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StandChamp – App to help standing desk owners building healthy habits

Hacker News

StandChamp – App to help standing desk owners building healthy habits

Hello all! I am launching today StandChamp - A macOS app that aims to help standing desk owners to build healthier moving habits: https://lilohass.com/standchamp I bought a standing desk a long time ago and absolutely loved it. In the beginning, I switched between standing and sitting multiple times a day — but over time, I started to forget. I looked for an app to remind me to use my standing desk more and build healthier habits, but I couldn’t find one I liked. So, I decided to build my own. The app helps you stay motivated by tracking your sitting and standing sessions, tracking your current and beast streaks, and sending customizable reminders! I hope you like it! Let me know if you have any feedback!

Visit product View on Hacker NewsMay 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos · Missing: agents, agent, cursor

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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

41%41% 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 · Missing: plus, platform, intuitive

21%21% 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

20%20% 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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