StopAddict – A gamified, minimalist tracker to quit addictions

Hacker News

Hi HN, I built StopAddict, a minimalist, gamified app to help people quit addictions like nicotine, porn, social media, sugar, etc. I made it because existing habit trackers felt too generic and didn’t motivate me during tough days. StopAddict is focused specifically on helping you quit: • Gamified streaks: Earn XP and level up for every 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: See your journey over weeks and months. • Private-first: All your data stays private, with secure export options. I’m currently testing it with early users and would love your feedback on what would make it truly helpful for you. Link: https://www.stopaddict.net/ Happy to answer any questions!

Visit product View on Hacker NewsJun 28, 2025

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Did not reach leaderboard

Launch Intel predictions

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

73%73% 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: month, users · Missing: mobile apps, ios, personal

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

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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

36%36% 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

22%22% 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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