a habit tracker that only lets you track one habit

Hacker News

I kept failing at habit apps. I'd add 6 habits, miss a day, feel guilty, stop opening the app entirely. So I built the opposite: an app that only lets you track one habit at a time. The idea is that one habit done beats ten habits planned. Pick one thing, show up daily, and only move on when it's automatic. It's free with ads. Would love your feedback on whether this constraint feels liberating or limiting. iOS: https://apps.apple.com/app/id1549454193 Android: https://play.google.com/store/apps/details?id=com.davvie.hab...

Visit product View on Hacker NewsJan 26, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

84%84% predicted probability of success on Indie Hackers, 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: ios, apps, google · Missing: mobile apps, personal, entrepreneurs

61%61% 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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

32%32% 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

31%31% 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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