One Weekend: How I Found 3,000 Open-Source AI Projects (and You Can Too)

I gave myself 48 hours to map the open-source AI landscape. Not to use any of it — just to understand what exists. I came out with a list of ~3,000 projects, categorized. The method is repeatable, and it doesn't require knowing where to start.

The awesome-* GitHub repos are the closest thing to a curated index. Start with:

These aren't just lists — each entry is a link with a one-line description. I scraped them into a single file and deduplicated. That alone gave me ~1,200 unique projects.

GitHub's topic system is underrated for discovery. Searching by topic surfaces projects that maintainers themselves tagged. High-signal topics:

Searching topic:llm stars:>100 gives you the projects people actually use. Dropping the star threshold and sorting by recently-updated gives you the long tail.

This is where it gets powerful. Pick a tool you already trust and look at what it depends on, or what depends on it. If you use one inference runtime, the projects wrapping it, fine-tuning for it, or benchmarking it are all one hop away. Two hops and you've tripled your map.

ArXiv papers often link a reference implementation. Searching GitHub for the paper title or the repo mentioned in the abstract finds code that hasn't been "discovered" yet — sometimes months before it hits an awesome list.

The 3,000 projects collapse into maybe 30 real categories, and maybe 200 that are actively maintained. The signal-to-noise is brutal, but the discovery method is fast once you know it.

The biggest surprise: the local-inference ecosystem ( llama.cpp and everything built on it) is far more active than the API-wrapper projects. People are building tools that run on the model, not around someone's API.

Has anyone else mapped this space? I'd love to compare categorization — I suspect I'm missing whole branches.