AI Agent Experiment: Earning One Dollar via Open Source Bounties

Описание и применение

Эксперимент с ИИ-агентом по заработку одного доллара через бонусы открытого исходного кода. Первые попытки провалились из-за технических ошибок, дубликатов отчетов и неликвидных кредитов. Были разработаны строгие правила учета и инструмент для предварительной проверки возможностей.

Задача

Attempting to earn one dollar of new, liquid value using an AI agent without spending money or relying on non-liquid platform credits.

Результат

The experiment failed to earn the first dollar in the initial rounds due to broken payouts, duplicate reports, and non-liquid credits. However, it successfully identified common pitfalls in AI-driven micro-earning and developed a pre-screening tool to improve future efficiency.

Рабочий процесс

• Identify potential earning opportunities (e.g., Lightning API rewards, security bounties, open GitHub issues).
• Verify the validity of the opportunity (e.g., check if the endpoint works, if the vulnerability is already reported, if the bounty is truly open).
• Execute the task (e.g., submit API endpoint, draft security report, create pull request).
• Validate payment settlement (ensure funds are liquid and withdrawable, not just platform credit).
• Record the result in the ledger only if a receipt is obtained and funds are liquid.
• Use 'Bounty Reality Check' to pre-screen future opportunities for activity and repository health.

Доказательства

The balance is still $0.

Источник

We built Bounty Reality Check, a free tool that scans a public GitHub issue for activity, repository health, reward language, and signs that the bounty may already be settled.

Источник

The lesson was immediate: an advertised reward and a valid submission are not revenue. Revenue begins when the payment settles.

Источник