An Android app to gamify exercise tracking

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Hey HN! Over the last few months I've been working on a mobile app written in React Native with a C# backend. The app is supposed to be a simple and minimal exercise tracker with components of gamification and social interaction. The idea behind creating this was to learn a bit more of the fullstack development flow and pick up some skills regarding product and design (which I thought would be hard, and it turns out it was still much harder than I'd expected haha). The app is currently only on the Android store due to it being cheaper to license than the Apple store. There are still definitely bugs within the app, however I thought it would be good to get user feedback earlier in the apps life cycle! Feel free to leave any feedback here or on the play store page. Happy to hear any sort of feedback, thanks for reading and have a good day all :)

Visit product View on Hacker NewsApr 27, 2023

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

88%88% 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 · Strong signals: apple, apps, user · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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

31%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, revenue

28%28% 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, intuitive

24%24% predicted probability of success on AppSumo, 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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