๐—ช๐—ต๐—ฎ๐˜ ๐—ถ๐—ณ ๐ซ๐ž๐ฅ๐ข๐š๐›๐ฅ๐ฒ ๐—ฎ๐˜‚๐˜๐—ผ๐—บ๐—ฎ๐˜๐—ถ๐—ป๐—ด ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜€๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐ญ๐š๐ฌ๐ค๐ฌ ๐˜„๐—ฎ๐˜€ ๐Ÿ๐ข๐ง๐š๐ฅ๐ฅ๐ฒ ๐˜„๐—ถ๐˜๐—ต๐—ถ๐—ป ๐—ฟ๐—ฒ๐—ฎ๐—ฐ๐—ต?!

We all know the grind of working with data, even with AI tools: every experiment starts with re-explaining everything, every iteration needs you to prompt, wait, review, correct, and repeat. And the moment you close the session, everything learned is gone.

It makes us the bottleneck, and this hinders human-AI collaboration...

So I built ๐Ž๐ฉ๐ž๐ง๐ƒ๐š๐ญ๐š๐’๐œ๐ข, an autonomous agent purpose-built for DS/ML, and tested it on Kaggle. I enrolled in a recent competition, ran the agent with no hints, no guidance, while ironing my shirts.

In one shot, it landed AUC 0.95, a top-30% finish out of 3K+ teams and 36K+ submissions using hashtag#Anthropic's Claude Sonnet 4.6. (More on this in README)