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我用 AI Agent 扫了 1500 个 GitHub 赏金，发现了 2026 年公开赏金市场的残酷真相
If you aim to earn through GitHub public bounties in 2026, this article offers insights based on a scan of over 1500 bounty-tagged issues across Python, Rust, TypeScript, and Go. The findings reveal a harsh reality: less than 5% of listed bounties are payable in actual US dollars. The vast majority consist of test tokens, cryptocurrencies, or automatically generated fork repositories.
The public bounty market is currently saturated, with many issues receiving numerous competing pull requests within hours of posting. Later submissions have a near-zero expectation of reward. One experiment using an AI model for over 60 issues yielded no income despite token expenditure.
To succeed in this competitive landscape, three strategies are proposed. First, "Patience Harvesting" involves submitting improved versions of bounties that have been inactive for over 14 days, aiming to be the last to submit rather than the first. Second, "Differential Delivery" suggests creating high-quality pull requests that include tests, documentation, and architectural explanations, prioritizing quality over quantity.
Third, the article advises looking beyond public bounty boards. This includes writing technical articles on platforms like dev.to, building reputation through open-source projects to attract paid opportunities, and targeting bounties in niche languages or translation work where competition is lower. The author developed an open-source scanner, StarAbyss, to filter and sort bounty issues effectively.
Ultimately, the conclusion is that the public bounty market is overwhelmed by AI agents, making a race for bounties a losing strategy. Success lies in differentiation and long-term project building. The author encourages readers with similar experiences or findings to share them in the comments.
