Cu

Cut back alcohol in a warm and supportive app

Hacker News

Cut back alcohol in a warm and supportive app

I've always been a heavy drinker and tried multiple time to cut back alcohol, but failed every time. As a mobile developer, I had the idea to create a dedicated app to track alcohol consumption in quick and fun way. That's why I've developed Drylendar, an iOS app translated in French (I'm French) and English that allows people to track their drink every day in a beautiful calendar. To help people staying motivated, I've added medals to unlock and possibility to add friends to share calendars and positive energy. My app is available on App Store for 10 days, and it's been 215 days that I didn't drink alcohol at all, I've never felt healthier & happier!

Visit product View on Hacker NewsOct 6, 2023

Actual performance

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11comments

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

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TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

53%53% 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

33%33% 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

23%23% 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

17%17% 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.

Incorrect prediction on native model

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