Simple Workout – Weightlifting Tracking App

Hacker News

Hello! I got a little tired of the UI in other popular Android fitness tracking apps, so I decided to make my own. Google Play: https://play.google.com/store/apps/details?id=com.harrisontu... I've stripped away all the other features, and left what I thought was essential. I tried to optimise for ease-of-use (and speed!). Let me know if there's anything you think I missed. Check it out! Hope you like it. Its the first app I've ever published. Built with Google Flutter, if anyone is curious about that development experience. Also happy to consider open-sourcing this! Cheers, Harry

Visit product View on Hacker NewsMay 22, 2019

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

87%87% 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.

TrustMRRFits verified-revenue profile · Strong signals: apps, google, way · Missing: mobile apps, ios, personal

66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

58%58% 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

47%47% predicted probability of success on AppSumo, 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

44%44% 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 · Strong signals: arr · Missing: mrr, revenue, profit

19%19% 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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