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Track Habits by taking pics

Hacker News

Track Habits by taking pics

Folksable is a photo habit tracking app, people share photo updates of their habits with friends to keep them accountable. Problem it addresses is many people want accountability from their friends and peers for doing self improvement activities, and they might not be comfortable doing this on instagram/twtr and other social media bcz it’s designed to show off and they might not be vulnerable enough to show things they are bad at and want to grow in. Folksable is designed to give them that social space & accountability to grow and develop habits, It achieves this by requiring sharing photo updates of their habits on the app and providing gamification features like streaks & social profile

Visit product View on Hacker NewsMar 25, 2024

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · 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.

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% 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

27%27% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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