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What I learned from looking at 900 most popular open source AI tools

Named practitioner synthesis

#194

T3digested
Topic
Practitioner Notes
First seen
2026-07-16 19:08:00
Last seen
2026-07-16 19:08:00

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  • Blog / Newsletter2026-07-16 19:07:19
    What I learned from looking at 900 most popular open source AI tools

    <p>[<em><a href="https://news.ycombinator.com/item?id=39709912">Hacker News discussion</a>, <a href="https://www.linkedin.com/posts/chiphuyen_generativeai-aiapplications-llmops-activity-7174153467844820993-ztSE">LinkedIn discussion</a>, <a href="https://twitter.com/chipro/status/1768388213008445837">Twitter thread</a></em>]</p> <p><strong>Update (Feb 2026)</strong>: <em>The full list of open source AI repos is hosted at <a href="https://goodailist.com">Good AI List</a>, updated daily. It’s balooned to 15K repos, and you can submit missing repos. You can also find some of them on my <a href="https://github.com/stars/chiphuyen/lists/cool-llm-repos">cool-llm-repos</a> list on GitHub.</em></p> <p>Four years ago, I did an analysis of the <a href="https://huyenchip.com/2020/06/22/mlops.html">open source ML ecosystem</a>. Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models.</p> <h2 id="data">Data</h2> <p>I