Kadai –> AI friendly, discoverable project tools

Hacker News

I kept building this for basically every project I built, so decided to make a standard version. tl;dr: - add actions to .kadai/actions in your repo - run `bunx kadai` - get a little CLI menu to run your actions actions are also 'AI friendly', meaning there's an .mcp.json setup that points to kadai so AI's can discover any actions you add to kadai basically you can share scripts/actions across your team and also expose them to AI at the same time

Visit product View on Hacker NewsFeb 26, 2026

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

87%87% 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

29%29% 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, revenue

21%21% 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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