PogiFit – Nutrition and Workout Tracker

Hacker News

Hi HN, I just launched PogiFit, an app that tracks both nutrition and workouts. The idea is to keep everything in one place: macros, food database, workout routines, exercise history. It has two parts: Food: macro diary (auto or manual), option to just track calories + protein, supplement reminders, nutrition guides, different macros per day, and a food database of 100k+ items across all supported languages. Workout: routine library (home, gym, powerlifting, bodybuilding, etc.), 240+ exercises, routine builder, simple workout tracker, progress history, and 1RM calculators. I built it with Laravel, PicoCSS, jQuery, MySQL, and packaged it as a mobile app with Cordova. The biggest challenge has been by far keeping 21 languages up to date with every new feature and content, and working around Cordova’s limitations compared to native apps. Available here: Play Store - https://play.google.com/store/apps/details?id=com.pogifit.mo... Desktop web - https://pogi.fit

Visit product View on Hacker NewsSep 25, 2025

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Actual performance

1points

4comments

Did not reach leaderboard

Launch Intel predictions

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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 · Missing: mobile apps, ios, personal

62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: builder · Missing: plus, platform, intuitive

51%51% 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

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, google, apps · Missing: agents, macos, agent

23%23% 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 · 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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