Persist OS – Durable decisions for AI code

Hacker News

In the past few months, I’ve been building and shipping things extremely fast. Some of the MVPs that I built for my friends were completely vibe-coded and shipped within a day to show a demo. While it was a great experience working with AI tools, I wanted to make sure some basics were followed, decisions of what and why written down, obvious things not skipped and that it could scale. That’s why I built persist-os. It’s a CLI that runs locally. No network required. It records the architecture decisions and standards in plain files in the repo and 'persist doctor' checks them - it flags if anything contradicts or is missing, or if the task is done with no tests. Because the rules live in your repo files, it doesn't matter if you start a new session or the session is compacted - the standards and rules survive. MIT licensed. Here’s how you can try it: npx persist-os@latest init

Visit product View on Hacker NewsJun 23, 2026

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

94%94% 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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

50%50% 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

31%31% 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

27%27% 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

23%23% 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.

Incorrect prediction on native model

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