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Built my first iOS app to quit smoking

Hacker News

Built my first iOS app to quit smoking

Hey everyone, I’ve been a smoker for years. Tried dozens of apps, tried quitting cold turkey more times than I can count. Nothing really clicked. Most apps I found either felt too robotic, too motivational ("Just quit now!"), or they didn’t fit how I needed to change — gradually. So... I built my own app. It’s called QuitFlow, and it’s my very first iOS app (yep, 45k lines of code later). I’m actually a backend developer (Go - primary language) by profession — React Native gave me a way in, and honestly, props to AI code editors too. Without them, this would still be sitting half-finished on my laptop. But this app is different because it’s built by someone who gets it — not just a dev team guessing what smokers need. Here’s what QuitFlow focuses on: - Track both electric (vape) and non-electric (cigarette, weed, etc.) smoking in one app. - Connect every smoke with a craving — QuitFlow links each smoking session to what triggered it, so you can see deeper patterns over time. - Understand your habits — quickly spot when, where, and why you smoke the most, and make smarter decisions to avoid those situations. - Encourages delay, not just quitting — the more you delay your next smoke, the more you stretch the gaps between sessions, the less you smoke overall. - Built-in habit tracker — quitting gets easier when you replace the habit, not just fight it. - Simple health tracking — track symptoms like coughing, poop, gastritis, and sleep, and notice real improvements. Privacy: Requires no permissions. App Store Link: https://apps.apple.com/us/app/quit-smoking-tracker-quitflow/... I’d love your feedback — whether you’re trying to quit, cut down, or just better understand your smoking patterns. - Check it out if you're curious - Ask me anything about the app, building it solo, or quitting strategies — I'm here. Thanks for reading.

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
93%93% 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: apple, apps, code · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs
66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
38%38% 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
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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