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Manage coding norms across your AI agents
Hacker NewsManage coding norms across your AI agents
Working on a tiny repo to keep coding norms in sync across AI agents. Trying to make it simpler than Ruler / RuleSync. Would love feedback on: 1) Is this the right way to manage coding norms as agent use balloons? 2) Would it be useful to sync rules with your team through the VSCode extension? 3) What's the best way to enforce long-term norm consistency as norm and code count increase?
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, tiny · Missing: mac, macos, cursor92%92% 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.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting51%51% 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: io · Missing: https docs, excited, just released31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive15%15% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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