Open Source AI Gap Map
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
Current AI’s open-source AI map has deeply categorized 421 projects, with another 24,400 artifacts still uncategorized, and released the underlying dataset as MIT-licensed YAML plus schemas and notebooks. For production teams, the useful shift is not the web map but the reusable inventory: you can now programmatically audit open-source models, tools, datasets, and hardware candidates instead of relying on ad hoc GitHub search or vendor lists.
A newly-funded non-profit ($400m committed) has published a machine-readable index of the open source AI ecosystem: 421 deeply-profiled products (266 tools, 85 models, 50 datasets, 20 hardware projects) plus 16,185 tracked GitHub repos, all released as MIT-licensed YAML you can query directly. If you're evaluating open-weight models or self-hosted infra, this gives you a structured, citable dependency inventory to source alternatives and assess maturity across the stack rather than relying on scattered leaderboards.