StopAddict – Quit addictions by leveling up every day you stay clean

Hacker News

Hey HN, I built StopAddict, a minimalist app that helps you quit addictions or bad habits by turning the process into a simple XP and level-up system. Every day you stay clean, you earn XP. If you relapse, you don’t lose everything — just your momentum. The goal is to make progress feel tangible, without guilt, spam, or pressure. ⸻ Why I built it: Most addiction apps felt bloated, medical, or too serious. I just wanted a quiet space to track discipline — like a gamified habit tracker with no fluff. I’m 18 and just getting started in SaaS. This is one of my first projects built solo in public (Next.js + MongoDB + Vercel). ⸻ Features: • Track multiple addictions (ex: nicotine, porn, social media) • XP system + streaks + level • Anonymous & mobile-friendly • Dark mode (because of course) ⸻ Upcoming: • Milestones & badges • Daily reflections • Optional community support (opt-in only) ⸻ Would love feedback: • Does this resonate with you? • What would make it more helpful without overcomplicating it? • Are XP/streak mechanics motivating or too gamified? Thanks for reading You can try it here: https://www.stopaddict.net/

Visit product View on Hacker NewsJun 26, 2025

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

83%83% 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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% 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 · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, 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

38%38% 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: saas · Missing: arr, mrr, revenue

31%31% 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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