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HeroSnatch – User-friendly workout tracker for CrossFit athletes

Hacker News

HeroSnatch – User-friendly workout tracker for CrossFit athletes

Hey Hacker News family, My name is Mike. I'm a marketer from HeroSnatch app and I'm super excited to introduce our product to the community! HeroSnatch (FREE) is a user-friendly workout tracker that helps CrossFit athletes plan their workouts, keep personal records always up-to-date, achieve best results by concentrating efforts on weak points, adjust training plans to stay in balance, find friends or teammates and connect with other athletes. Our goal is to give athletes the ability to keep their records in a single safe place. Unlike other WOD (Workout of the Day) tracking apps, we put the user first and try to deliver the best experience with a clean UI and beautiful data visualization. Here's what you can do with HeroSnatch: - Find a suitable workout among existing; - Challenge yourself with Benchmarks or Opens; - Build new AMRAP, AFAP and EMOM workouts by yourself; - Create a plain WOD as a text if you got stuck with a builder; - Keep up with your stats by following an activity calendar; - Monitor your progress and workout modality balance; - Share results to your feed and comment other athletes; - Compete with friends for the top of a leaderboard. Download on the App Store: https://apple.co/2O7V1fr Get in on Google Play: http://bit.ly/hsgoogleplay Or use our web version: https://herosnatch.com/ Feel free to share your thoughts in comments and don't be shy to ask any questions. We at HeroSnatch would be very grateful for your honest feedback about our app! Drop me a DM if you need some additional info: Email: zelenskymike@gmail.com Instagram: @michaelzelensky Telegram: @michaelzelensky Twitter: @michaelzelensky

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, google · Missing: mobile apps, ios, entrepreneurs
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly, builder · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
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: training · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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