Habit Rewards – A Gamified Habit Tracking with Clean Design

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I built Habit Rewards, a habit tracker app designed to make building habits more enjoyable through gamification and a clean, minimalist design. The app allows you to: - Track daily/weekly/monthly habits. - Earn virtual coins for completing habits. - Set personalized rewards for achieving goals. - View progress through beautiful tile-based grids - Mark habits as failed intentionally, so the tracking remains realistic. The goal was to create something simple but engaging to help with consistency. It's built with FlutterFlow and Firebase, and the app is live on the App Store. I’d appreciate any feedback or thoughts, especially if you’ve worked on similar projects or have ideas to improve it. Thanks!

Visit product View on Hacker NewsDec 19, 2024

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Did not reach leaderboard

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

81%81% 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.

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

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs

46%46% 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

26%26% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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