StopAddict – Quit addictions with a clean, gamified tracker

Hacker News

Hey HN, I made StopAddict, a lightweight app that helps you quit addictions and bad habits using a gamified progress system. You get XP and level up each day you stay clean. If you relapse, you don’t lose everything, but your momentum resets, helping you build discipline without guilt. ⸻ Why I built it I couldn’t find a tool that made quitting feel like progress instead of punishment. Most addiction apps felt heavy, medical, or too clinical. I just wanted a clean, minimalist space to track my days and feel a small sense of achievement each time I resisted. I’m 18, building SaaS solo to learn and help others while I document my journey. ⸻ Features • Track any addiction (nicotine, porn, social media, etc.) • Daily streaks, XP, and leveling system • Fully private and anonymous • Mobile-first, clean design ⸻ Roadmap • Milestone rewards • Optional daily reflection prompts • Community leaderboard (opt-in only) ⸻ I’d love your feedback: • Would this help you or someone you know? • Is the gamified approach motivating, or would you prefer a simpler counter? • What feature would make it genuinely useful for you? Thanks for checking it out. Try it here : https://www.stopaddict.net/

Visit product View on Hacker NewsJun 27, 2025

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

64%64% 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.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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