PunchLab – track punches given to punching bag with an App

Hacker News

Hi HN, we just released an Android App that is able to measure and react to strikes thrown at a punching bag. Check out https://punchlab.net It uses data coming from the smartphone's own sensors and analyzes the signal to detect and measure punches. The App is bag-agnostic and it's able to be accurate enough to track also the fastest athletes (check out this video with a Pro fighter giving up to 7 punches per seconds: https://www.youtube.com/watch?v=nwaHmgj45aQ ) let me know what do you think about it, there are many features we are working on!

Visit product View on Hacker NewsApr 23, 2018

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

66%66% 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.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, ide · Missing: https docs, excited, exist

38%38% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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