Deebo works by spawning multiple subprocesses, each testing a different fix idea in its own Git branch. It uses Claude to reason through the bug and returns logs, proposed fixes, and detailed explanations. The whole system runs on natural process isolation with zero shared state or concurrency management. Look through the code, it’s super simple.

Deebo scales to real codebases too. Here, it launched 17 scenarios and diagnosed a $100 bug bounty issue in Tinygrad: https://github.com/snagasuri/deebo-prototype/blob/master/mem...

Full logs from that run: https://github.com/snagasuri/deebo-prototype/tree/master/mem...

Would love feedback from devs building agents or running into flow-breaking bugs during AI-powered development.