GratefulMe – A privacy-focused gratitude journal app

Hacker News

I built a gratitude journaling app that implements CBT techniques to help users develop a daily mindfulness practice. Research shows that consistent gratitude practice can significantly improve mental wellbeing, and I wanted to create a tool that makes this accessible. The app focuses on these core principles: - Privacy: All data stays on your device with an offline-first approach - Simplicity: Clean interface optimized for quick daily entries - Consistency: Streak tracking and gentle notifications to build a habit - Organization: Category system to identify patterns in what brings you joy Tech details: - Built with React Native/Expo - Local SQLite database using expo-sqlite and drizzle-orm - In-app purchases via RevenueCat - Minimalist architecture with a focus on performance As a developer, the most valuable lessons came from handling the entire process solo - from initial concept through development to App Store submission and payment infrastructure. The current roadmap includes customizable notifications, dark mode, and an achievements system. Link: https://apps.apple.com/app/apple-store/id6742772020 Any feedback or suggestions would be valuable for future iterations.

Visit product View on Hacker NewsMar 28, 2025

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, user · Missing: mac, agents, macos

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

TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

29%29% 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: revenue · Missing: arr, mrr, profit

16%16% 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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