Gamified Fitness – Would You Use This?

Hacker News

Working out is hard to stay consistent with. It’s often boring, repetitive, and lacks interaction. Gyms take time, and at-home workouts (Peloton, YouTube, etc.) lose engagement fast. That’s why I’m building Pulse. It's a mobile app that gamifies workouts using AI motion detection. Think Guitar Hero or Dance Dance Revolution, but for fitness. You get instant feedback, can perfect your form, time your moves perfectly, and compete for high scores. No remotes or extra gear needed. Just the ability to perform moves like a jumping jack, push up, or a squat. I’m building this now and would love your honest feedback: 1. Does this sound like something you’d actually use? 2. What would make it most engaging for you? Drop your thoughts! I’m all ears!

Visit product View on Hacker NewsOct 30, 2025

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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 · Strong signals: using · Missing: mac, agents, macos

66%66% 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

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

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

35%35% predicted probability of success on TrustMRR, 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

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

13%13% 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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