Visualizing Apple Health workout data (stats, trends, insights)

Hacker News

I've just launched this little iOS app as an alternative to Apple Fitness, which is cluttered and chaotic when it comes to visualizing basic workout stats and metrics. The idea is to focus on a clean, minimalistic design and only show high-level metrics that are actually useful, e.g. how often did I work out this week/month/year, cardio vs strength vs mobility, surf session count in February, etc. It's free and offline. Also, no signup, no ads, no data sharing, no social feeds, and no notifications. Just open it and get a quick glance at the state of your workout game within 2s. It's built in native Swift with liquid glass. Feedback is quite good so far, but it seems hard to get initial traction in the App Store, especially with all the AI slob these days. Would deeply appreciate some early downloads and honest reviews.

Visit product View on Hacker NewsMar 24, 2026

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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, visual, open · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, month, fitness · Missing: mobile apps, personal, entrepreneurs

53%53% 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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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.

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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