I made a gamified fitness app that features a social network and AI

Hacker News

Hi everyone, I launched calcium.fitness last year. It was called Stryd initially. I shared with communities and got ~100 users, plus feedback for the initial MVP launch. calcium.fitness is a gamified fitness app with a community. - Find a routine to participate in. - Track your progress. - Custom routines: Use AI or our tools to create a routine that's just right for you. - Compete and climb ranks: Earn XP points, compete with others, and climb the ranks. - Connect: Find and connect with like-minded individuals. I've poured my heart into making fitness more engaging, and I'd love for you to be a part of this journey. Try out the app at calcium.fitness or check it out on Product Hunt: https://www.producthunt.com/posts/calcium and let me know what you think! The feedback so far has been invaluable. What features would you love to see? How can I make fitness more fun and engaging for you? Thanks!

Visit product View on Hacker NewsFeb 29, 2024

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3points

Did not reach leaderboard

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

65%65% 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: user · Missing: mac, agents, macos

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

AppSumoMay struggle as an AppSumo deal · Strong signals: plus, users · Missing: platform, intuitive, reviews

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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