Reference
Learn
Plain-language guides that lead to real records and their sources. New here? Start here.
Explainers
- What open weight and open source actually meanIf I can download a model, what am I allowed to do with it?
- How to read a model cardWhat should I check before relying on a model?
- Choosing hosted access or local inferenceShould I use an API or run a model on my own machine?
- Understanding inference hardwareWhat determines whether a model can run on my hardware?
- How to read an AI evaluationWhat can a benchmark result tell me about my own task?
- Understanding training data disclosuresWhat does a provider reveal about the material used to train a model?
- How the American AI ecosystem fits togetherHow do chips, compute providers, labs, software projects, and application companies connect?
- Agents and robotics without the hypeWhat changes when an AI system can use tools or act in the physical world?
- Reading AI infrastructure and energy claimsWhat does a data-center announcement tell us about actual operating capacity?
- Finding AI policy and standards sourcesWhere can I find the original text behind a policy claim?
Hubs
- 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.
More reference
- Glossary : terms used across the catalog
- Places : organizations by documented headquarters state
- Timeline : open releases and news events by documented date
- Source library : every cited source and the records that cite it
- Data and reuse : the public data files and reuse terms
- Methodology : eligibility, openness, sources, and dates
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