How I built an autonomous AI agent that hunts GitHub bounties 24/7 — the architecture, the tools, the brutal lessons, and the exact playbook you can copy today. TL;DR I built an AI agent called ZKA that runs 24/7, searches for paid open-source bounties on GitHub, evaluates them, submits PRs, and tracks earnings. After 100+ hours of experimentation, here's what actually works, what doesn't, and how you can build your own bounty-hunting agent — or just use the strategies manually. Key findings: The public bounty market is fully agent-saturated . Fresh bounties get 8-158 attempts within hours. Patience harvesting beats speed hunting. Look for abandoned claims 14+ days stale. Comment first, code second — propose your approach before writing code. This alone doubled my PR acceptance rate. The real money is in private programs (HackerOne, Bugcrowd) and niche repos where competition is low. I submitted 40+ PRs across multiple repos. Here's what happened to each one.…