StopAddict – A minimalist, gamified app to quit addictions

Hacker News

Hi HN, I built StopAddict, a minimalist, gamified tool to help people quit addictions like nicotine, porn, social media, sugar, etc., by tracking streaks and earning XP for each day clean. Existing habit trackers felt too generic and didn’t give me the push I needed during the hardest moments. StopAddict is designed specifically for quitting: • Gamified streaks: Earn XP and level up each day you stay clean. • Relapse-friendly: If you relapse, your streak resets, but your XP remains, so you don’t lose all progress. • Progress charts: Visualize your journey over weeks and months. • Private-first: Your data stays private, with export options if needed. • Clean interface: No distractions, just your progress and goals. I would love your feedback on: • What would make a tool like this genuinely helpful for you? • Would you want community features, or do you prefer simplicity? • What has worked (or failed) for you when trying to quit an addiction? Link: https://www.stopaddict.net/ Thanks for reading, and happy to answer any questions.

Visit product View on Hacker NewsJun 29, 2025

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

70%70% 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: visual · Missing: mac, agents, macos

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

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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

24%24% 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 · 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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