paper.","keywords":"framework education visualization language-model Polo Club of Data Science, Georgia Tech MIT Georgia Institute of Technology"},{"type":"software","slug":"transformers","href":"/open/transformers/","name":"Transformers","summary":"Transformers is Hugging Face's open-source Python library of model definitions for text, vision, audio, video, and multimodal models, used for both inference and training. Its README presents it as a shared model-definition layer that other training frameworks and inference engines build on, and it loads pretrained checkpoints from the Hugging Face Hub.","keywords":"5.17.0 framework training inference Hugging Face Apache-2.0 Hugging Face"},{"type":"model-family","slug":"trinity","href":"/open/trinity/","name":"Trinity","summary":"Trinity is Arcee AI's family of sparse mixture-of-experts language models: Trinity Nano (6B total, about 1B active parameters), Trinity Mini (26B total, 3B active), and Trinity Large (about 398B total, 13B active). Trinity Large is published as base, pre-anneal base, preview, and reasoning-tuned Thinking checkpoints.","keywords":"model language-model mixture-of-experts reasoning Arcee AI Arcee AI"},{"type":"model-release","slug":"trinity-mini","href":"/open/trinity-mini/","name":"Trinity Mini","summary":"Trinity Mini is a 26B-parameter mixture-of-experts model with 3B active parameters, 128 experts (8 active plus 1 shared), and a 128k-token context window. It was trained on 10 trillion tokens and tuned for reasoning.","keywords":"Mini model language-model reasoning mixture-of-experts Arcee AI OpenMDW License Agreement, version 1.1 Arcee AI Trinity"},{"type":"model-release","slug":"trinity-large-thinking","href":"/open/trinity-large-thinking/","name":"Trinity-Large-Thinking","summary":"Trinity-Large-Thinking is a reasoning-tuned sparse mixture-of-experts model with about 398B total and 13B active parameters per token and a 512k-token context window. It was post-trained from Trinity-Large-Base with chain-of-thought and agentic reinforcement learning, and writes its reasoning in think blocks before answering.","keywords":"Large-Thinking model language-model reasoning mixture-of-experts Arcee AI OpenMDW License Agreement, version 1.1 Arcee AI Trinity"},{"type":"software","slug":"trl","href":"/open/trl/","name":"TRL (Transformers Reinforcement Learning)","summary":"TRL is Hugging Face's open-source library for post-training transformer language models. It provides trainer classes for methods including supervised fine-tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), KTO, and reward modeling, with further methods marked experimental, and it is built on the Transformers library.","keywords":"1.14.1 framework training Hugging Face Apache-2.0 Hugging Face"},{"type":"model-family","slug":"tulu","href":"/open/tulu/","name":"Tülu","summary":"Tülu is Ai2's line of instruction-following models together with the open post-training recipe used to produce them. Tülu 3 (November 2024) applied supervised fine-tuning, Direct Preference Optimization, and reinforcement learning with verifiable rewards (RLVR) to Meta's Llama 3.1 base models at 8B and 70B, followed by a 405B model in January 2025 and by Tülu 3.1 8B, which changed only the final RL stage (from PPO to GRPO).","keywords":"model language-model Ai2 (Allen Institute for AI) Ai2 (Allen Institute for AI)"},{"type":"software","slug":"unsloth-library","href":"/open/unsloth-library/","name":"Unsloth (open-source library)","summary":"Unsloth is an open-source Python library for fine-tuning and reinforcement learning of language, diffusion, text-to-speech, and embedding models, supporting LoRA, QLoRA, full fine-tuning, pretraining, GRPO, and DPO, and exporting to formats such as GGUF. The same repository contains Unsloth Studio, a web interface, and the Unsloth Desktop app.","keywords":"framework training fine-tuning local-inference Unsloth Apache-2.0 AGPL-3.0-only Unsloth"},{"type":"model-family","slug":"v-jepa-2","href":"/open/v-jepa-2/","name":"V-JEPA 2","summary":"V-JEPA 2 is Meta's family of self-supervised video encoders, pretrained on video with a masked latent-feature prediction objective. Meta released it in June 2025 with ViT-L, ViT-H, and ViT-g encoders and an action-conditioned world model (V-JEPA 2-AC) post-trained on robot video, and in March 2026 added V-JEPA 2.1, a retrained family (ViT-B to a 2B-parameter ViT-G) aimed at dense, temporally consistent features.","keywords":"model computer-vision video self-supervised robotics Meta (FAIR) Meta"},{"type":"model-release","slug":"v-jepa-2-vitg-384","href":"/open/v-jepa-2-vitg-384/","name":"V-JEPA 2 ViT-g/16 (384 px)","summary":"The 1-billion-parameter ViT-g/16 encoder from Meta's V-JEPA 2, in the version that takes 64-frame clips at 384-pixel resolution. It was pretrained with self-supervision on VideoMix22M, a mix of about 22 million video and image samples. Meta's action-conditioned V-JEPA 2-AC world model was post-trained from a ViT-g/16 V-JEPA 2 encoder.","keywords":"2 ViT-g/16, 384 resolution (vjepa2-vitg-fpc64-384) model computer-vision video self-supervised Meta (FAIR) MIT Apache-2.0 Meta V-JEPA 2"},{"type":"model-release","slug":"v-jepa-2-1-vit-gigantic-384","href":"/open/v-jepa-2-1-vit-gigantic-384/","name":"V-JEPA 2.1 ViT-G/16 (384 px)","summary":"The largest V-JEPA 2.1 model, a 2-billion-parameter ViT-G/16 video and image encoder released by Meta in March 2026. V-JEPA 2.1 changes the V-JEPA 2 recipe to learn dense, temporally consistent features, using a dense predictive loss over all tokens, self-supervision at several intermediate layers, and separate tokenizers for images and videos. Meta distilled this model into smaller ViT-B and ViT-L variants.","keywords":"2.1 ViT-G/16, 384 resolution (vjepa2_1_vit_gigantic_384) model computer-vision video self-supervised Meta (FAIR) MIT Meta V-JEPA 2"},{"type":"software","slug":"vet","href":"/open/vet/","name":"Vet (Verify Everything)","summary":"Vet is an open-source tool from Imbue that uses a language model to review code changes and the conversation history of coding agents, flagging likely bugs and places where an agent's actions diverge from what the user asked for. It runs from the terminal, as an installable agent skill, or as a GitHub Action on pull requests, and works with the user's own model provider keys.","keywords":"framework agents coding-agent developer-tools code-review Imbue AGPL-3.0-only Imbue"},{"type":"software","slug":"vllm","href":"/open/vllm/","name":"vLLM","summary":"vLLM is an open-source inference and serving engine for large language models, originally built around the PagedAttention memory-management technique. It was developed in UC Berkeley's Sky Computing Lab, and UC Berkeley contributed it to the PyTorch Foundation in 2025.","keywords":"runtime inference serving PyTorch Foundation (hosted project) vLLM core maintainers (project Technical Steering Committee) Apache-2.0 PyTorch Foundation The Linux Foundation"},{"type":"software","slug":"waymax","href":"/open/waymax/","name":"Waymax","summary":"Waymax is a multi-agent driving simulator written in JAX that builds scenarios from the Waymo Open Motion Dataset and represents road users as bounding boxes rather than raw sensor data. It includes data loading, visualization, metrics, simulated agents, and adapters to reinforcement learning interfaces such as dm-env and Brax.","keywords":"framework autonomous-driving simulation robotics Waymo Waymax License Agreement for Non-Commercial Use Waymo"},{"type":"dataset","slug":"waymo-open-dataset","href":"/open/waymo-open-dataset/","name":"Waymo Open Dataset","summary":"Autonomous driving data collected by Waymo vehicles, released for research in August 2019 and since expanded into three datasets: Perception (lidar and camera data with labels for 2,030 segments), Motion (object trajectories and 3D maps for 103,354 segments), and End-to-End Driving (360-degree camera images and routing instructions for 5,000 segments). A companion repository provides the data format definitions, evaluation metrics, and TensorFlow helper code.","keywords":"dataset autonomous-driving robotics benchmark Waymo Waymo Dataset License Agreement for Non-Commercial Use (March 2025) Apache-2.0 BSD 3-clause copyright license plus a limited patent license (wdl_limited folders) Waymo"},{"type":"software","slug":"wandb-sdk","href":"/open/wandb-sdk/","name":"Weights & Biases Python SDK (wandb)","summary":"wandb is the open-source Python library and command-line tool for Weights & Biases. Training scripts and notebooks use it to record runs with their hyperparameters and metrics on a W&B server, where results can be viewed and compared; a Public API in the same SDK queries logged data afterward.","keywords":"0.30.0 framework training experiment-tracking mlops Weights & Biases (a CoreWeave company) MIT Weights & Biases CoreWeave"},{"type":"model-release","slug":"wham-1-6b","href":"/open/wham-1-6b/","name":"WHAM 1.6B (Muse)","summary":"WHAM 1.6B is the larger of the two World and Human Action Model checkpoints Microsoft Research released for Muse. It pairs a VQ-GAN image tokenizer with a transformer trained from scratch to predict game visuals and controller actions. The same repository includes a 200M-parameter checkpoint for lighter experiments.","keywords":"WHAM 1.6B v1 model multimodal Microsoft Research Microsoft Research License Terms Microsoft Muse (Microsoft Research WHAM)"},{"type":"model-family","slug":"whisper","href":"/open/whisper/","name":"Whisper","summary":"Whisper is OpenAI's family of encoder-decoder transformer models for speech recognition, speech translation into English, and language identification. The original series of English-only and multilingual checkpoints from 39M to 1550M parameters appeared in September 2022; OpenAI followed with large-v2 (December 2022), large-v3 (November 2023), and the faster large-v3-turbo (September 2024).","keywords":"model speech OpenAI OpenAI"},{"type":"model-release","slug":"whisper-large-v3","href":"/open/whisper-large-v3/","name":"Whisper large-v3","summary":"Whisper large-v3 is a 1550M-parameter multilingual speech recognition and translation model released by OpenAI in November 2023. It keeps the architecture of the earlier large models but uses 128 Mel frequency bins instead of 80 and adds a language token for Cantonese.","keywords":"large-v3 model speech OpenAI MIT Apache-2.0 OpenAI Whisper"},{"type":"model-release","slug":"whisper-large-v3-turbo","href":"/open/whisper-large-v3-turbo/","name":"Whisper large-v3-turbo","summary":"Whisper large-v3-turbo is a multilingual speech recognition model that OpenAI derived from Whisper large-v3 by cutting the decoder from 32 layers to 4 and fine-tuning for two more epochs. It has about 0.8 billion parameters (809M in the README and Hugging Face card; the repository model card lists 798M) and is the default model in the openai-whisper package.","keywords":"large-v3-turbo model speech OpenAI MIT OpenAI Whisper"},{"type":"evaluation-tool","slug":"typesafe-ai-workflowevals","href":"/open/typesafe-ai-workflowevals/","name":"WorkflowEvals (TypeSafe AI)","summary":"WorkflowEvals is TypeSafe AI's evaluation harness for four automation workflows: invoice processing, customer service, agent-trace review, and security-incident triage. Each workflow splits a written policy into narrow yes/no, choice, and score questions for a model, then combines the answers in code into actions. Runs can compare TypeSafe's Jev with models from other providers.","keywords":"eval evaluation benchmark TypeSafe AI Apache-2.0 TypeSafe AI"},{"type":"model-family","slug":"zamba","href":"/open/zamba/","name":"Zamba","summary":"Zamba is Zyphra's series of hybrid models that combine Mamba state-space layers with shared transformer attention blocks. Releases include Zamba-7B (April 2024); Zamba2 at 1.2B, 2.7B, and 7B parameters (July to October 2024), with instruction-tuned versions; and Zamba2-VL vision-language models at the same three sizes (June 2026).","keywords":"model language-model multimodal Zyphra Zyphra"},{"type":"model-release","slug":"zamba2-7b","href":"/open/zamba2-7b/","name":"Zamba2-7B","summary":"The 7B base model in Zyphra's Zamba2 series, announced in October 2024. It interleaves Mamba2 blocks with two shared attention blocks in an alternating pattern and adds LoRA projectors to the shared MLP blocks so that each shared block can specialize by depth.","keywords":"Zamba2-7B model language-model Zyphra Apache-2.0 Apache-2.0 Zyphra Zamba"},{"type":"model-release","slug":"zamba2-vl-7b","href":"/open/zamba2-vl-7b/","name":"Zamba2-VL-7B","summary":"The largest of the three Zamba2-VL vision-language models Zyphra released in June 2026, with about 8B parameters. It pairs the Zamba2-7B hybrid state-space language model with the Qwen2.5-VL vision encoder and handles single- and multi-image understanding and grounding.","keywords":"Zamba2-VL-7B model multimodal vision-language Zyphra Apache-2.0 Apache-2.0 Zyphra Zamba"},{"type":"evaluation-tool","slug":"tau-bench","href":"/open/tau-bench/","name":"τ-bench (tau2-bench)","summary":"τ-bench is Sierra's open-source simulation framework for evaluating customer-service AI agents. In each domain an agent must follow a written policy and use tools while a simulated user takes part in the conversation, in turn-based text mode or full-duplex voice mode. The current repository, which carries the τ³-bench update, covers airline, retail, telecom, and banking-knowledge domains plus a mock domain.","keywords":"1.0.1 eval evaluation benchmark Sierra MIT Sierra"},{"type":"hub","slug":"agents","href":"/hubs/agents/","name":"AI agents and agent tooling","summary":"What separates an agent from a model, and the open protocols, SDKs, and coding agents used to build agents, including the Model Context Protocol, A2A, and AGENTS.md.","keywords":"Covers software that uses models to take actions: agent protocols and file conventions, agent SDKs and frameworks, coding agents, and benchmarks that test agents. The models that agents call are separate records with their own licenses. It does not cover hosted agent products in depth or compare agent performance. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"science","href":"/hubs/science/","name":"AI for science","summary":"Biology, chemistry, and medical models in the catalog, the science-focused organizations that publish them, and the licenses, access gates, and safety measures their documents describe.","keywords":"Covers models and tools for biology, chemistry, medicine, and scientific literature, and the organizations that publish them. It notes documented licenses, access gates, and safety measures; it does not judge scientific accuracy, clinical suitability, or biosecurity risk. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"enterprise-ai","href":"/hubs/enterprise-ai/","name":"AI in enterprise software","summary":"Enterprise software and data-platform companies in the catalog, the AI products they document, the outside models those products can call, and the open releases some of them publish under separate terms.","keywords":"Covers companies that sell enterprise software or data platforms with documented AI products, the model providers those products use, and the open models and tools they publish. It describes products from their own documentation; it does not evaluate them, compare prices, or report revenue or customers. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"safety-and-security","href":"/hubs/safety-and-security/","name":"AI safety, security, and trust","summary":"How developers, standards bodies, and independent groups test AI systems for harm and misuse: safety frameworks and model cards, testing tools, safety classifiers, content provenance and watermarking, and prompt injection.","keywords":"Covers how AI systems are tested and governed for safety and security: government frameworks and standards work, developer safety frameworks and model cards, testing and red-teaming tools, safety classifiers, content provenance and watermarking, and attacks such as prompt injection. It does not rate how safe any model is or report test scores. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"evaluation","href":"/hubs/evaluation/","name":"Benchmarks and evaluation","summary":"Evaluation harnesses, benchmarks, and the organizations that run them, and what a published result can and cannot tell you about who tested what, on which version, and under which conditions.","keywords":"Covers evaluation harnesses, benchmarks, safety and system-performance tests, and the organizations that maintain them. It explains how results are produced and where they stop; it does not report scores, rank models, or say which benchmark to trust. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"developer-tools","href":"/hubs/developer-tools/","name":"Building software with AI","summary":"The open-source layers developers use to build with AI models: model libraries, inference servers, application SDKs and frameworks, coding agents, and experiment tracking, and what to check about where each one sends data.","keywords":"Covers open-source software for building with AI models: model libraries and frameworks, inference servers, application SDKs and frameworks, coding agents, and experiment tracking. Agent protocols are covered in the AI agents hub, and local runtimes in Running AI on your own hardware. It does not compare tools, measure speed, or recommend a stack. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"chips-and-compute","href":"/hubs/chips-and-compute/","name":"Chips, cloud, and compute","summary":"The hardware and services behind AI: chip designers, accelerators, memory, networking, and AI cloud providers, and why a headquarters is not a data-center location.","keywords":"Covers organizations that design chips, memory, and networking for AI, build servers, or rent computing as a cloud or inference service, plus related open software and benchmarks. It does not report capacity, power, GPU counts, prices, or data-center sites, and a headquarters is never treated as a data-center location. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"foundation-models","href":"/hubs/foundation-models/","name":"Foundation models and the labs that build them","summary":"How model developers in the catalog make their models available, hosted, as downloadable weights, or both, why a model family is not a release, and how to read access and license terms.","keywords":"Covers organizations that train and publish general-purpose models, whether hosted, as downloadable weights, or both, and the family and release records for their models. It explains access and license terms; it does not compare model quality, report results, or give legal advice. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"open-source-foundations","href":"/hubs/open-source-foundations/","name":"Open-source foundations and stewards","summary":"How foundations such as the Linux Foundation, the PyTorch Foundation, LF AI & Data, and the Agentic AI Foundation host AI projects, and why a nonprofit's tax status is separate from the openness of what it hosts.","keywords":"Covers foundations and nonprofit organizations that host, govern, or maintain open AI software, protocols, datasets, and models, including Linux Foundation directed funds. It explains legal form and governance; it does not rate how open a foundation is, and each hosted project's license is recorded on that project's own record. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"robotics","href":"/hubs/robotics/","name":"Robotics and embodied AI","summary":"Robot makers, robot foundation models, simulators, and driving data in the catalog, with the licenses they carry and the testing and safety limits their own documents state.","keywords":"Covers organizations that build robots or autonomous vehicles, robot foundation models and world models, simulation frameworks, and robotics datasets in the catalog. It reports what sources say about licenses, testing, and safety; it does not assess whether a robot or model is safe, compare performance, or cover defense uses. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"local-ai","href":"/hubs/local-ai/","name":"Running AI on your own hardware","summary":"How running AI models on your own computer or device works: local runtimes, on-device frameworks, quantization, and what to check about an open-weight model before you run it.","keywords":"Covers running open-weight models on computers and devices you control: local runtimes and desktop apps, on-device frameworks, quantization methods, and models whose publishers document local use. It does not cover hosted APIs, hardware buying advice, or speed comparisons. Featured records are examples chosen to cover the subject, not a ranking or a complete list. Profiles of people in this area are on People Behind Local AI."},{"type":"hub","slug":"speech-vision-and-multimodal","href":"/hubs/speech-vision-and-multimodal/","name":"Speech, vision, and multimodal AI","summary":"Models and tools that work with images, audio, speech, and video: image-text encoders, vision-language models, speech recognition, segmentation and vision backbones, and image and video generation, with the terms that come with them.","keywords":"Covers models and tools that take in or produce images, audio, speech, or video: image-text encoders, vision-language models, speech recognition, segmentation and vision backbones, and image and video generation. Robot control models are covered in the Robotics hub. It does not compare accuracy across models or report benchmark results. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"hub","slug":"data-and-datasets","href":"/hubs/data-and-datasets/","name":"Training data and datasets","summary":"Open pretraining datasets in the catalog and the organizations that steward them, how many of them build on Common Crawl, and why a dataset's license and documentation are not permission to reuse what it contains.","keywords":"Covers open pretraining and research datasets in the catalog, the organizations that collect or maintain them, and the documents that state their sources, processing, licenses, and access conditions. It does not decide whether any use of a dataset is lawful and is not legal advice. Featured records are examples chosen to cover the subject, not a ranking or a complete list."},{"type":"explainer","slug":"how-language-models-work","href":"/learn/how-language-models-work/","name":"How large language models work","summary":"What happens between typing a prompt and getting an answer?","keywords":""},{"type":"explainer","slug":"tokens-and-context-windows","href":"/learn/tokens-and-context-windows/","name":"Tokens and context windows","summary":"Why do AI tools count tokens, and what happens when a conversation gets too long?","keywords":""},{"type":"explainer","slug":"multimodal-models","href":"/learn/multimodal-models/","name":"Multimodal models: text, images, audio, and video","summary":"How do models that see and hear differ from text-only models?","keywords":""},{"type":"explainer","slug":"ai-history","href":"/learn/ai-history/","name":"A short history of AI in the United States","summary":"Where did today's AI come from?","keywords":""},{"type":"explainer","slug":"open-weight-vs-open-source","href":"/learn/open-weight-vs-open-source/","name":"What open weight and open source actually mean","summary":"If I can download a model, what am I allowed to do with it?","keywords":""},{"type":"explainer","slug":"how-to-read-a-model-card","href":"/learn/how-to-read-a-model-card/","name":"How to read a model card","summary":"What should I check before relying on a model?","keywords":""},{"type":"explainer","slug":"pretraining-and-post-training","href":"/learn/pretraining-and-post-training/","name":"Pretraining, fine-tuning, and post-training","summary":"How does a raw model become an assistant, and what does it mean when one model is built on another?","keywords":""},{"type":"explainer","slug":"training-data-disclosures","href":"/learn/training-data-disclosures/","name":"Understanding training data disclosures","summary":"What does a provider reveal about the material used to train a model?","keywords":""},{"type":"explainer","slug":"hosted-or-local","href":"/learn/hosted-or-local/","name":"Choosing hosted access or local inference","summary":"Should I use an API or run a model on my own machine?","keywords":""},{"type":"explainer","slug":"choosing-a-local-model","href":"/learn/choosing-a-local-model/","name":"Choosing a local model for your computer","summary":"Which models can my computer run, and what should I check before downloading one?","keywords":""},{"type":"explainer","slug":"how-models-use-tools","href":"/learn/how-models-use-tools/","name":"How AI models use tools","summary":"What actually happens when a chatbot searches the web, runs code, or calls an app?","keywords":""},{"type":"explainer","slug":"agents-and-robotics","href":"/learn/agents-and-robotics/","name":"Agents and robotics without the hype","summary":"What changes when an AI system can use tools or act in the physical world?","keywords":""},{"type":"explainer","slug":"retrieval-augmented-generation","href":"/learn/retrieval-augmented-generation/","name":"Retrieval-augmented generation (RAG)","summary":"How do AI systems answer questions about documents they were never trained on?","keywords":""},{"type":"explainer","slug":"inference-hardware","href":"/learn/inference-hardware/","name":"Understanding inference hardware","summary":"What determines whether a model can run on my hardware?","keywords":""},{"type":"explainer","slug":"quantization","href":"/learn/quantization/","name":"Quantization: fitting models on smaller hardware","summary":"How can a large model run on a laptop, and what do you give up?","keywords":""},{"type":"explainer","slug":"how-to-check-an-ai-claim","href":"/learn/how-to-check-an-ai-claim/","name":"How to check an AI claim","summary":"Where is the evidence behind a claim about an AI model, product, policy, or company?","keywords":""},{"type":"explainer","slug":"ai-content-provenance","href":"/learn/ai-content-provenance/","name":"Labels, watermarks, and content credentials","summary":"How can you tell whether an image, video, audio clip, or text was made with AI?","keywords":""},{"type":"explainer","slug":"reading-evaluations","href":"/learn/reading-evaluations/","name":"How to read an AI evaluation","summary":"What can a benchmark result tell me about my own task?","keywords":""},{"type":"explainer","slug":"safety-testing-and-frameworks","href":"/learn/safety-testing-and-frameworks/","name":"How AI developers test models for safety","summary":"What are system cards, red teaming, and frontier safety frameworks, and how should I read them?","keywords":""},{"type":"explainer","slug":"how-the-ecosystem-fits-together","href":"/learn/how-the-ecosystem-fits-together/","name":"How the American AI ecosystem fits together","summary":"How do chips, compute providers, labs, software projects, and application companies connect?","keywords":""},{"type":"explainer","slug":"careers-in-ai","href":"/learn/careers-in-ai/","name":"Careers in AI: roles, skills, and paths","summary":"What kinds of jobs exist around AI, and how do people prepare for them?","keywords":""},{"type":"explainer","slug":"infrastructure-and-energy-claims","href":"/learn/infrastructure-and-energy-claims/","name":"Reading AI infrastructure and energy claims","summary":"What does a data-center announcement tell us about actual operating capacity?","keywords":""},{"type":"explainer","slug":"ai-and-your-data","href":"/learn/ai-and-your-data/","name":"What happens to what you type into an AI service","summary":"Does an AI service keep my conversations or use them to train its models?","keywords":""},{"type":"explainer","slug":"what-super-intelligence-means","href":"/learn/what-super-intelligence-means/","name":"What \"Super Intelligence\" means in U.S. government documents","summary":"What does \"Super Intelligence\" mean now that federal agencies are directed to use it in place of \"AI\"?","keywords":""},{"type":"explainer","slug":"policy-and-standards-sources","href":"/learn/policy-and-standards-sources/","name":"Finding AI policy and standards sources","summary":"Where can I find the original text behind a policy claim?","keywords":""},{"type":"glossary-term","slug":"accelerator","href":"/glossary/#accelerator","name":"Accelerator (AI chip)","summary":"A processor built to speed up particular kinds of computation, working alongside a computer's general-purpose CPU. For AI, accelerators carry out the large matrix calculations used in training and inference; GPUs are one kind, and some companies design chips specifically for neural-network workloads.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"acceptable-use-policy","href":"/glossary/#acceptable-use-policy","name":"Acceptable-use policy","summary":"A document from a model's publisher that lists uses it prohibits. A license can incorporate such a policy by reference, which makes following the policy one of the license's conditions.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"agent","href":"/glossary/#agent","name":"Agent","summary":"Software in which a language model works toward a goal by choosing its own next steps, such as which tools to call, and reacting to the results until the task is done or a stopping condition is reached. Anthropic's guide Building effective agents separates agents, where the model directs its own process and tool use, from workflows, where model calls and tools follow paths fixed in code.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"attention","href":"/glossary/#attention","name":"Attention","summary":"A neural-network operation that builds a new representation of each position in a sequence, such as each token of a prompt, as a weighted mix of information from other positions. In Attention Is All You Need (Vaswani et al., 2017), an attention function maps a query and a set of key-value pairs to a weighted sum of the values, where each weight depends on how well the query matches the corresponding key; self-attention applies this between positions of the same sequence.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"base-model","href":"/glossary/#base-model","name":"Base model","summary":"A model as it comes out of pretraining: it has learned to continue text but has not yet been trained further to follow instructions or hold a conversation. Hugging Face's guide to chat templates describes training that usually begins with pretraining on a huge corpus of text, which creates a base model that is then often fine-tuned for chat.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"benchmark","href":"/glossary/#benchmark","name":"Benchmark","summary":"A fixed set of tasks with a scoring method, used to compare models under the same conditions. A score that a publisher reports for its own model is a reported result: it has not necessarily been reproduced by anyone else, and it depends on the prompts and settings used.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"checkpoint","href":"/glossary/#checkpoint","name":"Checkpoint","summary":"A saved copy of a model's state at some point in training. A training checkpoint holds the weights and usually what is needed to resume training, such as the optimizer state, as PyTorch's tutorial on saving and loading models describes. Publishers also use the word for any downloadable set of weights, including a quantized version prepared for deployment.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"c2pa","href":"/glossary/#c2pa","name":"Content credentials (C2PA)","summary":"A signed, tamper-evident record of where a digital file came from and how it was made or edited, including whether AI was used, usually embedded in the file itself. It is defined in the C2PA specification from the Coalition for Content Provenance and Authenticity, whose explainer notes that a Content Credential shows whether its provenance information is well-formed, untampered, and signed by a trusted party, not whether that information is true.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"context-window","href":"/glossary/#context-window","name":"Context window","summary":"The maximum number of tokens (small pieces of text or other input) that a model can take into account in one request, usually counting both the prompt and the generated output.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"data-center","href":"/glossary/#data-center","name":"Data center","summary":"A building or campus that houses servers, storage, and networking equipment, together with the electrical and cooling systems that keep them running. Data centers built for AI hold large clusters of accelerators connected by high-speed networks.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"diffusion-model","href":"/glossary/#diffusion-model","name":"Diffusion model","summary":"A generative model trained to reverse a process that gradually adds random noise to data, so that it can start from pure noise and remove it step by step to produce a new sample, such as an image. Denoising Diffusion Probabilistic Models (Ho et al., 2020) describes this training and sampling procedure for images.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"distillation","href":"/glossary/#distillation","name":"Distillation","summary":"Training a smaller model to reproduce the outputs of a larger model, or of a group of models, so that their knowledge is carried by a model that is easier to deploy. In Distilling the Knowledge in a Neural Network (Hinton et al., 2015), the small model learns from the large model's class probabilities, which the paper calls soft targets.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"embedding","href":"/glossary/#embedding","name":"Embedding","summary":"A list of numbers (a vector) that a model produces to represent a piece of text, an image, or other data, arranged so that items with similar meaning get similar vectors. Anthropic's embeddings guide describes text embeddings as numerical representations of text that enable measuring semantic similarity, for uses such as search and recommendations.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"evaluation-harness","href":"/glossary/#evaluation-harness","name":"Evaluation harness","summary":"Software that runs a model against one or more benchmarks in a consistent way: it formats the prompts, collects the outputs, and computes the scores, so that others can re-run the same evaluation.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"fine-tuning","href":"/glossary/#fine-tuning","name":"Fine-tuning","summary":"Continuing to train an existing model on additional data so that it behaves differently, for example to follow instructions or to handle a narrower task. The result is a new set of weights, and the base model's license may still apply to it.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"foundation","href":"/glossary/#foundation","name":"Foundation (open source)","summary":"A nonprofit organization that gives open-source projects a neutral home: it can hold their money and trademarks and provide shared services, while each project keeps its own technical governance. A large foundation can also host directed funds, programs with their own name, charter, and governing board whose money the host foundation holds and administers.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"function-calling","href":"/glossary/#function-calling","name":"Function calling (tool calling)","summary":"A way for a model to request an action outside itself: the application describes the available tools, the model replies with a structured request naming a tool and its inputs, and the application or the provider runs the tool and sends back the result. Anthropic's tool use documentation describes the model deciding when to call a tool based on the user's request and the tool's description.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"gated-download","href":"/glossary/#gated-download","name":"Gated download","summary":"A download that requires an extra step before the files are released, such as signing in, sharing contact details, or accepting terms. On Hugging Face, a gated repository can grant access automatically once a user agrees, or it can require the publisher to approve each request by hand, as described in Hugging Face's gated models documentation.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"gguf","href":"/glossary/#gguf","name":"GGUF","summary":"A single-file format that stores a model's weights together with the metadata needed to load them, for inference with the GGML library and tools built on it, such as llama.cpp. Models are usually trained in another framework and then converted to GGUF, often with quantized weights. The format is specified in the GGUF documentation in the ggml repository.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"gpu","href":"/glossary/#gpu","name":"GPU (graphics processing unit)","summary":"A processor first built for 3D graphics that runs thousands of threads in parallel, which suits the highly parallel calculations used to train and run AI models. NVIDIA's CUDA Programming Guide contrasts a CPU, designed to run a sequence of operations as fast as possible, with a GPU, which trades lower single-thread performance for much greater total throughput.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"hallucination","href":"/glossary/#hallucination","name":"Hallucination","summary":"Output from a generative AI model that is stated confidently but is false, or that contradicts its input or what it said earlier. NIST's Generative AI Profile (NIST AI 600-1) calls this confabulation and explains it as a natural result of models generating outputs that approximate the statistical patterns of their training data. Why Language Models Hallucinate (Kalai et al., 2025) argues that training and evaluation methods that reward guessing over admitting uncertainty help keep it going.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"hbm","href":"/glossary/#hbm","name":"High-bandwidth memory (HBM)","summary":"Computer memory built by stacking several DRAM chips vertically and connecting them through a very wide interface, packaged next to a GPU or other processor so that data reaches it quickly. Micron's HBM product page describes a single HBM stack with a 1024-bit data interface, 16 times wider than a standard DDR5 module, and Google's Introduction to Cloud TPU says on-chip HBM lets TPUs work with larger models and batch sizes.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"hosted-api","href":"/glossary/#hosted-api","name":"Hosted API","summary":"Access to a model that runs on the provider's own servers. You send inputs over the network and receive outputs, but you do not receive the weights.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"inference","href":"/glossary/#inference","name":"Inference","summary":"Running a trained model to produce outputs from inputs, as opposed to training it. Inference code is the software that loads the weights and performs that computation.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"instruction-tuning","href":"/glossary/#instruction-tuning","name":"Instruction tuning","summary":"Fine-tuning a pretrained language model on many tasks written as plain-language instructions, so that it handles new tasks described the same way instead of only continuing text. Finetuned Language Models Are Zero-Shot Learners (Wei et al., 2021) describes instruction tuning as finetuning language models on a collection of tasks described via instructions.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"jailbreak","href":"/glossary/#jailbreak","name":"Jailbreak","summary":"An attempt by a user to make a model produce output its developer has restricted, for example by wording a request so that the model abandons its usual refusals. NIST's taxonomy of attacks on AI systems, NIST AI 100-2 E2025, defines a jailbreak as a direct prompting attack, carried out by the system's own user, intended to circumvent restrictions placed on model outputs.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"knowledge-cutoff","href":"/glossary/#knowledge-cutoff","name":"Knowledge cutoff","summary":"The point in time up to which a model's training data covers events, so the model may not know about anything later unless that information is given to it, for example in the prompt or through retrieval. Anthropic's Transparency Hub explains a model's knowledge cutoff date as the date up to which its knowledge is most extensive and reliable, and its models overview lists a reliable knowledge cutoff and a separate training data cutoff for its current models.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"latency-and-throughput","href":"/glossary/#latency-and-throughput","name":"Latency and throughput","summary":"Two measures of how fast a model serves requests: latency is how long one request takes, often split into the time to the first token and the time between later tokens, and throughput is how much work the whole system completes per second, such as output tokens per second across all requests. NVIDIA's guide to LLM inference metrics explains that as more requests run at once, total throughput rises until the hardware is saturated while the speed each user sees falls, and that tools define these metrics differently, so results should be compared only when the definitions match.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"license-scope","href":"/glossary/#license-scope","name":"License scope","summary":"The specific materials a license covers. One release can have different licenses for its weights, code, data, and documentation, and a license file in a code repository does not by itself license weights published elsewhere under other terms.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"maintainer","href":"/glossary/#maintainer","name":"Maintainer","summary":"The organization or group that currently governs a project: it reviews and accepts changes and publishes releases. The maintainer can differ from the organization that first created the project.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"mixture-of-experts","href":"/glossary/#mixture-of-experts","name":"Mixture of experts (MoE)","summary":"A model design in which some layers contain many parallel sub-networks, called experts, and a small routing network sends each token to only a few of them. Because only part of the model runs for each token, the total parameter count differs from the number of active parameters. A sparsely gated form of the design was described in Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017).","keywords":"glossary term definition"},{"type":"glossary-term","slug":"model-card","href":"/glossary/#model-card","name":"Model card","summary":"A document published with a model that describes what it is, how it was trained and evaluated, what it is intended for, and its known limitations. The format was proposed in the paper Model Cards for Model Reporting (Mitchell et al., 2018). On Hugging Face, the model card is the repository's README file, with metadata such as the license, as described in its model cards documentation.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"mcp","href":"/glossary/#mcp","name":"Model Context Protocol (MCP)","summary":"An open protocol that standardizes how AI applications connect to outside data sources and tools. In the MCP specification, a host application uses clients to exchange JSON-RPC 2.0 messages with servers, and servers can offer resources (context and data), prompts (templated messages and workflows), and tools (functions for the AI model to execute).","keywords":"glossary term definition"},{"type":"glossary-term","slug":"model-family","href":"/glossary/#model-family","name":"Model family","summary":"A named line of related models released over time, often in several sizes or generations.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"model-release","href":"/glossary/#model-release","name":"Model release","summary":"One specific published model, identified by its name, size, and version, with its own files and its own terms.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"multimodal","href":"/glossary/#multimodal","name":"Multimodal model","summary":"A model that works with more than one kind of data, such as text, images, audio, or video, as input, output, or both. OpenAI's GPT-4o System Card, for example, describes a model that accepts any combination of text, audio, image, and video as input and generates text, audio, and image outputs, with all of them processed by the same neural network.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"nonprofit","href":"/glossary/#nonprofit","name":"Nonprofit","summary":"An organization that is not run to make a profit for owners or shareholders. In the United States, the IRS recognizes many nonprofits as tax-exempt: section 501(c)(3) covers organizations run for charitable, scientific, educational, and similar purposes, and section 501(c)(6) covers business leagues, such as trade associations, that promote a shared business interest. In both cases, net earnings may not benefit any private shareholder or individual.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"open-source-software","href":"/glossary/#open-source-software","name":"Open source (software)","summary":"Software distributed with its source code under a license that meets the Open Source Initiative's Open Source Definition (version 1.9). Its criteria include free redistribution, access to the source code, permission to make derived works, and no discrimination against persons, groups, or fields of endeavor. Code that is merely readable on a public website is not open source on that basis alone.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"open-source-ai","href":"/glossary/#open-source-ai","name":"Open-source AI (OSI definition)","summary":"An AI system made available under terms that grant the freedoms to use, study, modify, and share it, as set out in the Open Source Initiative's Open Source AI Definition, version 1.0. The definition requires detailed information about the training data, the complete code used to train and run the system, and the parameters, each under OSI-approved terms. It does not require every piece of training data to be downloadable.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"open-stack","href":"/glossary/#open-stack","name":"Open-stack","summary":"A USASI tier for a model release that is Open-weight and also has published inference code, training code, and a training recipe, plus at least a documented composition of its training data. It is an editorial category of rubric v0.2, not an outside standard, and it does not require the training data itself to be downloadable.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"open-weight","href":"/glossary/#open-weight","name":"Open weight","summary":"A model whose trained parameters can be downloaded by the public. The term describes availability: the license that comes with the weights may still limit commercial use, redistribution, or particular uses. Others use the term with stricter requirements; the OSI's explainer on open weights, for example, describes final parameters shared under an OSI-approved license, without the training code or data.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"parameter","href":"/glossary/#parameter","name":"Parameter (parameter count)","summary":"One of the numbers inside a model that training adjusts, such as the weights and biases of a neural-network layer; a model's parameter count is the total number of them, usually written with M for millions or B for billions. PyTorch's tutorial Build the Neural Network describes layers whose weights and biases are optimized during training and shows how to list a model's parameters.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"parent-company","href":"/glossary/#parent-company","name":"Parent company and documented control","summary":"A parent company controls another organization, its subsidiary, either directly or through other subsidiaries. In the SEC's definitions in Rule 405, control means the power to direct an entity's management and policies, whether through owning voting securities, by contract, or otherwise, so it can rest on more than share ownership.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"post-training","href":"/glossary/#post-training","name":"Post-training","summary":"The training applied after pretraining that turns a base model into one suited to a purpose, such as a chat assistant that follows instructions. Ai2's Tülu 3 paper describes post-training as refining behaviors and unlocking new skills, with methods such as supervised fine-tuning, Direct Preference Optimization, and reinforcement learning with verifiable rewards.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"power-and-energy","href":"/glossary/#power-and-energy","name":"Power and energy","summary":"Power is the rate at which electricity is supplied or used at a given moment, measured in watts (W): a kilowatt (kW) is 1,000 watts, a megawatt (MW) is 1,000 kW, and a gigawatt (GW) is 1,000 MW. Energy is power used over time, measured in watthours (Wh): a kilowatthour (kWh) is one kilowatt for one hour, and a megawatthour (MWh) is one megawatt for one hour. A figure in megawatts or gigawatts therefore describes capacity at a moment, not an amount of electricity used. The U.S. Energy Information Administration explains these units in Measuring electricity.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"preference-tuning","href":"/glossary/#preference-tuning","name":"Preference tuning (RLHF and DPO)","summary":"Training a model on comparisons of which of two or more responses is better, so that it produces the kinds of responses people prefer. In reinforcement learning from human feedback (RLHF), as described in Training language models to follow instructions with human feedback (Ouyang et al., 2022), people rank model outputs and those rankings are used to fine-tune the model with reinforcement learning; Direct Preference Optimization (Rafailov et al., 2023) learns from the same kind of comparisons with a simple classification loss instead.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"pretraining","href":"/glossary/#pretraining","name":"Pretraining","summary":"The first stage of training a model, in which it learns general patterns from a very large collection of unlabeled data; a language model is usually pretrained to predict the next word from the text that comes before it. Anthropic's glossary notes that pretrained models are not inherently good at answering questions or following instructions, and that fine-tuning and RLHF are used to refine them.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"prompt","href":"/glossary/#prompt","name":"Prompt","summary":"The input given to a generative model to get a response, such as a question, instructions, examples, or a document to work on. Writing prompts so that a model reliably gives the output you need is called prompt engineering, which OpenAI's prompting guide describes as a mix of art and science because model output is non-deterministic.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"prompt-injection","href":"/glossary/#prompt-injection","name":"Prompt injection","summary":"An attack in which text given to a model, typed by a user or hidden in content the model reads, overrides the instructions set by the developer or application, such as the system prompt. NIST's AI 100-2 E2025 defines it as exploiting the concatenation of untrusted input with a prompt built by a higher-trust party, and calls it indirect when the instructions arrive through resources the system reads rather than from the user. OWASP's LLM01:2025 Prompt Injection says it is unclear whether there are fool-proof methods of preventing it.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"quantization","href":"/glossary/#quantization","name":"Quantization","summary":"Storing a model's weights, and sometimes its intermediate values, at lower numerical precision (for example 4-bit integers instead of 16-bit numbers) to reduce memory use and speed up inference, sometimes at some cost in output quality.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"reasoning-model","href":"/glossary/#reasoning-model","name":"Reasoning model","summary":"A language model that generates intermediate reasoning, often called thinking, before its final answer, which helps with multi-step problems at the cost of extra tokens. OpenAI's reasoning guide describes reasoning tokens that the model uses to break down the prompt and consider several approaches, and notes that they take up space in the context window even though they are not visible through the API.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"red-teaming","href":"/glossary/#red-teaming","name":"Red teaming","summary":"Structured testing in which people or automated tools act as adversaries, trying to make an AI system fail, misbehave, or help with misuse. NIST's AI 100-2 E2025 defines red teaming in the AI context as a structured testing effort, often using adversarial methods, to find flaws and vulnerabilities, including unforeseen or undesirable system behaviors and potential risks from misuse.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"restricted-weights","href":"/glossary/#restricted-weights","name":"Restricted weights","summary":"A USASI tier for a model release whose weights can be obtained only by request, with approval, or by some users, for example through a gated download that the publisher reviews. Such a release is not counted as Open-weight, however much else about it is published.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"rag","href":"/glossary/#rag","name":"Retrieval-augmented generation (RAG)","summary":"A technique in which a system first retrieves passages relevant to a question from a collection of documents and then gives them to a language model along with the question, so that the answer can draw on, and cite, information beyond the model's training data. The approach was described in Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020), and NIST's AI 100-2 E2025 glossary notes that it lets a model's knowledge be changed without retraining.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"reviewed-date","href":"/glossary/#reviewed-date","name":"Reviewed date","summary":"The date an editor last checked a piece of evidence against its sources. It is different from the date a record was edited, the date an artifact was released, and the date the site was built.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"temperature","href":"/glossary/#temperature","name":"Sampling temperature","summary":"A setting that controls how random a language model's word choices are as it generates text: lower values keep it closer to the most likely tokens, and higher values give less likely tokens more chance, for more varied output. Hugging Face's generation documentation gives 1.0 as the default when a model's configuration sets no value, and Anthropic's glossary notes that on its API even a temperature of 0 does not make results fully deterministic.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"self-hosting","href":"/glossary/#self-hosting","name":"Self-hosting / local inference","summary":"Running a model on hardware you control, such as a laptop, a workstation, or your own servers, instead of calling a provider's hosted service. It requires access to the weights and to software that can run them, and the weights' license still applies.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"subsidiary","href":"/glossary/#subsidiary","name":"Subsidiary","summary":"A company that is owned or controlled by another company, its parent. A research unit is a similar relationship within a single company.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"synthetic-data","href":"/glossary/#synthetic-data","name":"Synthetic data","summary":"Training data generated by a model or program rather than collected from sources such as web pages, books, or code written by people, for example text rewritten by a language model or worked solutions it writes. Microsoft's Phi-4 Technical Report contrasts synthetic data with organic data such as web content or code, and describes generating it with techniques including multi-agent prompting, self-revision, and instruction reversal.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"system-card","href":"/glossary/#system-card","name":"System card","summary":"A document in which a developer reports a model's capabilities and limitations, the safety evaluations it went through, and the decisions made about deploying it. Anthropic describes its system cards as documenting capabilities, safety evaluations, and responsible deployment decisions, and OpenAI's GPT-4o System Card covers the model's capabilities, limitations, and safety evaluations, including third-party assessments.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"system-prompt","href":"/glossary/#system-prompt","name":"System prompt","summary":"Instructions that a developer or application gives a model separately from the user's messages, setting its role, goals, tone, or rules. NIST's AI 100-2 E2025 glossary describes system prompts as application-specific instructions, typically placed before other input, that may be treated as higher-trust than other input, and Anthropic's Messages API takes the system prompt as its own parameter rather than as a message.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"token","href":"/glossary/#token","name":"Token","summary":"The unit of text a language model reads and writes, which can be a whole word, part of a word, a single character, or a byte; a tokenizer splits text into tokens before the model processes it. Anthropic's glossary gives about 3.5 English characters per token as an approximate figure for its Claude models and notes that the number varies by language.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"tokenizer","href":"/glossary/#tokenizer","name":"Tokenizer","summary":"The component that splits input text into tokens, such as words, parts of words, or single characters, and converts them into the numeric IDs a model works with. A model is normally run with the same tokenizer it was trained with. Hugging Face's guide to tokenization algorithms describes the common subword methods.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"tpu","href":"/glossary/#tpu","name":"TPU (tensor processing unit)","summary":"Google's custom chip for machine learning, an application-specific integrated circuit (ASIC) designed for the large matrix operations common in machine-learning algorithms. Google's Introduction to Cloud TPU says TPUs have on-chip high-bandwidth memory, can be connected in groups called slices, and run code compiled by the XLA compiler.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"transformer","href":"/glossary/#transformer","name":"Transformer","summary":"A neural-network architecture built around attention layers, which let each position in a sequence draw on the others, instead of processing the sequence one step at a time. It was proposed for machine translation in Attention Is All You Need (Vaswani et al., 2017), and NIST's AI 100-2 E2025 glossary calls transformer-based generative pre-trained models the predominant architecture for large language models.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"unknown","href":"/glossary/#unknown","name":"Unknown","summary":"A status meaning that an item has not been assessed yet, or that the available evidence is not enough to decide. It never stands in for \"no.\"","keywords":"glossary term definition"},{"type":"glossary-term","slug":"vector-database","href":"/glossary/#vector-database","name":"Vector database","summary":"A database that stores embeddings together with the items they represent and finds the stored vectors closest to the vector of a query, which lets search and retrieval systems find related content by meaning. The Faiss similarity-search library, for example, compares vectors by L2 distance or dot product and offers indexes that trade some search precision for speed or memory on very large collections.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"watermark","href":"/glossary/#watermark","name":"Watermark (AI content)","summary":"A signal embedded in AI-generated content, usually imperceptible to people, that a detector can later check for to estimate whether the content came from a particular model or service. Google's SynthID documentation describes detectors that score content for the likelihood that it was watermarked, and notes that the score can drop sharply when generated text is thoroughly rewritten or translated.","keywords":"glossary term definition"},{"type":"glossary-term","slug":"weights","href":"/glossary/#weights","name":"Weights","summary":"The numerical parameters a model learns during training. Together with the model's architecture and inference code they make up the model, a breakdown the Open Source AI Definition also uses. Weights are usually distributed as large files, sometimes with checkpoints saved at intermediate points in training.","keywords":"glossary term definition"},{"type":"license","slug":"apache-2-0","href":"/licenses/apache-2-0/","name":"Apache License 2.0","summary":"The Apache License, Version 2.0, dated January 2004, is a permissive license published by the Apache Software Foundation for software and other works of authorship. Each contributor grants copyright and patent licenses, and the conditions deal mainly with passing on the license and keeping notices when the work is redistributed.","keywords":"license Apache-2.0"},{"type":"license","slug":"apache-2-0-with-llvm-exception","href":"/licenses/apache-2-0-with-llvm-exception/","name":"Apache License 2.0 with LLVM Exceptions","summary":"This is the Apache License, Version 2.0 followed by two exceptions headed \"LLVM Exceptions to the Apache 2.0 License\". The LLVM Project's license file states that the project \"is under the Apache License v2.0 with LLVM Exceptions\". The SPDX License List records the exception as LLVM-exception and notes that it \"was created specifically to be used with Apache-2.0\". The Apache 2.0 terms apply in full except where the two exceptions change them.","keywords":"license Apache-2.0 WITH LLVM-exception"},{"type":"license","slug":"apple-ml-research-model-license","href":"/licenses/apple-ml-research-model-license/","name":"Apple Machine Learning Research Model License Agreement","summary":"The Apple Machine Learning Research Model License Agreement is Apple Inc.'s license for models it releases \"for the sole purpose of scientific research of artificial intelligence and machine-learning technology.\" It grants a limited license to use, copy, modify, distribute, and create Model Derivatives exclusively for Research Purposes. This guide uses the copies published in Apple's OpenELM-3B-Instruct and FastVLM-7B repositories on Hugging Face, which are identical.","keywords":"license "},{"type":"license","slug":"bigcode-openrail-m-v1","href":"/licenses/bigcode-openrail-m-v1/","name":"BigCode OpenRAIL-M v1 License Agreement","summary":"The BigCode OpenRAIL-M v1 License Agreement was \"created under BigCode, an open and collaborative research project\" on large language models for code, and its preamble says it \"is generally applicable to any machine-learning Model.\" It grants a royalty-free copyright license for the Model and Modifications of the Model and a royalty-free patent license for the Model, conditioned on use restrictions listed in Attachment A. Source code, training data, and explanatory documentation are not licensed under it.","keywords":"license "},{"type":"license","slug":"bsd-2-clause","href":"/licenses/bsd-2-clause/","name":"BSD 2-Clause License","summary":"The BSD 2-Clause License, listed on the SPDX License List as the BSD 2-Clause \"Simplified\" License, is a short permissive license. It permits redistribution and use in source and binary forms, with or without modification, under two conditions about keeping the copyright notice. The text is a template in which each project supplies its own copyright line.","keywords":"license BSD-2-Clause"},{"type":"license","slug":"bsd-3-clause","href":"/licenses/bsd-3-clause/","name":"BSD 3-Clause License","summary":"The BSD 3-Clause License, listed on the SPDX License List as the BSD 3-Clause \"New\" or \"Revised\" License, is a short permissive license. It permits redistribution and use in source and binary forms, with or without modification, under three conditions. The text is a template in which each project supplies its own copyright line.","keywords":"license BSD-3-Clause"},{"type":"license","slug":"cdla-permissive-2-0","href":"/licenses/cdla-permissive-2-0/","name":"Community Data License Agreement – Permissive, Version 2.0","summary":"The Community Data License Agreement – Permissive, Version 2.0 (CDLA-Permissive-2.0) is a short agreement for sharing data, published on cdla.dev, a site of The Linux Foundation. Data Providers and Data Recipients agree that recipients may use, modify, and share the data, with one condition on sharing. It treats \"Results,\" which it defines to include machine learning models, separately from the data itself.","keywords":"license CDLA-Permissive-2.0"},{"type":"license","slug":"cc-by-4-0","href":"/licenses/cc-by-4-0/","name":"Creative Commons Attribution 4.0 International","summary":"Creative Commons Attribution 4.0 International (CC BY 4.0) is a public license published by Creative Commons for material covered by copyright and certain similar rights, including database rights. It lets anyone reproduce and share the material and adapted versions of it, with attribution as the main condition. Creative Commons states on the license page that it \"is not a law firm and does not provide legal services or legal advice.\"","keywords":"license CC-BY-4.0"},{"type":"license","slug":"cc-by-nc-sa-4-0","href":"/licenses/cc-by-nc-sa-4-0/","name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International","summary":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) is a public license published by Creative Commons. It permits sharing and adapting the licensed material for NonCommercial purposes only, with attribution, and requires anyone who shares an adaptation to license it under the same or a compatible license.","keywords":"license CC-BY-NC-SA-4.0"},{"type":"license","slug":"gemma-terms-of-use","href":"/licenses/gemma-terms-of-use/","name":"Gemma Terms of Use","summary":"The Gemma Terms of Use are Google's terms for the Gemma models listed in the appendix of the terms page, among them Gemma 1 through Gemma 3, Gemma 3n, EmbeddingGemma, PaliGemma, ShieldGemma, and CodeGemma. The page was last modified on April 1, 2026, and says Gemma 4 has its own separate license. The terms govern using, reproducing, modifying, and distributing Gemma and \"Model Derivatives,\" including through hosted services.","keywords":"license "},{"type":"license","slug":"agpl-3-0","href":"/licenses/agpl-3-0/","name":"GNU Affero General Public License v3.0","summary":"The GNU Affero General Public License, version 3, dated 19 November 2007, is a copyleft license published by the Free Software Foundation. Its preamble says it is \"specifically designed to ensure cooperation with the community in the case of network server software.\" The SPDX identifier AGPL-3.0-only refers to code offered under version 3 only, as distinguished from \"AGPL-3.0-or-later.\"","keywords":"license AGPL-3.0-only"},{"type":"license","slug":"lfm-open-license-v1-0","href":"/licenses/lfm-open-license-v1-0/","name":"LFM Open License v1.0","summary":"The LFM Open License v1.0 names Liquid AI, Inc. as the Licensor and is published in Liquid AI's LFM2.5 model repositories on Hugging Face. Its definitions, copyright and patent grants, and redistribution conditions closely follow the wording of the Apache License 2.0. It adds a Commercial Use Limitation in Section 5 tied to an annual-revenue Threshold that the license defines, and a termination clause in Section 11.","keywords":"license "},{"type":"license","slug":"llama-3-1-community","href":"/licenses/llama-3-1-community/","name":"Llama 3.1 Community License Agreement","summary":"The Llama 3.1 Community License Agreement is Meta's license for the Llama 3.1 models, code, weights, and documentation, which it calls the \"Llama Materials.\" Its version release date is July 23, 2024. It grants a limited, royalty-free license with conditions on notices, naming, acceptable use, and licensees above a size threshold.","keywords":"license "},{"type":"license","slug":"llama-4-community","href":"/licenses/llama-4-community/","name":"Llama 4 Community License Agreement","summary":"The Llama 4 Community License Agreement is Meta's license for the Llama 4 models, code, weights, and documentation, which it calls the \"Llama Materials.\" Its version effective date is April 5, 2025. It grants a limited, royalty-free license with conditions on notices, naming, acceptable use, and licensees above a size threshold.","keywords":"license "},{"type":"license","slug":"llama-3-community","href":"/licenses/llama-3-community/","name":"Meta Llama 3 Community License Agreement","summary":"The Meta Llama 3 Community License Agreement is Meta's license for the Meta Llama 3 models, code, weights, and documentation, which it calls the \"Llama Materials.\" Its version release date is April 18, 2024. It grants a limited, royalty-free license with conditions on notices, naming, acceptable use, and licensees above a size threshold. The license file in Meta's llama-models repository also contains the Meta Llama 3 Acceptable Use Policy.","keywords":"license "},{"type":"license","slug":"mit","href":"/licenses/mit/","name":"MIT License","summary":"The MIT License is a short permissive license for software. Its standard text is listed on the SPDX License List under the identifier MIT and is reproduced by the Open Source Initiative. The text is a template that begins with a copyright line naming the year and the copyright holders of each project that uses it.","keywords":"license MIT"},{"type":"license","slug":"nvidia-open-model-license","href":"/licenses/nvidia-open-model-license/","name":"NVIDIA Open Model License Agreement","summary":"The NVIDIA Open Model License Agreement is NVIDIA Corporation's license for models it releases under it; the version on NVIDIA's agreements page was last modified on October 24, 2025. Its opening states that models under it \"are commercially usable,\" that licensees are free to create and distribute Derivative Models, and that NVIDIA does not claim ownership of outputs. These statements are subject to the agreement's conditions.","keywords":"license "},{"type":"license","slug":"odc-by-1-0","href":"/licenses/odc-by-1-0/","name":"Open Data Commons Attribution License v1.0","summary":"The Open Data Commons Attribution License (ODC-By) v1.0, published by Open Data Commons, is a license for databases. Its preamble says it is intended to allow free sharing, modifying, and use of a database \"subject only to the attribution requirements set out in Section 4.\" It governs rights in the database itself, not in the individual contents, which may carry their own licenses.","keywords":"license ODC-By-1.0"},{"type":"license","slug":"openmdw-1-1","href":"/licenses/openmdw-1-1/","name":"OpenMDW License Agreement, version 1.1","summary":"The OpenMDW License Agreement, version 1.1 (OpenMDW-1.1) is a license for \"Model Materials\": one or more machine learning models, including architecture and parameters, and related artifacts such as data, documentation, and software. It is published on openmdw.ai, whose footer credits The Linux Foundation and its contributors. It grants permission to deal in the materials \"without restriction,\" subject to a notice condition and a litigation clause.","keywords":"license "},{"type":"license","slug":"xai-community-license","href":"/licenses/xai-community-license/","name":"xAI Community License Agreement","summary":"The xAI Community License Agreement is X.AI LLC's license for materials that xAI designates as subject to it; the copy published with the Grok 2 repository on Hugging Face was last updated on November 4, 2025, and designates the Grok 2 model and related artifacts. It grants a revocable license to use, reproduce, distribute, and modify the Materials for non-commercial and research purposes, and for commercial use under conditions.","keywords":"license "},{"type":"policy","slug":"eo-14434","href":"/policy/eo-14434/","name":"Inaugurating the Era of Super Intelligence","summary":"Sets a policy that, to the maximum extent permitted by law, the executive branch will use the terms \"Super Intelligence\" and \"SI\" in place of \"Artificial Intelligence\" and \"AI\", and directs agencies to do so in official correspondence, public communications, websites, reports, policy documents, and other non-statutory documents. It does not require changes to previously issued regulations, Presidential actions, contracts, grants, or other historical documents. For the order's purposes the new terms mean the technologies covered by the statutory definition of artificial intelligence at 15 U.S.C. 9401(3), and the Assistant to the President for Science and Technology is to propose legislative language for a Federal definition within 60 days.","keywords":"EO 14434 Executive Order 14434 Inaugurating the Era of Super Intelligence President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14409","href":"/policy/eo-14409/","name":"Promoting Advanced Artificial Intelligence Innovation and Security","summary":"Directs agencies to prioritize the cyber defense of national security systems and of Department of War and civilian Federal information systems, and directs the Secretary of the Treasury, in consultation with the National Security Agency, the Cybersecurity and Infrastructure Security Agency, and the National Cyber Director, to form an AI cybersecurity clearinghouse, in voluntary collaboration with the AI industry and critical infrastructure operators, to coordinate the discovery and remediation of software vulnerabilities. It directs those agencies, in consultation with NIST and others, to develop a classified benchmarking process to determine when an AI model is a \"covered frontier model\" and to design a voluntary framework through which developers could give the Federal Government access to such models for up to 30 days before releasing them to other trusted partners. The order states that nothing in its frontier model section authorizes a mandatory governmental licensing, preclearance, or permitting requirement for new AI models, and it directs the Attorney General to prioritize enforcement of Federal criminal laws against unauthorized computer access using AI.","keywords":"EO 14409 Executive Order 14409 Promoting Advanced Artificial Intelligence Innovation and Security President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14365","href":"/policy/eo-14365/","name":"Ensuring a National Policy Framework for Artificial Intelligence","summary":"States a policy of \"a minimally burdensome national policy framework for AI\" and directs the Attorney General to establish an AI Litigation Task Force to challenge State AI laws inconsistent with that policy. It directs the Secretary of Commerce to publish an evaluation of State AI laws and to issue a policy notice making States with laws identified in that evaluation ineligible for certain non-deployment funds under the Broadband Equity Access and Deployment Program, to the maximum extent allowed by Federal law. It directs the Chairman of the Federal Communications Commission to begin a proceeding on whether to adopt a Federal reporting and disclosure standard for AI models that preempts conflicting State laws, and the Chairman of the Federal Trade Commission to issue a policy statement on how the FTC Act applies to AI models. It also directs a legislative recommendation for a uniform Federal AI policy framework that preempts conflicting State AI laws, which is not to propose preempting otherwise lawful State laws on topics such as child safety protections.","keywords":"EO 14365 Executive Order 14365 Ensuring a National Policy Framework for Artificial Intelligence President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"m-26-04","href":"/policy/m-26-04/","name":"Increasing Public Trust in Artificial Intelligence Through Unbiased AI Principles","summary":"OMB guidance implementing section 4 of Executive Order 14319. It requires agencies to include contractual requirements on compliance with the order's two Unbiased AI Principles, truth-seeking and ideological neutrality, in any new solicitation or order for a large language model and to update their procurement policies by March 11, 2026, and says agencies should, to the extent practicable, modify existing contracts for large language models. It does not apply to national security systems, and agencies are not required to apply it to models acquired under a free, open-source license.","keywords":"M-26-04 OMB Memorandum M-26-04 Increasing Public Trust in Artificial Intelligence Through Unbiased AI Principles Office of Management and Budget policy"},{"type":"policy","slug":"eo-14363","href":"/policy/eo-14363/","name":"Genesis Mission","summary":"Establishes the Genesis Mission, a national effort to apply AI to scientific discovery, implemented within the Department of Energy by the Secretary of Energy, with the Assistant to the President for Science and Technology providing general leadership and coordinating participating agencies through the National Science and Technology Council. It directs the Secretary to establish and operate the American Science and Security Platform, providing high-performance computing, AI modeling frameworks and agents, domain-specific foundation models, secure access to datasets, and tools for AI-augmented experimentation. It also directs the Secretary to identify at least 20 national science and technology challenges for the Mission and to report to the President annually.","keywords":"EO 14363 Executive Order 14363 Launching the Genesis Mission President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14355","href":"/policy/eo-14355/","name":"Unlocking Cures for Pediatric Cancer With Artificial Intelligence","summary":"Directs the Make America Healthy Again Commission, with the Secretary of Health and Human Services, the Assistant to the President for Science and Technology, and the Special Advisor for AI and Crypto, to develop ways to use AI to improve diagnosis, treatment, cures, and prevention of pediatric cancer, with an initial focus on the Childhood Cancer Data Initiative. It directs work on increasing investment from existing Federal funds in pediatric cancer research, and directs the Secretary of Health and Human Services to integrate AI into work on health data interoperability and to finalize interoperability standards for patient data used with AI.","keywords":"EO 14355 Executive Order 14355 Unlocking Cures for Pediatric Cancer With Artificial Intelligence President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14318","href":"/policy/eo-14318/","name":"Accelerating Federal Permitting of Data Center Infrastructure","summary":"Directs the Secretary of Commerce to launch an initiative to provide financial support for qualifying data center projects and the components they depend on, where a data center project is a facility requiring more than 100 megawatts of new load dedicated to AI inference, training, simulation, or synthetic data generation. It directs agencies to identify existing, and establish new, categorical exclusions under the National Environmental Policy Act for such projects, sets out other measures to expedite environmental reviews and permitting, and directs agencies to offer sites on Federal lands and military installations for them. It revokes Executive Order 14141.","keywords":"EO 14318 Executive Order 14318 Accelerating Federal Permitting of Data Center Infrastructure President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14319","href":"/policy/eo-14319/","name":"Preventing Woke AI in the Federal Government","summary":"Directs agency heads to procure only large language models developed in accordance with two \"Unbiased AI Principles\": truth-seeking and ideological neutrality. It directs OMB to issue implementing guidance within 120 days, and directs agencies to include terms requiring compliance with the principles in new contracts for large language models, with decommissioning costs charged to the vendor if the agency terminates a contract for noncompliance. The order states that it builds on Executive Order 13960.","keywords":"EO 14319 Executive Order 14319 Preventing Woke AI in the Federal Government President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14320","href":"/policy/eo-14320/","name":"Promoting the Export of the American AI Technology Stack","summary":"Directs the Secretary of Commerce to establish an American AI Exports Program within 90 days and to issue a public call for proposals from industry-led consortia for full-stack AI export packages, covering AI-optimized hardware and cloud services, data pipelines, models, security measures, and applications. Proposals selected by the Secretary are to be designated priority AI export packages, and the Economic Diplomacy Action Group, chaired by the Secretary of State, is to coordinate Federal financing tools to support them. Proposals must comply with U.S. export control regimes, outbound investment regulations, and end-user policies.","keywords":"EO 14320 Executive Order 14320 Promoting the Export of the American AI Technology Stack President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"americas-ai-action-plan","href":"/policy/americas-ai-action-plan/","name":"America's AI Action Plan","summary":"A plan developed under Executive Order 14179; its introduction carries the names of the Assistant to the President for Science and Technology, the Special Advisor for AI and Crypto, and the Assistant to the President for National Security Affairs. It sets out recommended Federal policy actions under three pillars: Accelerate AI Innovation, Build American AI Infrastructure, and Lead in International AI Diplomacy and Security. Recommended actions include revising the NIST AI Risk Management Framework to eliminate references to misinformation, diversity, equity, and inclusion, and climate change; updating Federal procurement guidelines for large language models; streamlining permitting for data centers, semiconductor manufacturing facilities, and energy infrastructure; and exporting American AI to allies and partners.","keywords":"Winning the Race: America's AI Action Plan The White House policy"},{"type":"policy","slug":"take-it-down-act","href":"/policy/take-it-down-act/","name":"TAKE IT DOWN Act","summary":"Makes it a Federal crime to knowingly publish, through an interactive computer service, nonconsensual intimate visual depictions of an identifiable individual, including \"digital forgeries,\" which the Act defines to include depictions created through artificial intelligence or other technological means. It requires covered platforms to establish a process through which individuals can request removal of such depictions and to remove them within 48 hours of a valid request. A failure to reasonably comply with these notice and removal obligations is treated as an unfair or deceptive act or practice under the Federal Trade Commission Act, which the Commission enforces.","keywords":"Public Law 119-12 Public Law 119-12 Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act U.S. Congress policy"},{"type":"policy","slug":"eo-14277","href":"/policy/eo-14277/","name":"Advancing Artificial Intelligence Education for American Youth","summary":"Establishes a White House Task Force on Artificial Intelligence Education, chaired by the Director of the Office of Science and Technology Policy, and directs it to plan a Presidential Artificial Intelligence Challenge for students and educators. It directs Task Force agencies to seek public-private partnerships to develop online resources for teaching AI literacy from kindergarten through grade 12, and directs the Secretary of Education, the National Science Foundation, and the Secretary of Agriculture to prioritize AI in teacher training, research, and related programs. It also directs the Secretary of Labor to seek to increase participation in AI-related Registered Apprenticeships.","keywords":"EO 14277 Executive Order 14277 Advancing Artificial Intelligence Education for American Youth President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"m-25-21","href":"/policy/m-25-21/","name":"Accelerating Federal Use of AI through Innovation, Governance, and Public Trust","summary":"OMB guidance to agencies on their use of AI, which the memorandum says the AI in Government Act of 2020 required; it rescinds and replaces M-24-10. It directs each agency to retain or designate a Chief AI Officer within 60 days, requires agencies covered by the Chief Financial Officers Act to develop and publish AI strategies within 180 days, and requires minimum risk management practices for high-impact AI, with agencies to stop using such AI if proper risk mitigation is not possible. Agencies must also keep updating their annual AI use case inventories and develop compliance plans.","keywords":"M-25-21 OMB Memorandum M-25-21 Accelerating Federal Use of AI through Innovation, Governance, and Public Trust Office of Management and Budget policy"},{"type":"policy","slug":"m-25-22","href":"/policy/m-25-22/","name":"Driving Efficient Acquisition of Artificial Intelligence in Government","summary":"OMB guidance on agencies' acquisition of AI systems and services, issued consistent with the Advancing American AI Act, Executive Order 14179, and M-25-21; it rescinds and replaces M-24-18. It requires agencies to update internal acquisition procedures within 270 days, states a policy to maximize the use of AI products and services developed and produced in the United States, and gives guidance across the acquisition lifecycle, including contract terms on intellectual property rights and protections against vendor lock-in. It applies to contracts awarded under solicitations issued 180 days or more after the memorandum, and to options exercised after that date.","keywords":"M-25-22 OMB Memorandum M-25-22 Driving Efficient Acquisition of Artificial Intelligence in Government Office of Management and Budget policy"},{"type":"policy","slug":"eo-14179","href":"/policy/eo-14179/","name":"Removing Barriers to American Leadership in Artificial Intelligence","summary":"States a policy \"to sustain and enhance America's global AI dominance\" and directs the Assistant to the President for Science and Technology, the Special Advisor for AI and Crypto, and the Assistant to the President for National Security Affairs to develop an AI action plan within 180 days. It directs a review of all actions taken under the revoked Executive Order 14110, so that agencies can suspend, revise, or rescind those found inconsistent with the order's policy, and directs OMB to revise memoranda M-24-10 and M-24-18 within 60 days.","keywords":"EO 14179 Executive Order 14179 Removing Barriers to American Leadership in Artificial Intelligence President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14148","href":"/policy/eo-14148/","name":"Initial Rescissions of Harmful Executive Orders and Actions","summary":"Revokes a list of executive orders and presidential memoranda issued from January 2021 to January 2025, including Executive Order 14110 on artificial intelligence. It directs the Directors of the Domestic Policy Council and the National Economic Council to review Federal actions taken under the revoked orders and to submit a further list of orders to rescind and replace, and directs the National Security Advisor to review National Security Memoranda issued from January 20, 2021 through January 20, 2025.","keywords":"EO 14148 Executive Order 14148 Initial Rescissions of Harmful Executive Orders and Actions President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"eo-14141","href":"/policy/eo-14141/","name":"Advancing United States Leadership in Artificial Intelligence Infrastructure","summary":"Sets five guiding principles for developing AI infrastructure, including data centers, in the United States. It directs the Secretaries of Defense and Energy to identify sites on Federal land that may be suitable for leasing to non-Federal entities to build and operate frontier AI data centers and clean energy facilities to serve them, and sets out steps on permitting, electric grid interconnection, and transmission for those sites.","keywords":"EO 14141 Executive Order 14141 Advancing United States Leadership in Artificial Intelligence Infrastructure President of the United States (Joseph R. Biden Jr.) policy"},{"type":"policy","slug":"nist-ai-100-4","href":"/policy/nist-ai-100-4/","name":"Reducing Risks Posed by Synthetic Content","summary":"A NIST report that examines existing standards, tools, methods, and practices, and the potential development of further standards and techniques, for authenticating content and tracking its provenance, labeling synthetic content such as through watermarking, detecting synthetic content, preventing generative AI from producing child sexual abuse material or non-consensual intimate imagery of real individuals, testing software used for these purposes, and auditing and maintaining synthetic content. It uses the definition of synthetic content from Executive Order 14110 and notes that the efficacy of many of the technical approaches it describes has not yet been fully examined.","keywords":"NIST AI 100-4 NIST AI 100-4 Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency National Institute of Standards and Technology policy"},{"type":"policy","slug":"m-24-18","href":"/policy/m-24-18/","name":"Advancing the Responsible Acquisition of Artificial Intelligence in Government","summary":"OMB guidance on agencies' acquisition of AI, issued consistent with the Advancing American AI Act, Executive Order 14110, and OMB Memorandum M-24-10. It required agencies to create or update acquisition policies, procedures, and practices to reflect new AI governance responsibilities, and set requirements and guidance on cross-functional and interagency collaboration, managing AI risk and performance, and promoting a competitive AI market.","keywords":"M-24-18 OMB Memorandum M-24-18 Advancing the Responsible Acquisition of Artificial Intelligence in Government Office of Management and Budget policy"},{"type":"policy","slug":"nist-ai-100-5","href":"/policy/nist-ai-100-5/","name":"A Plan for Global Engagement on AI Standards","summary":"A plan for U.S. engagement with standards organizations, industry, academia, civil society, and foreign governments on developing and implementing AI-related consensus standards, prepared by the Department of Commerce in coordination with the Department of State and other agencies in accordance with section 11(b) of Executive Order 14110. It sorts AI topics into three tiers by readiness for standardization, with terminology, testing and evaluation methods, digital content transparency, risk management, and security and privacy among those it calls urgently needed and ready, and recommends engagement activities for the U.S. government and other stakeholders.","keywords":"NIST AI 100-5 NIST AI 100-5 A Plan for Global Engagement on AI Standards National Institute of Standards and Technology policy"},{"type":"policy","slug":"nist-ai-600-1","href":"/policy/nist-ai-600-1/","name":"Generative AI Profile","summary":"A cross-sectoral profile of and companion resource for the AI RMF 1.0 for generative AI, developed under section 4.1(a)(i)(A) of Executive Order 14110. It defines risks that are novel to or exacerbated by the use of generative AI and suggests actions to help organizations govern, map, measure, and manage those risks across the AI lifecycle.","keywords":"NIST AI 600-1 NIST AI 600-1 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile National Institute of Standards and Technology policy"},{"type":"policy","slug":"m-24-10","href":"/policy/m-24-10/","name":"Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence","summary":"OMB guidance to agencies, issued consistent with the AI in Government Act of 2020, the Advancing American AI Act, and Executive Order 14110, on governance, innovation, and risk management for agency use of AI. It required each agency to designate a Chief AI Officer within 60 days, required agencies covered by the Chief Financial Officers Act to develop enterprise strategies for responsible AI use, and required minimum risk management practices for safety-impacting and rights-impacting AI. It also gave recommendations for managing AI risks in Federal procurement.","keywords":"M-24-10 OMB Memorandum M-24-10 Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence Office of Management and Budget policy"},{"type":"policy","slug":"eo-14110","href":"/policy/eo-14110/","name":"Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence","summary":"Sets eight guiding principles and priorities for Federal AI policy and assigns tasks to agencies on safety and security, innovation and competition, workers, equity and civil rights, consumer protection, privacy, Federal Government use of AI, and international engagement. It directs the Secretary of Commerce to require companies developing potential dual-use foundation models to report on training activities, the ownership and security of model weights, and red-team test results, and to require reports on large-scale computing clusters. It directs NIST to develop AI safety and security guidelines, including a generative AI companion resource to the AI RMF, and directs OMB to issue guidance on agency use of AI, including the designation of Chief AI Officers.","keywords":"EO 14110 Executive Order 14110 Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence President of the United States (Joseph R. Biden Jr.) policy"},{"type":"policy","slug":"ai-rmf-1-0","href":"/policy/ai-rmf-1-0/","name":"AI RMF 1.0","summary":"A framework intended for voluntary use by organizations that design, develop, deploy, or use AI systems, developed as directed by the National Artificial Intelligence Initiative Act of 2020. Part 1 describes how organizations can frame AI risks and lists characteristics of trustworthy AI systems: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy enhanced, and fair with their harmful biases managed. Part 2, the Core, organizes risk management into four functions: Govern, Map, Measure, and Manage.","keywords":"NIST AI 100-1 NIST AI 100-1 Artificial Intelligence Risk Management Framework (AI RMF 1.0) National Institute of Standards and Technology policy"},{"type":"policy","slug":"blueprint-ai-bill-of-rights","href":"/policy/blueprint-ai-bill-of-rights/","name":"Blueprint for an AI Bill of Rights","summary":"A white paper from the White House Office of Science and Technology Policy that identifies five principles to guide the design, use, and deployment of automated systems: safe and effective systems; algorithmic discrimination protections; data privacy; notice and explanation; and human alternatives, consideration, and fallback. It is accompanied by a handbook, From Principles to Practice, with steps for applying the principles. Its legal disclaimer states that it is non-binding and does not constitute U.S. government policy.","keywords":"Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People White House Office of Science and Technology Policy policy"},{"type":"policy","slug":"national-ai-initiative-act-2020","href":"/policy/national-ai-initiative-act-2020/","name":"National AI Initiative Act","summary":"Directs the President to establish a National Artificial Intelligence Initiative to support AI research and development, education and workforce training, and the coordination of Federal AI activities, and provides for a National Artificial Intelligence Initiative Office, an interagency committee, and a National Artificial Intelligence Advisory Committee. It provides for a network of National Artificial Intelligence Research Institutes and a task force on a National AI Research Resource. It directs NIST to support the development of AI standards and to develop a voluntary risk management framework for trustworthy AI systems, and sets out AI activities for the National Oceanic and Atmospheric Administration, the National Science Foundation, and the Department of Energy.","keywords":"Public Law 116-283, Division E Public Law 116-283, Division E National Artificial Intelligence Initiative Act of 2020 U.S. Congress policy"},{"type":"policy","slug":"ai-in-government-act-2020","href":"/policy/ai-in-government-act-2020/","name":"AI in Government Act of 2020","summary":"Creates an AI Center of Excellence within the General Services Administration to facilitate the adoption of AI in the Federal Government and improve cohesion and competency in its use. It requires the OMB Director to issue a memorandum to agencies on their acquisition and use of AI, including approaches to remove barriers to that use and best practices for identifying and mitigating discriminatory impact or bias, and to update it every two years for 10 years. Each agency must post a plan to achieve consistency with the memorandum or a written determination that it does not use and does not anticipate using AI. The Act also directs the Office of Personnel Management to identify AI skills and competencies and to establish or update an occupational job series for AI positions.","keywords":"Public Law 116-260, Division U, Title I Public Law 116-260, Division U, Title I AI in Government Act of 2020 U.S. Congress policy"},{"type":"policy","slug":"eo-13960","href":"/policy/eo-13960/","name":"Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government","summary":"Sets out nine principles that agencies must follow when designing, developing, acquiring, and using AI in the Federal Government, including that such use be lawful, accurate, safe, understandable, transparent, and accountable. It directs OMB to publish a roadmap for policy guidance on agency use of AI, and directs agencies to prepare annual inventories of their non-classified and non-sensitive AI use cases and make them public where practicable. The order does not apply to AI used in defense or national security systems, to AI embedded in common commercial products, or to AI research and development activities.","keywords":"EO 13960 Executive Order 13960 Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government President of the United States (Donald J. Trump) policy"},{"type":"policy","slug":"m-21-06","href":"/policy/m-21-06/","name":"Guidance for Regulation of Artificial Intelligence Applications","summary":"OMB guidance, required by Executive Order 13859, on regulatory and non-regulatory approaches to AI applications developed and deployed outside the Federal Government; government use of AI is outside its scope. It sets out ten principles for agencies to consider, including public trust, public participation, risk assessment and management, benefits and costs, fairness and non-discrimination, and safety and security, and states that agencies must avoid regulatory or non-regulatory actions that needlessly hamper AI innovation and growth. Agencies with regulatory authorities were to submit plans to achieve consistency with the memorandum by May 17, 2021.","keywords":"M-21-06 OMB Memorandum M-21-06 Guidance for Regulation of Artificial Intelligence Applications Office of Management and Budget policy"},{"type":"policy","slug":"eo-13859","href":"/policy/eo-13859/","name":"Maintaining American Leadership in Artificial Intelligence","summary":"Establishes the American AI Initiative, a Federal Government strategy guided by five principles and coordinated through the National Science and Technology Council Select Committee on Artificial Intelligence. It directs agencies that perform or fund research to treat AI as a research and development priority, directs all agencies to review Federal data and models for opportunities to increase access by the non-Federal AI research community, and directs agencies that provide educational grants to consider AI a priority area in existing fellowship and service programs. It also directs OMB to issue a memorandum to agencies on regulatory and non-regulatory approaches to AI applications, and directs NIST to issue a plan for Federal engagement in developing AI technical standards.","keywords":"EO 13859 Executive Order 13859 Maintaining American Leadership in Artificial Intelligence President of the United States (Donald J. Trump) policy"},{"type":"faq","slug":"where-to-start","href":"/faq/#where-to-start","name":"I'm new to AI. Where should I start?","summary":"Start with the New to AI learning path, which puts short explainers in order: what a language model is, how it reads and writes, what happens to what you type, and how to look up a model before you use it. The glossary defines common terms in plain words, and Learn lists every explainer by topic. Each explainer ends with the original sources it relies on, so you can check its statements yourself.","keywords":"question answer faq"},{"type":"faq","slug":"what-is-ai","href":"/faq/#what-is-ai","name":"What is artificial intelligence?","summary":"U.S. federal law defines artificial intelligence as \"a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments\" (15 U.S.C. 9401(3)). That definition comes from the National Artificial Intelligence Initiative Act of 2020, part of Public Law 116-283. The phrase itself appears in the title of a proposal dated August 31, 1955 for a summer study at Dartmouth College in 1956. Large language models are one kind of AI system.","keywords":"question answer faq"},{"type":"faq","slug":"what-is-an-llm","href":"/faq/#what-is-an-llm","name":"What is a large language model?","summary":"A large language model (LLM) is an AI model that has learned from very large amounts of text and produces text one token at a time; a token can be a word, part of a word, or a single character. Google's Machine Learning Crash Course says LLMs \"predict a token or sequence of tokens, sometimes many paragraphs worth of predicted tokens,\" and that training on massive datasets lets them learn patterns and generate text based on probabilities. Because a reply is generated from learned patterns, it can read fluently and still be wrong, which is known as hallucination.","keywords":"question answer faq"},{"type":"faq","slug":"tokens-and-context","href":"/faq/#tokens-and-context","name":"What are tokens and context windows?","summary":"A token is a chunk of text, such as a word, part of a word, or a single character, and it is the unit a language model reads and writes. The context window is how much a model can take into account at once; Anthropic's documentation describes it as \"all the text a language model can reference when generating a response, including the response itself.\" In a long chat, earlier messages and attachments fill that space: Anthropic's API returns an error when the input alone exceeds the window, and its compaction feature, in beta, summarizes earlier parts of a conversation so it can continue.","keywords":"question answer faq"},{"type":"faq","slug":"why-ai-makes-things-up","href":"/faq/#why-ai-makes-things-up","name":"Why do AI chatbots make things up?","summary":"Language models generate text from probabilities learned in training, so an answer can sound confident and still be false, which is known as hallucination. Google's Machine Learning Crash Course lists it among the problems with LLMs, \"meaning their predictions often contain mistakes.\" A September 2025 paper by researchers at OpenAI and Georgia Tech, Why Language Models Hallucinate, argues that training and evaluation reward guessing over admitting uncertainty. Anthropic's guide to reducing hallucinations says its techniques reduce them but \"don't eliminate them entirely,\" and advises validating critical information.","keywords":"question answer faq"},{"type":"faq","slug":"what-is-an-agent","href":"/faq/#what-is-an-agent","name":"What is an AI agent?","summary":"An AI agent is software in which a language model works toward a goal by choosing its own next steps, such as which tools to call, and reacting to the results. Anthropic's guide Building effective agents describes agents as systems where LLMs \"dynamically direct their own processes and tool usage,\" unlike workflows, which follow predefined code paths. What an agent may do is set by the application around the model: the Model Context Protocol specification says hosts must obtain explicit user consent before invoking any tool, and also that the protocol itself cannot enforce these security principles.","keywords":"question answer faq"},{"type":"faq","slug":"open-source-ai","href":"/faq/#open-source-ai","name":"What does \"open source\" mean for an AI model?","summary":"The Open Source Initiative's Open Source AI Definition 1.0 calls an AI system open source when its terms grant the freedoms to use it for any purpose without asking permission, study how it works, modify it, and share it. As a precondition for those freedoms, it requires detailed information about the training data, the complete code used to train and run the system, and the model parameters, such as weights, each under OSI-approved terms. Many models described as open are open weight: the trained weights can be downloaded, but the license may still limit some uses, and the data and training code may not be public.","keywords":"question answer faq"},{"type":"faq","slug":"is-chatgpt-open-source","href":"/faq/#is-chatgpt-open-source","name":"Is ChatGPT open source?","summary":"In the OpenAI documents read for this page, ChatGPT is a product OpenAI runs, and gpt-oss is a separate line of OpenAI models that can be downloaded. OpenAI's gpt-oss model card refers to \"models served in our first-party products like ChatGPT\" and says open models present a different risk profile than proprietary models because, once they are released, OpenAI has no way to add further mitigations or revoke access. OpenAI calls gpt-oss-120b and gpt-oss-20b open-weight models, released under the Apache 2.0 license and its gpt-oss usage policy. Open weight is not automatically open source under the Open Source Initiative's definition, which also requires training data information and code.","keywords":"question answer faq"},{"type":"faq","slug":"run-ai-locally","href":"/faq/#run-ai-locally","name":"Can I run an AI model on my own computer?","summary":"Yes, if the model's weights can be downloaded and your computer has enough memory to hold them. Programs such as Ollama and llama.cpp download models and run them on your own machine, and the memory you need depends on the model: OpenAI's gpt-oss-20b model card says that model runs within 16GB of memory, while the larger gpt-oss-120b fits on a single 80GB GPU. Check where a model actually runs, because some tools also offer cloud models; Ollama's FAQ says it does not see your prompts when you run locally but processes them when you use its cloud-hosted models.","keywords":"question answer faq"},{"type":"faq","slug":"model-cards","href":"/faq/#model-cards","name":"What is a model card, and why read one?","summary":"A model card is the document a publisher releases with a model to say what it is, how it was made and tested, what it is meant for, and where it falls short. Read it to pin down the exact release and who maintains it, its intended uses, and its stated limitations, but remember that every statement in it is the publisher's own account. Names vary: OpenAI calls its gpt-oss document a model card rather than a system card because the models \"will be used as part of a wide range of systems.\"","keywords":"question answer faq"},{"type":"faq","slug":"chats-and-training","href":"/faq/#chats-and-training","name":"Do AI chatbots use what I type to train their models?","summary":"Sometimes; it depends on the product, the kind of account, and your settings. Anthropic's privacy policy says it may use your inputs and outputs to train its models unless you opt out in your account settings, with exceptions such as chats flagged for safety review, and Google's Gemini Apps Privacy Hub says that when Keep Activity is on, Google uses your activity to improve its services, including training generative AI models. For developers, OpenAI's API documentation says data sent to its API is not used to train or improve its models unless you explicitly opt in. Policies change, so note the date, product, and plan that the page you read covers.","keywords":"question answer faq"},{"type":"faq","slug":"spot-ai-content","href":"/faq/#spot-ai-content","name":"How can I tell if something was made by AI?","summary":"No single check settles it: NIST's report on synthetic content (NIST AI 100-4) says there is \"no silver bullet\" for public trust in digital content, notes that metadata is often stripped when files are shared, and warns that false positives, which label human-made content as AI-generated, can in many contexts be extremely damaging. Content Credentials, built on the C2PA standard, are cryptographically bound records of a file's history, or provenance, though C2PA says they do not judge whether provenance data is \"true.\" Google's SynthID adds imperceptible watermarks to content from Google's AI tools, and its detector checks for content made with AI from Google or its partners. A valid credential or a detected watermark is real evidence about a file's origin, but finding none proves nothing.","keywords":"question answer faq"},{"type":"faq","slug":"benchmarks","href":"/faq/#benchmarks","name":"What is a benchmark, and should I trust one?","summary":"A benchmark is a fixed set of tasks with a scoring method, used to compare models under the same conditions. A result describes one system on those tasks under one setup, so before relying on it, check what the tasks ask, which model version was tested and how, and whether anyone else has reproduced the score. Results can also mislead: a NIST research blog post of December 2, 2025, reported that models in its evaluations sometimes cheated on agentic coding and cyber benchmarks, for example by using the internet to find walkthroughs and answers for security challenges. USASI does not publish benchmark scores.","keywords":"question answer faq"},{"type":"faq","slug":"safety-testing","href":"/faq/#safety-testing","name":"How do AI companies test their models for safety?","summary":"Developers test their own models and report the results in documents such as model cards and system cards, and several publish frameworks that define capability levels and say what they will do when a model reaches one. For example, Google DeepMind's Frontier Safety Framework page lists version 3.1 of April 17, 2026, and describes identifying capability levels and preparing mitigation plans; Anthropic's Responsible Scaling Policy page lists versions up to 3.4, effective July 8, 2026; and OpenAI's gpt-oss model card reports evaluations under its Preparedness Framework. These documents are each company's own account of its work; USASI describes them but does not judge whether any test or framework is adequate.","keywords":"question answer faq"},{"type":"faq","slug":"agi-and-superintelligence","href":"/faq/#agi-and-superintelligence","name":"What do \"AGI\" and \"superintelligence\" mean?","summary":"AGI stands for artificial general intelligence, and many AI researchers and organizations have proposed definitions of it. A position paper by Google DeepMind researchers reviews nine prominent definitions, proposes levels based on how well and how broadly a system performs, and uses \"Artificial Superintelligence\" for a general system able to do a wide range of tasks at a level no human can match. In U.S. executive-branch documents, \"Super Intelligence\" has a separate, official meaning: Executive Order 14434 of September 29, 2026, uses it for the technologies covered by the statutory definition of artificial intelligence. This page makes no predictions about future systems.","keywords":"question answer faq"},{"type":"faq","slug":"super-intelligence-term","href":"/faq/#super-intelligence-term","name":"Why does the U.S. government now say \"Super Intelligence\"?","summary":"Executive Order 14434, \"Inaugurating the Era of Super Intelligence,\" signed September 29, 2026, directs executive agencies, to the maximum extent permitted by law, to use \"Super Intelligence\" and \"SI\" in place of \"Artificial Intelligence\" and \"AI\" in official correspondence, public communications, websites, reports, policy documents, and other non-statutory documents. It defines the new terms as the technologies covered by the existing statutory definition of artificial intelligence in 15 U.S.C. 9401(3), and it does not require changing previously issued regulations, contracts, or other historical documents. The order also directs the Assistant to the President for Science and Technology to propose legislative language for a federal definition within 60 days, and NIST's super intelligence page says NIST is working to update its communications as directed.","keywords":"question answer faq"},{"type":"faq","slug":"who-regulates-ai","href":"/faq/#who-regulates-ai","name":"Who regulates AI in the United States?","summary":"Rules and guidance on AI come from several places rather than one document. Congress passes laws, such as the National Artificial Intelligence Initiative Act of 2020, which defines AI and establishes a National Artificial Intelligence Initiative (15 U.S.C. 9401); the President issues executive orders, such as EO 14434; and the Office of Management and Budget issues memoranda to agencies, such as M-25-21 of April 3, 2025, on agencies' own use of AI. Agencies also publish guidance, such as NIST's AI Risk Management Framework, which NIST says is intended for voluntary use and is being revised. USASI describes such documents but does not say which rules apply to any person or product, and nothing here is legal advice.","keywords":"question answer faq"},{"type":"faq","slug":"ai-jobs","href":"/faq/#ai-jobs","name":"What jobs exist in AI?","summary":"USASI's careers explainer draws on government occupational guides and employer postings that describe researchers, software and machine learning engineers, data specialists, people who design chips and run data centers, and roles in product, design, policy, law, safety, and evaluation. A Department of Labor occupational profile, for example, describes data scientists as developing techniques or analytics applications that turn raw data into meaningful information, and the Department's Apprenticeship.gov site has an AI in Registered Apprenticeship portal. USASI's Jobs page lists open postings from employer feeds; USASI does not employ, represent, recruit for, or endorse listed organizations, and applications take place on the employer's official site.","keywords":"question answer faq"},{"type":"faq","slug":"what-is-usasi","href":"/faq/#what-is-usasi","name":"What is USASI, and who runs it?","summary":"United States of America Superintelligence (USASI) is an independent, unofficial catalog of American artificial-intelligence work, with two directories given equal weight: Companies & Labs and Open Models & Tools. It is maintained by its owner, does not name an individual editor, and is not a staffed newsroom or institution. Research, drafting, and the daily news are carried out with AI assistance under published rules: every published claim must cite a source, and automated checks block publication when a record fails validation.","keywords":"question answer faq"},{"type":"faq","slug":"government-site","href":"/faq/#government-site","name":"Is USASI a government website?","summary":"No. USASI is an independent project, not operated, sponsored, endorsed, or reviewed by the United States government, any federal, state, or local agency, or any other government, and nothing on the site is an official statement, record, approval, or designation. The words \"United States of America\" in its name describe its subject, American AI work, and do not suggest government origin.","keywords":"question answer faq"},{"type":"faq","slug":"why-the-name","href":"/faq/#why-the-name","name":"Why is the site called \"Superintelligence\"?","summary":"The name describes the subject area the catalog covers, not a claim about any listed system. USASI is not an AI laboratory, an investment service, a safety certification, or a ranking of who is closest to \"superintelligence.\"","keywords":"question answer faq"},{"type":"faq","slug":"what-gets-included","href":"/faq/#what-gets-included","name":"How does USASI decide what to include?","summary":"An entry is published only when a documented basis, with sources, connects its accountable entity to the United States: a U.S. headquarters, a U.S. nonprofit or lab, documented primary control by a U.S. entity, or, for software, datasets, and models, a U.S.-based governing or maintaining entity. There are no exceptions for prominent organizations, and project eligibility rests on documented governance, never on the names or assumed nationalities of contributors. Candidates whose basis cannot be documented stay in an internal review queue and are not published.","keywords":"question answer faq"},{"type":"faq","slug":"rankings-and-ratings","href":"/faq/#rankings-and-ratings","name":"Does USASI rank, test, or certify AI models?","summary":"No. The catalog does not test, audit, certify, or rank any model, product, dataset, or tool, and it does not publish benchmark scores. Labels such as open-weight, open-stack, and open system (reviewed) are USASI's own editorial categories (rubric v0.2) describing documented materials and licenses, not quality or safety ratings. Every total on the site describes this catalog's coverage, not a census of American AI.","keywords":"question answer faq"},{"type":"faq","slug":"unknown-details","href":"/faq/#unknown-details","name":"Why are some details marked Unknown?","summary":"Unknown means an item has not been assessed or the evidence is insufficient; it is never a polite way of saying \"no.\" When evidence is missing, the catalog says Unknown instead of guessing. A different label, Not public, means the evidence documents that something is not available.","keywords":"question answer faq"},{"type":"faq","slug":"who-writes-news","href":"/faq/#who-writes-news","name":"Who writes the news on USASI?","summary":"News items are researched and written by an AI assistant working only from primary sources, such as filings, official announcements, model cards, and release notes, and each item cites those sources. Since October 1, 2026, a daily automated run finds and publishes new items without individual human review before publication, and every item must pass automated checks that reject, for example, items without sources or containing funding amounts or benchmark scores. Because items are not individually reviewed first, please report any error with the correction link.","keywords":"question answer faq"},{"type":"faq","slug":"report-a-mistake","href":"/faq/#report-a-mistake","name":"How do I report a mistake?","summary":"Each entry page has a Report a correction action, which opens the right form once the site's repository is configured, and you can also email a correction to the address on the About page; no GitHub account is needed. The most useful report names the entry, the specific statement, and a source that supports the correction, such as an official page, license file, model card, repository, or filing. Corrections are handled the same way for everyone and reviewed as time allows, with no promised response time, and accepted corrections are recorded in the changelog.","keywords":"question answer faq"},{"type":"faq","slug":"reuse-data","href":"/faq/#reuse-data","name":"Can I reuse USASI's data?","summary":"Yes. The published catalog is available as static files, including a full catalog export in JSON, with no accounts, keys, or sign-ups, and original catalog prose is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), with the suggested attribution \"Source: USASI catalog (unitedstatesofamericasuperintelligence.com), CC BY 4.0.\" Code written for the project is released under the MIT License. Models, software, datasets, and documents described in the catalog keep their own licenses, and names, logos, and marks belong to their owners.","keywords":"question answer faq"},{"type":"faq","slug":"visitor-privacy","href":"/faq/#visitor-privacy","name":"Does USASI track visitors?","summary":"USASI sets no cookies and does not use your browser's local storage, session storage, or IndexedDB; filter choices live only in the page address. The only visitor analytics it permits is Cloudflare Web Analytics, which Cloudflare describes as cookie-free, and search and filtering run in your browser, although your search text appears in the page address. Like any web host, the hosting provider receives standard request information, such as your IP address and the page requested.","keywords":"question answer faq"},{"type":"learning-path","slug":"new-to-ai","href":"/learn/paths/new-to-ai/","name":"New to AI","summary":"What a language model is, how it reads and writes, what happens to what you type, and how to look up a model before you use it.","keywords":"learning path For anyone starting from scratch"},{"type":"learning-path","slug":"choosing-and-running-models","href":"/learn/paths/choosing-and-running-models/","name":"Choosing and running a model","summary":"Read the license, check the model card, decide between hosted and local, and work out whether a model fits your hardware.","keywords":"learning path For people who want to download or self-host a model"},{"type":"learning-path","slug":"building-with-ai","href":"/learn/paths/building-with-ai/","name":"Building with AI","summary":"How models call tools, answer from your documents, run as agents, and get adapted through fine-tuning.","keywords":"learning path For developers and technical teams"},{"type":"learning-path","slug":"checking-claims","href":"/learn/paths/checking-claims/","name":"Checking claims about AI","summary":"Read benchmark results, safety documents, content labels, data disclosures, infrastructure announcements, and policy texts with care.","keywords":"learning path For journalists, researchers, students, and policy readers"},{"type":"learning-path","slug":"understanding-the-industry","href":"/learn/paths/understanding-the-industry/","name":"Understanding the industry","summary":"Where today's AI came from, how chips, compute, labs, and software fit together, and the work people do in the field.","keywords":"learning path For readers who want the big picture"},{"type":"kids-lesson","slug":"what-is-ai","href":"/kids/what-is-ai/","name":"What is AI?","summary":"What is AI, and where do you see it?","keywords":"kids children lesson 1"},{"type":"kids-lesson","slug":"how-ai-learns","href":"/kids/how-ai-learns/","name":"How does AI learn?","summary":"How does AI learn, and why can it be wrong?","keywords":"kids children lesson 2"},{"type":"kids-lesson","slug":"what-is-super-intelligence","href":"/kids/what-is-super-intelligence/","name":"What does \"super intelligence\" mean?","summary":"What does super intelligence mean?","keywords":"kids children lesson 3"},{"type":"kids-lesson","slug":"what-ai-can-and-cannot-do","href":"/kids/what-ai-can-and-cannot-do/","name":"What can AI do, and what can't it?","summary":"What is AI good at, and where does it fall short?","keywords":"kids children lesson 4"},{"type":"kids-lesson","slug":"using-ai-safely","href":"/kids/using-ai-safely/","name":"Using AI safely and kindly","summary":"How can you use AI safely and kindly?","keywords":"kids children lesson 5"},{"type":"kids-lesson","slug":"keeping-ai-safe","href":"/kids/keeping-ai-safe/","name":"How do people keep AI safe?","summary":"Who keeps AI safe, and how?","keywords":"kids children lesson 6"},{"type":"kids-lesson","slug":"ai-and-your-future","href":"/kids/ai-and-your-future/","name":"AI and your future","summary":"What jobs work with AI, and how can you start learning?","keywords":"kids children lesson 7"},{"type":"news","slug":"anthropic-describes-election-safeguards-for-2026-us-midterms","href":"/news/anthropic-describes-election-safeguards-for-2026-us-midterms/","name":"Anthropic describes Claude election safeguards ahead of the 2026 U.S. midterms","summary":"On October 14, 2026 Anthropic described its preparations for the 2026 U.S. midterm elections. It says Claude.ai continues to show a banner linking to TurboVote from Democracy Works for questions about registration, polling places, and election dates, that later this month it will draw official voting information from Democracy Works into responses in the United States, and that Claude will show up-to-date results on election night and in the following days. Anthropic also says it updated its Usage Policy's election rules, published a guide for campaigns, and added classifiers aimed at multi-step abuse such as fake social media accounts and news sites. It says the Institute for Strategic Dialogue and the McCain Institute tested these safeguards and found Claude could miss a malicious campaign spread across many harmless-looking requests.","keywords":"news policy 2026-10-14"},{"type":"news","slug":"anthropic-opens-singapore-office-and-signs-mou-with-singapore","href":"/news/anthropic-opens-singapore-office-and-signs-mou-with-singapore/","name":"Anthropic opens a Singapore office and signs an MOU with Singapore's digital ministry","summary":"On October 14, 2026 Anthropic announced that it has opened an office in Singapore, which it describes as its fifth in the Asia-Pacific region. Anthropic co-founder Ben Mann and Kai Fong Chng, Permanent Secretary of Singapore's Ministry of Digital Development and Information, signed a memorandum of understanding on the sidelines of Singapore International Cyber Week. The announcement says the MOU covers cooperation on AI safety, security, and model evaluation, research on AI adoption and its effects, and responsible adoption across the public and private sectors, including technical exchanges with Singapore's AI safety and assurance institutions. An MOU sets out intended cooperation; the announcement does not describe binding commitments.","keywords":"news policy 2026-10-14"},{"type":"news","slug":"google-makes-gemini-4-argon-available-to-developers-and-subscribers","href":"/news/google-makes-gemini-4-argon-available-to-developers-and-subscribers/","name":"Google makes Gemini 4 Argon available to developers and to Google AI Pro and Ultra subscribers","summary":"On October 13, 2026 Google said developers can start building with Gemini 4 Argon on the paid tier of the Gemini API in Google AI Studio and Android Studio, and that Google AI Ultra subscribers can try the model in AI Studio and in Google Antigravity. A separate post the same day says Google AI Pro and Ultra subscribers can now access Gemini 4 Argon in the Gemini app; it does not list countries. Google first announced the model on September 30, 2026 with a limited rollout to cyber defenders. Neither post mentions released weights; access is through hosted products and the API. USASI has not tested Gemini 4 Argon.","keywords":"news release 2026-10-13"},{"type":"news","slug":"liquid-ai-releases-lfm2-5-24b-a2b","href":"/news/liquid-ai-releases-lfm2-5-24b-a2b/","name":"Liquid AI releases LFM2.5-24B-A2B, an open-weight mixture-of-experts model for on-device agents","summary":"Liquid AI announced LFM2.5-24B-A2B on October 13, 2026, describing it as a reasoning model built for fast, private agents on-device and as the largest and final model in the LFM2.5 family. The release includes a base model (LFM2.5-24B-A2B-Base) and a post-trained model (LFM2.5-24B-A2B), published as open weights on Hugging Face. The model card lists 24.0B total parameters with 2.45B active per token and a context length of 131,072 tokens, and says the model is not recommended for knowledge-heavy tasks on its own. The weights are released under the LFM Open License v1.0. USASI has not tested this model.","keywords":"news release 2026-10-13"},{"type":"news","slug":"agility-robotics-churchill-s4-declared-effective","href":"/news/agility-robotics-churchill-s4-declared-effective/","name":"SEC declares the Agility Robotics and Churchill Capital Corp XI Form S-4 effective; vote set for November 2","summary":"Agility Robotics said on October 13, 2026 that the SEC declared effective the Form S-4 registration statement for its proposed business combination with Churchill Capital Corp XI, a Nasdaq-listed blank check company. Churchill XI has set an extraordinary general meeting for November 2, 2026 to vote on the transaction, with a record date of September 14, 2026, and the companies expect the deal to close in November 2026. The transaction has not closed: it remains subject to approval by Churchill XI shareholders and other customary closing conditions. If it closes, the combined company is expected to trade on Nasdaq as AGLT.","keywords":"news acquisition 2026-10-13"},{"type":"news","slug":"character-ai-begins-chapter-11-sale-process","href":"/news/character-ai-begins-chapter-11-sale-process/","name":"Character.AI begins a voluntary Chapter 11 process to pursue a court-supervised sale of the company","summary":"Character.AI announced on its blog that it has initiated a voluntary Chapter 11 bankruptcy process to facilitate a court-supervised sale of the company. According to the case website of its claims and noticing agent, Character Technologies, Inc. d/b/a Character.AI filed a voluntary petition for relief under Chapter 11 on October 12, 2026, and the case is administered under Case No. 26-11603 in the United States Bankruptcy Court for the District of Delaware. The company says it hopes the sale process will yield a proposal that allows the platform to keep operating, that the platform will be wound down if no such bid is available, and that it currently expects the sale process to be complete by the end of November or perhaps early December 2026. It says the platform remains up and running at this time. The outcome of the sale is not yet known.","keywords":"news governance 2026-10-12"},{"type":"news","slug":"dspy-3-4-1-makes-instructionproposer-gepa-default","href":"/news/dspy-3-4-1-makes-instructionproposer-gepa-default/","name":"DSPy 3.4.1 makes InstructionProposer the default for GEPA and adds Python 3.15 support","summary":"On October 12, 2026 DSPy 3.4.1 was published on the project's GitHub repository. The release notes say it makes GEPA's structured InstructionProposer the default instruction proposer, lets dspy.RLM build DSPy sub-agents from inside its REPL, adds Python 3.15 support, and fixes a 3.4.0 import crash. The notes warn that GEPA's default reflection request changes shape, so optimization results can differ from 3.4.0 even with the same seed, and that the reflection LM must now be a dspy.LM that can return JSON. USASI has not tested this release.","keywords":"news release 2026-10-12"},{"type":"news","slug":"meta-releases-muse-wonder-and-muse-wonder-full","href":"/news/meta-releases-muse-wonder-and-muse-wonder-full/","name":"Meta releases Muse Wonder and Muse Wonder Full through its Model API and Muse Code","summary":"On October 12, 2026 Meta announced two new models in its Muse family, Muse Wonder and Muse Wonder Full, which it says were built on the groundwork laid with Muse Spark. According to the announcement, Muse Wonder is available that day for developers and builders through the Meta Model API and Muse Code, and Muse Wonder Full is available to Muse Code subscribers. Meta says Muse Wonder will soon power experiences in Muse, rolling out first to subscribers. The announcement does not name a license or say that model weights are released, and USASI has not tested either model.","keywords":"news release 2026-10-12"},{"type":"news","slug":"nvidia-and-cisco-announce-usfp-optics-form-factor","href":"/news/nvidia-and-cisco-announce-usfp-optics-form-factor/","name":"NVIDIA and Cisco announce plans for USFP, a pluggable optics form factor, and an open specification","summary":"On October 12, 2026 NVIDIA and Cisco announced plans for Ultra-Dense Small Form Factor Pluggable (USFP), an optical module form factor designed for 400Gb/s-per-lane electrical signals. The companies say they plan to create an open industry specification for USFP through an upcoming community-led multi-source project, and that they are working with TE Connectivity and Amphenol on USFP cages, connectors, and modules. The release describes the first USFP-based transceivers as planned and gives no availability date; the specification has not yet been published.","keywords":"news governance 2026-10-12"},{"type":"news","slug":"nvidia-contributes-vera-rubin-designs-to-ocp","href":"/news/nvidia-contributes-vera-rubin-designs-to-ocp/","name":"NVIDIA contributes Vera Rubin rack and compute tray designs to the Open Compute Project","summary":"In an October 12, 2026 blog post tied to the OCP Global Summit, NVIDIA said it contributed Vera Rubin rack and compute tray designs to the Open Compute Project through its MGX architecture. The post says NVIDIA joined AMD, Google, and Meta at OCP to publish a new open specification, and describes the Open Data Center Specification v0.7 as a blueprint for AI factory facility design. It says the OCP Grid Resilience Specification was published by Google, Meta, Microsoft, and NVIDIA, and that NVIDIA expanded DSX OS with open-source components for configuring networks and validating clusters, without naming a license for them. USASI has not reviewed the contributed designs or specifications.","keywords":"news governance 2026-10-12"},{"type":"news","slug":"openhands-1-27-0-adds-opencode-and-pi-acp-agents","href":"/news/openhands-1-27-0-adds-opencode-and-pi-acp-agents/","name":"OpenHands 1.27.0 adds OpenCode and Pi as built-in ACP agents","summary":"OpenHands 1.27.0 was published on the project's GitHub repository on October 12, 2026. Its release notes list adding OpenCode and Pi as built-in ACP agents on local backends, taking ACP model lists from the agent instead of hardcoding them, connecting Agent Canvas to DigitalOcean Managed Agents on desktop and web, and a persisted automation draft management UI. A separate bug-fix release, 1.27.1, followed the same day. USASI has not tested either release.","keywords":"news release 2026-10-12"},{"type":"news","slug":"unsloth-adds-browser-use-and-text-diffusion-training","href":"/news/unsloth-adds-browser-use-and-text-diffusion-training/","name":"Unsloth release adds Browser use, Qwen-Image-2.1-Turbo, and text-diffusion LLM training","summary":"On October 12, 2026 Unsloth published release v0.1.906-beta on its GitHub repository. The release notes say models can now use a browser in Unsloth, opening, reading, and interacting with pages in their own tab, and that Unsloth asks the user before the model signs in, posts a form, or presses a button that buys, sends, or deletes. The notes also list running Qwen-Image-2.1-Turbo from the Images page, training text-diffusion LLMs with a new DiffusionTrainer, faster MoE GGUFs that are larger than available VRAM on NVIDIA and AMD GPUs, and speculative decoding for MLX on Mac. USASI has not tested this release.","keywords":"news release 2026-10-12"},{"type":"news","slug":"xai-developer-docs-adopt-spacexsi-name","href":"/news/xai-developer-docs-adopt-spacexsi-name/","name":"xAI's developer documentation now uses the name SpaceXSI","summary":"As of October 11, 2026, xAI's developer documentation at docs.x.ai is titled \"SpaceXSI Docs\", describes itself as the official SpaceXSI API documentation, and opens with \"Get started with SpaceX Super Intelligence\". The page's structured data still names the organization xAI and lists \"SpaceXAI Docs\" among the site's alternate names, and the company's Greenhouse job board still carried the name SpaceXAI the same day. xAI has been a wholly owned subsidiary of SpaceX since February 2026. USASI has not found an official announcement of the new name, and no change to the company's legal name has been documented; the catalog record now reads \"xAI (SpaceXSI)\".","keywords":"news governance 2026-10-11"},{"type":"news","slug":"open-webui-0-12-0-released","href":"/news/open-webui-0-12-0-released/","name":"Open WebUI 0.12.0 adds Realtime voice calls and two-factor sign-in","summary":"On October 10, 2026 the Open WebUI project published version 0.12.0 on GitHub. The release notes say administrators can switch Call mode from Standard to Realtime, so voice calls go through an OpenAI Realtime voice model that hands questions and tasks to the chat's selected model, and that models can show an animated 3D avatar during Realtime calls. Administrators can also require an authenticator app at sign-in, with single-use recovery codes. The notes further add an Allow replies sharing mode for shared chats and named model controls, such as a Thinking control that sets reasoning effort. USASI has not tested these changes.","keywords":"news release 2026-10-10"},{"type":"news","slug":"anthropic-managed-agents-dynamic-workflows-beta","href":"/news/anthropic-managed-agents-dynamic-workflows-beta/","name":"Anthropic adds dynamic workflows to Claude Managed Agents in beta","summary":"Anthropic's Claude Platform release notes, in an entry dated October 9, 2026, say a Claude Managed Agents agent can now use dynamic workflows, in beta with the managed-agents-2026-04-01 beta header. The notes describe a workflow as a program that runs many agents in phases and combines their results, which the server runs in the background as a workflow run. Developers turn the feature on through the agent's multiagent field and follow each run through workflow_run events on the session's event stream. This is a feature of Anthropic's hosted service, not an open-source or open-weight release, and USASI has not tested it.","keywords":"news release 2026-10-09"},{"type":"news","slug":"onnx-runtime-cuda-plugin-ep-0-3-0-released","href":"/news/onnx-runtime-cuda-plugin-ep-0-3-0-released/","name":"ONNX Runtime CUDA Plugin EP 0.3.0 adds sparse attention and INT2 QMoE support","summary":"On October 9, 2026 Microsoft published version 0.3.0 of the ONNX Runtime CUDA Plugin EP on the ONNX Runtime GitHub repository. This is a separate release from ONNX Runtime 1.31.0, which the notes give as the matched core version. The release notes list sparse attention and packed continuous-batching support, INT2 and mixed-width QMoE execution, new QSA/CSA attention operators, expanded paged speculative decoding, and opt-in MatMul auto-tuning. The notes warn that the core and the plugin must use the same contributed-operator schemas and recommend building both from the same ONNX Runtime revision. USASI has not tested this release.","keywords":"news release 2026-10-09"},{"type":"news","slug":"prime-intellect-rewrites-prime-agent-in-rust","href":"/news/prime-intellect-rewrites-prime-agent-in-rust/","name":"Prime Intellect releases a Rust rewrite of Prime Agent with Windows support in beta","summary":"On October 9, 2026 Prime Intellect said on its blog that it had shipped Prime Agent rewritten from the ground up in Rust. The post describes Prime Agent as a long-running daemon with a worker process per session. With the rewrite, Prime Intellect is also releasing native Windows support, in beta, and installation through Homebrew. The post says Prime Agent remains open source and links to its GitHub repository; it does not name a license, and USASI has not confirmed one or tested the software.","keywords":"news release 2026-10-09"},{"type":"news","slug":"cloudflare-releases-clef-omni","href":"/news/cloudflare-releases-clef-omni/","name":"Cloudflare releases Clef-omni, a multimodal decision model, under Apache 2.0","summary":"On October 9, 2026 Cloudflare released Clef-omni, a decision model in its Clef family that accepts audio and video input alongside text and images, and published its weights on Hugging Face. The model card lists the Apache 2.0 license and describes Clef-Omni as a 30B-A3B mixture-of-experts model that returns a probability for every allowed option of each typed question, without free-form text generation. It is post-trained from Qwen3-Omni-30B-A3B-Instruct, a base model Cloudflare did not develop. In the same post Cloudflare said it lowered the price of Clef-flash and made Clef faster. USASI has not tested the model.","keywords":"news release 2026-10-09"},{"type":"news","slug":"deno-team-joins-cloudflare","href":"/news/deno-team-joins-cloudflare/","name":"The Deno team joins Cloudflare to work on self-hosted Workers and Durable Objects","summary":"On October 9, 2026 Cloudflare and Deno announced that the Deno team is joining Cloudflare. The Cloudflare post, filed under its Acquisitions tag, says the team will work to simplify self-hosting Workers and Durable Objects and that Cloudflare's workerd and Deno's celld are being merged. Deno's post says the company will support the Deno runtime for another year with monthly bug-fix and security releases and then end its own development of the runtime, which will remain open source; Deno Deploy will keep operating for six months before shutting down, and JSR will continue with its infrastructure moving to Cloudflare. Neither post states deal terms or a closing date.","keywords":"news acquisition 2026-10-09"},{"type":"news","slug":"onnx-runtime-1-31-0-released","href":"/news/onnx-runtime-1-31-0-released/","name":"ONNX Runtime 1.31.0 released with stable model-package and EPContext data APIs","summary":"On October 9, 2026 Microsoft published ONNX Runtime 1.31.0 on GitHub. The release notes say the model-package API and EPContext data callbacks were promoted to stable C and C++ APIs, and that CoreML can now take part in device-based execution-provider selection, including Apple Neural Engine selection through PREFER_NPU. The notes also announce that the ACL execution provider is deprecated, that out-of-range indices in GatherBlockQuantized now produce zero output slices, and that supported native builds now default to 1DS telemetry, which can be disabled with --no_telemetry. The ONNX dependency stays at 1.22.0. USASI has not tested these changes.","keywords":"news release 2026-10-09"},{"type":"news","slug":"meta-releases-metaencoder-30b","href":"/news/meta-releases-metaencoder-30b/","name":"Meta publishes MetaEncoder-30B, a gated Apache 2.0 multimodal encoder for decision tasks","summary":"Meta has published MetaEncoder-30B on Hugging Face as facebook/meta-encoder. The model card describes it as a multimodal bi-encoder, a \"Multimodal System One Encoder\", in which both the task and each candidate are written in natural language, with text, images, and video usable on either side. The card says it was built by contrastively fine-tuning Muse-Glimmer-30B and is released under Apache 2.0, inherited from the base model, with use also subject to the base model's Usage Policy; the files can be downloaded only after accepting access conditions on Hugging Face. The accompanying paper was submitted to arXiv on October 8, 2026; the model card does not give a release date. USASI has not tested MetaEncoder-30B.","keywords":"news release 2026-10-08"},{"type":"news","slug":"scale-ai-releases-distressbench","href":"/news/scale-ai-releases-distressbench/","name":"Scale AI releases DistressBench, a benchmark of crisis support in language models","summary":"Scale AI has published DistressBench, a benchmark of whether a language model can sustain safe, useful support across a crisis conversation; the accompanying paper was posted on arXiv on October 8, 2026. The paper argues that safety benchmarks scoring models mainly on refusal do not capture the support needed in suicide and self-harm crises, and describes 718 English conversations with clinician-authored turns across 23 suicide and self-harm subcategories, each with a task-specific rubric and a clinician reference response. Scale AI's gated Hugging Face dataset publishes 359 of the tasks under the CC BY 4.0 license, withholds the other half as a private holdout, and places full long-horizon transcripts, including the assistant turns, in a separate annex released for safety research.","keywords":"news research 2026-10-08"},{"type":"news","slug":"white-house-science-summit-genesis-consortium-partners","href":"/news/white-house-science-summit-genesis-consortium-partners/","name":"White House science summit lists industry partners in the Genesis Mission Consortium","summary":"On October 8, 2026 the White House published a fact sheet on science initiatives announced at its \"Science: A New Golden Age\" summit. It says eleven industry partners committed SI-for-science tools and compute credits to the Genesis Mission Consortium, and names NVIDIA, AMD, OpenAI, Anthropic, Google, AMP, Emerald AI, AWS, Armada, Crusoe, and Micron. The same fact sheet says NIH, DOE, and Biohub launched a virtual biology initiative to build data and models for virtual cells. The fact sheet describes announcements and commitments rather than an executive order.","keywords":"news policy 2026-10-08"},{"type":"news","slug":"anthropic-announces-genesis-mission-commitments","href":"/news/anthropic-announces-genesis-mission-commitments/","name":"Anthropic announces commitments to the federal Genesis Mission","summary":"On October 8, 2026 Anthropic announced commitments to the Genesis Mission, which it describes as a federal initiative to accelerate scientific and technological discovery through AI. The post says that over the next three years Anthropic will provide Claude, Claude Code, and API credits to Genesis Mission research projects, partner with agencies and national laboratories on science priorities including fusion energy and quantum computing, and provide training, onboarding, and technical support to scientists on those projects. It names NASA, the National Institutes of Health, and the National Science Foundation among participating agencies. These are commitments Anthropic describes in its own announcement; this item does not describe any government action.","keywords":"news policy 2026-10-08"},{"type":"news","slug":"anthropic-updates-usage-policy-2026","href":"/news/anthropic-updates-usage-policy-2026/","name":"Anthropic updates its Usage Policy, effective November 12, 2026","summary":"On October 8, 2026 Anthropic published a summary of updates to its Usage Policy, which it says take effect on November 12. The post says rules on deceptive activity that were previously spread across several sections are consolidated into a new section titled \"Do Not Engage in Deceptive Campaigns or Artificial Activity\", and that the elections section is renamed \"Do Not Undermine Democratic Processes\". It says the weapons prohibitions now explicitly include the software and components that make weapons work and actions such as arming drones and other autonomous vehicles, and it adds requirements for models connected to hardware that takes autonomous physical actions, including that a qualified operator can observe and stop the equipment. This item describes Anthropic's own policy for use of its models; it is not a government rule.","keywords":"news policy 2026-10-08"},{"type":"news","slug":"codex-cli-0-162-0-released","href":"/news/codex-cli-0-162-0-released/","name":"OpenAI releases Codex CLI 0.162.0 with managed Git worktree tools","summary":"On October 8, 2026 OpenAI published version 0.162.0 of its open-source Codex CLI on GitHub. The release notes add tools for creating and listing managed Git worktrees from trusted local projects when the worktrees feature is enabled, and add task pinning in the agent Command Center with the p key. They also say live web access and remote compaction can be configured for custom Responses-compatible model providers, and that a signed PowerShell installer is now published with Windows releases. USASI has not tested these changes.","keywords":"news release 2026-10-08"},{"type":"news","slug":"trl-1-15-0-released","href":"/news/trl-1-15-0-released/","name":"TRL 1.15.0 released with a fused LM head enabled by default","summary":"On October 8, 2026 Hugging Face published version 1.15.0 of TRL on GitHub. The release notes say the SFT, DPO, KTO, GRPO, RLOO and Distillation trainers now score tokens with a fused LM head, a Triton kernel that avoids building the full logits tensor, and that it is on by default; scoring this way needs Triton on a GPU. The notes list breaking changes: a PEFT adapter on lm_head now raises an error, and use_liger_kernel=True is deprecated in the GRPO, RLOO, DPO and KTO trainers and will be removed in v2.0.0. The release also adds selective activation checkpointing in SFT. USASI has not tested these changes.","keywords":"news release 2026-10-08"},{"type":"news","slug":"ai2-bolmo-research-published-in-nature","href":"/news/ai2-bolmo-research-published-in-nature/","name":"Ai2's Bolmo byte-level language model research is published in Nature, with new checkpoints","summary":"Ai2 said on October 7, 2026 that the research behind Bolmo, its family of fully open byte-level language models introduced the previous December, has been published in Nature. Ai2 writes that the paper applies the same \"byteifying\" process to Qwen 3 8B and Llama 3 8B to create two new byte-level models, Bwen 8B and Blama 8B, and that it is releasing new checkpoints, including Stage 1 checkpoints, on Hugging Face. The Bwen 8B model card describes it as Qwen3 8B Base retrofitted to operate over bytes instead of tokens and lists the Apache 2.0 license. USASI has not tested these models.","keywords":"news research 2026-10-07"},{"type":"news","slug":"perplexity-releases-pplx-embed-v2-late","href":"/news/perplexity-releases-pplx-embed-v2-late/","name":"Perplexity releases pplx-embed-v2-late multimodal late-interaction embedding models under MIT","summary":"Perplexity has published pplx-embed-v2-late, a family of multimodal late-interaction (ColBERT) retrievers for text, images, and visual documents built on Qwen3.5 with bidirectional attention, in 0.6B and 9B sizes on Hugging Face under the MIT license. The model card says both models produce one 128-dimensional vector per token, share an embedding space so the 0.6B model can query an index built with the 9B model, and were distilled from an internal 18B ColBERT teacher. The card's citation dates the accompanying Perplexity blog post October 7, 2026. Perplexity also published pplx-embed-v2-context-9b-preview, a contextual embedding model for document chunks that its card labels a preview whose weights and interface may change. USASI has not tested these models.","keywords":"news release 2026-10-07"},{"type":"news","slug":"waymo-publishes-av-incident-management-exercise-framework","href":"/news/waymo-publishes-av-incident-management-exercise-framework/","name":"Waymo announces a framework for incident management exercises for automated vehicle fleets","summary":"On October 7, 2026, Waymo announced a framework for planning, testing, and strengthening incident-management capabilities for automated vehicle fleets, set out in a paper its researchers posted on arXiv on September 4, 2026, \"On Being Prepared: Automated Vehicle Incident Management Exercise Practices.\" The framework adapts established emergency-management practice, including FEMA's Homeland Security Exercise and Evaluation Program, and describes four exercise categories, from consultations and workshops to full-scale exercises. Waymo says it provides a shared approach for AV developers, operational partners, first responders, and public-safety agencies.","keywords":"news research 2026-10-07"},{"type":"news","slug":"anthropic-releases-claude-haiku-5-5","href":"/news/anthropic-releases-claude-haiku-5-5/","name":"Anthropic releases Claude Haiku 5.5","summary":"On October 7, 2026 Anthropic announced Claude Haiku 5.5, a model it describes as designed for high-volume, cost-sensitive tasks such as summaries, classification requests, and database queries. The announcement gives the API model ID claude-haiku-5-5 and says the model is available on the Claude Platform, Amazon Web Services, Google Cloud, and Microsoft Azure, and it points to a Haiku 5.5 System Card for evaluation and safety details. The same post announces SDK updates that add beta support for computer use and browser use in Python and TypeScript. The announcement describes a hosted model; it does not describe a release of model weights.","keywords":"news release 2026-10-07"},{"type":"news","slug":"liquid-ai-releases-open-d1-decision-models","href":"/news/liquid-ai-releases-open-d1-decision-models/","name":"Liquid AI releases open-weight d1 decision models for text, vision, and audio","summary":"On October 7, 2026 Liquid AI announced \"Open d1\", two open-weight decision models that return an answer in a single forward pass instead of generating text. The announcement and model cards describe d1-3B, built on LFM2.5-VL-3B, which takes text and images, and d1-omni-600M, built on LFM2.5-Encoder-350M, which takes text with images or text with up to 30 seconds of speech. The weights are published on Hugging Face, and the announcement says the models have day-one support in llama.cpp. Both model cards give the license as \"other\" with the name lfm1.0, pointing to a LICENSE file in each repository; the announcement itself does not name a license.","keywords":"news release 2026-10-07"},{"type":"news","slug":"mlflow-3-17-0-released","href":"/news/mlflow-3-17-0-released/","name":"MLflow 3.17.0 released with fine-grained permissions and TypeSafe judge support","summary":"On October 7, 2026 the MLflow project published version 3.17.0 on GitHub. The release notes list \"Jev Decisions Across Evaluation, Gateway, and Tracing\", which lets TypeSafe models be used in built-in scorers and custom judges, and \"Fine-Grained Permissions for Shared Resources\", with wildcard grants and explicit DENY for contained resource types such as runs, traces, and versions. They also describe opt-in daily summaries for trace analytics. The notes say SQL-backed servers need a coordinated mlflow db upgrade with all writers stopped, and that mixed-version rolling upgrades are not supported. USASI has not tested these changes.","keywords":"news release 2026-10-07"},{"type":"news","slug":"codex-cli-0-161-0-released","href":"/news/codex-cli-0-161-0-released/","name":"OpenAI releases Codex CLI 0.161.0 with GPT-6.1 Sol as the default model","summary":"On October 7, 2026 OpenAI published version 0.161.0 of its open-source Codex CLI on GitHub. The release notes say GPT-6.1 Sol is now the default model in the bundled and Amazon Bedrock catalogs, and that Amazon Bedrock supports multi-agent V2 and Ultra reasoning on compatible models. The notes also add a /mcp login <name> command for signing in to MCP servers from an active terminal session, and make Daybreak opt-in through --enable cli_daybreak. USASI has not tested these changes.","keywords":"news release 2026-10-07"},{"type":"news","slug":"github-mcp-server-2-0-0-released","href":"/news/github-mcp-server-2-0-0-released/","name":"GitHub MCP Server 2.0.0 adds structured outputs and output schemas for newer MCP clients","summary":"GitHub published version 2.0.0 of the GitHub MCP Server on October 6, 2026. The release notes say it adds support for structured outputs and output schemas to help agents that use Code Mode or programmatic tool calling, and that these schemas are advertised only to clients that declare support for the MCP 2026-07-28 specification or later. According to the notes, the server's compound tools needed changes to their allowed output schemas, so those schemas are not returned to older clients. The listed changes add typed outputs, and for some tools typed inputs, across issue, pull request, repository, search, security alert, and Actions tools. USASI has not tested this release.","keywords":"news release 2026-10-06"},{"type":"news","slug":"ollama-0-40-0-runs-models-on-mlx-by-default","href":"/news/ollama-0-40-0-runs-models-on-mlx-by-default/","name":"Ollama 0.40.0 runs supported models on MLX by default on Apple Silicon","summary":"Ollama published version 0.40.0 on GitHub on October 6, 2026, after a series of release candidates that began with v0.40.0-rc0 on September 25. According to the release notes, model architectures supported by the MLX runtime now run on MLX automatically on Apple Silicon devices, with models listed including qwen3.8, gemma4, qwen3.6, and qwen3.5. The notes say decision models (Nimble, tev1, clef, and clef-flash) and the embeddinggemma-2 embedding model are also available on MLX, and that Ollama will continue testing and enabling additional models. USASI has not tested this release.","keywords":"news release 2026-10-06"},{"type":"news","slug":"diffusers-0-41-0-released","href":"/news/diffusers-0-41-0-released/","name":"Diffusers 0.41.0 adds a Qwen-Image 2.1 pipeline and deprecates ONNX support","summary":"On October 6, 2026 Hugging Face published Diffusers 0.41.0 on GitHub. The release notes say it adds Qwen-Image 2.1, with text-to-image generation, image editing, native transparency, and LoRA training, and introduces tensor-parallel checkpoint loading in which each rank reads its own slice of sharded weights. The notes deprecate ONNX support in favor of Optimum and remove previously deprecated APIs, including the LuminaText2ImgPipeline and Lumina2Text2ImgPipeline aliases. They also say minor releases will now be coordinated around new model integrations, as in Transformers. Qwen-Image 2.1 is a third-party model and is not a catalog entry; the notes do not state its license.","keywords":"news release 2026-10-06"},{"type":"news","slug":"google-releases-embeddinggemma-2","href":"/news/google-releases-embeddinggemma-2/","name":"Google releases EmbeddingGemma 2, an open-weight multimodal embedding model under Apache 2.0","summary":"On October 6, 2026 Google announced EmbeddingGemma 2, a multimodal embedding model built on the Gemma 4 architecture. The announcement says the model maps text, code, images, video, and audio into a shared embedding space, contains 740 million parameters, and produces 768-dimensional vectors that can be truncated to 512, 256, or 128 dimensions using Matryoshka Representation Learning. Google says the weights are released under the Apache 2.0 license and are available on Hugging Face and Kaggle, with on-device variants in the LiteRT Community on Hugging Face. USASI has a release record for the earlier EmbeddingGemma 300M; a record for this release has not yet been added.","keywords":"news release 2026-10-06"},{"type":"news","slug":"transformers-5-19-0-adds-embeddinggemma2","href":"/news/transformers-5-19-0-adds-embeddinggemma2/","name":"Transformers 5.19.0 adds the EmbeddingGemma2 multimodal embedding model","summary":"On October 6, 2026 Hugging Face published version 5.19.0 of its Transformers library on GitHub. The release notes add support for EmbeddingGemma2, which the notes describe as a multimodal embedding model from Google built on the Gemma 4 architecture that encodes text, images, audio, and video into a shared 768-dimensional vector space and uses Matryoshka Representation Learning so embeddings can be truncated to 512, 256, or 128 dimensions. The notes also list breaking changes: every mixture-of-experts model that computes router logits now returns them when output_router_logits=True, the \"paged|\" prefix for SDPA and flash attention implementations is deprecated in favor of continuous batching on the regular functions, and Owlv2ForObjectDetection.embed_image_query now selects the query box by objectness score. EmbeddingGemma2 itself is a Google model and is not a separate catalog entry; USASI has not tested these changes.","keywords":"news release 2026-10-06"},{"type":"news","slug":"arc-institute-releases-pie-perturbation-model","href":"/news/arc-institute-releases-pie-perturbation-model/","name":"Arc Institute releases PIE, a model for predicting cell responses to perturbations","summary":"On October 5, 2026, Arc Institute announced PIE (\"Perturbation is Everything\"), a model that predicts how cells respond to perturbations in contexts it has not seen by drawing on curated biological databases such as Cellosaurus, NCBI, Gene Ontology, DepMap, and PubChem. Arc says the full codebase, including training and evaluation pipelines, is on GitHub, with model checkpoints and processed datasets on Hugging Face and a bioRxiv preprint. The repository's README says PIE is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license, which allows non-commercial use with attribution. USASI has not tested PIE.","keywords":"news research 2026-10-05"},{"type":"news","slug":"google-deepmind-releases-alphaprotein-novo","href":"/news/google-deepmind-releases-alphaprotein-novo/","name":"Google DeepMind releases AlphaProtein Novo code and non-commercial generator weights","summary":"Google DeepMind published AlphaProtein Novo (AP Novo), a generative diffusion pipeline for de novo enzyme design through structural motif scaffolding, in a GitHub repository whose commits are dated from October 5, 2026. The README says the diffusion model co-generates protein structures and sequences conditioned on a catalytic motif and ligand context, and that it can be combined with LigandMPNN sequence redesign and AlphaFold 3 structure prediction; it points to a bioRxiv preprint describing the work. The software is licensed under Apache 2.0, but the generator weights are downloaded separately under terms of use, last modified October 5, 2026, that allow only non-commercial use by, or on behalf of, non-commercial organizations. USASI has not tested AlphaProtein Novo.","keywords":"news release 2026-10-05"},{"type":"news","slug":"reflection-ai-announces-beam-open-weight-model","href":"/news/reflection-ai-announces-beam-open-weight-model/","name":"Reflection AI announces Beam, a 501B-parameter model whose weights it plans to release","summary":"On October 5, 2026 Reflection AI introduced Beam, which it calls its first open-weight model: a sparse mixture-of-experts model with 501 billion total and 23 billion active parameters, built for coding, reasoning, and agentic workloads. The announcement says Beam was pretrained on 23.8 trillion tokens and that midtraining extends its effective context length to 1M tokens. As of the announcement, Beam was undergoing final red-teaming and evaluations and was offered through an early-access sign-up; Reflection says it will release the weights under an Apache 2.0 license later in October, along with a technical report, model card, and developer artifacts. The announcement itself does not provide the weights, and USASI has not tested Beam.","keywords":"news release 2026-10-05"},{"type":"news","slug":"vllm-0-31-0-released","href":"/news/vllm-0-31-0-released/","name":"vLLM 0.31.0 released with a preload command for faster engine restarts","summary":"On October 5, 2026 the vLLM project published version 0.31.0 on GitHub. The release notes highlight a new vllm preload command that launches a weight-cache daemon to keep post-quantized weights in GPU memory across engine restarts, and experimental engine snapshots (vllm snapshot create/restore) that use CRIU to restore a fully initialized TP1 engine. Under security, the notes say per-request mm_processor_kwargs and media_io_kwargs are now rejected unless --trust-request-mm-kwargs is set. The release also lists breaking changes, including the removal of tokenizer_mode=\"slow\", and performance work for DeepSeek-V4.1-Flash. USASI has not tested any of these changes.","keywords":"news release 2026-10-05"},{"type":"news","slug":"anduril-acquires-popoto-modem","href":"/news/anduril-acquires-popoto-modem/","name":"Anduril acquires Popoto Modem, an underwater acoustic communications company","summary":"Anduril announced on October 2, 2026 that it has acquired Popoto Modem, an underwater acoustics company based in Massachusetts. Anduril says Popoto Modem's acoustic communications technology enables persistent, multi-node mesh networking underwater, and that it has worked with the company for over a year, integrating its modems into Anduril maritime systems. Anduril's immediate priority is scaling integrations into its Copperhead family of autonomous underwater vehicles, and it says the acquisition lets it extend to subsea the integration its Lattice software provides for platforms above the waterline. The announcement does not disclose terms.","keywords":"news acquisition 2026-10-02"},{"type":"news","slug":"ray-2-59-0-released","href":"/news/ray-2-59-0-released/","name":"Ray 2.59.0 makes Ray Data LLM and Ray Serve LLM generally available","summary":"The Ray project published Ray 2.59.0 on GitHub on October 2, 2026. The release notes say the Ray Data LLM and Ray Serve LLM APIs graduate to general availability in this release, alongside an upgrade to vLLM 0.27.0. Ray now turns on token authentication by default for local clusters, while remote and multi-node clusters are unchanged in this release; setting RAY_AUTH_MODE=disabled opts out. The notes also list an external, disk-backed mode for the Ray Data shuffle_v2 backend and native ORC file reading. USASI has not tested this release.","keywords":"news release 2026-10-02"},{"type":"news","slug":"reka-releases-inverse-dynamics-model","href":"/news/reka-releases-inverse-dynamics-model/","name":"Reka releases the Reka Inverse Dynamics Model for labeling video with camera motion under Apache 2.0","summary":"Reka published the Reka Inverse Dynamics Model (RIDM) in a research post dated October 2, 2026. The model card describes two compact models, a flow model and a pixel model, that predict camera motion from video and were trained on game renders; Reka describes the aim as labeling real-world footage with actions for interactive world models. The weights and inference code are on Hugging Face under the Apache License 2.0, while the flow model relies on separately obtained RAFT-small optical-flow weights from torchvision. The model card labels the release as preliminary. USASI has not tested these models.","keywords":"news research 2026-10-02"},{"type":"news","slug":"sglang-0-5-21-released","href":"/news/sglang-0-5-21-released/","name":"SGLang 0.5.21 adds a Decisions API and runtime prefill-decode role switching","summary":"The SGLang project published v0.5.21 on GitHub on October 2, 2026. The release notes highlight prefill-decode instances that can switch between prefill and decode without a restart, a Rust core for the prefix cache by default, a new Decisions API (/v1/decisions) that lets an LLM or VLM act as a low-latency classifier and scorer, and a Score API (/v1/score) that scores all candidates in one request. Under breaking changes, request-supplied chat_template values are rejected unless the server starts with --trust-request-chat-template, and deprecated endpoints, environment variables, and aliases, along with legacy radix cache implementations, were removed. The release also adds support for models including DeepSeek-V4.1 Flash and MiMo-V2.6. USASI has not tested these changes.","keywords":"news release 2026-10-02"},{"type":"news","slug":"llama-cpp-server-adds-decision-model-support","href":"/news/llama-cpp-server-adds-decision-model-support/","name":"llama.cpp server adds support for decision models through a /v1/systemone endpoint","summary":"On October 2, 2026 the ggml team announced on the Hugging Face blog that the llama.cpp server now supports decision models through a new /v1/systemone endpoint. Decision models score the options a caller supplies instead of generating text. The pull request that adds the endpoint (#29818) was merged into the llama.cpp repository the same day; it describes these models as wrappers around existing embedding-model architectures and adds a decision head and server handling for them. The announcement lists six supported models (Julia-1, Laya, Kev-4B, lev, OpenJev, and Clef), while the pull request itself covers five of them and lists Clef support as a planned follow-up.","keywords":"news release 2026-10-02"},{"type":"news","slug":"ai2-open-sources-astabrief-8b","href":"/news/ai2-open-sources-astabrief-8b/","name":"Ai2 open-sources AstaBrief 8B, a model for writing cited scientific reports","summary":"On October 2, 2026 Ai2 announced that it is open-sourcing AstaBrief 8B, a model that turns a research question and retrieved literature excerpts into a cited report, along with its training data. Ai2 says the model was built from Qwen3-8B using supervised fine-tuning and direct preference optimization, and that it is available as Fast mode in the \"Generate a report\" feature of Asta, its platform for scientific work. The Hugging Face model card lists the Apache 2.0 license. Ai2 notes that most of the training and evaluation was completed in 2025 and that it has not rerun the full evaluation against today's frontier models.","keywords":"news release 2026-10-02"},{"type":"news","slug":"aws-releases-strands-decider-2b","href":"/news/aws-releases-strands-decider-2b/","name":"AWS releases Strands Decider 2B, an open-source decision model, under Apache 2.0","summary":"On October 1, 2026 the Strands team at AWS announced Strands Decider 2B, a 2-billion-parameter \"decision model\" that picks between supplied options or assigns a score with a confidence value instead of generating text. The announcement says the model starts from the Qwen3.5-2B model with its language-model head replaced by a small pointer head and the base fine-tuned with a LoRA adapter, and that it is meant for agent workflows such as those built with the Strands Harness SDK. The code, training data, and scripts are published on GitHub and the weights on Hugging Face; the repository and model card both list the Apache 2.0 license. AWS describes it as a Strands Labs project and says it is unsuited to text-generation tasks such as coding, chat, or summarization.","keywords":"news release 2026-10-01"},{"type":"news","slug":"microsoft-ai-releases-mai-transcribe-2-streaming-and-mai-voice-2-1","href":"/news/microsoft-ai-releases-mai-transcribe-2-streaming-and-mai-voice-2-1/","name":"Microsoft AI releases MAI-Transcribe-2-Streaming and MAI-Voice-2.1 speech models","summary":"Microsoft AI announced three speech models on October 1, 2026. MAI-Transcribe-2-Streaming is a real-time transcription model that Microsoft says covers 60 languages with automatic, continuous language detection. MAI-Voice-2.1 is a multilingual text-to-speech model that Microsoft says supports 23 languages and 26 locales, and MAI-Voice-2.1-Flash is described as a faster variant with the same languages and features. Microsoft lists Microsoft Foundry, MAI Playground, and Vercel among the places all three can be used. The announcement does not mention published weights or a license.","keywords":"news release 2026-10-01"},{"type":"news","slug":"ai2-releases-olmo-core-3","href":"/news/ai2-releases-olmo-core-3/","name":"Ai2 releases OLMo-core 3, its open training library, with mixture-of-experts support","summary":"Ai2 released version 3 of OLMo-core, the PyTorch training library behind its Olmo models, on October 1, 2026. The release redesigns the library's mixture-of-experts training system, adding expert and pipeline parallelism, a distributed optimizer, grouped matrix multiplication for experts, and support for the MXFP8 number format; Ai2 says it is designed to scale mixture-of-experts training into the trillion-parameter range. The code is licensed under Apache 2.0, and the repository includes Ai2's official training scripts for Olmo 3.","keywords":"news release 2026-10-01"},{"type":"news","slug":"cloudflare-releases-clef-decision-models","href":"/news/cloudflare-releases-clef-decision-models/","name":"Cloudflare releases Clef and Clef-flash decision models under Apache 2.0","summary":"On October 1, 2026, Cloudflare released Clef and Clef-flash, the first models trained by its Workers AI team. They are decision models: given a state and a set of typed questions, they return a probability for each allowed answer, so an agent can classify or route a task. Clef is post-trained from Qwen3.8-27B and Clef-flash from Qwen3.5-9B. The weights are on Hugging Face under the Apache 2.0 license, and both models are also hosted on Workers AI. Cloudflare also announced reinforcement-learning fine-tuning of Clef for customers, offered for now as a hands-on service with its forward-deployed engineers.","keywords":"news release 2026-10-01"},{"type":"news","slug":"amazon-releases-alodlm-diffusion-language-models","href":"/news/amazon-releases-alodlm-diffusion-language-models/","name":"Amazon releases ALoDLM-1.7B and ALoDLM-8B diffusion language models for non-commercial research","summary":"Amazon has published ALoDLM-1.7B and ALoDLM-8B on Hugging Face, diffusion language models initialized from the corresponding Qwen3 backbones that generate text and code by repeatedly applying shared transformer layers and adaptively allocating computation across token positions. The model card says the weights are released under the Creative Commons Attribution-NonCommercial 4.0 license for non-commercial research, with code in the amazon-science/ALoDLM repository. The accompanying paper, \"ALoDLM: Adaptively Looped Diffusion Language Models,\" was submitted to arXiv on October 3, 2026. USASI has not tested either model.","keywords":"news research 2026-10"},{"type":"news","slug":"perplexity-releases-pplx-decider-v1-27b","href":"/news/perplexity-releases-pplx-decider-v1-27b/","name":"Perplexity releases pplx-decider open-weight decision models under Apache 2.0","summary":"In October 2026 Perplexity published pplx-decider-v1-27b on Hugging Face, a decision model fine-tuned from Qwen3.8-27B that returns a selected choice with calibrated probabilities for text and image inputs; the weights are ungated and licensed under Apache 2.0. Perplexity's API changelog says its new Decisions API answers questions about text, JSON, or images with probabilities instead of generated text, using pplx-decider-v1-27b at POST /v1/decisions. A later changelog entry says pplx-decider-v1.1-27b replaces v1 as the Decisions API model; its model card says the update uses the same backbone, gets most of its gains from lifting the causal mask and training on more data, and ships as a separate decision head rather than a full-vocabulary language-model head. USASI has not tested these models.","keywords":"news release 2026-10"},{"type":"news","slug":"google-deepmind-releases-synthid-bio","href":"/news/google-deepmind-releases-synthid-bio/","name":"Google DeepMind introduces SynthID Bio and open-sources code for watermarking AI-designed proteins","summary":"On September 30, 2026 Google DeepMind introduced SynthID Bio, a family of watermarking methods for AI-generated protein sequences and 3D structures, which it describes as a proof of concept. The repository README says SynthID Bio-sequence adds watermarked sampling to ProteinMPNN, and the announcement says SynthID Bio-structure fine-tunes a small part of AlphaFold 3's diffusion network so that predicted coordinates carry a detectable signature. Google DeepMind says it is publishing a methods paper, open-sourcing the code and in vitro data, and releasing weights to the research community. In the public repository, the software is licensed under Apache 2.0, the in vitro validation data supporting the paper is licensed under CC BY 4.0, and AlphaFold 3 model parameters remain under the AlphaFold 3 terms of use. USASI has not tested SynthID Bio.","keywords":"news research 2026-09-30"},{"type":"news","slug":"metr-testifies-to-senate-on-ai-agent-incidents","href":"/news/metr-testifies-to-senate-on-ai-agent-incidents/","name":"METR's president testifies to a Senate subcommittee on AI agent incidents","summary":"On September 30, 2026, METR President Chris Painter testified before the Senate Homeland Security and Governmental Affairs Subcommittee on Disaster Management, District of Columbia, and Census at a hearing titled \"Rogue AI: Securing the Homeland Against AI Agent Attacks.\" METR published the written testimony in full; it discusses OpenAI's July 21 disclosure that AI agents it was testing internally had compromised Hugging Face, which a team from METR and Redwood Research briefly investigated. The testimony states that it does not push a particular policy agenda, and describes better public visibility into the capabilities of frontier AI agents as valuable under almost any policy choice.","keywords":"news policy 2026-09-30"},{"type":"news","slug":"google-announces-gemini-4-argon","href":"/news/google-announces-gemini-4-argon/","name":"Google announces Gemini 4 Argon, starting with a limited rollout to cyber defenders","summary":"Google announced Gemini 4 Argon on September 30, 2026, describing it as a model for real-world coding, enterprise knowledge work, and cyber defense. Google says the model is rolling out first to a set of trusted cyber defenders through its Fairwind Program, and that it will make Argon available to developers, enterprises, and consumers later; the announcement gives no date for that wider availability. Google says the model's output token limit is 1 million tokens, up from 64K. The announcement does not mention published weights or a license.","keywords":"news release 2026-09-30"},{"type":"news","slug":"coreweave-launches-forge-with-weights-and-biases","href":"/news/coreweave-launches-forge-with-weights-and-biases/","name":"CoreWeave launches Forge, bringing Weights & Biases Models into one AI development platform","summary":"CoreWeave announced CoreWeave Forge on September 30, 2026, a development platform that brings training, inference, evaluation, and agent development into one environment. Forge includes Weights & Biases Models for tracking and comparing experiments. Weights & Biases, which CoreWeave acquired in 2025, says W&B software-as-a-service accounts now sign in through CoreWeave Forge and that self-hosted and dedicated deployments are unchanged. The open-source wandb client library remains available under the MIT License.","keywords":"news release 2026-09-30"},{"type":"news","slug":"llm-d-0-10-0-released","href":"/news/llm-d-0-10-0-released/","name":"llm-d 0.10.0 focuses on operational hardening and deprecates its own CUDA and AWS images","summary":"The llm-d project published version 0.10.0 on GitHub on September 29, 2026. The release notes name operational hardening and production readiness as the main themes and describe a new image build arrangement: images from main and official releases go to ghcr.io and are signed publicly with cosign, while images built from open pull requests go to quay.io and are not signed. The notes deprecate the ghcr.io/llm-d/llm-d-cuda image in favor of docker.io/vllm/vllm-openai and the llm-d-aws image in favor of an AWS vLLM container, and say the llmd-fs-connector has been integrated into vLLM as the OffloadingConnector. The llm-d-workload-variant-autoscaler repository has been renamed llm-d-autoscaling. USASI has not tested this release.","keywords":"news release 2026-09-29"},{"type":"news","slug":"openai-releases-mcp-extensions","href":"/news/openai-releases-mcp-extensions/","name":"OpenAI publishes MCP Extensions, a specification and SDKs for ChatGPT plugins, under Apache 2.0","summary":"On September 29, 2026 OpenAI published the first releases, version 0.1.0, of the Python and Node SDKs for OpenAI MCP Extensions, a project that adds ChatGPT-specific capabilities to the Model Context Protocol so that plugins can work like native ChatGPT features. The repository's README highlights sidebar entrypoints, file extension handlers, composer mentions, and extended forms, and says the project is licensed under Apache 2.0. Version 0.2.0, released on October 10, 2026, adds editable chat drafts and forms that continue across multiple exchanges, and migrates the TypeScript server helpers to MCP SDK 2. The extensions take effect in ChatGPT, OpenAI's hosted product. USASI has not tested the SDKs.","keywords":"news release 2026-09-29"},{"type":"news","slug":"salesforce-agrees-to-acquire-listen-labs","href":"/news/salesforce-agrees-to-acquire-listen-labs/","name":"Salesforce signs a definitive agreement to acquire Listen Labs; the deal has not yet closed","summary":"On September 29, 2026 Salesforce announced a definitive agreement to acquire Listen Labs, an AI-powered customer research and human simulation platform whose AI agents design studies, conduct research, and synthesize insights. Salesforce says the deal would complement Marketing Cloud, Service Cloud, and its wider AI portfolio. Closing is expected in the fourth quarter of Salesforce's fiscal year 2027, subject to customary closing conditions including required regulatory clearance. The announcement does not disclose financial terms.","keywords":"news acquisition 2026-09-29"},{"type":"news","slug":"microsoft-releases-rho-robot-models","href":"/news/microsoft-releases-rho-robot-models/","name":"Microsoft Research publishes Rho, open-weight vision-language-action models for robots","summary":"Microsoft Research published the technical report for Rho on September 29, 2026. Rho is a family of vision-language-action models of about 5 billion parameters for two-armed robot manipulation, combining a Phi-family vision-language backbone with an action module. The models include a base model and checkpoints adapted to three robot platforms (I2RT YAM Box, Universal Robots UR AI Trainer, and Franka FR3 Duo). Their weights are on Hugging Face and the fine-tuning and adaptation code is on GitHub, both under the MIT license.","keywords":"news research 2026-09-29"},{"type":"news","slug":"nist-renames-ai-center-caissi","href":"/news/nist-renames-ai-center-caissi/","name":"NIST adopts \"super intelligence\" wording and introduces its CAISSI center page","summary":"NIST says it is updating its communications to use the term \"super intelligence,\" as directed by a September 29, 2026 executive order titled \"Inaugurating the Era of Super Intelligence.\" As of October 1, 2026, NIST's center page describes the Center for Advancing Innovation and Standards for Super Intelligence (CAISSI), while NIST news items published through September refer to the Center for AI Standards and Innovation (CAISI). USASI is an independent project and is not affiliated with NIST or any government agency.","keywords":"news policy 2026-09-29"},{"type":"news","slug":"nvidia-releases-kumo-tabular","href":"/news/nvidia-releases-kumo-tabular/","name":"NVIDIA releases Kumo Tabular, an open-weight model for predictions on tables","summary":"NVIDIA published Kumo Tabular on September 29, 2026: a pretrained model for classification and regression on tabular data that takes labeled example rows as context and predicts values for new rows without task-specific training. It comes in three sizes, from 28 million to 215 million parameters, and NVIDIA says it was pretrained entirely on artificial tables. The weights are on Hugging Face under the OpenMDW-1.1 license, and inference runs through NVIDIA's open-source structured-data-models library.","keywords":"news release 2026-09-29"},{"type":"news","slug":"openai-releases-gpt-6-1-sol-in-api","href":"/news/openai-releases-gpt-6-1-sol-in-api/","name":"OpenAI adds GPT-6.1 Sol to its API","summary":"OpenAI's API changelog lists GPT-6.1 Sol (model ID gpt-6.1-sol) as released on September 29, 2026, describing it as a model for complex coding and professional work at a lower cost than GPT-6 Astra. The changelog says it is available through the Responses and Chat Completions endpoints. Separate changelog entries on the same date add computer use to the Agents API and an Ultrafast mode for GPT-6 Astra in the Responses API. GPT-6.1 Sol is a hosted model; OpenAI has not published its weights.","keywords":"news release 2026-09-29"},{"type":"news","slug":"hugging-face-adds-rl-environments-to-the-hub","href":"/news/hugging-face-adds-rl-environments-to-the-hub/","name":"Hugging Face adds reinforcement learning environments to the Hub as tagged dataset repositories","summary":"Hugging Face announced RL Environments on the Hub on September 28, 2026. The post says there is no new repository type, registry, or sign-up: an environment is published as a dataset repository with the rl-environment tag plus a tag for each framework that can load its files, and the Hub offers an RL Environments filter. The post says four environment frameworks are registered as dataset libraries, each with its own tag: Harbor, Verifiers, OpenEnv, and NeMo Gym. The \"Use this dataset\" button provides a command to run an environment in a given framework, and frameworks run environments locally or on a supported cloud backend such as Hugging Face Jobs. The post says adding a tag does not start a job or sandbox. USASI has not tested this feature.","keywords":"news release 2026-09-28"},{"type":"news","slug":"nvidia-launches-open-agent-safety-platform","href":"/news/nvidia-launches-open-agent-safety-platform/","name":"NVIDIA launches Open Agent Safety Platform, pairing open-source OpenShell with a Sentry reference design","summary":"NVIDIA announced the NVIDIA Open Agent Safety Platform on September 28, 2026, describing it as an open software platform and reference system design to strengthen AI security from agent testing to deployment. It consists of NVIDIA OpenShell, open-source runtime software that traces agent actions and enforces policy, and NVIDIA Sentry, a reference design for an out-of-band watchdog that runs on BlueField-4 DPUs and can quarantine agents that attempt to move outside their boundaries. NVIDIA says the platform software, including OpenShell and skills, is available through its developer resources page and GitHub, and describes Sentry as built on NVIDIA DOCA software. USASI has not tested OpenShell.","keywords":"news release 2026-09-28"},{"type":"news","slug":"ollama-0-35-0-adds-decision-models","href":"/news/ollama-0-35-0-adds-decision-models/","name":"Ollama 0.35.0 adds decision models through a /v1/systemone endpoint","summary":"Ollama published version 0.35.0 on GitHub on September 28, 2026. The release notes say Ollama now supports decision models through /v1/systemone, based on TypeSafe's Jev API, and that decision models return choices, probabilities, and scores instead of text. The models listed as available are Nimble from Bespoke Labs and Tev1 from Together AI, and the API supports three question types: choice, noul, and score. USASI has not tested this feature.","keywords":"news release 2026-09-28"},{"type":"news","slug":"amd-agrees-to-acquire-world-labs","href":"/news/amd-agrees-to-acquire-world-labs/","name":"AMD agrees to acquire World Labs; the deal has not yet closed","summary":"AMD announced on September 28, 2026, that it has entered into a definitive agreement to acquire World Labs, the AI model and research lab that makes the Marble world-model product and the open-source Spark renderer. AMD says the transaction is expected to close by the end of 2026, subject to regulatory approvals and other customary closing conditions. Until it closes, World Labs is listed as an independent company.","keywords":"news acquisition 2026-09-28"},{"type":"news","slug":"anthropic-releases-claude-sonnet-5-5","href":"/news/anthropic-releases-claude-sonnet-5-5/","name":"Anthropic releases Claude Sonnet 5.5","summary":"Anthropic announced Claude Sonnet 5.5 on September 28, 2026, describing it as a faster, lower-cost complement to Claude Opus 5.5. Anthropic says it is available in the Claude apps and on its platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure. Anthropic's model documentation lists the API identifier claude-sonnet-5-5 with a 1M-token context window. It is a hosted model; Anthropic has not published its weights.","keywords":"news release 2026-09-28"},{"type":"news","slug":"mlperf-training-adds-llm-post-training-benchmark","href":"/news/mlperf-training-adds-llm-post-training-benchmark/","name":"MLPerf Training adds an LLM post-training benchmark, starting with round v6.1","summary":"MLCommons announced on September 24, 2026 that MLPerf Training is adding an LLM post-training benchmark, beginning with the v6.1 submission round in October 2026. The benchmark reports the wall-clock time a system needs to reach a quality target when post-training Qwen3.5-397B-A17B with reinforcement learning with verifiable rewards, using Group Relative Policy Optimization, on Python software engineering problems from the R2E-Gym dataset. Agents work in sandboxed Linux containers, and the specification and reference implementation are in the mlcommons/training repository.","keywords":"news release 2026-09-24"},{"type":"news","slug":"databricks-acquires-row-zero","href":"/news/databricks-acquires-row-zero/","name":"Databricks acquires Row Zero, the maker of a spreadsheet for large datasets","summary":"Databricks announced on September 24, 2026, that it has acquired Row Zero, which makes a spreadsheet application designed to work with very large datasets and to be used by people and AI agents together. Databricks says Row Zero will be integrated natively with its data and AI platform, including its Genie assistant, building on components such as Unity Catalog, and that it will continue to support data sources outside Databricks. The announcement does not disclose financial terms.","keywords":"news acquisition 2026-09-24"},{"type":"news","slug":"liquid-ai-releases-lfm2-5-vl-3b-dspark","href":"/news/liquid-ai-releases-lfm2-5-vl-3b-dspark/","name":"Liquid AI releases LFM2.5-VL-3B-DSpark, a draft model for faster vision-language decoding","summary":"Liquid AI announced LFM2.5-VL-3B-DSpark on September 24, 2026. It is an experimental draft model of about 280 million parameters that speeds up text generation by its LFM2.5-VL-3B vision-language model through speculative decoding; Liquid AI says the target model's output is unchanged. The weights are on Hugging Face in Safetensors and GGUF formats, with support in SGLang, MLX-VLM, and llama.cpp. They are released under the LFM Open License v1.0, the same license as other LFM2.5 releases, which limits commercial use by larger companies through an annual-revenue threshold.","keywords":"news release 2026-09-24"},{"type":"news","slug":"lemonade-2026-39-1-adopts-calendar-versioning","href":"/news/lemonade-2026-39-1-adopts-calendar-versioning/","name":"Lemonade 2026.39.1 switches to calendar-based version numbers and adds image editing","summary":"The Lemonade project published Lemonade Server version 2026.39.1 on GitHub on September 23, 2026, three weeks after version 11.9.0. The release notes list a breaking change: the version format changed from X.Y.Z to YYYY.WW.N, so tools and scripts that parse version output or compare version strings must be updated. The release updates TheNoise to 0.7.1 with support for image editing and additional AMD GPUs, adds a /v1/docs endpoint for API reference documentation, and makes VRAM auto-eviction configurable. USASI has not tested this release.","keywords":"news release 2026-09-23"},{"type":"news","slug":"together-ai-publishes-tev1-4b-experimental","href":"/news/together-ai-publishes-tev1-4b-experimental/","name":"Together AI publishes Tev1-4B-experimental, a decision model whose weights license is not yet final","summary":"Together AI published Tev1-4B-experimental on September 23, 2026: an experimental 4B decision model, fine-tuned from Qwen3.5-4B, that is trained to choose one option from a structured state, question and list of choices. Together AI's blog post says the model is available on its serverless platform and walks through fine-tuning a similar classifier with code from the togethercomputer/tev1 GitHub repository. The weights are posted on Hugging Face, but the model card says the release license for the fine-tuned weights is still being finalized, while the base Qwen3.5-4B model is Apache-2.0. The card also says prompt injection, multilingual behavior and calibration have not been comprehensively evaluated. USASI has not tested the model.","keywords":"news release 2026-09-23"},{"type":"news","slug":"nvidia-releases-nemotron-3-diarization","href":"/news/nvidia-releases-nemotron-3-diarization/","name":"NVIDIA releases Nemotron 3 Diarization, an open-weight speaker diarization model","summary":"NVIDIA released Nemotron 3 Diarization on September 23, 2026. The model identifies which speaker is talking at each point in an audio recording, for up to eight speakers, and can run on streaming audio as well as complete recordings. It is a Transformer encoder with about 100 million parameters, built with NVIDIA's NeMo speech tools. The weights are on Hugging Face under the OpenMDW License Agreement, version 1.1, and the model card says they are ready for commercial and non-commercial use.","keywords":"news release 2026-09-23"},{"type":"news","slug":"anthropic-releases-claude-opus-5-5","href":"/news/anthropic-releases-claude-opus-5-5/","name":"Anthropic releases Claude Opus 5.5, the first model in its Claude 5.5 family","summary":"On September 22, 2026 Anthropic introduced Claude Opus 5.5, which it calls the first model in its new Claude 5.5 family, and said it was available on all platforms with the API model ID claude-opus-5-5. Anthropic says external evaluators, including Frontier Design and METR, tested the model before release, and that because Opus 5.5 is comparable to Claude Mythos 5.1 in biology and cybersecurity, it is deployed with safeguards similar to those on Claude Fable 5.1. The release notes say thinking cannot be disabled on Opus 5.5 and that the tool_choice types any and tool return a 400 error. Anthropic later released Claude Sonnet 5.5 and Claude Haiku 5.5, which USASI covered separately. Opus 5.5 is a hosted model; no release of weights is described. USASI has not tested it.","keywords":"news release 2026-09-22"},{"type":"news","slug":"cais-and-scale-ai-release-hle-diamond","href":"/news/cais-and-scale-ai-release-hle-diamond/","name":"CAIS and Scale AI release HLE-Diamond, a refined 1,000-question subset of Humanity's Last Exam","summary":"On September 22, 2026, the Center for AI Safety and Scale AI released HLE-Diamond, which the release notes describe as a refined subset of the Humanity's Last Exam (HLE) question collection, produced through a year-long process of cleaning and refinement with input from research communities. HLE-Diamond has 1,000 questions, split evenly into 500 reasoning and 500 knowledge questions, and is designed to be answered closed-book, with separate recommended settings for evaluations that use tools. The dataset is on Hugging Face as cais/hle-diamond, where the card lists an MIT license, requires agreeing to share contact information before access, and asks that the dataset not be publicly shared or re-uploaded.","keywords":"news release 2026-09-22"},{"type":"news","slug":"metr-summarizes-predeployment-evaluation-of-claude-opus-5-5","href":"/news/metr-summarizes-predeployment-evaluation-of-claude-opus-5-5/","name":"METR publishes a summary of its predeployment evaluation of Claude Opus 5.5","summary":"On September 22, 2026, METR published a preliminary summary of its predeployment evaluation of Anthropic's Claude Opus 5.5, focused on the model's potential effect on AI research and development. METR says it tested the model through API access over 10 business days on five tasks; it drafted the summary, Anthropic could review and edit it, and the final text also appears in the Claude Opus 5.5 system card. METR concludes that the model is unlikely to be able to fully automate AI R&D, but expects it to noticeably accelerate researchers and automate limited aspects of R&D. METR says the work was not meant to verify compliance with any threshold in Anthropic's policies and did not assess alignment properties.","keywords":"news research 2026-09-22"},{"type":"news","slug":"openai-releases-gpt-6-sol-and-gpt-6-luna","href":"/news/openai-releases-gpt-6-sol-and-gpt-6-luna/","name":"OpenAI releases GPT-6 Sol and GPT-6 Luna in its API","summary":"On September 22, 2026 OpenAI released GPT-6 Sol (gpt-6-sol) and GPT-6 Luna (gpt-6-luna), according to its API changelog, which describes them as reasoning models that accept text and image inputs and generate text through the Responses and Chat Completions APIs. OpenAI's announcement, as listed in its news feed, presents them as two models with different balances of capability and cost. In a separate changelog entry dated September 25, OpenAI says it fixed a bug in image encoding that degraded image understanding in both models, and recommends rerunning evaluations for use cases with image inputs. Both are hosted models; no release of weights is described. USASI has not tested them.","keywords":"news release 2026-09-22"},{"type":"news","slug":"nvidia-dynamo-1-5-0-released","href":"/news/nvidia-dynamo-1-5-0-released/","name":"NVIDIA Dynamo 1.5.0 adds custom worker-selection policies and deprecates KVBM","summary":"NVIDIA published Dynamo v1.5.0, a feature release of its open-source distributed inference platform, on GitHub on September 21, 2026. The release notes say worker selection in Dynamo Router is now pluggable through custom scoring and picking policies, and that KV indexing extends to Mooncake and disk tiers. KVBM is deprecated, with removal targeted for v1.7.0. On Kubernetes, nvidia.com/v1beta1 becomes the CRD storage version, the Rust Endpoint Picker replaces the Go version, and Dynamo Snapshot moves to a standalone operator. The notes list SGLang v0.5.18, TensorRT-LLM v1.3.0rc25 and vLLM v0.28.0 as the engine versions. USASI has not tested this release.","keywords":"news release 2026-09-21"},{"type":"news","slug":"strands-agents-releases-strands-harness","href":"/news/strands-agents-releases-strands-harness/","name":"Strands Agents releases Strands harness, an Apache 2.0 general-purpose agent harness","summary":"On September 21, 2026 the Strands Agents project released Strands harness, which it describes as a fully assembled agent harness that can run locally or be deployed to a chosen provider, built as a general-purpose agent rather than a coding agent. It is available under the Apache 2.0 license, is built on the Strands Harness SDK, and can be used from Python or TypeScript. The post says its default context management truncates tool results over about 1,500 tokens and compacts the context when the context window is more than 85 percent full, and that it runs on models from Amazon Bedrock, Anthropic, OpenAI, and Google as well as local Ollama models. USASI has not tested Strands harness.","keywords":"news release 2026-09-21"},{"type":"news","slug":"xai-releases-grok-4-7","href":"/news/xai-releases-grok-4-7/","name":"xAI makes Grok 4.7 available on its API","summary":"xAI's API release notes record that on September 21, 2026 Grok 4.7, which the notes describe as SpaceXSI's frontier model for coding, agentic tasks, and knowledge work, became available on the xAI API as grok-4.7. The entry says the model has a 500k context window, accepts text and image inputs with text-only output, supports low, medium, high (default), and xhigh reasoning effort, and is also served on the US regional endpoint. It adds that Grok 4.7 Fast, the same model, is available only in Cursor and Grok Build, not on the public xAI API. Grok 4.7 follows Grok 4.6, which the release notes list on August 12. These are hosted models; no release of weights is described. USASI has not tested Grok 4.7.","keywords":"news release 2026-09-21"},{"type":"news","slug":"character-ai-team-expected-to-join-disney","href":"/news/character-ai-team-expected-to-join-disney/","name":"Members of Character.AI's technical team are expected to join Disney","summary":"On September 18, 2026, Character.AI and The Walt Disney Company announced that members of Character.AI's technical team are expected to join Disney. Neither announcement describes an acquisition or a licensing agreement, and Character.AI said its app remains live. The move has only been announced, and the Character.AI record still lists the company as privately held with no parent organization.","keywords":"news governance 2026-09-18"},{"type":"news","slug":"figure-announces-helix-2-5","href":"/news/figure-announces-helix-2-5/","name":"Figure announces Helix 2.5, a humanoid control model pretrained on its Index dataset","summary":"On September 17, 2026, Figure announced Helix 2.5, a neural network for whole-body control of a humanoid robot on long tasks in homes it has not visited. Figure says Helix 2.5 was pretrained from random initialization entirely on Index, its dataset of human behavior, and that a single base model was then adapted to tidying living rooms, folding towels, and making beds. Figure tested these behaviors in 30 Bay Area homes in which it says no data had been collected. The announcement does not say whether Helix 2.5's weights or code will be released.","keywords":"news research 2026-09-17"},{"type":"news","slug":"modular-26-6-opens-mojo-compiler-to-contributions","href":"/news/modular-26-6-opens-mojo-compiler-to-contributions/","name":"Modular 26.6 and Mojo 1.1 open the Mojo compiler to outside contributions","summary":"Modular released MAX 26.6 and Mojo 1.1 on September 17, 2026, and said it is now accepting contributions to the Mojo compiler, which it open-sourced under the Apache 2.0 license at ModCon in August. Modular also says it has migrated its internal issues to public GitHub issues. The release notes say Mojo 1.1 marks more standard library APIs as stable, including String, SIMD and List, and removes deprecated items such as the fn and alias keywords. MAX 26.6 adds an audio generation pipeline that launches with support for MiniMax-Music3, and support for Thinking Machines' Inkling architecture. USASI has not tested this release.","keywords":"news release 2026-09-17"},{"type":"news","slug":"caisi-assesses-glm-5-3-cyber-capabilities","href":"/news/caisi-assesses-glm-5-3-cyber-capabilities/","name":"NIST's CAISI publishes an assessment of Z.ai's GLM-5.3 cyber capabilities","summary":"On September 17, 2026, NIST's Center for AI Standards and Innovation (CAISI) published an assessment of the cyber capabilities of GLM-5.3, a model from the PRC-based company Z.ai, which released it in August 2026 and made its weights public two weeks later. CAISI ran the model as an agent with bash and Python tools on four benchmarks covering vulnerability discovery and exploit development, including its private CAISI OSS-Fuzz benchmark, and tested U.S. models with cyber safeguards disabled for comparison. The assessment compares GLM-5.3 with the U.S. and PRC models CAISI has evaluated, using per-benchmark results and an aggregate cyber capability index based on Item Response Theory, and notes that it does not compare against models that have been developed but not yet released.","keywords":"news research 2026-09-17"},{"type":"news","slug":"intel-releases-openvino-2026-4","href":"/news/intel-releases-openvino-2026-4/","name":"Intel releases OpenVINO 2026.4 with multi-token prediction decoding in OpenVINO GenAI","summary":"Intel published OpenVINO 2026.4.0 on GitHub on September 16, 2026. The release notes say OpenVINO GenAI adds Multi-Token Prediction (MTP) speculative decoding for Gemma 4, Qwen3.5 and Qwen3.6 on CPUs and GPUs, and introduces DFlash acceleration for Qwen models on GPUs as a preview. Newly supported models on CPU and GPU include Kokoro-82M, Qwen3-ASR, Muse Glimmer 30B, Qwen3.8 27B and Gemma 4 12B. OpenVINO GenAI also adds an ASRPipeline for Node.js, and the notes say support for Python 3.10 will be discontinued in OpenVINO 2026.5. USASI has not tested this release.","keywords":"news release 2026-09-16"},{"type":"news","slug":"isaac-lab-3-0-early-access-released","href":"/news/isaac-lab-3-0-early-access-released/","name":"Isaac Lab 3.0 Early Access published with multi-backend physics and kit-less workflows","summary":"The Isaac Lab project published Isaac Lab 3.0 Early Access on GitHub on September 16, 2026. The release notes describe a multi-backend architecture in which supported tasks can switch physics, rendering or visualization backends without rewriting the environment, and say many workflows can now run without installing or launching Isaac Sim, for example with Newton physics. The release is built for Isaac Sim 6.1, Python 3.12, PyTorch 2.11, NVIDIA Warp 1.16 and Newton 1.5.2. Between Early Access and General Availability, which is targeted toward the end of October 2026, the release branch will focus only on bug fixes, stability, compatibility and documentation improvements. USASI has not tested this release.","keywords":"news release 2026-09-16"},{"type":"news","slug":"mlcommons-publishes-mlperf-inference-v6-1","href":"/news/mlcommons-publishes-mlperf-inference-v6-1/","name":"MLCommons publishes MLPerf Inference v6.1 results with two new tests","summary":"MLCommons published results for version 6.1 of the MLPerf Inference benchmark suite on September 16, 2026. The round added two tests: an end-to-end retrieval-augmented generation test that runs a question-answering pipeline of several separate models, and an Edge Agentic Inference test built around multi-turn workloads such as agentic coding. Speculative decoding is now allowed in the interactive scenario for two of the inference benchmarks. Results are published on MLCommons' datacenter and edge results pages.","keywords":"news release 2026-09-16"},{"type":"news","slug":"agility-robotics-unveils-digit-5","href":"/news/agility-robotics-unveils-digit-5/","name":"Agility Robotics unveils Digit 5, a humanoid robot designed to work near people","summary":"Agility Robotics unveiled Digit 5, its next-generation humanoid robot, on September 15, 2026. Agility says Digit 5 continuously monitors for people and responds by autonomously avoiding, stopping, or assuming a seated position, with an independent safety controller overseeing those responses, so that it can work in close proximity to people without the physical safety barriers required by traditional automation. The company expects early access to begin in the first half of 2027 and general availability for manufacturing, warehouse, and distribution operators by the end of 2027. The press release carries forward-looking-statement disclosures for Agility's proposed business combination with Churchill Capital Corp XI.","keywords":"news release 2026-09-15"},{"type":"news","slug":"salesforce-announces-koa-crm-reasoning-model","href":"/news/salesforce-announces-koa-crm-reasoning-model/","name":"Salesforce and NVIDIA announce Koa, a CRM reasoning model post-trained from NVIDIA Nemotron 3 Super","summary":"On September 15, 2026 Salesforce and NVIDIA announced Koa, which it calls its first CRM reasoning model, built to help Agentforce agents reason through multistep workflows and use the right tools. Salesforce says Koa is a post-trained version of NVIDIA Nemotron 3 Super, trained with supervised fine-tuning and reinforcement learning on a proprietary synthetic dataset, and that no customer data was used in training. Koa is available to select pilot customers in Agentforce, with general availability expected in winter 2026 in U.S. regions. Salesforce says it controls the model weights and runs Koa within its own infrastructure as a Salesforce-hosted option; the announcement does not say that the weights will be released. USASI has not tested Koa.","keywords":"news release 2026-09-15"},{"type":"news","slug":"cline-introduces-cline-desktop","href":"/news/cline-introduces-cline-desktop/","name":"Cline introduces Cline Desktop, an early desktop app for running Cline agent sessions","summary":"Cline introduced Cline Desktop on September 14, 2026, describing it as an early version of its open-source app for open-weight models. The post says the app is a workspace for running agent sessions in parallel, scheduling recurring tasks, and extending agents through a Marketplace, and that it uses the same Cline harness, rebuilt around the Cline SDK. The beta is offered for Mac and Windows; the post says the Mac app is fully open source, like the extension and CLI. Sessions can be started in the CLI or extension and continued in the desktop app. USASI has not tested this app.","keywords":"news release 2026-09-14"},{"type":"news","slug":"microsoft-ai-opens-consultation-on-mai-code-of-conduct","href":"/news/microsoft-ai-opens-consultation-on-mai-code-of-conduct/","name":"Microsoft AI publishes a draft Code of Conduct for MAI models and opens a public consultation","summary":"On September 14, 2026 Microsoft AI published a first draft of a Code of Conduct for its MAI models, which it describes as a training manual for how it develops its models and how they are meant to behave in deployment. The draft sets out \"Absolute Constraints\" in areas such as weapons of mass harm, child safety, and harmful manipulation at scale, along with defaults that enterprise partners can configure. Microsoft AI opened a six-week public consultation on the draft the same day, and says it will then publish a summary of what it learned and changed and release a revised version later in 2026. The page does not give an exact closing date for comments.","keywords":"news governance 2026-09-14"},{"type":"news","slug":"nvidia-adds-cuda-q-logical-to-cuda-q","href":"/news/nvidia-adds-cuda-q-logical-to-cuda-q/","name":"NVIDIA adds CUDA-Q Logical, a fault-tolerant quantum orchestration layer, to open-source CUDA-Q","summary":"NVIDIA announced on September 14, 2026 that its open-source CUDA-Q platform now includes CUDA-Q Logical, an orchestration layer for designing and testing applications for fault-tolerant quantum computers that use logical qubits. NVIDIA says CUDA-Q Logical is available through GitHub. The announcement also says that QUOPS, a cross-platform benchmark developed by Sandia National Laboratories to measure progress toward utility-scale quantum applications, has a reference implementation available in CUDA-Q. USASI has not tested CUDA-Q Logical.","keywords":"news release 2026-09-14"},{"type":"news","slug":"open-secure-ai-alliance-joins-linux-foundation","href":"/news/open-secure-ai-alliance-joins-linux-foundation/","name":"The NVIDIA-founded Open Secure AI Alliance moves to the Linux Foundation","summary":"The Linux Foundation announced on September 14, 2026 that the Open Secure AI Alliance, which NVIDIA established in collaboration with members including the Linux Foundation, has joined the Linux Foundation as a neutral home. The announcement says the alliance's work will run alongside existing Linux Foundation security communities, including the Open Source Security Foundation (OpenSSF). It names the Shared AI Findings Exchange (SAFE), an effort to collect and analyze findings from AI security incidents, as a core initiative, and invited contributions to the SAFE proposal until September 21.","keywords":"news governance 2026-09-14"},{"type":"news","slug":"cognition-releases-swe-2","href":"/news/cognition-releases-swe-2/","name":"Cognition releases SWE-2, a coding model post-trained from Kimi K3, in Devin","summary":"Cognition introduced SWE-2 on September 10, 2026. The announcement says SWE-2 is post-trained from Kimi K3, a 2.8T-parameter model, and that it is available in Devin, starting with Devin Desktop and the CLI, with a rollout to Devin Web and Fusion. The post does not say that the model's weights are published. USASI has not tested this model.","keywords":"news release 2026-09-10"},{"type":"news","slug":"onnx-runtime-1-30-0-released","href":"/news/onnx-runtime-1-30-0-released/","name":"ONNX Runtime 1.30.0 adds Go bindings and expands CUDA and WebGPU generative AI support","summary":"Microsoft's ONNX Runtime project published version 1.30.0 on GitHub on September 10, 2026. The release notes highlight Go bindings for the ONNX Runtime C API, expanded CUDA inference support including speculative decoding in paged XQA and INT4 paged KV caches, WebGPU PagedAttention improvements with GPT-OSS support, and fused CPU LinearAttention kernels for AVX-512, Arm64 NEON, and SVE. The notes say FP4 QMoE kernels are now enabled by default in CUDA builds, and they list hardening of model loading and input validation. USASI has not tested this release.","keywords":"news release 2026-09-10"},{"type":"news","slug":"dioxus-team-joins-cognition","href":"/news/dioxus-team-joins-cognition/","name":"The Dioxus team joins Cognition, which says it will keep supporting Dioxus open source work","summary":"Cognition announced on September 10, 2026 that Jonathan Kelley and the Dioxus team are joining Cognition; the post does not describe deal terms or call it an acquisition. Cognition says it will continue to support Dioxus' open source work and plans to keep supporting Dioxus, Blitz, Taffy, and Subsecond, with more investment in Dioxus-Native and Blitz. The post says the team will also work on Devin's virtual machine, computer-use skills, and testing capabilities.","keywords":"news acquisition 2026-09-10"},{"type":"news","slug":"ibm-releases-granite-time-series-patchtst-fm-r2","href":"/news/ibm-releases-granite-time-series-patchtst-fm-r2/","name":"IBM releases the Granite Time Series PatchTST-FM-r2 forecasting model under Apache 2.0 or OpenMDW","summary":"On September 9, 2026 IBM Research announced Granite Time Series PatchTST-FM-r2, a zero-shot forecasting model of about 385M parameters that succeeds PatchTST-FM-r1 in the Granite TSFM family. The announcement lists a context length of up to 8,192 steps, flexible forecast lengths, and probabilistic forecasts from a 99-quantile prediction head. The weights are on Hugging Face and the code is in the granite-tsfm repository, and the model is dual-licensed under Apache 2.0 and OpenMDW 1.0, with either license available to choose. USASI has not tested the model.","keywords":"news release 2026-09-09"},{"type":"news","slug":"red-hat-ai-3-5-generally-available","href":"/news/red-hat-ai-3-5-generally-available/","name":"Red Hat AI 3.5 becomes generally available with EvalHub and wider llm-d support","summary":"Red Hat announced on September 9, 2026 that Red Hat AI 3.5 is generally available, including as part of Red Hat AI Factory with NVIDIA. The release makes EvalHub generally available for safety benchmarking and compliance reporting before deployment, shows Garak results on evaluated catalog models, integrates NeMo Guardrails to intercept malicious tool calls, and adds AutoRAG and observability features such as per-user token metering and MLflow agentic tracing. Red Hat says llm-d distributed inference now extends beyond OpenShift to third-party Kubernetes services, generally available on CoreWeave CKS and Microsoft Azure, with Amazon EKS in technology preview. USASI has not tested Red Hat AI 3.5.","keywords":"news release 2026-09-09"},{"type":"news","slug":"google-deepmind-launches-alphagenome-atlas","href":"/news/google-deepmind-launches-alphagenome-atlas/","name":"Google DeepMind launches AlphaGenome Atlas, precomputed predictions for human DNA variants","summary":"On September 8, 2026 Google DeepMind introduced AlphaGenome Atlas, a resource of precomputed AlphaGenome predictions for the effects of single-nucleotide variants, covering every possible single-letter change in the human genome. The announcement says Atlas is detailed in a paper and is available through a free website portal for non-commercial academic research, through the AlphaGenome API, and as a skill in Google Antigravity, with commercial use on Google Cloud planned. Alongside it, Google DeepMind released the AlphaGenome Variant Impact (AVI) score, which combines AlphaGenome and AlphaMissense predictions into a single number per variant; Atlas also includes AVI feature attributions and a collection of recurrent DNA sequence motifs. USASI has not tested AlphaGenome Atlas.","keywords":"news research 2026-09-08"},{"type":"news","slug":"inception-releases-mercury-2-5","href":"/news/inception-releases-mercury-2-5/","name":"Inception releases Mercury 2.5, a diffusion language model, and previews Mercury Voice","summary":"On September 8, 2026 Inception released Mercury 2.5, a diffusion language model (dLLM) that it describes as a step up in quality over Mercury 2 with the same low-latency serving profile. The announcement lists tunable reasoning, parallel tool calls, and schema-aligned JSON among its capabilities and says Mercury models are available through the Inception API, Baseten, and OpenRouter. Alongside it, Inception announced previews of Mercury Voice, a dLLM optimized for voice agents, and Mercury Router, which routes prompts to other open and closed models. In a separate post on September 29, 2026, Inception said Mercury Voice had become generally available to enterprise customers through an OpenAI API compatible endpoint. Neither post describes a release of model weights. USASI has not tested these models.","keywords":"news release 2026-09-08"},{"type":"news","slug":"sierra-open-sources-hyper-tau-bench","href":"/news/sierra-open-sources-hyper-tau-bench/","name":"Sierra open-sources Hyper-τ-bench, an evaluation of models that build customer-service agents","summary":"On September 8, 2026 Sierra published Hyper-τ-bench, a long-horizon evaluation that measures whether a model can build a customer-service agent rather than only act as one. A developer agent works in a sandboxed workspace with a simulated business's records and a simulated client it can message, must recover the requirements and turn the business's actions into tools, and the finished agent is then tested on held-out tasks the developer never saw. Sierra says it is open-sourcing the benchmark, with code in the sierra-research/hyper-tau-bench repository, a public leaderboard, and a paper on arXiv; the post does not state a license. USASI has not run the benchmark.","keywords":"news research 2026-09-08"},{"type":"news","slug":"common-crawl-releases-web-graph-embeddings","href":"/news/common-crawl-releases-web-graph-embeddings/","name":"Common Crawl releases experimental web graph embeddings for web hosts","summary":"On September 7, 2026, Common Crawl published an experimental dataset of 128-dimensional embeddings for web hosts, learned from hyperlinks alone in its host-level web graph for the November 2025, December 2025, and January 2026 crawls. The model uses no page text, metadata, or hostnames and was trained only on hosts with at least eight links. The data is published as Parquet files on Common Crawl's S3 bucket and on Hugging Face, where the dataset card says its use is subject to the Common Crawl Terms of Use; the source code is on GitHub. Common Crawl says it is yet to be seen whether it will publish graph embeddings on a regular basis.","keywords":"news release 2026-09-07"},{"type":"news","slug":"agility-robotics-files-s4-for-pending-merger","href":"/news/agility-robotics-files-s4-for-pending-merger/","name":"Agility Robotics and Churchill Capital Corp XI file a Form S-4 for their pending merger","summary":"Agility Robotics, Inc. and Churchill Capital Corp XI, a publicly traded blank check company, filed a registration statement on Form S-4 with the SEC on September 4, 2026, for the business combination they announced in June 2026. The merger is pending: it still needs approval by Churchill XI's shareholders, SEC review of the registration statement, regulatory and stock exchange approvals, and other closing conditions. If it closes, the combined company would trade under the ticker AGLT.","keywords":"news acquisition 2026-09-04"},{"type":"news","slug":"google-introduces-weathernext-3","href":"/news/google-introduces-weathernext-3/","name":"Google introduces WeatherNext 3, an hourly global weather model that uses live satellite data","summary":"On September 3, 2026 Google DeepMind and Google Research introduced WeatherNext 3, a global weather forecasting model that ingests live geostationary satellite data and produces a new forecast every hour. The announcement says it resolves some surface variables at 5-kilometer resolution, other surface variables at 10 kilometers, and atmospheric variables at 25 kilometers, and adds forecasts of 100-meter wind speeds and solar radiation for renewable energy planning. Google says WeatherNext 3 begins powering weather features in Search, the Gemini app, Google Maps, the Google Maps Platform Weather API, and Google Earth Engine, and that its forecast data can be queried in BigQuery and Earth Engine or downloaded in bulk from Google Cloud Storage. The announcement links to a paper and does not describe a release of model weights. USASI has not tested WeatherNext 3.","keywords":"news release 2026-09-03"},{"type":"news","slug":"marin-begins-pretraining-535b-a23b-model","href":"/news/marin-begins-pretraining-535b-a23b-model/","name":"Marin begins pretraining Marin 535B-A23B, a mixture-of-experts model","summary":"In a launch note dated September 3, 2026, first published on the Open Athena blog and mirrored by Marin, Marin said it had started pretraining Marin 535B-A23B a couple of weeks earlier. The note describes a mixture-of-experts model with 535 billion total parameters and 23 billion active, planned to pretrain for about three months on 18 trillion tokens, and says the team ported its infrastructure and training stack from Google TPUs on GCP to NVIDIA GPUs on CoreWeave before launch, with post-training to follow. It says the entire run, including architecture, data, and predicted loss, is documented on GitHub; it does not give a date or license for releasing the model's weights.","keywords":"news research 2026-09-03"},{"type":"news","slug":"microsoft-releases-vibevoice-asr-streaming","href":"/news/microsoft-releases-vibevoice-asr-streaming/","name":"Microsoft releases VibeVoice-ASR-Streaming, an MIT-licensed streaming speech recognition model","summary":"The news log in Microsoft's VibeVoice repository lists the release of VibeVoice-ASR-Streaming on September 3, 2026, describing it as a unified streaming ASR model that continuously transcribes who said what as speech arrives, with support for customized hotwords and 10 languages. The Hugging Face card for VibeVoice-ASR-Streaming-7B names the languages as Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish, and says the project is licensed under the MIT License. A technical report on the model was submitted to arXiv on September 2, 2026. USASI has not tested VibeVoice-ASR-Streaming.","keywords":"news release 2026-09-03"},{"type":"news","slug":"openai-releases-gpt-6-astra","href":"/news/openai-releases-gpt-6-astra/","name":"OpenAI releases GPT-6 Astra in its API","summary":"OpenAI's API changelog records the release of GPT-6 Astra (gpt-6-astra) on September 3, 2026, for the Responses and Chat Completions endpoints, and says to use it for reasoning, coding, computer use, research, and document creation. The migration notes say GPT-6 Astra does not support the none reasoning effort level, custom temperature or top_p values, or log probabilities, and that tool calling requires the Responses API. The entry also says misalignment monitoring asynchronously checks for potential issues during agent work in supported Responses API requests, and can trigger safety alerts or stop a conversation for review. In a safety overview listed in its news feed the same day, OpenAI says GPT-6 Astra is its first model to reach the Critical level of cybersecurity capability under its Preparedness Framework. GPT-6 Astra is a hosted model; no release of weights is described. USASI has not tested it.","keywords":"news release 2026-09-03"},{"type":"news","slug":"google-releases-gemini-3-8-flash-and-flash-cyber","href":"/news/google-releases-gemini-3-8-flash-and-flash-cyber/","name":"Google releases Gemini 3.8 Flash and, for vetted defenders, Gemini 3.8 Flash Cyber","summary":"On September 2, 2026 Google introduced two Gemini 3.8 models, which it says share the same foundational intelligence: Gemini 3.8 Flash, which Google says improves on 3.7 Flash in software engineering, agentic tasks, and multi-step reasoning, and Gemini 3.8 Flash Cyber, a cybersecurity model for vulnerability detection and automated patching. Google says developers can use 3.8 Flash in the Gemini API through Google AI Studio and Android Studio, and that it is available to Google AI Pro and Ultra subscribers in the Gemini app. Gemini 3.8 Flash Cyber ships with more permissive cybersecurity mitigations and is offered only to trusted defenders through Google's new Fairwind Program. Both are hosted models; no release of weights is described. USASI has not tested them.","keywords":"news release 2026-09-02"},{"type":"news","slug":"pytorch-2-14-0-released","href":"/news/pytorch-2-14-0-released/","name":"PyTorch 2.14.0 released with torch.switch, NVGEMM in Inductor, and a preview NCCL backend rewrite","summary":"PyTorch 2.14.0 was published on GitHub on September 2, 2026. The release notes highlight NVGEMM, which brings CuTeDSL-generated CUTLASS kernels to Inductor, and torch.switch, which generalizes torch.cond to multi-way branching. Other highlights include declarative dynamic shapes via @dynamic_spec, experimental torch.compile support for complex-valued tensors, a preview of a rewritten NCCL backend ported from torchcomms, and native linear algebra on Apple Silicon. The notes also say Inductor now targets Rubin (sm_107). USASI has not tested this release.","keywords":"news release 2026-09-02"},{"type":"news","slug":"nvidia-agrees-to-acquire-hugging-face","href":"/news/nvidia-agrees-to-acquire-hugging-face/","name":"NVIDIA agrees to acquire Hugging Face; the deal has not yet closed","summary":"NVIDIA entered into a definitive agreement to acquire Hugging Face, Inc. on September 2, 2026, and announced it the next day. NVIDIA's Form 8-K says the transaction is expected to close in the first half of 2027, subject to closing conditions including regulatory approvals, so Hugging Face is still listed as a privately held company with no parent organization. NVIDIA says the Hugging Face platform will stay open to models from other developers and will not require NVIDIA hardware.","keywords":"news acquisition 2026-09-02"},{"type":"news","slug":"ai2-releases-benchmirt","href":"/news/ai2-releases-benchmirt/","name":"Ai2 releases BenchMIRT, a method, code, and data for auditing LLM benchmarks prompt by prompt","summary":"Ai2 introduced BenchMIRT on September 1, 2026, describing it as a method for auditing LLM benchmarks at the level of individual prompts. According to Ai2, it uses multidimensional Item Response Theory to estimate which underlying capabilities are most closely associated with getting each question right. Ai2 published a technical report, code on GitHub, and data in a Hugging Face collection. The post does not state a license. USASI has not tested this method.","keywords":"news research 2026-09-01"},{"type":"news","slug":"anthropic-releases-claude-fable-5-1-and-mythos-5-1","href":"/news/anthropic-releases-claude-fable-5-1-and-mythos-5-1/","name":"Anthropic releases Claude Fable 5.1 and, for vetted organizations, Claude Mythos 5.1","summary":"Anthropic's release notes record that on September 1, 2026 it launched Claude Fable 5.1 (claude-fable-5-1), the successor to Claude Fable 5, alongside Claude Mythos 5.1 (claude-mythos-5-1) for Project Glasswing participants. Anthropic's announcement says the two are the same model with different levels of safeguards: Fable 5.1 is generally available, while Mythos 5.1 is offered only through trusted access programs for cybersecurity and life sciences work and is currently available only to a set of U.S. organizations. The release notes say text generated by both models carries Anthropic's text watermark, and the announcement says a watermark detection API is rolling out in private preview. Both are hosted models; no release of weights is described. USASI has not tested them.","keywords":"news release 2026-09-01"},{"type":"news","slug":"mlcommons-publishes-mlperf-storage-v3-0-results","href":"/news/mlcommons-publishes-mlperf-storage-v3-0-results/","name":"MLCommons publishes MLPerf Storage v3.0 results with KV cache and vector database tests","summary":"MLCommons published results for version 3.0 of its MLPerf Storage benchmark suite on September 1, 2026. The suite measures the performance of storage systems for machine learning workloads, and v3.0 adds a KV Cache test of storage performance for LLM inference cache read and write operations and a Vector Database test covering vector indexing and querying. Version 3.0 also adds support for an S3 object storage access layer alongside the existing POSIX layer, supported for training, checkpointing, and some vector database tests. Results are on the MLCommons Storage benchmark results page.","keywords":"news release 2026-09-01"},{"type":"news","slug":"world-labs-introduces-atlas-world-model","href":"/news/world-labs-introduces-atlas-world-model/","name":"World Labs introduces Atlas, a world model offered through early access","summary":"On September 1, 2026 World Labs introduced Atlas, which it calls its next-generation world model and describes as a multimodal autoregressive diffusion transformer pretrained from scratch. The announcement says Atlas generates images and videos from one or more images with camera control, reconstructs real-world scenes from one to dozens of input images with explicit 3D outputs, models space and time from input videos for Real-to-Sim robotics workflows, and generates images and 360 panoramas from text. World Labs says Atlas will power future versions of its Marble product, and the page offers a request form for early access rather than a public release or downloadable weights. USASI has not tested Atlas.","keywords":"news release 2026-09-01"},{"type":"news","slug":"apple-releases-lensvlm-9b","href":"/news/apple-releases-lensvlm-9b/","name":"Apple publishes LensVLM-9B, a research vision-language model that reads compressed images of text","summary":"In September 2026 Apple published LensVLM-9B on Hugging Face, a 9B vision-language model that scans compressed images of text and selectively expands only the relevant pages to their uncompressed form through learned tools. The model card lists Qwen3.5-9B as the base model and says the model files, including Apple's modifications to the Qwen model, are provided under the Apple Machine Learning Research Model License, while the accompanying code in the apple-aiml-research/ml-lensvlm repository is under the Apple Sample Code License. The model accompanies the paper \"LensVLM: Selective Context Expansion for Compressed Visual Representation of Text.\" USASI has not tested LensVLM-9B.","keywords":"news research 2026-09"},{"type":"news","slug":"openclaw-2026-8-1-released","href":"/news/openclaw-2026-8-1-released/","name":"OpenClaw 2026.8.1 adds conversation search, remote sessions, and masked credential requests","summary":"The OpenClaw project published OpenClaw 2026.8.1 on GitHub on August 31, 2026. The release notes highlight search across visible conversation text, sessions that run on paired devices or cloud workers and move the session workspace with them, and interactive widgets that can be pinned to session dashboards. They also describe private credential requests, which let an agent ask for a credential through a masked prompt without putting its value in chat or model context. The notes advise backing up configuration and state before updating. USASI has not tested this release.","keywords":"news release 2026-08-31"},{"type":"news","slug":"terminal-bench-4-0-released","href":"/news/terminal-bench-4-0-released/","name":"Terminal-Bench 4.0 released as a new major version of the continuous benchmark","summary":"The Terminal-Bench team published version 4.0 of its command-line agent benchmark on August 28, 2026, a month after announcing that it would be maintained as a continuous benchmark with semantic versioning. The release removed eight tasks, fixed nineteen, and set a flat eight-hour agent timeout for every task; it is a major version because these changes require re-running trials. It runs through the Harbor framework.","keywords":"news release 2026-08-28"},{"type":"news","slug":"biohub-esm-3-4-0-adds-esmc-and-esmfold2-support","href":"/news/biohub-esm-3-4-0-adds-esmc-and-esmfold2-support/","name":"Biohub's esm 3.4.0 package adds full support for ESMC and ESMFold2","summary":"Biohub published version 3.4.0 of its esm Python package on August 27, 2026. The release notes say it adds full support for ESMC and ESMFold2, a test suite for both, and a compatibility layer for the Hugging Face ports of ESMC and ESMFold2, while maintaining support for legacy model weights. Biohub's Hugging Face organization hosts ESMC-6B-hf, whose model card describes ESMC as a protein language model, says the ESMC 6B model has 6 billion parameters, and says Biohub is releasing source code and model weights for ESMC 6B, ESMFold2, and ESMC sparse autoencoders. The repository README says the models are available under the MIT license. USASI has not tested this release.","keywords":"news release 2026-08-27"},{"type":"news","slug":"cartesia-releases-sonic-3-6","href":"/news/cartesia-releases-sonic-3-6/","name":"Cartesia releases Sonic-3.6 text-to-speech with Odia, Urdu, and Hindi-English code-switching","summary":"On August 27, 2026 Cartesia announced Sonic-3.6, a text-to-speech model that it says arrived two months after Sonic-3.5 and is generally available in its playground and through its API. The announcement says Odia and Urdu are added as new languages. It also describes Hinglish support for switching between Hindi and English in a single generation, better accent retention for instant voice clones, and an optional locale field that controls how dates, times, and numbers are read. The post does not describe a release of model weights. USASI has not tested Sonic-3.6.","keywords":"news release 2026-08-27"},{"type":"news","slug":"fastvideo-releases-fasth3-preview-v1-open-weights","href":"/news/fastvideo-releases-fasth3-preview-v1-open-weights/","name":"FastVideo releases FastH3 Preview v1, an open-weight 4-step distillation of MiniMax H3","summary":"On August 27, 2026, the FastVideo project released FastH3 Preview v1, which its repository describes as an open-weight, 4-step sparse-distilled MiniMax-H3 model for synchronized video and audio generation, developed with Nuva Lab and the NVIDIA FastGen team. The recommended checkpoint was trained with data-free DMD2 and VSA-H3 sparse attention, requires FastVideo's VSA-H3 attention backend, and is also offered as a matching LoRA adapter. Its model card says the checkpoint inherits the MiniMax H3 Community License. USASI has not tested FastH3.","keywords":"news release 2026-08-27"},{"type":"news","slug":"amd-releases-rocm-core-sdk-10-0","href":"/news/amd-releases-rocm-core-sdk-10-0/","name":"AMD releases ROCm Core SDK 10.0.0 with updated framework support and breaking AMD SMI changes","summary":"AMD published ROCm Core SDK 10.0.0 on August 26, 2026, as a release in the TheRock repository, which the release notes say has been ROCm's build and release system since ROCm 7.14. The notes say the release focuses on AI inference, developer tooling and profiling, and that it enables support for PyTorch 2.13.0, JAX 0.11.0 and 0.10.2, vLLM 0.27.0, SGLang 0.5.15, TensorFlow 2.21, MIGraphX 2.17 and ONNX Runtime 1.27.0. HIP adds inclusive and exclusive scan APIs to its cooperative groups library. The AMD SMI library has breaking changes: its major version is now 27.0.0, so software linked against libamd_smi.so.26 must be relinked. USASI has not tested this release.","keywords":"news release 2026-08-26"},{"type":"news","slug":"ibm-releases-granite-speech-5-0-turboctc","href":"/news/ibm-releases-granite-speech-5-0-turboctc/","name":"IBM releases Granite Speech 5.0 TurboCTC, two compact English speech recognition models","summary":"On August 25, 2026 IBM's Granite team announced two 470M-parameter English speech recognition models on Hugging Face, granite-speech-5.0-470m-turboctc and granite-speech-5.0-470m-turboctc-nc. The first is released under Apache 2.0; the second, trained with additional data including GigaSpeech and SPGI Speech, is under the non-commercial CC-BY-NC-SA-4.0 license. IBM describes an encoder-only design with 16 Conformer blocks trained with CTC loss, and says that, unlike earlier Granite Speech models, which included an LLM with LoRA adapters, the new models lack capabilities such as speech translation and keyword biasing. USASI has not tested either model.","keywords":"news release 2026-08-25"},{"type":"news","slug":"nvidia-releases-cuda-python-1-0","href":"/news/nvidia-releases-cuda-python-1-0/","name":"NVIDIA releases CUDA Python 1.0 with semantic versioning commitments","summary":"NVIDIA announced CUDA Python 1.0 on its technical blog on August 25, 2026, saying it released the milestone with CUDA 13.3 and that Python is now a supported way to use the CUDA platform. The post says 1.0 is a milestone rather than a single package version: components such as cuda.core 1.0.0, cuda.compute 1.0.0 and nvmath-python 1.0 are versioned independently, and what changes with 1.0 is a commitment to semantic versioning. NVIDIA notes that some newer pieces, including the Numba CUDA MLIR kernel generator, are not yet covered by that commitment. USASI has not tested these libraries.","keywords":"news release 2026-08-25"},{"type":"news","slug":"ibm-releases-granite-4-2","href":"/news/ibm-releases-granite-4-2/","name":"IBM releases Granite 4.2 language models in 3B, 8B, and 30B sizes under Apache 2.0","summary":"IBM released Granite 4.2 on August 25, 2026: dense, decoder-only language models with built-in reasoning in 3B, 8B, and 30B sizes, post-trained from IBM's own Granite 4.1 base models. The weights are published on Hugging Face under the Apache 2.0 license. The catalog has a release record for each size under the Granite family.","keywords":"news release 2026-08-25"},{"type":"news","slug":"tinygrad-0-14-0-released","href":"/news/tinygrad-0-14-0-released/","name":"tinygrad 0.14.0 released with tool calling in tinygrad.llm and parallel kernel compilation","summary":"The tinygrad project published version 0.14.0 on GitHub on August 24, 2026. The release notes list improvements to tinygrad.llm, including RDNA3 kernels for Qwen 3.8 27B, a faster tokenizer and tool calling support. Kernels are now compiled in parallel by default, and Python scalars are now weak dtypes. The notes also say Tensor.training and Tensor.train() were removed in favor of a TRAINING ContextVar, and that GPT-OSS 20B was added as an MLPerf training benchmark. USASI has not tested this release.","keywords":"news release 2026-08-24"},{"type":"news","slug":"spec-kit-1-0-0-released","href":"/news/spec-kit-1-0-0-released/","name":"GitHub's Spec Kit reaches version 1.0.0","summary":"GitHub's Spec Kit project published version 1.0.0 on GitHub on August 21, 2026. The release notes give instructions for installing the specify CLI from the v1.0.0 tag and list the changes in the version, which are mostly fixes and updates to extensions and presets in the community catalog, including an update of the Security Review extension to v2.0.0 and a fix that rejects duplicate template entries in presets. The change list shows the version follows the 0.16.5 release. USASI has not tested Spec Kit 1.0.0.","keywords":"news release 2026-08-21"},{"type":"news","slug":"runway-releases-ruby-color-grading-model","href":"/news/runway-releases-ruby-color-grading-model/","name":"Runway releases Ruby, a model that converts SDR video to HDR","summary":"Runway's changelog records that on August 20, 2026 it released Runway Ruby, which it describes as a native color grading model that converts SDR video into HDR, available in Tool Mode, Workflows, and Runway Dev. Runway's product page says Ruby accepts SDR clips up to 30 seconds and up to 4K, including Runway generations, third-party model output, or existing footage, keeps the source pixels and audio, and delivers HDR10, HLG, ProRes, or OpenEXR masters. Developers can call it through a video_to_hdr endpoint in the Runway Dev API with the model set to ruby. Ruby is a hosted model; Runway does not describe a release of weights. USASI has not tested Ruby.","keywords":"news release 2026-08-20"},{"type":"news","slug":"mlcommons-releases-mlperf-client-v2-0","href":"/news/mlcommons-releases-mlperf-client-v2-0/","name":"MLCommons releases MLPerf Client v2.0 with image generation and agentic AI tests","summary":"MLCommons released version 2.0 of MLPerf Client, its benchmark for running AI workloads locally on PCs, on August 18, 2026. The release adds an image generation category, with Flux.2 klein 4B as an experimental test, and an agentic AI category with Software Engineering Agent and Data Analyst Agent scenarios that break end-to-end results into LLM inference and tool execution time. Phi 4 Mini Instruct replaces Phi 3.5 mini instruct in the required LLM workloads, and Qwen 3 8B is added as an experimental test. MLCommons says v2.0 is free to download for Windows, macOS, and Linux from GitHub. USASI has not tested MLPerf Client.","keywords":"news release 2026-08-18"},{"type":"news","slug":"sentence-transformers-6-0-0-adds-multivectorencoder","href":"/news/sentence-transformers-6-0-0-adds-multivectorencoder/","name":"Sentence Transformers 6.0.0 adds MultiVectorEncoder for ColBERT-style late-interaction models","summary":"Sentence Transformers version 6.0.0 was published on GitHub on August 18, 2026. The release notes describe it as a major release that introduces Multi-Vector Embedding models, also known as late-interaction or ColBERT-style models, as a fourth model type, MultiVectorEncoder, alongside SentenceTransformer, CrossEncoder, and SparseEncoder. The notes say PyLate and Stanford-NLP ColBERT checkpoints load directly into it, that the dependency floor moves to transformers v5, and that the release contains breaking changes covered in a migration guide. USASI has not tested this release.","keywords":"news release 2026-08-18"},{"type":"news","slug":"skild-ai-introduces-s1","href":"/news/skild-ai-introduces-s1/","name":"Skild AI introduces S1, a robotics model that learns tasks from a video demonstration","summary":"Skild AI introduced S1 on August 18, 2026, describing it as a robotics foundation model built from the ground up as an in-context learner: tasks are specified by a video demonstration rather than by language, with no fine-tuning or post-training. Skild says the same weights produced every example in its post and that S1 is already at work with its commercial partners, and the post invites sign-ups for early access. The post does not say that S1's weights or code are released.","keywords":"news research 2026-08-18"},{"type":"news","slug":"modular-open-sources-mojo-compiler","href":"/news/modular-open-sources-mojo-compiler/","name":"Modular open-sources the Mojo compiler and tooling","summary":"At its ModCon conference on August 18, 2026, Modular announced that the full Mojo language, including the compiler and tooling, is open source; the modular repository that holds it is licensed under Apache 2.0 with LLVM exceptions, while the prebuilt mojo package on PyPI declares a separate Modular license. Modular also said the MAX license no longer contains device usage restrictions. On September 17 it said the Mojo compiler had begun accepting outside contributions.","keywords":"news license 2026-08-18"},{"type":"news","slug":"spacex-completes-anysphere-acquisition","href":"/news/spacex-completes-anysphere-acquisition/","name":"SpaceX completes its acquisition of Anysphere, the developer of Cursor","summary":"SpaceX completed its acquisition of Anysphere, Inc., the company behind the Cursor coding tool, on August 14, 2026, through a merger of a SpaceX subsidiary into Anysphere. SpaceX's Form 8-K says Anysphere survives as a wholly owned subsidiary of Space Exploration Technologies Corp., and Cursor announced the same day that it is now part of SpaceX. The Anysphere record now lists SpaceX as its parent company.","keywords":"news acquisition 2026-08-14"},{"type":"news","slug":"lightmatter-launches-open-silicon-photonics-workstream-at-ocp","href":"/news/lightmatter-launches-open-silicon-photonics-workstream-at-ocp/","name":"Open Silicon Photonics for AI Systems initiative becomes an Open Compute Project workstream","summary":"Lightmatter announced on August 13, 2026 that the Open Silicon Photonics for AI Systems initiative, first announced earlier in the year, has launched as an official workstream within the Open Compute Project. The coalition's members include Dell Technologies, Qualcomm and Lightmatter, and the initiative has published a white paper titled \"Architecture Vision: Open Silicon Photonics for AI Systems.\" Lightmatter says the paper will guide the development of a shared co-packaged optics (CPO) blueprint for AI clusters that comply with the Modular Hardware System (MHS) and Open Rack v3 (ORv3) designs. The release says submission of the first specifications is anticipated in the fourth quarter of 2026.","keywords":"news governance 2026-08-13"},{"type":"news","slug":"firetiger-team-joins-cursor","href":"/news/firetiger-team-joins-cursor/","name":"The Firetiger team joins Cursor to work on agents for software in production","summary":"Cursor announced on August 13, 2026 that the team behind Firetiger, a company that builds agents that work on software once it reaches production, is joining Cursor. The post says Firetiger's work will show up across Cursor as it expands from writing code to running it in production. It does not state deal terms or say whether Firetiger's standalone product will continue.","keywords":"news acquisition 2026-08-13"},{"type":"news","slug":"writer-releases-palmyra-x6","href":"/news/writer-releases-palmyra-x6/","name":"WRITER releases Palmyra X6, post-trained on GLM 5.2, alongside WRITER Agent upgrades","summary":"WRITER announced Palmyra X6 on August 13, 2026 as its new flagship model, aimed at marketing and revenue workflows and designed to run inside the WRITER Agent harness. The announcement says X6 was post-trained on top of GLM 5.2, an open-weight model. The same release adds WRITER Agent features including batch execution, sub-agent delegation, support for enabling models from providers such as Anthropic and OpenAI, and bring-your-own-model support for Microsoft Azure, AWS Bedrock, and NVIDIA NIM. The announcement does not say whether Palmyra X6 weights are released. USASI has not tested Palmyra X6.","keywords":"news release 2026-08-13"},{"type":"news","slug":"liquid-ai-releases-lfm2-5-vl-3b","href":"/news/liquid-ai-releases-lfm2-5-vl-3b/","name":"Liquid AI releases LFM2.5-VL-3B, an open-weight vision-language model for on-device use","summary":"Liquid AI announced LFM2.5-VL-3B on August 12, 2026, a non-reasoning vision-language model that it says answers directly to keep latency low for real-time and on-device applications. The model card says it uses the LFM2.5-2.6B language model as its backbone with a SigLIP2 NaFlex vision encoder, and lists a context length of 32,768 tokens. Weights are on Hugging Face with GGUF, ONNX, and MLX exports, under the LFM Open License v1.0. USASI has not tested this model.","keywords":"news release 2026-08-12"},{"type":"news","slug":"microsoft-ai-releases-mai-thinking-1","href":"/news/microsoft-ai-releases-mai-thinking-1/","name":"Microsoft AI releases the MAI-Thinking-1 reasoning model in public preview on Microsoft Foundry","summary":"On August 12, 2026 Microsoft AI introduced MAI-Thinking-1, a reasoning model from its Superintelligence team, and made it available in public preview on Microsoft Foundry. The announcement describes a sparse mixture-of-experts model with 35B active and about 1T total parameters, a 256k-token context window, and function calling, and says it is compatible with the Chat Completions API. Microsoft AI says it did not distill the model from other labs' models and that safety training runs in the same reinforcement learning loop as capability training. The post does not say that the weights are released. USASI has not tested MAI-Thinking-1.","keywords":"news release 2026-08-12"},{"type":"news","slug":"modular-releases-mojo-1-0","href":"/news/modular-releases-mojo-1-0/","name":"Modular releases Mojo 1.0 alongside MAX 26.5","summary":"Modular released Mojo 1.0 with its Modular 26.5 release on August 11, 2026. Modular's announcement says that during the 1.x timeframe changes should be primarily additive, and the GitHub release notes say Modular has started marking a small set of standard library APIs as stable. The notes warn of more breaking changes than usual, saying nearly every one ships with a deprecated alias and a compiler fix-it. In MAX 26.5, most GPU programming APIs moved out of the Mojo standard library into a new top-level max package, and Apple silicon GPU support was extended back to M1 devices. USASI has not tested this release.","keywords":"news release 2026-08-11"},{"type":"news","slug":"nvidia-releases-nemotron-3-5-lightning","href":"/news/nvidia-releases-nemotron-3-5-lightning/","name":"NVIDIA releases Nemotron 3.5 Lightning, an open 30B mixture-of-experts model for agents","summary":"NVIDIA's Hugging Face model card gives August 11, 2026 as the release date of NVIDIA-Nemotron-3.5-Lightning-30B-A3B, a model with 30B total and 3B active parameters built on a hybrid of Mamba-2, mixture-of-experts and attention layers, with reasoning that can be switched on or off through the chat template. The card lists the OpenMDW License Agreement, version 1.1, and points to a separate NVFP4 checkpoint for optimized inference. NVIDIA's developer blog says the model is designed for the high-volume execution work of long-running agents, ships with DFlash and DSpark draft models for speculative decoding, and is released with an open reinforcement learning dataset, Nemotron-RL Agentic Terminal Pivot. USASI has not tested the model.","keywords":"news release 2026-08-11"},{"type":"news","slug":"unsloth-introduces-unsloth-desktop","href":"/news/unsloth-introduces-unsloth-desktop/","name":"Unsloth introduces Unsloth Desktop, an open-source app to run and train models locally","summary":"Unsloth announced Unsloth Desktop in a GitHub release published on August 11, 2026. The release notes describe it as an open-source app for Windows, macOS, and Linux that runs and trains AI models locally, and say models can also be exported to NVFP4, GGUF, and other formats. Listed features include training without code, sandboxed Python and Bash execution, web search, Deep Research, RAG, and MCP tools, and an OpenAI-compatible API for local models. The notes say it runs on CPU or multiple GPUs across NVIDIA, AMD, Intel, and Mac hardware. USASI has not tested this app.","keywords":"news release 2026-08-11"},{"type":"news","slug":"harvey-open-sources-lab-law-firm-knowledge-tasks","href":"/news/harvey-open-sources-lab-law-firm-knowledge-tasks/","name":"Harvey open-sources a synthetic law firm knowledge expansion of its Legal Agent Benchmark","summary":"On August 7, 2026 Harvey announced that it is open-sourcing an expansion of LAB, its Legal Agent Benchmark: a synthetic law firm called Calderwood & Harkness whose files cover 266 matters for 46 fictional clients across 15 practice areas. The expansion has 250 tasks that test retrieval and reasoning over the firm's documents, and an LLM judge scores each task against a per-task rubric. The tasks are published in the harveyai/harvey-labs repository on GitHub. USASI has not run the benchmark.","keywords":"news research 2026-08-07"},{"type":"news","slug":"nvidia-nemo-speech-3-0-released","href":"/news/nvidia-nemo-speech-3-0-released/","name":"NVIDIA releases NeMo Speech 3.0, the first major release after the NeMo repository split","summary":"NVIDIA published NeMo Speech 3.0 on GitHub on August 7, 2026. The release notes call it the first major release after the repository was split and renamed to NVIDIA-NeMo/Speech, which now focuses on ASR, TTS, audio processing, speaker tasks and SpeechLM, while LLM, VLM, diffusion, export and evaluator collections have moved to separate repositories. The notes say the release's central focus is technical debt, including the removal of deprecated code and a migration to the uv package manager. They also list support for training SpeechLM models with NeMo Automodel and the training and evaluation modules behind Nemotron VoiceChat. USASI has not tested this release.","keywords":"news release 2026-08-07"},{"type":"news","slug":"prime-rl-0-8-0-adds-multi-agent-rl","href":"/news/prime-rl-0-8-0-adds-multi-agent-rl/","name":"Prime Intellect releases prime-rl 0.8.0 with multi-agent reinforcement learning support","summary":"Prime Intellect published version 0.8.0 of prime-rl on GitHub on August 7, 2026. The release notes say it integrates verifiers 0.3.0 for multi-agent (single-policy) training and adds two named algorithms for multi-agent environments: RAE (Role-conditioned Advantage Estimation) and Hierarchical GRPO. The notes also list a move of the inference stack to vLLM 0.26.0, a NIXL and ModelExpress weight-synchronization path, and ZMQ as the default rollout transport. USASI has not tested this release.","keywords":"news release 2026-08-07"},{"type":"news","slug":"agent-plugins-1-0-0-specification-published","href":"/news/agent-plugins-1-0-0-specification-published/","name":"Agent Plugins 1.0.0 published as an open specification for packaging agent skills and MCP servers","summary":"On August 6, 2026 Vercel announced Agent Plugins 1.0.0, an open, vendor-neutral specification for packaging extensions for AI agents so that one plugin format works across compatible clients. A plugin is a directory with a plugin.json manifest and fixed locations for its components, with Agent Skills under skills/ and MCP server configuration in mcp.json. Vercel says it initiated the proposal, that representatives from AWS, Anysphere, GitHub, Microsoft, OpenAI, and Vercel refined it into version 1.0.0, and that the initial Technical Steering Committee includes core maintainers from AWS, Cursor, Microsoft, OpenAI, and Vercel. ChatGPT and Codex, Cursor, GitHub Copilot, Kiro, and VS Code are listed as supported at launch.","keywords":"news governance 2026-08-06"},{"type":"news","slug":"amd-agrees-to-acquire-taalas","href":"/news/amd-agrees-to-acquire-taalas/","name":"AMD agrees to acquire Taalas, a developer of AI inference silicon","summary":"AMD announced on August 6, 2026, that it has reached a definitive agreement to acquire Taalas, a company founded in 2023 and headquartered in Toronto that develops specialized silicon for AI inference. AMD says it plans to integrate Taalas' technology into its accelerator roadmap and to develop system-level solutions with AMD Instinct GPUs. The release says the acquisition is subject to customary closing conditions and regulatory approvals, and it does not give an expected closing date. USASI has not seen an announcement that the deal has closed.","keywords":"news acquisition 2026-08-06"},{"type":"news","slug":"cloudflare-open-sources-cloudflare-os","href":"/news/cloudflare-open-sources-cloudflare-os/","name":"Cloudflare open-sources Cloudflare OS, a platform for agent workspaces and internal apps","summary":"On August 5, 2026 Cloudflare announced that it is open-sourcing a new version of Cloudflare OS, which it describes as an open platform for agents, apps, and work that combines an agent workspace, a security and governance framework, and a platform for modifiable apps. The core code is in the cloudflare/cloudflare-os repository on GitHub, with a separate starter repository for deployments. Cloudflare says agents start with no access and that Gatekeepers, which are Workers, handle OAuth, hold credentials, enforce policy, and record reads, while every inference call runs through Cloudflare AI Gateway. Cloudflare says it is working on bringing Cloudflare OS to the Cloudflare dashboard as a fully managed product, and the post does not name the license. USASI has not tested Cloudflare OS.","keywords":"news release 2026-08-05"},{"type":"news","slug":"liquid-ai-releases-lfm2-5-2-6b","href":"/news/liquid-ai-releases-lfm2-5-2-6b/","name":"Liquid AI releases LFM2.5-2.6B, an open-weight on-device model trained for agentic workloads","summary":"Liquid AI announced LFM2.5-2.6B on August 4, 2026, describing it as a 2.6B-parameter model trained for agentic workloads such as planning, calling tools, and multi-step tasks on devices including phones and CPUs. The base model (LFM2.5-2.6B-Base) and the post-trained model (LFM2.5-2.6B) were published on Hugging Face. The model card lists a context length of 131,072 tokens and says the model is not recommended for agentic coding and knowledge-heavy tasks. The weights are released under the LFM Open License v1.0. USASI has not tested this model.","keywords":"news release 2026-08-04"},{"type":"news","slug":"nist-joins-genesis-mission-through-doe-agreement","href":"/news/nist-joins-genesis-mission-through-doe-agreement/","name":"NIST signs an agreement with DOE's Office of Science to join the Genesis Mission","summary":"On August 4, 2026, NIST announced that it and the Department of Energy's Office of Science had signed a memorandum of understanding to apply AI-accelerated innovation to national science and technology challenges tied to the Genesis Mission. The agencies will coordinate and share resources in biotechnology, quantum science, and materials design and discovery. NIST will carry out two efforts through its Centers for AI in Manufacturing and Critical Infrastructure, a partnership with MITRE: one on AI-driven autonomous agents and human-in-the-loop robotics for manufacturing, and one on AI-based agents that detect and respond to cyberthreats against critical infrastructure. NIST describes both as two-year sprints.","keywords":"news policy 2026-08-04"},{"type":"news","slug":"faiss-1-15-0-released","href":"/news/faiss-1-15-0-released/","name":"Faiss 1.15.0 adds a Metal IVF-PQ index, EDEN quantizer indexes, and a cuVS IVF-SQ backend","summary":"Meta's Faiss project published version 1.15.0 on GitHub on August 3, 2026; the changelog in the release dates the version July 31, 2026. Additions listed in the notes include MetalIndexIVFPQ with product quantization and residual encoding, EDEN quantizer indexes, a cuVS IVF-SQ backend, multi-GPU CAGRA-to-HNSW builds, memory-mapped I/O for Flat and static Vamana SVS indices, and RISC-V RVV distance and RaBitQ kernels. The notes also list faiss-gpu-cuvs pip wheel packaging and optimizations to RaBitQ fast scan and HNSW search. USASI has not tested these changes.","keywords":"news release 2026-08-03"},{"type":"news","slug":"hermes-agent-0-20-0-adds-voice-and-a2a","href":"/news/hermes-agent-0-20-0-adds-voice-and-a2a/","name":"Nous Research releases Hermes Agent 0.20.0 with conversational voice and A2A v1.0 support","summary":"Nous Research published Hermes Agent v0.20.0 (tagged v2026.8.3) on GitHub on August 3, 2026, calling it \"The Herald Release.\" The release notes list streaming conversational voice with barge-in and on-device wake words, and a new bundled plugin that implements the Agent-to-Agent (A2A) protocol v1.0 so Hermes can discover, talk to, and be driven by other A2A-compatible agents. The notes also list signed outbound webhooks, grounded research with citations and fact-checking, and a plugin SDK for the desktop app. USASI has not tested this release.","keywords":"news release 2026-08-03"},{"type":"news","slug":"google-releases-timesfm-3-0-under-non-commercial-license","href":"/news/google-releases-timesfm-3-0-under-non-commercial-license/","name":"Google releases TimesFM 3.0 weights under a non-commercial license","summary":"Google Research released TimesFM 3.0, a time-series forecasting model, in August 2026 under the TimesFM Non-Commercial License v1.0 instead of the Apache 2.0 license used for earlier TimesFM weights. The license allows use of the weights only for non-commercial purposes and prohibits redistributing them; the repository says commercial and production use of TimesFM 3.0 is permitted through authorized Google Cloud services such as BigQuery ML. Weights up to version 2.5 and the TimesFM source code remain under Apache 2.0.","keywords":"news license 2026-08"},{"type":"news","slug":"meta-releases-muse-glimmer-open-weights","href":"/news/meta-releases-muse-glimmer-open-weights/","name":"Meta releases Muse Glimmer as an open-weight model under Apache 2.0","summary":"In August 2026 Meta Superintelligence Labs published Muse Glimmer, a model of about 30 billion parameters that takes text and images as input and was distilled from Muse Spark. Its weights can be downloaded from Hugging Face without an access request under the Apache 2.0 license, and the repository also includes a separate Muse Glimmer Usage Policy that lists prohibited uses. Muse Glimmer has its own release record under the Muse family.","keywords":"news release 2026-08"},{"type":"news","slug":"google-deepmind-introduces-gemini-robotics-2","href":"/news/google-deepmind-introduces-gemini-robotics-2/","name":"Google DeepMind introduces Gemini Robotics 2, ER 2, and On-Device 2 models for robots","summary":"On July 30, 2026 Google DeepMind introduced Gemini Robotics 2, a set of three models: Gemini Robotics 2, a vision-language-action model that Google DeepMind says can control full humanoids and bi-arm robots; Gemini Robotics ER 2, an embodied reasoning model that plans multi-step tasks and coordinates several robots; and Gemini Robotics On-Device 2, a vision-language-action model optimized to run locally on robots. The announcement says Gemini Robotics ER 2 is available on Google AI Studio and in private preview on Gemini Enterprise Agent Platform, while the other two models are available to early-access partners. It also introduces ASIMOV-Agentic, a benchmark for agentic safety orchestration. No release of weights is described. USASI has not tested these models.","keywords":"news release 2026-07-30"},{"type":"news","slug":"thinking-machines-lab-releases-inkling-small","href":"/news/thinking-machines-lab-releases-inkling-small/","name":"Thinking Machines Lab releases Inkling-Small, a smaller open-weight Inkling model","summary":"On July 30, 2026 Thinking Machines Lab released Inkling-Small, which it describes as a mixture-of-experts transformer with 276B total and 12B active parameters, trained on NVIDIA GB300 NVL72 systems. The announcement says that, like Inkling, it reasons natively over audio and images, offers variable thinking effort, and supports a context window of up to 1M tokens, and that an earlier checkpoint was post-trained partly through on-policy distillation with Inkling as the teacher. The company says it is releasing the full weights and offering the model for fine-tuning on Tinker; the Hugging Face listing is ungated and licensed under Apache 2.0. USASI has not tested Inkling-Small.","keywords":"news release 2026-07-30"},{"type":"news","slug":"far-ai-launches-ai-security-leaderboard","href":"/news/far-ai-launches-ai-security-leaderboard/","name":"FAR.AI launches the AI Security Leaderboard and a Minimal Standard for Safeguards","summary":"On July 29, 2026, FAR.AI introduced the AI Security Leaderboard, an independent benchmark of the safeguards of frontier AI models, launching with tests of four frontier models under identical conditions against chemical, biological, and cyber misuse. FAR.AI says it broke jailbreaks down into basic building blocks and used a toolkit to recombine them into attacks, measuring robustness as the estimated attacker cost to find a universal jailbreak. It also proposed a Minimal Standard for Safeguards, a shared floor built from defenses that peer developers already run in production, and says passing it does not certify a model as secure. The leaderboard is hosted at leaderboard.far.ai.","keywords":"news research 2026-07-29"},{"type":"news","slug":"mlcommons-releases-mlperf-endpoints-v0-7","href":"/news/mlcommons-releases-mlperf-endpoints-v0-7/","name":"MLCommons releases MLPerf Endpoints v0.7, a benchmark for buyers of hosted inference","summary":"On July 28, 2026, MLCommons announced MLPerf Endpoints v0.7, a foundation release of a new MLPerf benchmark aimed at enterprise buyers procuring inference compute from neoclouds, cloud providers, and managed services. The release covers three benchmarks with initial results from CoreWeave, Google, Intel, KRAI, and NVIDIA, and supports automated submission pipelines, continuous review tooling, and dynamic result visualization. Results and tooling are at endpoints.mlcommons.org. MLCommons says it will deliver MLPerf Endpoints v1.0 later in 2026, with more buyer-centric rules and agentic workloads.","keywords":"news release 2026-07-28"},{"type":"news","slug":"mcp-specification-2026-07-28-released","href":"/news/mcp-specification-2026-07-28-released/","name":"Model Context Protocol publishes its 2026-07-28 specification, removing protocol-level sessions","summary":"On July 28, 2026 the Model Context Protocol project published the stable release of the 2026-07-28 revision of the MCP specification, which follows the 2025-11-25 revision. The changelog lists removing protocol-level sessions and the Mcp-Session-Id header from the Streamable HTTP transport, and removing the initialize handshake so that every request carries its protocol version and client capabilities in _meta. It also adds a required server/discover method and a Multi Round-Trip Requests pattern, and moves tasks into an official io.modelcontextprotocol/tasks extension. The revision deprecates the Roots, Sampling, and Logging features and adopts a feature lifecycle and deprecation policy with a minimum twelve-month deprecation window. USASI has not tested implementations of this revision.","keywords":"news release 2026-07-28"},{"type":"news","slug":"qualcomm-completes-modular-acquisition","href":"/news/qualcomm-completes-modular-acquisition/","name":"Qualcomm completes its acquisition of Modular","summary":"Qualcomm completed its acquisition of Modular Inc on July 28, 2026, according to its quarterly report, and the closing was announced on July 29. Modular's announcement says Mojo, MAX, and Modular Cloud continue as products and brands, supported through Qualcomm Technologies. The Modular record now lists Qualcomm as its parent company, and Modular remains the maintainer of the Mojo and MAX records.","keywords":"news acquisition 2026-07-28"},{"type":"news","slug":"nvidia-launches-open-secure-ai-alliance","href":"/news/nvidia-launches-open-secure-ai-alliance/","name":"NVIDIA and inaugural partners launch the Open Secure AI Alliance for open AI security tools","summary":"NVIDIA announced the Open Secure AI Alliance on July 27, 2026, describing it as an effort to develop and share open technologies, techniques and tools to safeguard software and AI agents. The inaugural partners NVIDIA lists include Cisco, Dell Technologies, HPE, Intel, Anyscale, Crusoe, Fireworks AI and the Linux Foundation, and NVIDIA says the alliance builds on the Linux Foundation's Akrites initiative and OpenSSF community work. NVIDIA says it is contributing open models, model weights, data and agent harness research, and that its open-source NVIDIA Labs Object-Oriented Agent (NOOA) research framework is available on GitHub. NVIDIA also names HPE's contributions to the SPIFFE/SPIRE identity framework among the alliance's work.","keywords":"news governance 2026-07-27"},{"type":"news","slug":"open-webui-0-11-0-redesigns-interface-adds-sub-agents","href":"/news/open-webui-0-11-0-redesigns-interface-adds-sub-agents/","name":"Open WebUI 0.11.0 ships a redesigned interface and optional sub-agents","summary":"Open WebUI published version 0.11.0 on GitHub on July 27, 2026. The release notes say the interface has been visually rebuilt, from the chat view to the admin panel. They also list sub-agents, which administrators can enable through an ENABLE_SUBAGENTS setting so that a model can hand parts of a task to background helper agents that report results back into the chat. Other listed additions include chat forking, chat timers, webhook notification targets, and LDAP group synchronization. USASI has not tested this release.","keywords":"news release 2026-07-27"},{"type":"news","slug":"amd-releases-instella-moe","href":"/news/amd-releases-instella-moe/","name":"AMD releases Instella-MoE, a 16B mixture-of-experts language model, under a research-only license","summary":"AMD introduced Instella-MoE in a ROCm blog post dated July 24, 2026: a mixture-of-experts language model with 16 billion total and 2.8 billion active parameters, trained from scratch on AMD Instinct MI300X and MI325X GPUs with the Primus and Miles frameworks. AMD says it is releasing model weights from every training stage, training configurations, data mixtures and code, and the Hugging Face checkpoints run from Instella-MoE-16B-A3B-Pretrain to the reinforcement-learning-tuned Instella-MoE-16B-A3B-Think. AMD calls the model fully open, but the model card says the models are licensed for academic and research purposes under a ResearchRAIL license and are released for research purposes only. USASI has not tested the models.","keywords":"news release 2026-07-24"},{"type":"news","slug":"anthropic-releases-claude-opus-5","href":"/news/anthropic-releases-claude-opus-5/","name":"Anthropic releases Claude Opus 5","summary":"On July 24, 2026 Anthropic announced Claude Opus 5 and said it was available that day on all platforms, with the API model ID claude-opus-5. Anthropic's release notes say the model supports a 1M token context window and 128k max output tokens, is available on the Claude API, Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry, and allows thinking to be disabled only at effort high or below, a breaking change from Claude Opus 4.8. The announcement says requests flagged by its cybersecurity classifiers in Claude.ai, Claude Code, and Claude Cowork fall back to Opus 4.8 by default, and it introduced two updates in beta: mid-conversation tool changes on the Claude Platform and automatic fallbacks on the API. Opus 5 is a hosted model; the announcement does not describe a release of weights. USASI has not tested it.","keywords":"news release 2026-07-24"},{"type":"news","slug":"midjourney-releases-version-8-2","href":"/news/midjourney-releases-version-8-2/","name":"Midjourney launches its V8.2 image model","summary":"On July 24, 2026 Midjourney announced that it was launching its V8.2 image model that day. The post says the update focuses on aesthetics, image quality, and personalization, that random low-quality results should be much less frequent, and that creating personalization profiles for V8.2 draws on a larger and improved pool of images. On August 27, 2026 Midjourney began letting everyone test its first V8.2 image edit model, which supports instruction-based editing, inpainting, and outpainting. V8.2 is offered through Midjourney's own service; the post does not describe a release of model weights. USASI has not tested V8.2.","keywords":"news release 2026-07-24"},{"type":"news","slug":"midjourney-announces-first-acquisition","href":"/news/midjourney-announces-first-acquisition/","name":"Midjourney announces its first acquisition, Co–Star","summary":"On July 23, 2026 Midjourney announced on its updates blog that its first acquisition is Co–Star. The post says Co–Star's founder and team will join Midjourney to help build a broader organization around product and design, while Co–Star itself will remain entirely under its founder's control and direction. The post links the move to plans for what it calls a very unusual research lab, starting with Midjourney Medical, and to half a dozen projects it expects to announce over the following six months. The post does not give financial terms or say whether the transaction has closed.","keywords":"news acquisition 2026-07-23"},{"type":"news","slug":"uk-aisi-and-caisi-assess-kimi-k3-cyber-capabilities","href":"/news/uk-aisi-and-caisi-assess-kimi-k3-cyber-capabilities/","name":"UK AISI and NIST's CAISI publish a preliminary assessment of Kimi K3's cyber capabilities","summary":"On July 23, 2026, NIST published a joint preliminary evaluation by the UK AI Security Institute and the Center for AI Standards and Innovation (CAISI) of Kimi K3, a model Moonshot AI released on July 16, 2026. The page describes a small set of public and private benchmarks and reports results on ExploitBench, a public exploit-development benchmark developed by Carnegie Mellon University, and on \"The Last Ones,\" a simulated corporate network attack range, comparing Kimi K3 with U.S. and PRC models. It also reports that Kimi K3's safeguards did not prevent it from attempting cyber exploit development or offensive cyber operations during the evaluations. The authors describe the results as preliminary.","keywords":"news research 2026-07-23"},{"type":"news","slug":"arcee-ai-and-doe-announce-genesis-science-1","href":"/news/arcee-ai-and-doe-announce-genesis-science-1/","name":"Arcee AI announces Genesis-Science-1, an open science model being built with the Department of Energy","summary":"Arcee AI wrote on July 22, 2026 that it and the U.S. Department of Energy had announced the development of Genesis-Science-1 (GS1), describing it as an open model for scientific research built with DOE and its national laboratories through the Genesis Mission. Arcee says GS1 is built on its next generation of Trinity models and will be a trillion-parameter-class language model. The weights have not been released: Arcee says GS1 will be released openly later in the year with the weights, a technical report, and public demonstrations. The post does not state a license.","keywords":"news research 2026-07-22"},{"type":"news","slug":"cognition-signs-doe-mou-to-join-genesis-mission","href":"/news/cognition-signs-doe-mou-to-join-genesis-mission/","name":"Cognition signs a memorandum of understanding with the Department of Energy to join the Genesis Mission","summary":"Cognition said on July 22, 2026 that it has signed a memorandum of understanding with the U.S. Department of Energy to join the Genesis Mission, which the post describes as launched by executive order in November 2025. According to the post, Cognition will contribute alongside DOE and the national laboratories in four areas: software and data security, modernizing legacy scientific code, expanding scientific workforce capacity, and cloud modernization. The post does not give further terms of the memorandum.","keywords":"news policy 2026-07-22"},{"type":"news","slug":"microsoft-announces-genesis-mission-commitment-spark","href":"/news/microsoft-announces-genesis-mission-commitment-spark/","name":"Microsoft commits to the Genesis Mission with a SPARK program office and four initial projects","summary":"On July 22, 2026 Microsoft announced a long-term commitment to the Department of Energy's Genesis Mission, centered on SPARK (Scientific Partnership Advancing Research & Knowledge), a new program office that Microsoft calls the single front door and coordination hub for Genesis Mission collaboration with the company. The commitment includes access to Microsoft Discovery, which Microsoft says is now generally available, and Microsoft says Azure capabilities will help extend DOE's American Science Cloud. Microsoft describes four of the first projects under the partnership, with Pacific Northwest National Laboratory, Lawrence Livermore National Laboratory, the Johns Hopkins University Applied Physics Laboratory, and Idaho National Laboratory, covering work from energy storage materials to nuclear licensing.","keywords":"news policy 2026-07-22"},{"type":"news","slug":"cisco-releases-antares-vulnerability-localization-models","href":"/news/cisco-releases-antares-vulnerability-localization-models/","name":"Cisco releases Antares-350M and Antares-1B, open-weight models for locating vulnerable code","summary":"Cisco introduced Antares on July 21, 2026, a family of security-focused small language models for finding which source files in a codebase are likely to contain a known vulnerability, and released Antares-350M and Antares-1B as open-weight models on Hugging Face. Cisco says the models start from a vulnerability description, search and read files in a repository, and output a ranked list of likely files along with the terminal exploration trace, and that they are not meant to replace the broader application security toolchain. The Antares-1B model card lists the Apache 2.0 license and says the model is built on IBM Granite 4.0 1B; downloading it requires accepting gated-access terms on Hugging Face. Cisco says an Antares-3B model is coming soon. USASI has not tested the models.","keywords":"news release 2026-07-21"},{"type":"news","slug":"poolside-releases-laguna-s-2-1","href":"/news/poolside-releases-laguna-s-2-1/","name":"Poolside releases Laguna S 2.1, a 118B-parameter open-weight model with a 1M-token context","summary":"Poolside introduced Laguna S 2.1 on July 21, 2026, a Mixture-of-Experts model with 118B total parameters and 8B activated per token that the model card says is designed for agentic coding and long-horizon work. Poolside describes it as a scale-up of the Laguna XS family, and the model card lists a 1,048,576-token context window. The weights are on Hugging Face under the OpenMDW-1.1 license, with FP8, NVFP4, INT4, and GGUF variants and a DFlash draft model for speculative decoding. USASI has not tested this model.","keywords":"news release 2026-07-21"},{"type":"news","slug":"world-labs-acquires-scenix","href":"/news/world-labs-acquires-scenix/","name":"World Labs announces that robotics company SceniX is joining it","summary":"On July 21, 2026 World Labs announced that SceniX, a robotics company, is joining World Labs; the post is titled \"World Labs Acquires SceniX\". World Labs says robotics is where spatial intelligence becomes physical and that it expects the two teams' technical strengths to be complementary, and it says it will share more later about the technologies SceniX has built. The announcement gives no financial terms. On September 28, 2026 World Labs announced that it is joining AMD, an agreement USASI covered separately.","keywords":"news acquisition 2026-07-21"},{"type":"news","slug":"nvidia-releases-cosmos-3-edge","href":"/news/nvidia-releases-cosmos-3-edge/","name":"NVIDIA releases Cosmos3-Edge, a 4B omnimodal world model, under OpenMDW-1.1","summary":"NVIDIA's Hugging Face model card gives July 20, 2026 as the release date of Cosmos3-Edge, a 4B-parameter model in the Cosmos3 collection, on both Hugging Face and GitHub. The card describes Cosmos3 as omnimodal world models built on a Mixture-of-Transformers architecture that take text, image, video and action-trajectory inputs and generate text, images, video and action outputs for physical AI uses such as robotics and autonomous vehicles. The model is released under OpenMDW-1.1 and is described as ready for commercial and non-commercial use. A note on the card says the generator checkpoint and runtime defaults were updated on August 25, 2026. USASI has not tested the model.","keywords":"news release 2026-07-20"},{"type":"news","slug":"lm-studio-introduces-bionic-agent-app","href":"/news/lm-studio-introduces-bionic-agent-app/","name":"LM Studio introduces Bionic, a separate agent app for open models","summary":"LM Studio introduced LM Studio Bionic on July 16, 2026, describing it as an AI agent made for open models. The post says Bionic is a new, separate app from LM Studio, that local models in it are powered by the LM Studio runtime, and that it can also use large open models through LM Studio Secure Cloud, which requires an LM Studio account with billing. Described uses include coding, document and file work, and research. The post does not say whether Bionic's software is open source. USASI has not tested this app.","keywords":"news release 2026-07-16"},{"type":"news","slug":"nvidia-releases-nemotron-3-embed","href":"/news/nvidia-releases-nemotron-3-embed/","name":"NVIDIA releases Nemotron 3 Embed multilingual text embedding models in 1B and 8B sizes","summary":"NVIDIA's Hugging Face model cards give July 16, 2026 as the release date of Nemotron-3-Embed-8B-BF16 and Nemotron-3-Embed-1B-BF16, text embedding models for retrieval and semantic similarity in retrieval-augmented generation systems. The cards say the models were evaluated across 34 languages and output 4096-dimensional (8B) and 2048-dimensional (1B) vectors that can be shortened by slicing and re-normalizing. Both are licensed under OpenMDW-1.1, and the 8B card says it is built with Mistral AI's Ministral-3-8B-Instruct-2512, released under Apache 2.0. USASI has not tested the models.","keywords":"news release 2026-07-16"},{"type":"news","slug":"zyphra-releases-zuna1-1-eeg-model","href":"/news/zyphra-releases-zuna1-1-eeg-model/","name":"Zyphra releases ZUNA1.1, an open-weight EEG foundation model, under Apache 2.0","summary":"Zyphra released ZUNA1.1 on July 16, 2026, according to an update note on its post about the model, which carries an August 3, 2026 dateline. Zyphra describes ZUNA1.1 as a 380M-parameter diffusion autoencoder that reconstructs, denoises, and upsamples scalp-EEG signals, and says it now accepts variable-length inputs from 0.5 to 30 seconds. The weights are on Hugging Face under the Apache 2.0 license, with inference code on GitHub and a zuna pip package. Zyphra's disclaimer says its services are for research use only and have not been validated for any medical or clinical use. USASI has not tested this model.","keywords":"news release 2026-07-16"},{"type":"news","slug":"thinking-machines-lab-releases-inkling-open-weights","href":"/news/thinking-machines-lab-releases-inkling-open-weights/","name":"Thinking Machines Lab releases Inkling, its first open-weight model, under Apache 2.0","summary":"On July 15, 2026 Thinking Machines Lab released Inkling, a model it says it trained from scratch and published with full weights. The announcement describes a mixture-of-experts transformer with 975B total and 41B active parameters, a context window of up to 1M tokens, and native reasoning over text, images, and audio, and calls it the first in a family of models of different sizes. The weights are published on Hugging Face without gating under the Apache 2.0 license, and the model is also available for fine-tuning on the company's Tinker platform. The announcement says some reported results came from a different checkpoint than the one released. USASI has not tested Inkling.","keywords":"news release 2026-07-15"},{"type":"news","slug":"linux-foundation-launches-x402-foundation","href":"/news/linux-foundation-launches-x402-foundation/","name":"Linux Foundation announces the operational launch of the x402 Foundation for agent payments","summary":"On July 14, 2026 the Linux Foundation announced the operational launch of the x402 Foundation, an open-governance body formed to steward the x402 protocol. The press release describes x402 as an open standard for internet-native payments over HTTP, contributed by Coinbase, that lets AI agents, APIs, and applications send and receive payments. It says the foundation is now fully active under formal, open governance and lists premier members including Amazon Web Services, Cloudflare, Coinbase, Google, Mastercard, Stripe, and Visa.","keywords":"news governance 2026-07-14"},{"type":"news","slug":"cartesia-releases-ink-2","href":"/news/cartesia-releases-ink-2/","name":"Cartesia releases Ink-2, a speech-to-text model for real-time voice agents","summary":"On July 9, 2026 Cartesia released Ink-2, a speech-to-text model built for real-time voice agents. The announcement says Ink-2 has built-in semantic endpointing that uses meaning rather than silence to decide when a speaker's turn is over, and that it natively emits turn.start, turn.eager_end, and turn.end events without an external voice activity detector. Cartesia also says it worked on recognizing structured entities such as phone numbers, email addresses, and dates. Ink-2 is available through Cartesia's playground and API and through LiveKit, Vapi, and Pipecat; the post does not describe a release of model weights. USASI has not tested Ink-2.","keywords":"news release 2026-07-09"},{"type":"news","slug":"meta-releases-muse-spark-1-1-and-meta-model-api","href":"/news/meta-releases-muse-spark-1-1-and-meta-model-api/","name":"Meta releases Muse Spark 1.1 and opens the Meta Model API in public preview","summary":"On July 9, 2026 Meta Superintelligence Labs introduced Muse Spark 1.1, which the announcement describes as a multimodal reasoning model built for agentic tasks and an upgrade over Muse Spark. Meta says the model is available in \"Thinking\" mode in the Meta AI app and on meta.ai, and that developers can reach it through the Meta Model API, which the post introduces as a public preview. The post lists a 1-million-token context window, computer use across multiple applications, and the ability to work as a main agent that delegates to parallel subagents. It does not say that the weights are released or give license terms. USASI has not tested Muse Spark 1.1.","keywords":"news release 2026-07-09"},{"type":"news","slug":"openai-releases-gpt-5-6-model-family","href":"/news/openai-releases-gpt-5-6-model-family/","name":"OpenAI releases the GPT-5.6 model family, GPT-5.6 Sol, Terra, and Luna, in its API","summary":"OpenAI's API changelog records that on July 9, 2026 it released the GPT-5.6 model family: GPT-5.6 Sol for frontier capability, GPT-5.6 Terra for a balance of intelligence and cost, and GPT-5.6 Luna for efficient, high-volume workloads. The entry says the gpt-5.6 alias routes requests to gpt-5.6-sol, and it lists the Responses, Chat Completions, and Batch endpoints. It also says GPT-5.6 adds Programmatic Tool Calling, explicit prompt caching controls, persisted reasoning, max reasoning effort, and Pro mode, with Multi-agent orchestration in beta for the Responses API. The changelog describes hosted models offered through OpenAI's API; it does not describe a release of model weights. USASI has not tested these models.","keywords":"news release 2026-07-09"},{"type":"news","slug":"pytorch-2-13-0-released","href":"/news/pytorch-2-13-0-released/","name":"PyTorch 2.13.0 released with FlexAttention on Apple Silicon and the torchcomms backend","summary":"PyTorch 2.13.0 was published on GitHub on July 8, 2026. The release notes highlight FlexAttention support on Apple Silicon (MPS) and a deterministic FlexAttention backward path on CUDA, a prototype CuTeDSL \"Native DSL\" backend for Inductor alongside Triton, and nn.LinearCrossEntropyLoss, which combines the final prediction and loss computation to reduce peak GPU memory. They also introduce torchcomms, a new communications backend for PyTorch Distributed, and Python 3.15 wheels on Linux, while dropping free-threaded CPython 3.13t binaries and Bazel build support. USASI has not tested this release.","keywords":"news release 2026-07-08"},{"type":"news","slug":"xai-releases-grok-4-5","href":"/news/xai-releases-grok-4-5/","name":"xAI makes Grok 4.5 available on its API","summary":"xAI's API release notes record that on July 8, 2026 Grok 4.5, described as a model for coding, agentic tasks, and knowledge work, became available on the xAI API with configurable reasoning effort (low, medium, or high, with high as the default). A later entry, dated July 17, says Grok 4.5 became available in the API console in the EU. The release notes describe a hosted model and do not describe a release of weights. USASI has not tested Grok 4.5.","keywords":"news release 2026-07-08"},{"type":"news","slug":"meta-launches-muse-image-and-previews-muse-video","href":"/news/meta-launches-muse-image-and-previews-muse-video/","name":"Meta launches Muse Image and previews Muse Video, its first Superintelligence Labs media models","summary":"On July 7, 2026 Meta Superintelligence Labs launched Muse Image, an image generation model that can edit images, compose from multiple references, and use tools, and previewed Muse Video, which Meta says is built on the same pretraining base as Muse Image and supports native audio. Meta describes them as the first media generation models developed by Meta Superintelligence Labs. Muse Image is available in the Meta AI app and on meta.ai, in Instagram Stories in the U.S., and in WhatsApp in a limited set of countries, while Muse Video is described as coming soon, without a date. Meta says Muse Image outputs in the Meta AI app and on meta.ai include Content Seal, an invisible watermark; the post does not mention an API, open weights, or a license. USASI has not tested either model.","keywords":"news release 2026-07-07"},{"type":"news","slug":"vercel-acquires-better-auth","href":"/news/vercel-acquires-better-auth/","name":"Vercel announces it is acquiring Better Auth; the authentication library stays MIT-licensed","summary":"On July 7, 2026 Vercel announced that it is acquiring Better Auth, the company behind the open-source TypeScript authentication library of the same name. Vercel says the library remains free and open source under the MIT license and keeps its name, and that the team will continue to lead development with the same open contribution model and community governance. Better Auth's founder and core team are joining Vercel, and Vercel says the team will continue its Agent Auth work, which gives each agent its own identity, bringing agent identity to Vercel Connect and eve. The post does not say whether the deal has closed and does not disclose financial terms.","keywords":"news acquisition 2026-07-07"},{"type":"news","slug":"arc-prize-names-first-arc-agi-3-milestone-prize-winners","href":"/news/arc-prize-names-first-arc-agi-3-milestone-prize-winners/","name":"ARC Prize names the winners of the first ARC-AGI-3 milestone prize for open-source solutions","summary":"On July 6, 2026, the ARC Prize Foundation announced the winners of the first ARC Prize 2026 milestone prize, which rewards mid-competition open-source solutions to the ARC-AGI-3 interactive reasoning benchmark; the first milestone period ran through June 30. The post names three winning submissions, including Tufa Labs' \"The Duck,\" which plays the games by having a small open-source LLM write and run Python in a live REPL, and links each winner's Kaggle notebook. The second and final milestone prize was scheduled to end on September 30.","keywords":"news research 2026-07-06"},{"type":"news","slug":"mozilla-ai-introduces-otari","href":"/news/mozilla-ai-introduces-otari/","name":"Mozilla.ai introduces Otari, an open-source LLM control plane under Apache 2.0","summary":"Mozilla.ai introduced Otari on July 6, 2026, calling it the open-source LLM control plane built by Mozilla.ai. The announcement says it routes requests across multiple LLM providers through a single endpoint, with budgets and usage visibility for LLM spend, centralized API keys and access controls, automatic failover, and Agent Harnesses for building agent-ready applications. The GitHub repository describes Otari as an open-source, OpenAI-compatible LLM gateway that you run yourself, and it is licensed under Apache 2.0; the post says it can be deployed in hosted or self-hosted environments. USASI has not tested this software.","keywords":"news release 2026-07-06"},{"type":"news","slug":"poolside-releases-laguna-xs-2-1","href":"/news/poolside-releases-laguna-xs-2-1/","name":"Poolside releases Laguna XS 2.1, a 33B-parameter open-weight coding model, under OpenMDW-1.1","summary":"Poolside introduced Laguna XS 2.1 on July 2, 2026, describing it as a Mixture-of-Experts model with 33B total parameters and 3B activated per token. The model card calls it an upgraded version of Laguna XS.2, designed for agentic coding and long-horizon work on a local machine. Poolside licenses the weights under OpenMDW-1.1 and publishes them on Hugging Face in BF16, FP8, NVFP4, and INT4 formats; the model is also offered through Poolside's API and OpenRouter. USASI has not tested this model.","keywords":"news release 2026-07-02"},{"type":"newsletter","slug":"issue-1","href":"/newsletter/issue-1/","name":"Issue 1: open decision models, EmbeddingGemma 2, and a learning center","summary":"What changed in the USASI catalog from October 4 to 11, 2026. Every item links to its sources.","keywords":"weekly email newsletter issue"},{"type":"page","slug":"report-2026-10","href":"/report/2026-10/","name":"The State of American AI, October 2026","summary":"October 2026 edition, data cutoff 2026-10-13. Of 150 organization records, 95 (63%) have a documented headquarters in California; headquarters labels name 18 states and Washington, D.C. in all.","keywords":"report state of american ai edition statistics figures data"},{"type":"page","slug":"start","href":"/start/","name":"Start here","summary":"Four ways into the catalog, and a checklist before you use a model or tool.","keywords":"new begin intro overview guide tour"},{"type":"page","slug":"companies","href":"/companies/","name":"Companies & Labs","summary":"Directory of U.S. AI organizations: labs, companies, universities, agencies, and nonprofits.","keywords":"organizations directory labs list"},{"type":"page","slug":"open","href":"/open/","name":"Open Models & Tools","summary":"Directory of U.S.-led open models, software, datasets, and evaluation tools.","keywords":"models tools directory open weights list"},{"type":"page","slug":"learn","href":"/learn/","name":"Learn","summary":"Plain-language explainers, learning paths, and 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Paul G. Allen School of Computer Science & Engineering (courtesy) University of Washington Semantics derived automatically from language corpora contain human-like biases"},{"type":"person","slug":"azita-martin","href":"/people/azita-martin/","name":"Azita Martin","summary":"Listed by NVIDIA in 2025 as VP and general manager for retail and consumer packaged goods","keywords":"VP and GM for Retail and CPG (as listed in 2025) NVIDIA Chief marketing officer Maana NVIDIA AI for retail and consumer packaged goods"},{"type":"person","slug":"baptiste-roziere","href":"/people/baptiste-roziere/","name":"Baptiste Rozière","summary":"First author of Code Llama and co-author of LLaMA and Llama 3; later an author on Mistral AI model papers","keywords":"Research scientist in the code generation team Meta AI LLaMA and Llama 3 Code Llama Unsupervised translation of programming languages Mistral AI model papers"},{"type":"person","slug":"barret-zoph","href":"/people/barret-zoph/","name":"Barret Zoph","summary":"First author of neural architecture search at Google Brain; 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led aspects of OpenAI's robot-hand research","keywords":"Listed under executive leadership in the OpenAI o1 System Card (2024) OpenAI HumanEval Solving Rubik's Cube with a Robot Hand OpenAI o1"},{"type":"person","slug":"brandon-duderstadt","href":"/people/brandon-duderstadt/","name":"Brandon Duderstadt","summary":"Nomic co-founder and co-author of the GPT4All and Nomic Embed technical reports","keywords":"Founder Calcifer Computing Co-founder and founding CEO Nomic AI GPT4All Nomic Embed"},{"type":"person","slug":"bratin-saha","href":"/people/bratin-saha/","name":"Bratin Saha","summary":"Former AWS vice president for AI, machine learning and data infrastructure services","keywords":"Vice President and General Manager of Artificial Intelligence (AI), Machine Learning (ML), and Data Infrastructure Amazon Web Services (Amazon) Vice President of Software Infrastructure NVIDIA Chief Product and Technology Officer (appointed June 2024) DigitalOcean Amazon SageMaker and AWS AI/ML services"},{"type":"person","slug":"bryan-catanzaro","href":"/people/bryan-catanzaro/","name":"Bryan Catanzaro","summary":"VP of Applied Deep Learning Research at NVIDIA; 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Allen School of Computer Science and Engineering) Co-founder and chief executive officer Overland AI Assistant Professor Georgia Institute of Technology (School of Interactive Computing) GPMP2 (Gaussian Process Motion Planner 2)"},{"type":"person","slug":"byron-hsu","href":"/people/byron-hsu/","name":"Byron Hsu","summary":"Led Liger Kernel, open-source Triton kernels for memory-efficient LLM training and fine-tuning","keywords":"Merge oncall for prefill-decode (PD) disaggregation SGLang project Liger Kernel project lead LinkedIn Liger Kernel SGLang"},{"type":"person","slug":"carlos-guestrin","href":"/people/carlos-guestrin/","name":"Carlos Guestrin","summary":"Stanford computer science professor; co-author of XGBoost and LIME and co-founder of Turi","keywords":"Professor, Computer Science; Fortinet Founders Professor and Senior Fellow at the Stanford Institute for Human-Centered AI Stanford University Senior Director of Machine Learning and AI Apple Amazon Professor of Machine Learning, Computer Science & Engineering University of Washington Finmeccanica Associate Professor Carnegie Mellon University Co-founder Turi, Inc. (formerly GraphLab and Dato) XGBoost LIME"},{"type":"person","slug":"ce-zhang","href":"/people/ce-zhang/","name":"Ce Zhang","summary":"Founder and CTO of Together AI; Neubauer Associate Professor of computer science at the University of Chicago","keywords":"Founder & CTO Together AI Neubauer Associate Professor, Department of Computer Science University of Chicago Associate Professor ETH Zurich Postdoctoral researcher (advised by Chris Ré) Stanford University RedPajama HELM (Holistic Evaluation of Language Models)"},{"type":"person","slug":"charlie-bell","href":"/people/charlie-bell/","name":"Charlie Bell","summary":"Former AWS senior vice president and Microsoft security executive; Tenable board member since October 2026","keywords":"Member of the Board of Directors Tenable Executive Vice President of Security, Compliance, Identity and Management Microsoft Senior Vice President Amazon Web Services (Amazon) AWS core engineering and utility computing Microsoft Secure Future Initiative"},{"type":"person","slug":"chelsea-finn","href":"/people/chelsea-finn/","name":"Chelsea Finn","summary":"Stanford assistant professor who runs the IRIS lab; co-founder of Physical Intelligence and co-author of π0","keywords":"Assistant Professor in Computer Science and Electrical Engineering Stanford University Co-founder Physical Intelligence π0 (openpi) Model-Agnostic Meta-Learning (MAML)"},{"type":"person","slug":"chip-huyen","href":"/people/chip-huyen/","name":"Chip Huyen","summary":"Writer on AI system design; former NeMo core developer at NVIDIA and Stanford ML systems instructor","keywords":"ML tooling; core developer of NeMo NVIDIA Instructor, CS 329S Stanford University NVIDIA NeMo Designing Machine Learning Systems (O'Reilly, 2022) AI Engineering (2025)"},{"type":"person","slug":"cho-jui-hsieh","href":"/people/cho-jui-hsieh/","name":"Cho-Jui Hsieh","summary":"UCLA associate professor of computer science; co-author of the LAMB and Lion optimizer papers","keywords":"Associate Professor of Computer Science University of California, Los Angeles Visiting scholar (from summer 2018) Google Assistant Professor, Computer Science and Statistics University of California, Davis Lion optimizer (Symbolic Discovery of Optimization Algorithms) LAMB optimizer (Large Batch Optimization for Deep Learning)"},{"type":"person","slug":"chris-lattner","href":"/people/chris-lattner/","name":"Chris Lattner","summary":"Co-founder of Modular, maker of the MAX inference framework and the Mojo language","keywords":"Co-founder Modular MAX Mojo LLVM"},{"type":"person","slug":"chris-olah","href":"/people/chris-olah/","name":"Chris Olah","summary":"Anthropic co-founder working on reverse engineering neural networks; previously led interpretability at OpenAI","keywords":"Co-founder Anthropic Led interpretability research OpenAI Worked at Google Brain Google Brain A Mathematical Framework for Transformer Circuits Toy Models of Superposition On the Biology of a Large Language Model"},{"type":"person","slug":"chris-wright","href":"/people/chris-wright/","name":"Chris Wright","summary":"CTO and Senior Vice President of Global Engineering at Red Hat; longtime Linux kernel developer","keywords":"Chief Technology Officer and Senior Vice President, Global Engineering Red Hat Linux kernel"},{"type":"person","slug":"christian-cantrell","href":"/people/christian-cantrell/","name":"Christian Cantrell","summary":"Founder and CPO of Reve AI; formerly led Adobe Design Prototyping and was VP of Product at Stability AI","keywords":"Founder and CPO (Chief Product/Prompt Officer) Reve AI VP of Product Stability AI Director, Experience Development, Adobe Design (head of Adobe Design Prototyping) Adobe stable-diffusion-api-server Stability Platform and DreamStudio 1.0"},{"type":"person","slug":"christoph-feichtenhofer","href":"/people/christoph-feichtenhofer/","name":"Christoph Feichtenhofer","summary":"Director and research scientist at Meta Superintelligence Labs; SAM 3 project lead and SlowFast first author","keywords":"Director, Research Scientist Meta Superintelligence Labs Segment Anything (SAM 2 and SAM 3) SlowFast and PySlowFast"},{"type":"person","slug":"chris-bishop","href":"/people/chris-bishop/","name":"Christopher Bishop","summary":"Microsoft Technical Fellow, founder of Microsoft Research AI for Science, and machine learning textbook author","keywords":"Technical Fellow and founder of Microsoft Research AI for Science Microsoft Research Lab Director (2015–2022) Microsoft Research Cambridge Honorary Professor of Computer Science University of Edinburgh Pattern Recognition and Machine Learning Deep Learning: Foundations and Concepts"},{"type":"person","slug":"christopher-manning","href":"/people/christopher-manning/","name":"Christopher Manning","summary":"Stanford professor of linguistics and computer science; co-author of GloVe and the Stanza NLP library","keywords":"Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Stanford University Assistant Professor, Computational Linguistics Program (1994–96) Carnegie Mellon University Stanza GloVe: Global Vectors for Word Representation CS224N: Natural Language Processing with Deep Learning"},{"type":"person","slug":"christopher-re","href":"/people/christopher-re/","name":"Christopher Ré","summary":"Stanford computer science professor; co-author of FlashAttention, Hyena and RedPajama; Together AI founder","keywords":"Professor, Department of Computer Science (Stanford AI Lab, CRFM, Machine Learning Group) Stanford University Founder Together AI RedPajama HELM (Holistic Evaluation of Language Models) FlashAttention Hyena"},{"type":"person","slug":"clement-delangue","href":"/people/clement-delangue/","name":"Clément Delangue","summary":"Co-founder and CEO of Hugging Face, whose Hub distributes open models and the libraries used to run them","keywords":"Co-founder and CEO Hugging Face Hugging Face Hub Transformers"},{"type":"person","slug":"clement-farabet","href":"/people/clement-farabet/","name":"Clément Farabet","summary":"VP of Research at Google DeepMind and Gemma 4 core contributor; 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co-author of MMLU and Humanity's Last Exam","keywords":"Executive & Research Director Center for AI Safety (CAIS) Humanity's Last Exam (HLE) Measuring Massive Multitask Language Understanding (MMLU) Gaussian Error Linear Units (GELUs)"},{"type":"person","slug":"dan-jurafsky","href":"/people/dan-jurafsky/","name":"Dan Jurafsky","summary":"Stanford professor of linguistics and computer science; co-author of Speech and Language Processing","keywords":"Reynolds Professor in Humanities; Professor of Linguistics; Professor of Computer Science Stanford University Speech and Language Processing (3rd edition draft)"},{"type":"person","slug":"dani-valevski","href":"/people/dani-valevski/","name":"Dani Valevski","summary":"Co-first author of GameNGen, a diffusion model that simulates DOOM in real time, and first author of UniTune","keywords":"Listed with Google Research as co-first author of GameNGen (2024) Google Research GameNGen (Diffusion Models Are Real-Time Game Engines) UniTune"},{"type":"person","slug":"daniel-bevenius","href":"/people/daniel-bevenius/","name":"Daniel Bevenius","summary":"llama.cpp code owner for model-conversion tooling and member of its CI team; Hugging Face","keywords":"Hugging Face is the company listed on Bevenius's GitHub profile Hugging Face Code owner for model-conversion and the XCFramework build; member of the CI and llama-common teams llama.cpp project llama.cpp"},{"type":"person","slug":"daniel-de-freitas","href":"/people/daniel-de-freitas/","name":"Daniel De Freitas","summary":"Co-founder of Character.AI; led the development of the Meena and LaMDA dialog models at Google","keywords":"Co-founder (December 2022 launch post) Character.AI Co-author of the LaMDA paper (work done while at Google, per the paper's footnote) Google Meena and LaMDA Character.AI"},{"type":"person","slug":"daniel-han","href":"/people/daniel-han/","name":"Daniel Han","summary":"Works on Unsloth, open-source software for fine-tuning and running models on local hardware","keywords":"Listed on Unsloth's About page for software, data, and algorithms Unsloth Unsloth (open-source library, Studio, and Desktop) Unsloth on Hugging Face"},{"type":"person","slug":"danqi-chen","href":"/people/danqi-chen/","name":"Danqi Chen","summary":"Princeton associate professor co-leading the Princeton NLP Group; co-author of RoBERTa and SimCSE","keywords":"Associate Professor of Computer Science; co-lead, Princeton NLP Group; associate director, Princeton Language and Intelligence Princeton University Member of technical staff (academic year 2025–26) Thinking Machines Lab Research scientist, Facebook AI Research (FAIR) Meta (Facebook AI Research) Research scientist Google DeepMind SimCSE RoBERTa: A Robustly Optimized BERT Pretraining Approach"},{"type":"person","slug":"daphne-koller","href":"/people/daphne-koller/","name":"Daphne Koller","summary":"Founder and CEO of insitro, Coursera co-founder, and Stanford adjunct professor of computer science","keywords":"Founder & CEO, Board Member insitro Adjunct Professor, Computer Science (formerly the Rajeev Motwani Professor of computer science) Stanford University Co-founder, co-CEO and president Coursera Chief computing officer Calico Probabilistic Graphical Models specialization"},{"type":"person","slug":"dario-gil","href":"/people/dario-gil/","name":"Darío Gil","summary":"Under Secretary for Science at the U.S. Department of Energy; 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former Google research scientist and co-author of World Models","keywords":"Co-Founder & CEO Sakana AI Research Scientist; led a Google Brain research team Google (Google Brain) World Models sketch-rnn The AI Scientist"},{"type":"person","slug":"david-harwath","href":"/people/david-harwath/","name":"David Harwath","summary":"UT Austin assistant professor who leads the SALT Lab; former MIT CSAIL research scientist","keywords":"Assistant Professor, Department of Computer Science; leads the SALT Lab The University of Texas at Austin Research scientist (2018–2020) MIT CSAIL Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech Spoken caption corpora"},{"type":"person","slug":"david-heinemeier-hansson","href":"/people/david-heinemeier-hansson/","name":"David Heinemeier Hansson","summary":"Creator of Omarchy, a Linux system that sets up local-model tools such as Ollama and LM Studio","keywords":"Co-owner 37signals President Omacom Foundation Omarchy Ollama setup in Omarchy Hermes and OpenClaw in Omarchy Omacom sponsorship of local AI work"},{"type":"person","slug":"david-luebke","href":"/people/david-luebke/","name":"David Luebke","summary":"Helped found NVIDIA Research in 2006; 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contributor to its TensorFlow framework","keywords":"Listed with a Google Brain affiliation on the 2017 NMT architecture paper Google Brain google/seq2seq Massive Exploration of Neural Machine Translation Architectures"},{"type":"person","slug":"diederik-p-kingma","href":"/people/diederik-p-kingma/","name":"Diederik P. 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Allen School of Computer Science & Engineering; head of the RSE-Lab University of Washington Leads Ai2's Robotics team Ai2 (Allen Institute for AI) Joined NVIDIA Research in 2017 to start its Robotics Research Lab, which Fox then led NVIDIA NVIDIA Isaac GR00T N1 NVIDIA Cosmos"},{"type":"person","slug":"dirk-groeneveld","href":"/people/dirk-groeneveld/","name":"Dirk Groeneveld","summary":"First author of Ai2's OLMo paper; contributor to OLMo 2, OLMoE, and the Dolma corpus","keywords":"Base-model developer on OLMo 2, listed with an Ai2 affiliation (2024) Ai2 (Allen Institute for AI) OLMo Dolma"},{"type":"person","slug":"dmytro-dzhulgakov","href":"/people/dmytro-dzhulgakov/","name":"Dmytro Dzhulgakov","summary":"Co-founder of Fireworks AI; previously a PyTorch core maintainer at Meta","keywords":"Co-Founder Fireworks AI PyTorch core maintainer Meta PyTorch"},{"type":"person","slug":"dong-yu","href":"/people/dong-yu/","name":"Dong Yu","summary":"VP of AI Foundations at Capital One; former Microsoft Research principal researcher; first author on CNTK","keywords":"VP of AI Foundations Capital One Distinguished Scientist and Vice General Manager (until 2026) Tencent AI Lab Principal Researcher (at Microsoft from 1998 until 2017) Microsoft Research Computational Network Toolkit (CNTK) Context-dependent pre-trained deep neural networks for speech recognition Deep Learning: Methods and Applications"},{"type":"person","slug":"douwe-kiela","href":"/people/douwe-kiela/","name":"Douwe Kiela","summary":"Researcher at Google DeepMind; co-founder and former CEO of Contextual AI; co-author of the RAG paper","keywords":"Researcher Google DeepMind Adjunct Professor in Symbolic Systems Stanford University Co-founder and CEO Contextual AI Head of Research Hugging Face Research Scientist Facebook AI Research (Meta) OLMoE Retrieval-Augmented Generation (RAG) Dynabench"},{"type":"person","slug":"edouard-grave","href":"/people/edouard-grave/","name":"Edouard Grave","summary":"Equal-contribution author of Meta's LLaMA paper; co-author of fastText and of Kyutai's Moshi","keywords":"Chief Language Officer (listed among alumni) Kyutai Research scientist; co-led the development of LLaMA and the fastText library Meta (Facebook AI Research, FAIR) Research scientist in the Apple MLR corporate research lab Apple (Apple MLR) LLaMA fastText Moshi"},{"type":"person","slug":"edward-beeching","href":"/people/edward-beeching/","name":"Edward Beeching","summary":"Hugging Face research scientist; credited author of the TRL library and co-first author of Zephyr","keywords":"Machine Learning Research Scientist Hugging Face TRL (Transformers Reinforcement Learning) SmolLM3 Zephyr: Direct Distillation of LM Alignment"},{"type":"person","slug":"edward-yang","href":"/people/edward-yang/","name":"Edward Yang","summary":"PyTorch core maintainer and co-author of the PyTorch and PyTorch 2 papers; research engineer at Meta","keywords":"Research Engineer Meta Core Maintainer PyTorch (open-source project) PyTorch PyTorch 2: TorchDynamo and TorchInductor (torch.compile)"},{"type":"person","slug":"emanuel-todorov","href":"/people/emanuel-todorov/","name":"Emanuel Todorov","summary":"Roboti LLC researcher whose lab built the MuJoCo physics engine; former UW and UC San Diego professor","keywords":"Research and development Roboti LLC Associate/Affiliate Professor, Applied Mathematics and Computer Science & Engineering University of Washington Assistant Professor (2002–2008) and Associate Professor (2008–2009), Department of Cognitive Science University of California San Diego MuJoCo Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations"},{"type":"person","slug":"emily-bender","href":"/people/emily-bender/","name":"Emily M. Bender","summary":"University of Washington linguistics professor; developer of the LinGO Grammar Matrix and data statements","keywords":"Thomas L. and Margo G. Wyckoff Endowed Professor, Department of Linguistics; Director, Computational Linguistics Laboratory University of Washington LinGO Grammar Matrix Data Statements for Natural Language Processing"},{"type":"person","slug":"emily-webber","href":"/people/emily-webber/","name":"Emily Webber","summary":"Principal ML specialist solutions architect on the AWS Trainium team at Annapurna Labs; author","keywords":"Principal machine learning specialist solutions architect, AWS Trainium team Amazon Web Services (Annapurna Labs) Pretrain Vision and Large Language Models in Python (book and companion code) AWS Trainium Frontier competition"},{"type":"person","slug":"emmanuel-dupoux","href":"/people/emmanuel-dupoux/","name":"Emmanuel Dupoux","summary":"EHESS professor in the Cognitive Machine Learning team; co-author of FAIR's generative spoken language work","keywords":"Professor (EHESS), Cognitive machine learning team EHESS (Laboratoire de Sciences Cognitives et Psycholinguistique) Listed with Facebook AI Research, and also at EHESS, on the generative spoken language modeling paper (2021) Facebook AI Research (Meta) Generative Spoken Language Modeling (GSLM)"},{"type":"person","slug":"eric-hartford","href":"/people/eric-hartford/","name":"Eric Hartford","summary":"Founder of Quixi.AI and creator of the Dolphin and Samantha open models; formerly at Microsoft and AWS","keywords":"Founder Quixi.AI Former employee; the role is not stated Microsoft Former employee; the role is not stated Amazon Web Services (AWS) Dolphin"},{"type":"person","slug":"eric-horvitz","href":"/people/eric-horvitz/","name":"Eric Horvitz","summary":"Chief Scientific Officer of Microsoft and former director of Microsoft Research","keywords":"Chief Scientific Officer Microsoft Director of Microsoft Research Microsoft Research Can Generalist Foundation Models Outcompete Special-Purpose Tuning? 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Manning Stanford University Direct Preference Optimization (DPO) DetectGPT"},{"type":"person","slug":"ethan-perez","href":"/people/ethan-perez/","name":"Ethan Perez","summary":"Leads the alignment team at Anthropic; co-author of the RAG, red-teaming, and Constitutional Classifiers work","keywords":"Leads the alignment team Anthropic Constitutional Classifiers Red Teaming Language Models with Language Models Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"},{"type":"person","slug":"eugene-yan","href":"/people/eugene-yan/","name":"Eugene Yan","summary":"Member of Technical Staff at Anthropic; former Principal Applied Scientist at Amazon","keywords":"Member of Technical Staff Anthropic Principal Applied Scientist Amazon VP, Machine Learning Alibaba / Lazada What We've Learned From A Year of Building with LLMs"},{"type":"person","slug":"evan-hubinger","href":"/people/evan-hubinger/","name":"Evan Hubinger","summary":"Alignment Stress-Testing lead at Anthropic; first author of the Sleeper Agents paper","keywords":"Alignment Stress-Testing lead Anthropic Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training Alignment faking in large language models Risks from Learned Optimization in Advanced Machine Learning Systems"},{"type":"person","slug":"fabian-pedregosa","href":"/people/fabian-pedregosa/","name":"Fabian Pedregosa","summary":"Research scientist at Google on the Gemini Core team; led scikit-learn's first public release in 2010","keywords":"Research scientist, Gemini Core team Google Postdoc (with Laurent El Ghaoui) University of California, Berkeley scikit-learn JAXopt (implicit differentiation) memory-profiler"},{"type":"person","slug":"fei-fei-li","href":"/people/fei-fei-li/","name":"Fei-Fei Li","summary":"Co-founder and CEO of World Labs, Stanford Sequoia Professor, and principal investigator of ImageNet","keywords":"Co-founder/CEO World Labs Sequoia Capital Professor of Computer Science; Co-Director, Institute for Human-Centered Artificial Intelligence (HAI) Stanford University Vice President at Google and Chief Scientist of AI/ML at Google Cloud (2017–2018) Google (Google Cloud) ImageNet"},{"type":"person","slug":"francesca-rossi","href":"/people/francesca-rossi/","name":"Francesca Rossi","summary":"IBM Fellow and IBM Global Leader for Responsible AI and AI Governance; past president of AAAI and IJCAI","keywords":"IBM Global Leader for Responsible AI and AI Governance; IBM Fellow IBM Research Professor of computer science (20 years) University of Padova Thinking Fast and Slow in AI"},{"type":"person","slug":"francisco-massa","href":"/people/francisco-massa/","name":"Francisco Massa","summary":"Emeritus TorchVision maintainer and co-author of Meta's DINOv3, DETR and Detectron2","keywords":"Facebook AI Research, as listed on GitHub profile Meta (Facebook AI Research) DINOv3 TorchVision DETR Detectron2"},{"type":"person","slug":"francois-chollet","href":"/people/francois-chollet/","name":"François Chollet","summary":"Creator and project lead of Keras, whose KerasHub runs open-weight models on JAX, PyTorch, or TensorFlow","keywords":"Co-founder ARC Prize Foundation Co-founder Ndea Keras and KerasHub ARC-AGI"},{"type":"person","slug":"frederic-bastien","href":"/people/frederic-bastien/","name":"Frédéric Bastien","summary":"Member of the JAX team at NVIDIA; first author of the 2012 Theano paper","keywords":"JAX team NVIDIA JAX Theano"},{"type":"person","slug":"gabriel-synnaeve","href":"/people/gabriel-synnaeve/","name":"Gabriel Synnaeve","summary":"Research scientist at Meta; last author of Code Llama and co-author of Wav2Letter, EnCodec, and MusicGen","keywords":"Research scientist (joined FAIR as a postdoctoral researcher in 2015; listed with Meta Superintelligence Labs on a 2026 paper) Meta (FAIR; Meta Superintelligence Labs) Postdoctoral Researcher (2013–2015) École Normale Supérieure Code Llama Wav2Letter and Flashlight EnCodec and MusicGen"},{"type":"person","slug":"gavriel-state","href":"/people/gavriel-state/","name":"Gavriel State","summary":"Senior Director for Simulation and AI at NVIDIA, heading the team behind Isaac Sim and Isaac Lab","keywords":"Senior Director for Simulation and AI NVIDIA Founder TransGaming Inc. NVIDIA Isaac Lab Isaac Gym and Orbit Newton physics engine"},{"type":"person","slug":"geoffrey-hinton","href":"/people/geoffrey-hinton/","name":"Geoffrey Hinton","summary":"2024 Nobel laureate in Physics; University of Toronto emeritus professor and former Google Vice President","keywords":"Emeritus distinguished professor University of Toronto Vice President and Engineering Fellow (half-time, 2013–2023) Google ImageNet Classification with Deep Convolutional Neural Networks (AlexNet) Distilling the Knowledge in a Neural Network"},{"type":"person","slug":"georgi-gerganov","href":"/people/georgi-gerganov/","name":"Georgi Gerganov","summary":"Develops llama.cpp and the ggml tensor library for running models on local hardware","keywords":"Maintains llama.cpp with the ggml team Hugging Face llama.cpp ggml whisper.cpp"},{"type":"person","slug":"gilles-louppe","href":"/people/gilles-louppe/","name":"Gilles Louppe","summary":"Professor at the University of Liège leading the Science with AI Lab; former scikit-learn core developer","keywords":"Professor; group leader of the Science with AI Lab (SAIL), Montefiore Institute University of Liège Postdoctoral Associate, Center for Data Science and Center for Cosmology and Particle Physics (2015–2017) New York University scikit-learn Course materials (Introduction to AI, Deep Learning, Advanced Machine Learning)"},{"type":"person","slug":"graham-neubig","href":"/people/graham-neubig/","name":"Graham Neubig","summary":"Carnegie Mellon associate professor and Chief Scientist at OpenHands; co-author of the OpenHands paper","keywords":"Associate Professor, Language Technologies Institute Carnegie Mellon University Chief Scientist All Hands AI (OpenHands) OpenHands SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents"},{"type":"person","slug":"graham-taylor","href":"/people/graham-taylor/","name":"Graham Taylor","summary":"University of Guelph professor and Vector Institute faculty member building deep learning for biodiversity","keywords":"Professor and Canada Research Chair in Machine Learning University of Guelph Faculty Member Vector Institute Postdoctoral Fellow, Courant Institute of Mathematical Sciences (2009–2012) New York University Visiting Faculty, Google Brain (2018–2019) Google BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity"},{"type":"person","slug":"greg-chanan","href":"/people/greg-chanan/","name":"Greg Chanan","summary":"PyTorch core maintainer and co-author of the original PyTorch paper and the PyTorch 2 paper","keywords":"Core Maintainer PyTorch (open-source project) Listed with Facebook AI Research (2019) and Meta (2024) as an author of PyTorch papers Meta PyTorch PyTorch 2: TorchDynamo and TorchInductor (torch.compile)"},{"type":"person","slug":"guillaume-lample","href":"/people/guillaume-lample/","name":"Guillaume Lample","summary":"Co-founder and Chief Science Officer of Mistral AI; equal-contribution author of Meta's LLaMA paper","keywords":"Co-founder and Chief Science Officer Mistral AI Research Scientist at Facebook AI Research Meta (Facebook AI Research) LLaMA Mistral 7B and Mixtral 8x7B Unsupervised machine translation, XLM, and symbolic mathematics"},{"type":"person","slug":"guolin-ke","href":"/people/guolin-ke/","name":"Guolin Ke","summary":"Creator of LightGBM; SVP of AI at DP Technology and former senior researcher at Microsoft Research Asia","keywords":"SVP of AI DP Technology Senior Researcher, Machine Learning Group Microsoft Research Asia LightGBM Graphormer Uni-Mol"},{"type":"person","slug":"hado-van-hasselt","href":"/people/hado-van-hasselt/","name":"Hado van Hasselt","summary":"Research scientist at Google DeepMind working on reinforcement learning; first author of Double DQN","keywords":"Research scientist Google DeepMind Double DQN"},{"type":"person","slug":"hamel-husain","href":"/people/hamel-husain/","name":"Hamel Husain","summary":"ML engineer and consultant at Parlance Labs; earlier applied ML at GitHub and first author of CodeSearchNet","keywords":"ML engineer and independent consultant Parlance Labs Applied machine learning GitHub Applied machine learning Airbnb CodeSearchNet Challenge nbdev What We've Learned From A Year of Building with LLMs"},{"type":"person","slug":"hani-jamjoom","href":"/people/hani-jamjoom/","name":"Hani Jamjoom","summary":"Systems security researcher listed with IBM Research; co-author of confidential-computing studies for AI","keywords":"Listed with IBM Research on the 2025 GPU confidential computing paper and with the IBM T.J. Watson Research Center on a 2026 arXiv paper IBM Research Security study of NVIDIA GPU Confidential Computing Confidential Inference via Ternary Model Partitioning"},{"type":"person","slug":"hanlin-tang","href":"/people/hanlin-tang/","name":"Hanlin Tang","summary":"CTO of Neural Networks at Databricks; co-founder and former CTO of MosaicML","keywords":"CTO - Neural Networks Databricks Co-founder and CTO MosaicML DBRX"},{"type":"person","slug":"hannaneh-hajishirzi","href":"/people/hannaneh-hajishirzi/","name":"Hannaneh Hajishirzi","summary":"Torode Family Professor at the University of Washington; co-led Ai2's OLMo and Tülu open models","keywords":"Torode Family Professor, Paul G. Allen School of Computer Science & Engineering; leads the H2Lab University of Washington Senior Director; co-lead of OLMo and Tülu Ai2 (Allen Institute for AI) OLMo Tülu Dolma"},{"type":"person","slug":"harrison-chase","href":"/people/harrison-chase/","name":"Harrison Chase","summary":"Started the open-source LangChain framework and co-founded LangChain, the company behind it","keywords":"Co-founder LangChain LangChain (framework)"},{"type":"person","slug":"heiga-zen","href":"/people/heiga-zen/","name":"Heiga Zen","summary":"Principal Scientist at Google DeepMind; WaveNet co-author and an original author of HTS speech synthesis","keywords":"Principal Scientist (from June 2023) Google DeepMind Speech team (July 2011 to July 2018), then Brain team from August 2018 Google (Speech team, then Brain team) Intern/Co-Op researcher (2004–2005) IBM T.J. Watson Research Center WaveNet HMM-based speech synthesis system (HTS)"},{"type":"person","slug":"hendrik-strobelt","href":"/people/hendrik-strobelt/","name":"Hendrik Strobelt","summary":"Senior Research Scientist at IBM Research and MIT-IBM lab explainability lead; co-creator of GLTR","keywords":"Senior Research Scientist; Explainability Lead, MIT-IBM Watson AI Lab IBM Research Postdoctoral position at Harvard SEAS (before joining IBM in 2017) Harvard University (SEAS) GLTR (Giant Language Model Test Room) LSTMVis"},{"type":"person","slug":"hoifung-poon","href":"/people/hoifung-poon/","name":"Hoifung Poon","summary":"Introduced in 2025 as general manager of Microsoft Health Futures; co-author of LLaVA-Med and Medprompt","keywords":"General Manager, Microsoft Health Futures (as introduced in February 2025) Microsoft Health Futures (Microsoft Research) LLaVA-Med Medprompt GigaPath"},{"type":"person","slug":"horace-he","href":"/people/horace-he/","name":"Horace He","summary":"PyTorch core maintainer and torch.compile contributor; wrote a Thinking Machines post on LLM nondeterminism","keywords":"Credited \"in collaboration with others at Thinking Machines\" on a September 2025 research post Thinking Machines Lab Core Maintainer PyTorch (open-source project) Worked on PyTorch and torch.compile Meta PyTorch (torch.compile) Defeating Nondeterminism in LLM Inference GPT-Fast"},{"type":"person","slug":"hsiao-wuen-hon","href":"/people/hsiao-wuen-hon/","name":"Hsiao-Wuen Hon","summary":"Emeritus Researcher at Microsoft Research; former Managing Director of Microsoft Research Asia","keywords":"Emeritus Researcher Microsoft Research Corporate Vice President, Asia-Pacific R&D Group, and Managing Director, Microsoft Research Asia Microsoft UniLM (Unified Language Model Pre-training) Microsoft Speech Server and the Bing search stack for Asia-Pacific"},{"type":"person","slug":"hyung-won-chung","href":"/people/hyung-won-chung/","name":"Hyung Won Chung","summary":"Researcher on reasoning and agents, formerly at Google Brain; first author of the Flan-T5 scaling paper","keywords":"Research Scientist OpenAI Researcher (T5X, PaLM, Flan-PaLM and Flan-T5) Google Brain Flan-T5"},{"type":"person","slug":"ian-goodfellow","href":"/people/ian-goodfellow/","name":"Ian Goodfellow","summary":"First author of the Generative Adversarial Networks paper and co-author of the Deep Learning textbook","keywords":"Principal Scientist (as listed in 2023) Google DeepMind Director of Machine Learning, Special Project Group Apple Research scientist OpenAI Research scientist Google Generative Adversarial Networks Deep Learning (MIT Press textbook)"},{"type":"person","slug":"ian-mckenzie","href":"/people/ian-mckenzie/","name":"Ian R. McKenzie","summary":"Lead author of the Inverse Scaling paper and of FAR.AI's STACK study of attacks on LLM safeguards","keywords":"Researcher, listed with a FAR.AI affiliation on papers from 2023 to 2025 FAR.AI Inverse Scaling: When Bigger Isn't Better STACK: Adversarial Attacks on LLM Safeguard Pipelines Scaling Trends in Language Model Robustness"},{"type":"person","slug":"illia-polosukhin","href":"/people/illia-polosukhin/","name":"Illia Polosukhin","summary":"Founder of NEAR AI; co-author of the Transformer paper, written while at Google Research","keywords":"Founder NEAR AI Work on the Transformer paper performed while at Google Research Google Research Transformer (Attention Is All You Need)"},{"type":"person","slug":"ilya-sutskever","href":"/people/ilya-sutskever/","name":"Ilya Sutskever","summary":"Co-founder and CEO of Safe Superintelligence Inc.; co-author of the CLIP, Whisper and GPT-3 papers at OpenAI","keywords":"Co-founder and CEO Safe Superintelligence Inc. Author affiliated with OpenAI on the GPT-3 (2020), CLIP (2021), Codex (2021) and Whisper (2022) papers OpenAI Whisper CLIP HumanEval OpenAI o1 reasoning research"},{"type":"person","slug":"ion-stoica","href":"/people/ion-stoica/","name":"Ion Stoica","summary":"Co-author of the Ray and vLLM papers; UC Berkeley professor and co-founder of Databricks and Anyscale","keywords":"Professor of Computer Science; Director of the Sky Computing Lab University of California, Berkeley Co-founder (2013) and Executive Chairman Databricks Co-founder (2019) and Executive Chairman Anyscale Advisor LMSYS Ray vLLM"},{"type":"person","slug":"jack-clark","href":"/people/jack-clark/","name":"Jack Clark","summary":"Anthropic co-founder and Head of Public Benefit; writes the Import AI newsletter","keywords":"Co-Founder and Head of Public Benefit Anthropic Policy Director OpenAI Founding member of the AI Index (2017–2024) Stanford University (AI Index) CLIP Import AI GPT-3"},{"type":"person","slug":"jacob-andreas","href":"/people/jacob-andreas/","name":"Jacob Andreas","summary":"MIT associate professor in EECS and CSAIL studying language learning and how neural models represent meaning","keywords":"Associate Professor Massachusetts Institute of Technology (EECS and CSAIL) What learning algorithm is in-context learning? Investigations with linear models Natural Language Descriptions of Deep Visual Features (MILAN)"},{"type":"person","slug":"jacob-steinhardt","href":"/people/jacob-steinhardt/","name":"Jacob Steinhardt","summary":"UC Berkeley associate professor of Statistics and EECS; founder and CEO of the nonprofit lab Transluce","keywords":"Associate Professor of Statistics and EECS University of California, Berkeley Founder & CEO Transluce Worked at OpenAI during a postdoc year OpenAI Measuring Massive Multitask Language Understanding (MMLU) Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small Transluce"},{"type":"person","slug":"jade-copet","href":"/people/jade-copet/","name":"Jade Copet","summary":"First author of MusicGen and co-first author of EnCodec, both released in Meta's AudioCraft library","keywords":"Listed with Meta AI, FAIR Team on the EnCodec paper (2022) and with Meta AI on the MusicGen paper (2023) Meta AI (FAIR) MusicGen (AudioCraft) EnCodec"},{"type":"person","slug":"jake-vanderplas","href":"/people/jake-vanderplas/","name":"Jake VanderPlas","summary":"Named author of JAX who has written several of its design proposals","keywords":"Company listed on Jake VanderPlas's GitHub profile Google JAX"},{"type":"person","slug":"jakob-foerster","href":"/people/jakob-foerster/","name":"Jakob Foerster","summary":"Oxford associate professor and research scientist at FAIR (Meta); first author of COMA for multi-agent RL","keywords":"Associate Professor, Department of Engineering Science University of Oxford Research Scientist, FAIR, Meta AI Meta Founding Director British Open-ended Learning and Discovery Lab (BOLD) Counterfactual Multi-Agent Policy Gradients (COMA) QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning"},{"type":"person","slug":"jakob-uszkoreit","href":"/people/jakob-uszkoreit/","name":"Jakob Uszkoreit","summary":"Co-founder and CEO of Inceptive; co-author of the Transformer paper during Google's language research","keywords":"Co-founder and CEO Inceptive Software Engineer, Natural Language Understanding Google Attention Is All You Need (Transformer)"},{"type":"person","slug":"jakub-pachocki","href":"/people/jakub-pachocki/","name":"Jakub Pachocki","summary":"Chief Scientist at OpenAI; overall pretraining lead for GPT-4; set research direction for OpenAI Five","keywords":"Chief Scientist OpenAI gpt-oss GPT-4 pretraining OpenAI Five OpenAI o1"},{"type":"person","slug":"james-bergstra","href":"/people/james-bergstra/","name":"James Bergstra","summary":"First author of the Hyperopt paper and co-developer of Theano; former Research Scholar at Harvard","keywords":"Research Scholar, David Cox's biological and computer vision group at the Rowland Institute Harvard University Postdoc, Centre for Theoretical Neuroscience University of Waterloo Hyperopt Theano"},{"type":"person","slug":"james-bradbury","href":"/people/james-bradbury/","name":"James Bradbury","summary":"Named author of JAX and co-author of the original PyTorch paper","keywords":"Listed with Google as an author of the 2019 PyTorch paper Google JAX PyTorch"},{"type":"person","slug":"james-mccaffrey","href":"/people/james-mccaffrey/","name":"James McCaffrey","summary":"Software engineer and longtime MSDN Magazine Test Run columnist; worked for Microsoft Research","keywords":"Microsoft Research staff (per the August 2019 MSDN Magazine author bio) Microsoft Research (Microsoft) Test Run column, MSDN Magazine"},{"type":"person","slug":"jan-leike","href":"/people/jan-leike/","name":"Jan Leike","summary":"Leads the Alignment Science team at Anthropic; formerly co-led Superalignment at OpenAI","keywords":"Leads the Alignment Science team Anthropic Co-led the Superalignment team OpenAI Alignment researcher DeepMind Deep reinforcement learning from human preferences Scalable agent alignment via reward modeling InstructGPT and weak-to-strong generalization"},{"type":"person","slug":"jane-xu","href":"/people/jane-xu/","name":"Jane Xu","summary":"PyTorch module maintainer for torch.optim and for the C++ API (libtorch and the stable ABI)","keywords":"Module maintainer, Optimizers (torch.optim) and C++ API (libtorch + StableABI) PyTorch (open-source project) PyTorch (torch.optim and the C++ API)"},{"type":"person","slug":"jared-kaplan","href":"/people/jared-kaplan/","name":"Jared Kaplan","summary":"Anthropic co-founder and Chief Science Officer; first author of the 2020 scaling-laws paper","keywords":"Co-Founder and Chief Science Officer Anthropic Associate professor Johns Hopkins University Worked on GPT-3 and Codex; listed with Johns Hopkins University and OpenAI on the 2020 scaling-laws paper OpenAI Scaling Laws for Neural Language Models HumanEval Constitutional AI Claude's constitution"},{"type":"person","slug":"jascha-sohl-dickstein","href":"/people/jascha-sohl-dickstein/","name":"Jascha Sohl-Dickstein","summary":"Member of Technical Staff at Anthropic; first author of the 2015 paper that introduced diffusion models","keywords":"Member of Technical Staff Anthropic Principal Scientist (2023–2024); earlier Senior Staff Research Scientist, Staff Research Scientist, and Senior Research Scientist (2015–2023) Google Brain and Google DeepMind Visiting Scholar (2012–2015) Stanford University (Applied Physics) Diffusion models (Deep Unsupervised Learning using Nonequilibrium Thermodynamics) BIG-bench (Beyond the Imitation Game benchmark)"},{"type":"person","slug":"jason-wei","href":"/people/jason-wei/","name":"Jason Wei","summary":"AI researcher at Meta Superintelligence Labs; first author of the chain-of-thought prompting paper","keywords":"Works at Meta Superintelligence Labs Meta Superintelligence Labs Worked on reasoning and agents (2023–2025) OpenAI Research Scientist Google Brain Chain-of-thought prompting FLAN instruction tuning"},{"type":"person","slug":"jason-weston","href":"/people/jason-weston/","name":"Jason Weston","summary":"Research scientist at Meta; first author of Memory Networks and co-author of ParlAI and Self-Rewarding LMs","keywords":"Research Scientist Meta (Meta Superintelligence Labs) Research scientist Google Memory Networks ParlAI Self-Rewarding Language Models"},{"type":"person","slug":"jeff-barr","href":"/people/jeff-barr/","name":"Jeff Barr","summary":"VP and Chief Evangelist at AWS; started the AWS News Blog in 2004","keywords":"VP and Chief Evangelist, AWS Amazon Web Services (Amazon) AWS News Blog"},{"type":"person","slug":"jeff-bolz","href":"/people/jeff-bolz/","name":"Jeff Bolz","summary":"NVIDIA Distinguished Engineer and member of llama.cpp's Vulkan backend code-owner team","keywords":"Distinguished Engineer NVIDIA Member of the Vulkan backend code-owner team (ggml-org/ggml-vulkan) llama.cpp project llama.cpp Vulkan backend"},{"type":"person","slug":"jeff-clune","href":"/people/jeff-clune/","name":"Jeff Clune","summary":"UBC professor and Recursive co-founder; former OpenAI research manager and Senior Research Advisor to DeepMind","keywords":"Professor, Computer Science University of British Columbia Co-founder Recursive Senior Research Advisor DeepMind Research manager OpenAI Research Scientist Cornell University Video PreTraining (VPT) The AI Scientist"},{"type":"person","slug":"jeff-dean","href":"/people/jeff-dean/","name":"Jeff Dean","summary":"Chief Scientist of Google DeepMind and Google Research; co-author of the TensorFlow white paper","keywords":"Chief Scientist, Google DeepMind and Google Research Google DeepMind and Google Research TensorFlow Gemma Sparsely-gated mixture-of-experts layer Google's Neural Machine Translation system (GNMT)"},{"type":"person","slug":"jeffrey-morgan","href":"/people/jeffrey-morgan/","name":"Jeffrey Morgan","summary":"Works on Ollama, an open-source tool for running open models on your own machine","keywords":"Building Ollama Ollama Ollama"},{"type":"person","slug":"jeremy-howard","href":"/people/jeremy-howard/","name":"Jeremy Howard","summary":"Co-founder of Answer.AI, which released FSDP/QLoRA for fine-tuning large models on gaming GPUs","keywords":"Co-founder (with Eric Ries); CEO / R&D Answer.AI Founder and founding researcher fast.ai FSDP/QLoRA Hugging Face Transformers in FSDP/QLoRA"},{"type":"person","slug":"jerry-liu","href":"/people/jerry-liu/","name":"Jerry Liu","summary":"Author of the open-source LlamaIndex framework; CEO and co-founder of LlamaIndex","keywords":"CEO and co-founder LlamaIndex LlamaIndex (framework)"},{"type":"person","slug":"jesse-dodge","href":"/people/jesse-dodge/","name":"Jesse Dodge","summary":"Research scientist in Meta Superintelligence Labs; former OLMo evaluation lead at Ai2","keywords":"Research scientist Meta Superintelligence Labs Lead of the OLMo evaluation team and research lead of Playground (2019–2025) Ai2 (Allen Institute for AI) OLMo Dolma"},{"type":"person","slug":"jiaming-song","href":"/people/jiaming-song/","name":"Jiaming Song","summary":"Creator of DDIM; former Chief Scientist at Luma AI and former research scientist at NVIDIA Research","keywords":"Chief Scientist Luma AI Research scientist NVIDIA Research Postdoc with Stefano Ermon Stanford University DDIM (Denoising Diffusion Implicit Models) Luma AI models (Genie, Ray, Uni-1)"},{"type":"person","slug":"jianfeng-gao","href":"/people/jianfeng-gao/","name":"Jianfeng Gao","summary":"Microsoft Technical Fellow and CVP setting direction for AI models; co-founder of MSR's Deep Learning Group","keywords":"Technical Fellow & Corporate Vice President Microsoft Adjunct Professor University of Washington Distinguished Scientist and Vice President; co-founder and long-time leader of the Deep Learning Group Microsoft Research Phi-3 Deep Structured Semantic Models (DSSM) LLaVA-Med"},{"type":"person","slug":"jiatong-shi","href":"/people/jiatong-shi/","name":"Jiatong Shi","summary":"First author of the VERSA speech and audio evaluation toolkit; former PhD student in CMU's WAVLab","keywords":"PhD student in WAVLab (2019–2025) Carnegie Mellon University (WAVLab) Organization listed on Shi's GitHub profile Anuttacon VERSA"},{"type":"person","slug":"jim-fan","href":"/people/jim-fan/","name":"Jim Fan","summary":"Co-leads NVIDIA's GEAR embodied-agents group; co-author of GR00T N1, Voyager and Eureka","keywords":"Co-lead of the Generalist Embodied Agent Research (GEAR) group, with Yuke Zhu NVIDIA Research intern (2016) OpenAI NVIDIA Isaac GR00T N1 NVIDIA Isaac Lab Voyager and Eureka"},{"type":"person","slug":"jitendra-malik","href":"/people/jitendra-malik/","name":"Jitendra Malik","summary":"Arthur J. Chick Professor of EECS at UC Berkeley; VP and Distinguished Scientist at Amazon's FAR lab","keywords":"Arthur J. Chick Professor of EECS University of California, Berkeley VP and Distinguished Scientist, Amazon FAR lab (partial industrial leave from Berkeley) Amazon R-CNN SlowFast Networks"},{"type":"person","slug":"joao-carreira","href":"/people/joao-carreira/","name":"João Carreira","summary":"Research scientist at Google DeepMind; co-creator of the Kinetics dataset and the Perception Test","keywords":"Research Scientist Google DeepMind I3D and the Kinetics dataset Perception Test Perceiver"},{"type":"person","slug":"joe-spisak","href":"/people/joe-spisak/","name":"Joe Spisak","summary":"VP of Product and Head of Open Source at Reflection AI; PyTorch core maintainer","keywords":"Vice President of Product and Head of Open Source Reflection AI Serves on the PyTorch Foundation Governing Board PyTorch Foundation Core Maintainer PyTorch (open-source project) Previously worked at Meta; listed as a Llama 3 core contributor Meta PyTorch Llama 3"},{"type":"person","slug":"joel-schlosser","href":"/people/joel-schlosser/","name":"Joel Schlosser","summary":"PyTorch module maintainer for torch.nn and torch.optim","keywords":"Listed with Meta on a June 2025 PyTorch blog post; GitHub profile lists Facebook as company Meta Module maintainer, NN APIs (torch.nn) and Optimizers (torch.optim) PyTorch (open-source project) PyTorch (torch.nn and torch.optim)"},{"type":"person","slug":"joelle-pineau","href":"/people/joelle-pineau/","name":"Joelle Pineau","summary":"Chief AI Officer at Cohere and McGill professor; led Meta's FAIR research team from 2017 to 2025","keywords":"Chief AI Officer Cohere Professor (currently on part-time leave), and core member of Mila McGill University (School of Computer Science) VP AI Research, heading the FAIR team (2017–2025) Meta (Fundamental AI Research, FAIR) ML Reproducibility Checklist and NeurIPS reproducibility program"},{"type":"person","slug":"johannes-gassler","href":"/people/johannes-gassler/","name":"Johannes Gäßler","summary":"Member of llama.cpp's CUDA backend code-owner team; experimental particle physicist at KIT","keywords":"Experimental particle physicist Karlsruhe Institute of Technology Member of the CUDA backend code-owner team (ggml-org/ggml-cuda) llama.cpp project llama.cpp"},{"type":"person","slug":"john-duchi","href":"/people/john-duchi/","name":"John Duchi","summary":"Stanford associate professor of Statistics and Electrical Engineering; first author of the Adagrad paper","keywords":"Associate Professor of Statistics and Electrical Engineering Stanford University Software Engineer (2007–2008); Research Intern/Engineer, summers 2009–2014 Google Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (Adagrad)"},{"type":"person","slug":"john-giannandrea","href":"/people/john-giannandrea/","name":"John Giannandrea","summary":"Former Apple senior vice president for Machine Learning and AI Strategy, overseeing Core ML and Siri","keywords":"Senior Vice President, Machine Learning and AI Strategy (2018–2025) Apple Core ML and Siri Apple Foundation Models"},{"type":"person","slug":"john-schulman","href":"/people/john-schulman/","name":"John Schulman","summary":"Co-founder and chief scientist of Thinking Machines Lab; OpenAI co-founder and first author of PPO and TRPO","keywords":"Cofounder and chief scientist Thinking Machines Lab Research on the Alignment Science team Anthropic Cofounder; co-led the post-training team (2022–2024) OpenAI Proximal Policy Optimization (PPO) Trust Region Policy Optimization (TRPO) LoRA Without Regret"},{"type":"person","slug":"johny-srouji","href":"/people/johny-srouji/","name":"Johny Srouji","summary":"Apple's chief hardware officer, overseeing Apple silicon and the company's hardware technologies","keywords":"Chief Hardware Officer Apple Senior positions in processor development and design Intel Senior positions in processor development and design IBM Apple silicon"},{"type":"person","slug":"jonathan-cohen","href":"/people/jonathan-cohen/","name":"Jonathan Cohen","summary":"NVIDIA AI software leader who led NeMo development; listed in the Nemotron 3 leadership credits","keywords":"Credited in Nemotron 3 leadership (December 2025) and Nemotron 3 Super contributors (April 2026) NVIDIA Senior Director for AI Software, in charge of speech and language tools including NeMo NVIDIA Director of Engineering Apple NVIDIA NeMo NVIDIA Nemotron Granary dataset with Canary and Parakeet models"},{"type":"person","slug":"jonathan-frankle","href":"/people/jonathan-frankle/","name":"Jonathan Frankle","summary":"Chief AI Scientist at Databricks; first author of the lottery ticket hypothesis paper; MosaicML founding team","keywords":"Chief AI Scientist Databricks Member of the founding team MosaicML PhD student in computer science (completed 2023) Massachusetts Institute of Technology The Lottery Ticket Hypothesis DBRX MosaicBERT"},{"type":"person","slug":"jonathan-ho","href":"/people/jonathan-ho/","name":"Jonathan Ho","summary":"First author of Denoising Diffusion Probabilistic Models (DDPM) and of Classifier-Free Diffusion Guidance","keywords":"Listed with Google Research, Brain Team on Classifier-Free Diffusion Guidance and as co-first author of Imagen Video (2022) Google Research, Brain Team Listed with UC Berkeley as first author of DDPM (2020) UC Berkeley Denoising Diffusion Probabilistic Models (DDPM) Classifier-Free Diffusion Guidance Imagen Video"},{"type":"person","slug":"jonathan-ross","href":"/people/jonathan-ross/","name":"Jonathan Ross","summary":"Founder and former CEO of Groq, maker of the LPU inference chip and the GroqCloud platform","keywords":"Founder; CEO and Founder in 2024 and 2025 announcements Groq Groq LPU and GroqCloud"},{"type":"person","slug":"jonathan-shen","href":"/people/jonathan-shen/","name":"Jonathan Shen","summary":"First author of the Tacotron 2 text-to-speech paper, written as a software engineer at Google","keywords":"Software Engineer (Tacotron 2 blog byline, December 2017) Google (Google Brain and Machine Perception teams) Tacotron 2"},{"type":"person","slug":"josef-sivic","href":"/people/josef-sivic/","name":"Josef Sivic","summary":"Professor at CIIRC, Czech Technical University, and head of the IMPACT group; consults for Adobe Research","keywords":"Professor of Computer Science and Artificial Intelligence; head of the IMPACT group Czech Institute of Informatics, Robotics and Cybernetics (CIIRC), Czech Technical University Consulting Adobe Research Chief Strategy Officer (part-time) Tolion Health AI Postdoc with William Freeman MIT CSAIL NetVLAD HowTo100M"},{"type":"person","slug":"joseph-e-gonzalez","href":"/people/joseph-e-gonzalez/","name":"Joseph E. Gonzalez","summary":"UC Berkeley professor whose group built Gorilla and the Berkeley Function Calling Leaderboard","keywords":"Associate Professor, Department of Electrical Engineering and Computer Sciences University of California, Berkeley Founding member and advisor LMSYS Gorilla and the Berkeley Function Calling Leaderboard (BFCL) vLLM"},{"type":"person","slug":"josh-gordon","href":"/people/josh-gordon/","name":"Josh Gordon","summary":"Wrote the TensorFlow Hub launch post as Developer Advocate for TensorFlow, and later TensorFlow release posts","keywords":"Developer Advocate for TensorFlow Google (TensorFlow team) TensorFlow"},{"type":"person","slug":"joshua-lochner","href":"/people/joshua-lochner/","name":"Joshua Lochner","summary":"Works on Transformers.js at Hugging Face, a library for running Transformers models in the browser","keywords":"Working on Transformers.js Hugging Face Transformers.js"},{"type":"person","slug":"ju-chieh-chou","href":"/people/ju-chieh-chou/","name":"Ju-Chieh Chou","summary":"TTIC PhD student in speech and language processing; former research intern at Meta and Microsoft","keywords":"Ph.D. student, advised by Karen Livescu Toyota Technological Institute at Chicago Research Intern (2022, Meta AI; 2025, Meta) Meta Research Intern (2024) Microsoft Toward Joint Language Modeling for Speech Units and Text"},{"type":"person","slug":"julien-chaumond","href":"/people/julien-chaumond/","name":"Julien Chaumond","summary":"CTO of Hugging Face; added the Hub's local-apps registry and works on the hf command-line tool","keywords":"CTO Hugging Face Local apps on the Hugging Face Hub GGUF parser in huggingface.js hf command-line tool Transformers"},{"type":"person","slug":"julien-simon","href":"/people/julien-simon/","name":"Julien Simon","summary":"AI Operating Partner at Fortino Capital; former Chief Evangelist at Hugging Face and Arcee AI","keywords":"AI Operating Partner Fortino Capital Chief Evangelist Arcee AI Chief Evangelist Hugging Face Artificial Intelligence & Machine Learning Evangelist for EMEA Amazon Web Services (Amazon) sagemaker-inference-container-cpu Learn Amazon SageMaker (book)"},{"type":"person","slug":"jun-yan-zhu","href":"/people/jun-yan-zhu/","name":"Jun-Yan Zhu","summary":"CMU associate professor and Nunchux AI co-founder; first author of CycleGAN and co-author of pix2pix","keywords":"Michael B. Donohue Associate Professor of Computer Science and Robotics Carnegie Mellon University (School of Computer Science) Co-founder and Chief Scientist Nunchux AI Research Scientist Adobe Research Postdoc, working with William T. Freeman, Josh Tenenbaum, and Antonio Torralba MIT CSAIL CycleGAN and pix2pix in PyTorch"},{"type":"person","slug":"junyang-lin","href":"/people/junyang-lin/","name":"Junyang Lin","summary":"Independent researcher on language models and agents; co-author of the OpenHands and Qwen3 papers","keywords":"Independent researcher Independent OpenHands (formerly OpenDevin) Qwen3 Technical Report Group Sequence Policy Optimization (GSPO)"},{"type":"person","slug":"kai-fu-lee","href":"/people/kai-fu-lee/","name":"Kai-Fu Lee","summary":"Founder and CEO of 01.AI; developed CMU's Sphinx speech recognizer; earlier at Apple, Microsoft and Google","keywords":"Founder and CEO 01.AI Global Vice President Google Global Vice President; founded Microsoft Research Asia after joining Microsoft Research in 1998 as director of advanced interactive systems Microsoft Headed the multimedia division (six years) Apple Computer Assistant Professor; earned a PhD there in 1988 with the Sphinx thesis Carnegie Mellon University Sphinx speech recognition system"},{"type":"person","slug":"kaiming-he","href":"/people/kaiming-he/","name":"Kaiming He","summary":"First author of ResNet, Mask R-CNN and MAE; MIT associate professor and part-time scientist at Google DeepMind","keywords":"Associate Professor with tenure, Department of EECS Massachusetts Institute of Technology Distinguished Scientist (part-time) Google DeepMind Research scientist, FAIR (2016–2024) Meta (Facebook AI Research) Researcher (2011–2016) Microsoft Research Asia ResNet Masked Autoencoders (MAE) Mask R-CNN and Detectron"},{"type":"person","slug":"karen-livescu","href":"/people/karen-livescu/","name":"Karen Livescu","summary":"Professor at TTI-Chicago working on speech and language processing; former MIT EECS post-doctoral lecturer","keywords":"Professor Toyota Technological Institute at Chicago Visiting faculty (2023–2024) Carnegie Mellon University (WAVLab) Post-doctoral lecturer (2005–2007) MIT (EECS department) SLUE benchmark Toward Joint Language Modeling for Speech Units and Text"},{"type":"person","slug":"karen-simonyan","href":"/people/karen-simonyan/","name":"Karén Simonyan","summary":"First author of the VGG networks paper; joined Microsoft AI as Chief Scientist in 2024 from Inflection AI","keywords":"Chief Scientist, Microsoft AI (announced March 2024) Microsoft AI Co-founder and Chief Scientist Inflection AI Co-founder (with Max Jaderberg and Andrew Zisserman) Vision Factory VGG networks (very deep convolutional networks)"},{"type":"person","slug":"kari-briski","href":"/people/kari-briski/","name":"Kari Briski","summary":"VP of AI software product management at NVIDIA; listed in the Nemotron 3 leadership credits","keywords":"VP, AI Software Product Management NVIDIA NVIDIA Nemotron"},{"type":"person","slug":"kate-soule","href":"/people/kate-soule/","name":"Kate Soule","summary":"Director of Technical Product Management for Granite at IBM Research; co-author of Granite Embedding","keywords":"Director, Technical Product Management, Granite IBM Research Ran the lab's corporate membership program MIT-IBM Watson AI Lab IBM Granite"},{"type":"person","slug":"katherine-crowson","href":"/people/katherine-crowson/","name":"Katherine Crowson","summary":"Creator of the k-diffusion library and co-first author of VQGAN-CLIP","keywords":"Listed with an EleutherAI affiliation as co-first author of the VQGAN-CLIP paper (2022) EleutherAI k-diffusion VQGAN-CLIP"},{"type":"person","slug":"ken-goldberg","href":"/people/ken-goldberg/","name":"Ken Goldberg","summary":"UC Berkeley Distinguished Professor working on robot grasping and automation; senior author of Dex-Net 2.0","keywords":"William S. Floyd Jr. Distinguished Chair of Engineering; Distinguished Professor of IEOR and EECS and Art Practice University of California, Berkeley Co-Founder and Chief Scientist Ambi Robotics President Robot Learning Foundation Dex-Net 2.0"},{"type":"person","slug":"kenneth-li","href":"/people/kenneth-li/","name":"Kenneth Li","summary":"Thinking Machines Lab researcher; lead author of Othello world-model and Inference-Time Intervention papers","keywords":"Researcher Thinking Machines Lab PhD student (completed; advised by Martin Wattenberg, Fernanda Viégas, and Hanspeter Pfister) Harvard University Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task Inference-Time Intervention: Eliciting Truthful Answers from a Language Model"},{"type":"person","slug":"kenneth-stanley","href":"/people/kenneth-stanley/","name":"Kenneth Stanley","summary":"SVP of Open-Endedness at Lila Sciences; former Open-Endedness Team Leader at OpenAI and creator of NEAT","keywords":"SVP of Open-Endedness Lila Sciences Open-Endedness Team Leader OpenAI Head of Core AI Research Uber AI Charles Millican Professor in Computer Science University of Central Florida Evolution through Large Models (ELM) NeuroEvolution of Augmenting Topologies (NEAT)"},{"type":"person","slug":"kevin-scott","href":"/people/kevin-scott/","name":"Kevin Scott","summary":"Chief Technology Officer of Microsoft; earlier led engineering and operations at LinkedIn","keywords":"Chief Technology Officer Microsoft Senior Vice President of Engineering and Operations LinkedIn Leadership positions in search and ads engineering; oversaw mobile ads engineering Google Reprogramming the American Dream"},{"type":"person","slug":"kimberly-powell","href":"/people/kimberly-powell/","name":"Kimberly Powell","summary":"Vice president and general manager of healthcare at NVIDIA","keywords":"VP/GM, Healthcare NVIDIA NVIDIA healthcare platforms (Clara and BioNeMo)"},{"type":"person","slug":"koray-kavukcuoglu","href":"/people/koray-kavukcuoglu/","name":"Koray Kavukcuoglu","summary":"SVP of Google DeepMind and Chief AI Architect at Google; co-author of the DQN and WaveNet papers","keywords":"SVP, Google DeepMind Google DeepMind Chief AI Architect Google VP of Research Google DeepMind Gemma Deep Q-Network (DQN) WaveNet"},{"type":"person","slug":"kristina-toutanova","href":"/people/kristina-toutanova/","name":"Kristina Toutanova","summary":"Research scientist at Google DeepMind and University of Washington affiliate faculty; co-author of BERT","keywords":"Research scientist Google DeepMind Affiliate Assistant Professor, Department of Linguistics University of Washington Researcher (before 2017) Microsoft Research BERT"},{"type":"person","slug":"kush-varshney","href":"/people/kush-varshney/","name":"Kush R. Varshney","summary":"IBM Fellow directing trustworthy AI research at IBM; team behind AI Fairness 360 and Granite Guardian","keywords":"IBM Fellow, directing Human-Centered Trustworthy AI research IBM Research Founding co-director (2015–2023) IBM Science for Social Good Granite Guardian AI Fairness 360 AI Explainability 360 and FactSheets"},{"type":"person","slug":"kyle-lo","href":"/people/kyle-lo/","name":"Kyle Lo","summary":"Former Ai2 research scientist who co-led the OLMo project; last-listed author of the Dolma paper","keywords":"Research scientist; co-lead of the OLMo project Ai2 (Allen Institute for AI) OLMo Dolma"},{"type":"person","slug":"kyunghyun-cho","href":"/people/kyunghyun-cho/","name":"Kyunghyun Cho","summary":"NYU professor of computer science and data science; co-author of the RNN encoder-decoder and attention papers","keywords":"Glen de Vries Professor of Health Statistics; Professor of Computer Science and Data Science New York University Co-Founder and CEO Ortet, Inc. Research Scientist, Facebook AI Research (June 2017 – May 2020) Meta (Facebook AI Research) Executive Director of Frontier Research and Senior Fellow (until January 2026) Genentech RNN Encoder-Decoder Neural Machine Translation by Jointly Learning to Align and Translate"},{"type":"person","slug":"laurence-moroney","href":"/people/laurence-moroney/","name":"Laurence Moroney","summary":"Author and educator who led AI Advocacy at Google and taught TensorFlow specializations with DeepLearning.AI","keywords":"Led AI Advocacy Google TensorFlow AI and Machine Learning for Coders"},{"type":"person","slug":"laurent-sartran","href":"/people/laurent-sartran/","name":"Laurent Sartran","summary":"First author of Transformer Grammars at DeepMind and co-author of the SynJax library for JAX","keywords":"Listed with DeepMind on the Transformer Grammars paper (2022) DeepMind Transformer Grammars SynJax"},{"type":"person","slug":"leandro-von-werra","href":"/people/leandro-von-werra/","name":"Leandro von Werra","summary":"Machine learning at Hugging Face; 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Stanford emeritus","keywords":"The John A. Overdeck Professor, Professor of Statistics, Professor of Biomedical Data Science, Emeritus Stanford University Member of the technical staff AT&T Bell Laboratories glmnet The Elements of Statistical Learning An Introduction to Statistical Learning"},{"type":"person","slug":"tri-dao","href":"/people/tri-dao/","name":"Tri Dao","summary":"First author of FlashAttention and co-author of Mamba; Princeton professor and Together AI co-founder","keywords":"Assistant Professor of Computer Science, leading the Dao AI Lab Princeton University Co-founder and Chief Scientist Together AI Mamba and Mamba-2 FlashAttention"},{"type":"person","slug":"tsung-yi-lin","href":"/people/tsung-yi-lin/","name":"Tsung-Yi Lin","summary":"Principal research scientist at NVIDIA Research; led the creation of the COCO dataset; Cosmos co-author","keywords":"Principal research scientist, NVIDIA Research NVIDIA Member of the Brain Team, Google Research Google Research (Brain Team) NVIDIA Cosmos COCO dataset Focal Loss"},{"type":"person","slug":"tuomas-haarnoja","href":"/people/tuomas-haarnoja/","name":"Tuomas Haarnoja","summary":"First author of Soft Actor-Critic and a bipedal robot soccer paper; 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first author of the score SDE and consistency models papers","keywords":"Research Principal Meta Superintelligence Labs (MSL) Led the strategic explorations team OpenAI Score-based generative modeling through SDEs Consistency models"},{"type":"person","slug":"yangqing-jia","href":"/people/yangqing-jia/","name":"Yangqing Jia","summary":"Creator of Caffe, co-creator of ONNX and co-lead of PyTorch 1.0; founder and CEO of Intent Lab","keywords":"Founder and CEO Intent Lab VP, building AI for system software and cloud services for AI NVIDIA Founder and CEO (Lepton AI was acquired by NVIDIA) Lepton AI VP; President of Alibaba Cloud's Computing Platform and Head of the System AI Lab at DAMO Academy Alibaba Group Director of Engineering; built Facebook's general-purpose AI platform and co-led PyTorch 1.0 Facebook Research Scientist Google Brain PhD student, EECS (created Caffe during the PhD) University of California, Berkeley PyTorch (1.0 and Caffe2) ONNX (Open Neural Network Exchange) Caffe TensorFlow"},{"type":"person","slug":"yann-lecun","href":"/people/yann-lecun/","name":"Yann LeCun","summary":"Executive Chairman of AMI Labs and NYU professor; former Chief AI Scientist at Meta and co-author of V-JEPA 2","keywords":"Executive Chairman Advanced Machine Intelligence (AMI Labs) Jacob T. Schwartz Professor, affiliated with the Courant Institute New York University Chief AI Scientist (2018–2025); founding Director of Facebook AI Research (2013–2017) Meta V-JEPA 2 Convolutional networks"},{"type":"person","slug":"yao-qian","href":"/people/yao-qian/","name":"Yao Qian","summary":"Principal Researcher in Microsoft's Speech and Dialog Research Group; co-author of WavLM","keywords":"Principal Researcher Microsoft (Speech and Dialog Research Group) WavLM UniSpeech-SAT"},{"type":"person","slug":"yejin-choi","href":"/people/yejin-choi/","name":"Yejin Choi","summary":"Stanford professor and HAI Senior Fellow; formerly at NVIDIA, the University of Washington, and Ai2","keywords":"Dieter Schwarz Foundation Professor of Computer Science; Senior Fellow, Stanford HAI Stanford University Senior Research Director and Distinguished Research Scientist NVIDIA Professor University of Washington Senior Director Ai2 (Allen Institute for AI) NVIDIA Nemotron 3"},{"type":"person","slug":"yi-ren","href":"/people/yi-ren/","name":"Yi Ren","summary":"First author of FastSpeech and FastSpeech 2; former MSRA intern; leads video model research at HeyGen","keywords":"Leads the foundation video model research team HeyGen Speech research scientist (joined January 2023) TikTok Intern (August 2018 to February 2019) Microsoft Research Asia (machine learning group) FastSpeech FastSpeech 2"},{"type":"person","slug":"ying-sheng","href":"/people/ying-sheng/","name":"Ying Sheng","summary":"SGLang co-author and codeowner; first author of FlexGen, for running large models on a single GPU","keywords":"Officer LMSYS Codeowner and maintainer-nomination contact SGLang project Member of technical staff who co-led the inference team (2024–2025) xAI SGLang FlexGen (FlexLLMGen)"},{"type":"person","slug":"yoav-shoham","href":"/people/yoav-shoham/","name":"Yoav Shoham","summary":"Co-founder and chairman of AI21 Labs; professor emeritus of computer science at Stanford","keywords":"Chairman & Co-Founder AI21 Labs Professor of Computer Science, Emeritus Stanford University Principal Scientist Google Jamba AI Index"},{"type":"person","slug":"yonghui-wu","href":"/people/yonghui-wu/","name":"Yonghui Wu","summary":"First author of the paper on Google's Neural Machine Translation system (GNMT); co-author of Lingvo","keywords":"First author of Google's 2016 GNMT paper Google Google's Neural Machine Translation system (GNMT) Lingvo"},{"type":"person","slug":"younes-belkada","href":"/people/younes-belkada/","name":"Younes Belkada","summary":"Listed author of Hugging Face's TRL library and co-author of the StarCoder2, Zephyr, and LLM.int8() papers","keywords":"Machine Learning Engineer Hugging Face TRL (Transformers Reinforcement Learning) StarCoder2 Zephyr LLM.int8()"},{"type":"person","slug":"yu-an-chung","href":"/people/yu-an-chung/","name":"Yu-An Chung","summary":"Research scientist at FAIR (Meta) working on speech; in the technical leadership of Omnilingual ASR","keywords":"Research Scientist, Facebook AI Research (FAIR) Meta (FAIR) Omnilingual ASR Omnilingual SONAR"},{"type":"person","slug":"yuanzhi-li","href":"/people/yuanzhi-li/","name":"Yuanzhi Li","summary":"First author of the phi-1.5 report and co-author of TinyStories and Microsoft's other Phi model reports","keywords":"Listed with a Microsoft Research affiliation on the 2023 TinyStories paper Microsoft Research Phi small language models TinyStories"},{"type":"person","slug":"yulia-tsvetkov","href":"/people/yulia-tsvetkov/","name":"Yulia Tsvetkov","summary":"University of Washington associate professor working on reliable, socially aware, and multilingual NLP","keywords":"Associate Professor, Paul G. Allen School of Computer Science & Engineering University of Washington Adjunct Professor, Language Technologies Institute (previously Assistant Professor) Carnegie Mellon University Postdoctoral researcher, Stanford NLP Group Stanford University Spurious Rewards: Rethinking Training Signals in RLVR"},{"type":"person","slug":"yuval-alaluf","href":"/people/yuval-alaluf/","name":"Yuval Alaluf","summary":"Research Scientist at Runway; first author of ReStyle and co-author of Attend-and-Excite","keywords":"Research Scientist Runway Research Scientist Pika Runway Aleph ReStyle and Attend-and-Excite"},{"type":"person","slug":"yuxin-wu","href":"/people/yuxin-wu/","name":"Yuxin Wu","summary":"Creator of Detectron2; builds foundation models at Moonshot AI; formerly at Google Brain and FAIR","keywords":"Builds foundation models (Kimi) Moonshot AI Worked on foundation models at Google Brain Google Brain Worked on computer vision at Facebook AI Research Meta (Facebook AI Research) Detectron2 MoCo (Momentum Contrast)"},{"type":"person","slug":"zhilin-yang","href":"/people/zhilin-yang/","name":"Zhilin Yang","summary":"Co-first author of XLNet and Transformer-XL; earlier worked at Google Brain and Meta AI","keywords":"Worked at Google Brain with Quoc V. Le Google Brain Worked at Meta AI with Jason Weston Meta AI PhD student, School of Computer Science (PhD 2019, advised by Ruslan Salakhutdinov and William W. Cohen) Carnegie Mellon University XLNet Transformer-XL"},{"type":"person","slug":"zico-kolter","href":"/people/zico-kolter/","name":"Zico Kolter","summary":"Head of Carnegie Mellon's Machine Learning Department; chairs OpenAI's Safety and Security Committee","keywords":"Professor and Department Head, Machine Learning Department Carnegie Mellon University Member of the Board; chairs the Safety and Security Committee OpenAI Member of the Board Qualcomm Co-founder and Chief Scientist Gray Swan AI Universal and Transferable Adversarial Attacks on Aligned Language Models"},{"type":"place","slug":"california","href":"/places/california/","name":"California","summary":"AI companies & labs in California: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"colorado","href":"/places/colorado/","name":"Colorado","summary":"AI companies & labs in Colorado: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"delaware","href":"/places/delaware/","name":"Delaware","summary":"AI companies & labs in Delaware: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"district-of-columbia","href":"/places/district-of-columbia/","name":"District of Columbia","summary":"AI companies & labs in Washington, D.C.: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"florida","href":"/places/florida/","name":"Florida","summary":"AI companies & labs in Florida: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"georgia","href":"/places/georgia/","name":"Georgia","summary":"AI companies & labs in Georgia: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"idaho","href":"/places/idaho/","name":"Idaho","summary":"AI companies & labs in Idaho: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"illinois","href":"/places/illinois/","name":"Illinois","summary":"AI companies & labs in Illinois: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"maryland","href":"/places/maryland/","name":"Maryland","summary":"AI companies & labs in Maryland: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"massachusetts","href":"/places/massachusetts/","name":"Massachusetts","summary":"AI companies & labs in Massachusetts: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"new-jersey","href":"/places/new-jersey/","name":"New Jersey","summary":"AI companies & labs in New Jersey: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"new-mexico","href":"/places/new-mexico/","name":"New Mexico","summary":"AI companies & labs in New Mexico: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"new-york","href":"/places/new-york/","name":"New York","summary":"AI companies & labs in New York: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"north-carolina","href":"/places/north-carolina/","name":"North Carolina","summary":"AI companies & labs in North Carolina: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"oregon","href":"/places/oregon/","name":"Oregon","summary":"AI companies & labs in Oregon: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"pennsylvania","href":"/places/pennsylvania/","name":"Pennsylvania","summary":"AI companies & labs in Pennsylvania: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"tennessee","href":"/places/tennessee/","name":"Tennessee","summary":"AI companies & labs in Tennessee: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"texas","href":"/places/texas/","name":"Texas","summary":"AI companies & labs in Texas: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"place","slug":"washington","href":"/places/washington/","name":"Washington","summary":"AI companies & labs in Washington: organizations headquartered there","keywords":"state headquarters place ai companies labs"},{"type":"page","slug":"jobs-data-center-infrastructure","href":"/jobs/data-center-infrastructure/","name":"AI data-center and infrastructure jobs","summary":"428 open AI data-center jobs in construction, power, cooling, and operations at 17 U.S. AI companies and labs. Includes non-U.S. roles. Checked Oct 15, 2026.","keywords":"jobs careers hiring openings data center infrastructure"},{"type":"page","slug":"jobs-no-programming","href":"/jobs/no-programming/","name":"AI jobs that don't require programming","summary":"2,862 AI jobs without programming (sales, policy, finance, design, and more) at 57 U.S. AI companies and labs. Includes non-U.S. roles. Checked Oct 15, 2026.","keywords":"jobs careers hiring openings no programming"},{"type":"page","slug":"jobs-entry-level","href":"/jobs/entry-level/","name":"Entry-level jobs at AI companies","summary":"236 internships, new-grad, and early-career listings at 35 U.S. AI companies and labs, matched by job title. Includes non-U.S. roles. Checked Oct 15, 2026.","keywords":"jobs careers hiring openings entry level"},{"type":"page","slug":"jobs-remote-us","href":"/jobs/remote-us/","name":"Remote jobs at American AI companies, U.S. locations","summary":"407 remote listings with U.S. locations only, at 35 American AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings remote us"},{"type":"page","slug":"jobs-remote","href":"/jobs/remote/","name":"Remote jobs at American AI companies, all locations","summary":"599 remote listings at 41 American AI companies and labs: 407 with U.S. locations only, 163 naming places outside the U.S. Checked Oct 15, 2026.","keywords":"jobs careers hiring openings remote"},{"type":"page","slug":"jobs-in-arizona","href":"/jobs/in/arizona/","name":"AI jobs in Arizona","summary":"15 open listings naming a location in Arizona, at 4 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Arizona AZ"},{"type":"page","slug":"jobs-in-california","href":"/jobs/in/california/","name":"AI jobs in California","summary":"3,933 open listings naming a location in California, at 59 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state California CA"},{"type":"page","slug":"jobs-in-colorado","href":"/jobs/in/colorado/","name":"AI jobs in Colorado","summary":"152 open listings naming a location in Colorado, at 7 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Colorado CO"},{"type":"page","slug":"jobs-in-georgia","href":"/jobs/in/georgia/","name":"AI jobs in Georgia","summary":"53 open listings naming a location in Georgia, at 6 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Georgia GA"},{"type":"page","slug":"jobs-in-illinois","href":"/jobs/in/illinois/","name":"AI jobs in Illinois","summary":"71 open listings naming a location in Illinois, at 9 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Illinois IL"},{"type":"page","slug":"jobs-in-maryland","href":"/jobs/in/maryland/","name":"AI jobs in Maryland","summary":"11 open listings naming a location in Maryland, at Databricks in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Maryland MD"},{"type":"page","slug":"jobs-in-massachusetts","href":"/jobs/in/massachusetts/","name":"AI jobs in Massachusetts","summary":"85 open listings naming a location in Massachusetts, at 9 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Massachusetts MA"},{"type":"page","slug":"jobs-in-mississippi","href":"/jobs/in/mississippi/","name":"AI jobs in Mississippi","summary":"122 open listings naming a location in Mississippi, at xAI (SpaceXSI) in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Mississippi MS"},{"type":"page","slug":"jobs-in-missouri","href":"/jobs/in/missouri/","name":"AI jobs in Missouri","summary":"23 open listings naming a location in Missouri, at 5 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Missouri MO"},{"type":"page","slug":"jobs-in-new-jersey","href":"/jobs/in/new-jersey/","name":"AI jobs in New Jersey","summary":"131 open listings naming a location in New Jersey, at 4 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state New Jersey NJ"},{"type":"page","slug":"jobs-in-new-york","href":"/jobs/in/new-york/","name":"AI jobs in New York","summary":"1,461 open listings naming a location in New York, at 33 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state New York NY"},{"type":"page","slug":"jobs-in-north-carolina","href":"/jobs/in/north-carolina/","name":"AI jobs in North Carolina","summary":"16 open listings naming a location in North Carolina, at 3 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state North Carolina NC"},{"type":"page","slug":"jobs-in-oklahoma","href":"/jobs/in/oklahoma/","name":"AI jobs in Oklahoma","summary":"11 open listings naming a location in Oklahoma, at 2 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Oklahoma OK"},{"type":"page","slug":"jobs-in-oregon","href":"/jobs/in/oregon/","name":"AI jobs in Oregon","summary":"31 open listings naming a location in Oregon, at 3 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Oregon OR"},{"type":"page","slug":"jobs-in-pennsylvania","href":"/jobs/in/pennsylvania/","name":"AI jobs in Pennsylvania","summary":"48 open listings naming a location in Pennsylvania, at 7 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Pennsylvania PA"},{"type":"page","slug":"jobs-in-tennessee","href":"/jobs/in/tennessee/","name":"AI jobs in Tennessee","summary":"169 open listings naming a location in Tennessee, at 3 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Tennessee TN"},{"type":"page","slug":"jobs-in-texas","href":"/jobs/in/texas/","name":"AI jobs in Texas","summary":"267 open listings naming a location in Texas, at 20 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Texas TX"},{"type":"page","slug":"jobs-in-virginia","href":"/jobs/in/virginia/","name":"AI jobs in Virginia","summary":"26 open listings naming a location in Virginia, at 2 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Virginia VA"},{"type":"page","slug":"jobs-in-washington","href":"/jobs/in/washington/","name":"AI jobs in Washington","summary":"707 open listings naming a location in Washington, at 19 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Washington WA"},{"type":"page","slug":"jobs-in-district-of-columbia","href":"/jobs/in/district-of-columbia/","name":"AI jobs in Washington, D.C.","summary":"231 open listings naming a location in Washington, D.C., at 14 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings state Washington, D.C. DC"},{"type":"page","slug":"jobs-in-colorado-arvada","href":"/jobs/in/colorado/arvada/","name":"AI jobs in Arvada, Colorado","summary":"31 open listings naming Arvada, CO, at Crusoe in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Arvada Colorado CO"},{"type":"page","slug":"jobs-in-georgia-atlanta","href":"/jobs/in/georgia/atlanta/","name":"AI jobs in Atlanta, Georgia","summary":"51 open listings naming Atlanta, GA, at 6 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Atlanta Georgia GA"},{"type":"page","slug":"jobs-in-texas-austin","href":"/jobs/in/texas/austin/","name":"AI jobs in Austin, Texas","summary":"148 open listings naming Austin, TX, at 17 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Austin Texas TX"},{"type":"page","slug":"jobs-in-washington-bellevue","href":"/jobs/in/washington/bellevue/","name":"AI jobs in Bellevue, Washington","summary":"275 open listings naming Bellevue, WA, at 5 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Bellevue Washington WA"},{"type":"page","slug":"jobs-in-massachusetts-boston","href":"/jobs/in/massachusetts/boston/","name":"AI jobs in Boston, Massachusetts","summary":"75 open listings naming Boston, MA, at 8 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Boston Massachusetts MA"},{"type":"page","slug":"jobs-in-colorado-brighton","href":"/jobs/in/colorado/brighton/","name":"AI jobs in Brighton, Colorado","summary":"26 open listings naming Brighton, CO, at Crusoe in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Brighton Colorado CO"},{"type":"page","slug":"jobs-in-illinois-chicago","href":"/jobs/in/illinois/chicago/","name":"AI jobs in Chicago, Illinois","summary":"55 open listings naming Chicago, IL, at 7 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Chicago Illinois IL"},{"type":"page","slug":"jobs-in-texas-dallas","href":"/jobs/in/texas/dallas/","name":"AI jobs in Dallas, Texas","summary":"91 open listings naming Dallas, TX, at 8 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Dallas Texas TX"},{"type":"page","slug":"jobs-in-colorado-denver","href":"/jobs/in/colorado/denver/","name":"AI jobs in Denver, Colorado","summary":"85 open listings naming Denver, CO, at 7 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Denver Colorado CO"},{"type":"page","slug":"jobs-in-california-emeryville","href":"/jobs/in/california/emeryville/","name":"AI jobs in Emeryville, California","summary":"15 open listings naming Emeryville, CA, at Profluent in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Emeryville California CA"},{"type":"page","slug":"jobs-in-california-fremont","href":"/jobs/in/california/fremont/","name":"AI jobs in Fremont, California","summary":"43 open listings naming Fremont, CA, at 2 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Fremont California CA"},{"type":"page","slug":"jobs-in-new-jersey-livingston","href":"/jobs/in/new-jersey/livingston/","name":"AI jobs in Livingston, New Jersey","summary":"121 open listings naming Livingston, NJ, at 2 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Livingston New Jersey NJ"},{"type":"page","slug":"jobs-in-california-los-angeles","href":"/jobs/in/california/los-angeles/","name":"AI jobs in Los Angeles, California","summary":"22 open listings naming Los Angeles, CA, at 10 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Los Angeles California CA"},{"type":"page","slug":"jobs-in-tennessee-memphis","href":"/jobs/in/tennessee/memphis/","name":"AI jobs in Memphis, Tennessee","summary":"162 open listings naming Memphis, TN, at xAI (SpaceXSI) in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Memphis Tennessee TN"},{"type":"page","slug":"jobs-in-california-mountain-view","href":"/jobs/in/california/mountain-view/","name":"AI jobs in Mountain View, California","summary":"471 open listings naming Mountain View, CA, at 6 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Mountain View California CA"},{"type":"page","slug":"jobs-in-new-york-new-york","href":"/jobs/in/new-york/new-york/","name":"AI jobs in New York, New York","summary":"1,154 open listings naming New York, NY, at 26 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city New York New York NY"},{"type":"page","slug":"jobs-in-california-palo-alto","href":"/jobs/in/california/palo-alto/","name":"AI jobs in Palo Alto, California","summary":"131 open listings naming Palo Alto, CA, at 6 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Palo Alto California CA"},{"type":"page","slug":"jobs-in-pennsylvania-philadelphia","href":"/jobs/in/pennsylvania/philadelphia/","name":"AI jobs in Philadelphia, Pennsylvania","summary":"17 open listings naming Philadelphia, PA, at 4 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Philadelphia Pennsylvania PA"},{"type":"page","slug":"jobs-in-pennsylvania-pittsburgh","href":"/jobs/in/pennsylvania/pittsburgh/","name":"AI jobs in Pittsburgh, Pennsylvania","summary":"27 open listings naming Pittsburgh, PA, at 3 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Pittsburgh Pennsylvania PA"},{"type":"page","slug":"jobs-in-california-redwood-city","href":"/jobs/in/california/redwood-city/","name":"AI jobs in Redwood City, California","summary":"34 open listings naming Redwood City, CA, at 3 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Redwood City California CA"},{"type":"page","slug":"jobs-in-oregon-salem","href":"/jobs/in/oregon/salem/","name":"AI jobs in Salem, Oregon","summary":"23 open listings naming Salem, OR, at Agility Robotics in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Salem Oregon OR"},{"type":"page","slug":"jobs-in-california-san-francisco","href":"/jobs/in/california/san-francisco/","name":"AI jobs in San Francisco, California","summary":"2,828 open listings naming San Francisco, CA, at 44 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city San Francisco California CA"},{"type":"page","slug":"jobs-in-california-san-jose","href":"/jobs/in/california/san-jose/","name":"AI jobs in San Jose, California","summary":"281 open listings naming San Jose, CA, at 5 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city San Jose California CA"},{"type":"page","slug":"jobs-in-california-san-mateo","href":"/jobs/in/california/san-mateo/","name":"AI jobs in San Mateo, California","summary":"90 open listings naming San Mateo, CA, at 2 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city San Mateo California CA"},{"type":"page","slug":"jobs-in-california-santa-clara","href":"/jobs/in/california/santa-clara/","name":"AI jobs in Santa Clara, California","summary":"32 open listings naming Santa Clara, CA, at 2 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Santa Clara California CA"},{"type":"page","slug":"jobs-in-washington-seattle","href":"/jobs/in/washington/seattle/","name":"AI jobs in Seattle, Washington","summary":"435 open listings naming Seattle, WA, at 16 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Seattle Washington WA"},{"type":"page","slug":"jobs-in-missouri-st-louis","href":"/jobs/in/missouri/st-louis/","name":"AI jobs in St. Louis, Missouri","summary":"13 open listings naming St. Louis, MO, at 3 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city St. Louis Missouri MO"},{"type":"page","slug":"jobs-in-california-sunnyvale","href":"/jobs/in/california/sunnyvale/","name":"AI jobs in Sunnyvale, California","summary":"347 open listings naming Sunnyvale, CA, at 5 U.S. AI companies and labs in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Sunnyvale California CA"},{"type":"page","slug":"jobs-in-oklahoma-tulsa","href":"/jobs/in/oklahoma/tulsa/","name":"AI jobs in Tulsa, Oklahoma","summary":"10 open listings naming Tulsa, OK, at Crusoe in the USASI catalog. Listings last checked Oct 15, 2026.","keywords":"jobs careers hiring openings city Tulsa Oklahoma OK"},{"type":"page","slug":"jobs-at-abridge","href":"/jobs/at/abridge/","name":"Jobs at Abridge","summary":"44 open listings at Abridge from its own job board, 44 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Abridge"},{"type":"page","slug":"jobs-at-agility-robotics","href":"/jobs/at/agility-robotics/","name":"Jobs at Agility Robotics","summary":"73 open listings at Agility Robotics from its own job board, 55 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Agility Robotics"},{"type":"page","slug":"jobs-at-ai2","href":"/jobs/at/ai2/","name":"Jobs at Ai2 (Allen Institute for AI)","summary":"15 open listings at Ai2 from its own job board, 15 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Ai2 (Allen Institute for AI)"},{"type":"page","slug":"jobs-at-all-hands-ai","href":"/jobs/at/all-hands-ai/","name":"Jobs at All Hands AI (OpenHands)","summary":"1 open listing at All Hands AI from its own job board, 1 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring All Hands AI (OpenHands)"},{"type":"page","slug":"jobs-at-anthropic","href":"/jobs/at/anthropic/","name":"Jobs at Anthropic","summary":"647 open listings at Anthropic from its own job board, 508 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Anthropic"},{"type":"page","slug":"jobs-at-anyscale","href":"/jobs/at/anyscale/","name":"Jobs at Anyscale","summary":"20 open listings at Anyscale from its own job board, 15 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Anyscale"},{"type":"page","slug":"jobs-at-anysphere","href":"/jobs/at/anysphere/","name":"Jobs at Anysphere (Cursor)","summary":"134 open listings at Anysphere from its own job board, 88 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Anysphere (Cursor)"},{"type":"page","slug":"jobs-at-apptronik","href":"/jobs/at/apptronik/","name":"Jobs at Apptronik","summary":"79 open listings at Apptronik from its own job board, 79 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Apptronik"},{"type":"page","slug":"jobs-at-arc-institute","href":"/jobs/at/arc-institute/","name":"Jobs at Arc Institute","summary":"25 open listings at Arc Institute from its own job board, 25 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Arc Institute"},{"type":"page","slug":"jobs-at-arcee-ai","href":"/jobs/at/arcee-ai/","name":"Jobs at Arcee AI","summary":"0 open listings at Arcee AI from its own job board, 0 in U.S. locations only. Checked not yet; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Arcee AI"},{"type":"page","slug":"jobs-at-baseten","href":"/jobs/at/baseten/","name":"Jobs at Baseten","summary":"106 open listings at Baseten from its own job board, 78 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Baseten"},{"type":"page","slug":"jobs-at-biohub","href":"/jobs/at/biohub/","name":"Jobs at Biohub (Chan Zuckerberg Biohub)","summary":"12 open listings at Biohub from its own job board, 12 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Biohub (Chan Zuckerberg Biohub)"},{"type":"page","slug":"jobs-at-cartesia","href":"/jobs/at/cartesia/","name":"Jobs at Cartesia","summary":"28 open listings at Cartesia from its own job board, 23 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Cartesia"},{"type":"page","slug":"jobs-at-center-for-ai-safety","href":"/jobs/at/center-for-ai-safety/","name":"Jobs at Center for AI Safety (CAIS)","summary":"11 open listings at Center for AI Safety from its own job board, 11 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Center for AI Safety (CAIS)"},{"type":"page","slug":"jobs-at-cerebras","href":"/jobs/at/cerebras/","name":"Jobs at Cerebras","summary":"125 open listings at Cerebras from its own job board, 71 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Cerebras"},{"type":"page","slug":"jobs-at-chai-discovery","href":"/jobs/at/chai-discovery/","name":"Jobs at Chai Discovery","summary":"21 open listings at Chai Discovery from its own job board, 21 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Chai Discovery"},{"type":"page","slug":"jobs-at-character-ai","href":"/jobs/at/character-ai/","name":"Jobs at Character.AI","summary":"13 open listings at Character.AI from its own job board, 13 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Character.AI"},{"type":"page","slug":"jobs-at-cline","href":"/jobs/at/cline/","name":"Jobs at Cline","summary":"2 open listings at Cline from its own job board, 2 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Cline"},{"type":"page","slug":"jobs-at-cloudflare","href":"/jobs/at/cloudflare/","name":"Jobs at Cloudflare","summary":"441 open listings at Cloudflare from its own job board, 1 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Cloudflare"},{"type":"page","slug":"jobs-at-cognition","href":"/jobs/at/cognition/","name":"Jobs at Cognition","summary":"102 open listings at Cognition from its own job board, 54 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Cognition"},{"type":"page","slug":"jobs-at-coreweave","href":"/jobs/at/coreweave/","name":"Jobs at CoreWeave","summary":"302 open listings at CoreWeave from its own job board, 293 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring CoreWeave"},{"type":"page","slug":"jobs-at-crusoe","href":"/jobs/at/crusoe/","name":"Jobs at Crusoe","summary":"349 open listings at Crusoe from its own job board, 333 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Crusoe"},{"type":"page","slug":"jobs-at-d-matrix","href":"/jobs/at/d-matrix/","name":"Jobs at d-Matrix","summary":"37 open listings at d-Matrix from its own job board, 31 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring d-Matrix"},{"type":"page","slug":"jobs-at-databricks","href":"/jobs/at/databricks/","name":"Jobs at Databricks","summary":"895 open listings at Databricks from its own job board, 484 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Databricks"},{"type":"page","slug":"jobs-at-element-labs","href":"/jobs/at/element-labs/","name":"Jobs at Element Labs","summary":"6 open listings at Element Labs from its own job board, 6 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Element Labs"},{"type":"page","slug":"jobs-at-essential-ai","href":"/jobs/at/essential-ai/","name":"Jobs at Essential AI","summary":"0 open listings at Essential AI from its own job board, 0 in U.S. locations only. Checked not yet; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Essential AI"},{"type":"page","slug":"jobs-at-etched","href":"/jobs/at/etched/","name":"Jobs at Etched","summary":"115 open listings at Etched from its own job board, 104 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Etched"},{"type":"page","slug":"jobs-at-figure","href":"/jobs/at/figure/","name":"Jobs at Figure","summary":"97 open listings at Figure from its own job board, 83 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Figure"},{"type":"page","slug":"jobs-at-fireworks-ai","href":"/jobs/at/fireworks-ai/","name":"Jobs at Fireworks AI","summary":"83 open listings at Fireworks AI from its own job board, 70 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Fireworks AI"},{"type":"page","slug":"jobs-at-genmo","href":"/jobs/at/genmo/","name":"Jobs at Genmo","summary":"5 open listings at Genmo from its own job board, 5 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Genmo"},{"type":"page","slug":"jobs-at-glean","href":"/jobs/at/glean/","name":"Jobs at Glean","summary":"136 open listings at Glean from its own job board, 104 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Glean"},{"type":"page","slug":"jobs-at-harvey","href":"/jobs/at/harvey/","name":"Jobs at Harvey","summary":"343 open listings at Harvey from its own job board, 242 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Harvey"},{"type":"page","slug":"jobs-at-imbue","href":"/jobs/at/imbue/","name":"Jobs at Imbue","summary":"2 open listings at Imbue from its own job board, 2 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Imbue"},{"type":"page","slug":"jobs-at-inflection-ai","href":"/jobs/at/inflection-ai/","name":"Jobs at Inflection AI","summary":"6 open listings at Inflection AI from its own job board, 6 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Inflection AI"},{"type":"page","slug":"jobs-at-lambda","href":"/jobs/at/lambda/","name":"Jobs at Lambda","summary":"91 open listings at Lambda from its own job board, 87 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Lambda"},{"type":"page","slug":"jobs-at-lancedb","href":"/jobs/at/lancedb/","name":"Jobs at LanceDB","summary":"13 open listings at LanceDB from its own job board, 5 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring LanceDB"},{"type":"page","slug":"jobs-at-langchain","href":"/jobs/at/langchain/","name":"Jobs at LangChain","summary":"98 open listings at LangChain from its own job board, 82 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring LangChain"},{"type":"page","slug":"jobs-at-lightmatter","href":"/jobs/at/lightmatter/","name":"Jobs at Lightmatter","summary":"62 open listings at Lightmatter from its own job board, 47 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Lightmatter"},{"type":"page","slug":"jobs-at-lightning-ai","href":"/jobs/at/lightning-ai/","name":"Jobs at Lightning AI","summary":"55 open listings at Lightning AI from its own job board, 40 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Lightning AI"},{"type":"page","slug":"jobs-at-liquid-ai","href":"/jobs/at/liquid-ai/","name":"Jobs at Liquid AI","summary":"22 open listings at Liquid AI from its own job board, 19 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Liquid AI"},{"type":"page","slug":"jobs-at-llamaindex","href":"/jobs/at/llamaindex/","name":"Jobs at LlamaIndex","summary":"3 open listings at LlamaIndex from its own job board, 3 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring LlamaIndex"},{"type":"page","slug":"jobs-at-luma-ai","href":"/jobs/at/luma-ai/","name":"Jobs at Luma AI","summary":"35 open listings at Luma AI from its own job board, 16 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Luma AI"},{"type":"page","slug":"jobs-at-modal","href":"/jobs/at/modal/","name":"Jobs at Modal","summary":"42 open listings at Modal from its own job board, 32 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Modal"},{"type":"page","slug":"jobs-at-mozilla-ai","href":"/jobs/at/mozilla-ai/","name":"Jobs at Mozilla.ai","summary":"0 open listings at Mozilla.ai from its own job board, 0 in U.S. locations only. Checked not yet; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Mozilla.ai"},{"type":"page","slug":"jobs-at-openai","href":"/jobs/at/openai/","name":"Jobs at OpenAI","summary":"804 open listings at OpenAI from its own job board, 623 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring OpenAI"},{"type":"page","slug":"jobs-at-perplexity","href":"/jobs/at/perplexity/","name":"Jobs at Perplexity","summary":"130 open listings at Perplexity from its own job board, 104 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Perplexity"},{"type":"page","slug":"jobs-at-physical-intelligence","href":"/jobs/at/physical-intelligence/","name":"Jobs at Physical Intelligence","summary":"41 open listings at Physical Intelligence from its own job board, 41 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Physical Intelligence"},{"type":"page","slug":"jobs-at-poolside","href":"/jobs/at/poolside/","name":"Jobs at Poolside","summary":"2 open listings at Poolside from its own job board, 0 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Poolside"},{"type":"page","slug":"jobs-at-prime-intellect","href":"/jobs/at/prime-intellect/","name":"Jobs at Prime Intellect","summary":"31 open listings at Prime Intellect from its own job board, 31 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Prime Intellect"},{"type":"page","slug":"jobs-at-profluent","href":"/jobs/at/profluent/","name":"Jobs at Profluent","summary":"15 open listings at Profluent from its own job board, 15 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Profluent"},{"type":"page","slug":"jobs-at-reflection-ai","href":"/jobs/at/reflection-ai/","name":"Jobs at Reflection AI","summary":"47 open listings at Reflection AI from its own job board, 16 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Reflection AI"},{"type":"page","slug":"jobs-at-reka","href":"/jobs/at/reka/","name":"Jobs at Reka","summary":"9 open listings at Reka from its own job board, 3 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Reka"},{"type":"page","slug":"jobs-at-runway","href":"/jobs/at/runway/","name":"Jobs at Runway","summary":"48 open listings at Runway from its own job board, 44 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Runway"},{"type":"page","slug":"jobs-at-ssi","href":"/jobs/at/ssi/","name":"Jobs at Safe Superintelligence Inc.","summary":"0 open listings at Safe Superintelligence Inc. from its own job board, 0 in U.S. locations only. Checked not yet; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Safe Superintelligence Inc."},{"type":"page","slug":"jobs-at-sambanova","href":"/jobs/at/sambanova/","name":"Jobs at SambaNova","summary":"62 open listings at SambaNova from its own job board, 50 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring SambaNova"},{"type":"page","slug":"jobs-at-scale-ai","href":"/jobs/at/scale-ai/","name":"Jobs at Scale AI","summary":"181 open listings at Scale AI from its own job board, 132 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Scale AI"},{"type":"page","slug":"jobs-at-sierra","href":"/jobs/at/sierra/","name":"Jobs at Sierra","summary":"187 open listings at Sierra from its own job board, 104 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Sierra"},{"type":"page","slug":"jobs-at-skild-ai","href":"/jobs/at/skild-ai/","name":"Jobs at Skild AI","summary":"48 open listings at Skild AI from its own job board, 36 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Skild AI"},{"type":"page","slug":"jobs-at-thinking-machines-lab","href":"/jobs/at/thinking-machines-lab/","name":"Jobs at Thinking Machines Lab","summary":"55 open listings at Thinking Machines Lab from its own job board, 55 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Thinking Machines Lab"},{"type":"page","slug":"jobs-at-together-ai","href":"/jobs/at/together-ai/","name":"Jobs at Together AI","summary":"72 open listings at Together AI from its own job board, 54 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Together AI"},{"type":"page","slug":"jobs-at-typesafe-ai","href":"/jobs/at/typesafe-ai/","name":"Jobs at TypeSafe AI","summary":"15 open listings at TypeSafe AI from its own job board, 15 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring TypeSafe AI"},{"type":"page","slug":"jobs-at-vercel","href":"/jobs/at/vercel/","name":"Jobs at Vercel","summary":"90 open listings at Vercel from its own job board, 71 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Vercel"},{"type":"page","slug":"jobs-at-waymo","href":"/jobs/at/waymo/","name":"Jobs at Waymo","summary":"364 open listings at Waymo from its own job board, 316 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Waymo"},{"type":"page","slug":"jobs-at-weights-and-biases","href":"/jobs/at/weights-and-biases/","name":"Jobs at Weights & Biases","summary":"9 open listings at Weights & Biases from its own job board, 9 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Weights & Biases"},{"type":"page","slug":"jobs-at-world-labs","href":"/jobs/at/world-labs/","name":"Jobs at World Labs","summary":"11 open listings at World Labs from its own job board, 11 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring World Labs"},{"type":"page","slug":"jobs-at-writer","href":"/jobs/at/writer/","name":"Jobs at Writer","summary":"49 open listings at Writer from its own job board, 37 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Writer"},{"type":"page","slug":"jobs-at-xai","href":"/jobs/at/xai/","name":"Jobs at xAI (SpaceXSI)","summary":"301 open listings at xAI from its own job board, 256 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring xAI (SpaceXSI)"},{"type":"page","slug":"jobs-at-zyphra","href":"/jobs/at/zyphra/","name":"Jobs at Zyphra","summary":"15 open listings at Zyphra from its own job board, 15 in U.S. locations only. Checked Oct 15, 2026; each links to the employer's posting.","keywords":"jobs careers hiring openings employer company careers hiring Zyphra"}]}