Reference
Hubs
Short, sourced introductions to parts of the landscape. Each hub says what it covers and what it does not, suggests a reading path, and links to records and primary documents. Featured records are examples, not rankings.
- AI agents and agent toolingWhat 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.13 featured records · 7 reading steps
- AI for scienceBiology, 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.12 featured records · 7 reading steps
- AI in enterprise softwareEnterprise 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.12 featured records · 7 reading steps
- AI safety, security, and trustHow 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.14 featured records · 9 reading steps
- Benchmarks and evaluationEvaluation 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.11 featured records · 8 reading steps
- Building software with AIThe 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.22 featured records · 9 reading steps
- Chips, cloud, and computeThe hardware and services behind AI: chip designers, accelerators, memory, networking, and AI cloud providers, and why a headquarters is not a data-center location.12 featured records · 7 reading steps
- Foundation models and the labs that build themHow 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.14 featured records · 7 reading steps
- Open-source foundations and stewardsHow 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.12 featured records · 7 reading steps
- Robotics and embodied AIRobot 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.11 featured records · 7 reading steps
- Running AI on your own hardwareHow 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.13 featured records · 8 reading steps
- Speech, vision, and multimodal AIModels 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.16 featured records · 9 reading steps
- Training data and datasetsOpen 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.12 featured records · 7 reading steps
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