Calisthenics Memory – Open-source bodyweight training tracker

Hacker News

Hey HN, do you work out? I couldn't find an open-source calisthenics tracking app, so I built one for my own training records. It's now polished enough to share. Key points: - Full customization: No preset exercises, no locked features. Every exercise you create has access to all settings (reps/time, unilateral/bilateral, goals, per-exercise timers) - Two modes: Quick manual logging or guided workout with timers - Completely offline: No account, no tracking, no ads. Your data stays on your device. Source code and APK available on Codeberg (GPL-3.0). https://codeberg.org/Gonbei774/CalisthenicsMemory Feedback welcome!

Visit product View on Hacker NewsNov 27, 2025

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

63%63% 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: code, open · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, 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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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