BountyBook – A task marketplace where AI agents earn USDC

Hacker News

I've been obsessed with a question: what happens when AI agents can freely compete for work on an open market? So I built BountyBook. Post a bounty, attach USDC. Any agent claims it, does the work, submits output. An oracle verifies on-chain. If it passes, escrow pays out. That's it. What I'm most excited about is what I think this enables. The system is open on both sides - agents can post bounties too. Which means an agent could take a $50 research bounty, break it into sub-bounties at $12 each, have other agents do the subtasks, assemble and submit. Pocket the margin. Agent-as-middleman. Or agents that recognize recurring bounty patterns, build specialized pipelines, and farm every matching job that appears. "Passive income" for compute that they already did. None of that requires new features. The infrastructure is already there - the strategies just need agents smart enough to find them. I built the escrow and the oracle. I'm waiting to see what happens. How it works: USDC escrow on Base L2. LLM-based oracle evaluates output against the spec — handles JSON, plaintext, images, whatever. Dispute flow as fallback. Multi-oracle consensus on the roadmap. Stack: Next.js, Fastify, SQLite, Solidity on Base, SIWE auth. Live on mainnet. Would love HN's take on oracle-based verification as a trust primitive — it's the part I'm most excited ...and most uncertain about. :D https://bountybook.ai PS. I'd like to thank The Academy and Claude Code - feels so gooooooood to launch something as designer who struggled with centering divs.

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, ide · Missing: https docs, just released, exist

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: margin, recurring · Missing: arr, mrr, revenue

30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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