Archprint, infer architecture lint rules from your repo's import graph

Hacker News

This tool started as a research for me trying to answer the question "Does AI agents drift from your architecture conventions or if you need enforcement instead?". So I started out building a benchmark to test that premise and on the scope I tested, the agents did not drift and I had to stop that research line. So to reuse what I have learnt from that research, I started out trying to catch engineer's regressions through review and CI and create those conventions codebases already follows with the evidence instead of leaving them in someone's head. Before I started building this tool, I also mostly write the rules by hand and linter's documentations for every linter I wanted to use. But after my research, the tool automates the process and I'm excited to see how it can help other engineers too. I ran the full package workflow (scan, recommend) across 92,861 real public TypeScript repositories on github and it completed on every one without a single crash and the reversible install/uninstall round-trip held. I will appreciates any feedback you have on the rules it infers for your repo and how it can help you enforce them or not. The study is public here https://github.com/Tommkruix/agentrulebench and Archprint is public here https://github.com/Tommkruix/archprint also.

Visit product View on Hacker NewsSep 8, 2026

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

69%69% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

65%65% 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: excited, ide, io · Missing: https docs, just released, exist

38%38% 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 · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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