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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.

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I'm integrating the Yara-x rules engine into my C# hex editor. I'm working to maximize the performance and efficiency of the integration. I'd like to ask your opinion about this. I personally made this decision to expand the functionality of my hex editor by adding Yara-x support. This allows me to search for signatures in binary files in more detail. I think viewing the entire byte grid can help in malware research.

I implemented this using memory mapping files. I also divided the scanning methods into modes: small files are mapped completely, while large files are scanned in 16MB chunks with a small 64KB overlay to prevent a situation where half the signature is in one chunk and half is in another. I also used smarter memory management for performance with large files. Documentation is in the readme. But in short, this is an implementation that doesn't overload the garbage collector in C# and handles unsafe pointers and raw memory addresses. What's important is that I now have protection against bad rules that, for example, search for any byte, overloading the scanner. Such rules won't work, and the scanner will stop scanning so that the scanner doesn't crash with an error.

I can't say right now that this tool could be better than the others, because it's currently in development and I still have room for improvement, but it would be cool to hear people's opinions or accept other people's ideas for improving the tool.

(The native version with Yarax is not yet available in current releases, but the source code is available and you can compile or read it yourself.)

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