Build2me – Prove2Me for software agent swarms build code

Hacker News

Hi everyone, this is my first original post on Hacker News. I’d like to share my thoughts on the next generation of AI agents. I believe these agents will operate autonomously over long periods. While current chat interfaces are designed for conversation, the interfaces for next-gen agents should focus on delegation; they will be capable of gathering environmental data, interpreting requirements, and working continuously toward a goal for days or even a month. That is why I ported prove2me and built the build2me project. My inspiration came from prove2me , which uses Lean to run multi-agent systems for long-term mathematical proof verification—a process requiring extensive exploration and prolonged task execution. I believe this same logic applies to software development and long-term self-iterating projects. I invite you to try it out and please feel free to reach out with any feedback. Thank you.

Visit product View on Hacker NewsSep 14, 2026

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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