Gymmr – an app for finding workout partners

Hacker News

I initially developed Gymmr as a web app, but decided that mobile was the best platform given the increasing popularity of mobile chat apps. https://play.google.com/store/apps/details?id=com.connecteddeveloper.gymmr The app lets users find others who are into (or interested) in the same diet and fitness programs as they are. For example, someone who's on a ketogenic diet and doing the 5-3-1 program might want to meet people with similar plans/goals. It is also location-based and allows people to meet others who go to their gym. The idea is to break down some of the barriers to meeting others at the gym. I've started small, but I have future plans for Spotify integration, search, and anything that users might request.

Visit product View on Hacker NewsAug 22, 2019

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

78%78% 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, users · Missing: mobile apps, ios, personal

59%59% 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, user · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

46%46% 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

35%35% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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