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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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