Pour Decision – Alcohol Tracker and Mindful Drinking Companion

Hacker News

Hey there! I want to share the alcohol tracker app that I've made for iOS. The idea came to be when I decided I want to drink less, but not quit completely. I tried a bunch of solutions but I couldn't find the app quite right, so decided to build it myself. Existing apps that I've tried were either subscription-based, had a bad design/buggy, didn't come with a library of common drinks I can choose from etc. Pour Decision is my attempt to an app I wanted to exist. Hope y'all like it! Website: https://pourdecision.app App store: https://apps.apple.com/us/app/pour-decision/id6499468185

Visit product View on Hacker NewsJul 14, 2024

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

85%85% 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, apps · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

51%51% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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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