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The no bullshit approach to tracking your fitness progress
Hacker NewsThe no bullshit approach to tracking your fitness progress
Hello HN, I wanted a simple app to track my fitness progress. Every fitness tracker in the AppStore had too many features for my liking so I made fit-stitch (Free, iOS). It allows you to take snapshots and attach a weight and date to each one. When you've made some progress you can create a before and after picture with a few taps. Hope you find it useful. www.fit-stitch.com
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Did not reach leaderboard
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TrustMRRFits verified-revenue profile · Strong signals: ios, apps, fitness · Missing: mobile apps, personal, entrepreneurs71%71% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apps · Missing: mac, agents, macos39%39% 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, intuitive35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released28%28% 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, revenue18%18% 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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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