Just getting started

Bankr Noteworthy Projects Each project receives $250 cash and $250 in LLM credits. Dropper @hvjericho Aeko, Abhishek Pandey and Kritika Jha (@aekovera) Gavel Gate @0xgramajo Agentic Tellr Machine @vizinevizion Margin Call @davidbhurley Private Desk @jachian22 Risk Fork @agoragentic TPCP @ahn_going Nexus Trading Labs @borstxbt Lolo @ibuprod Idea Labs @hiiinternet Hedgr @avinashnayak27 Clock Out @chinesepowered Venue0 @winsznlabs Roux @_heisemma D3MYUR: Inoculum Agent @schmidtiest Vouch, Amanda and Max (@pegzeus) STAQ @basedkabeer, @dxfareed and @01CryptoGen

24 hours later, @CryptoStatuette's bounty to keep @kenjiquest cooking has pulled a 7x and sits at $380 in funding from 14 contributors 📸 poidh is for crowdfunding the outcomes you care about

Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by @asymptotelabs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: github.com/Asymptote-Labs/ag… (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.

runtime is in full swing. we have a hundred plus builders itching to launch cool projects this week in person in NYC. its builder season.

Runtime Agent Week registration is LIVE. Build online or IRL in NYC. Same prize pool. Same rules. Sept 13–19 Agents. Agent infra. Tokenized assets. Payments. Consumer apps. Social. Art. Memes. More info soon. Applications are open. Register now: luma.com/47rsvjir