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StopAddict – I built an app to help people quit their addictions

Hacker News

StopAddict – I built an app to help people quit their addictions

Hi HN, I built StopAddict, a simple web app designed to help people track and overcome their addictions — whether it’s porn, gambling, sugar, social media, or anything else. The concept is straightforward: each day you stay clean, you earn XP. Over time, you level up. The goal is to turn the recovery process into something measurable and motivating, without judgment. What it offers: • Daily streak and XP system • Visual progress tracking • Level progression to build momentum • Personalized addiction tracking • Mobile-friendly and anonymous There’s no sign-up required. You can just visit the site, choose what you want to quit, and start rebuilding day by day. I created StopAddict after struggling with some habits myself. I wanted something lightweight, clean, and rewarding — not a complex app or a clinical tracker. Live here: https://www.stopaddict.net/ Happy to answer questions, take feedback, or share how it was built. Thanks for reading.

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
84%84% 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
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
48%48% 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
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 · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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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