PrecisionRush: a tiny timing game inspired by the iPhone stopwatch

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PrecisionRush is a small free iOS timing game I built solo. Weirdly enough, the idea came from playing with the default iPhone stopwatch and thinking: what if this was an actual game? I’m not much of a gamer myself (I’ve only played a handful of games), but something about this tiny mechanic got stuck in my head, so I decided to build the version I personally wanted to play. The premise is simple: you get a target time, you start the timer, and your goal is to stop it as close to the target as possible. If you land within ~20ms it feels almost unreal. It’s a tiny concept, but it turned out to be surprisingly addictive. I shipped it last week and I’m currently in the “first real users” phase. I’ve only got a handful of ratings so far, but I’ve been genuinely happy just seeing people try it. One unexpected thing: a few users even tapped “Support the developer”, which I honestly didn’t think would happen. I do this on the side and I’m definitely not a top game designer or UX person - I just like building small things I enjoy and iterating on them. I haven’t done any marketing yet and I’m not trying to sell anything here. I’d just love honest feedback from people who enjoy minimal games or quick skill tests: - Does it stay fun after a few minutes, or does it get repetitive? - Is the UI/feedback loop satisfying and clear? - Anything obvious you’d improve or add? App Store link: https://apps.apple.com/ro/app/precisionrush/id6755596108 Thanks for reading.

Visit product View on Hacker NewsJan 26, 2026

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

87%87% 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, user · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

17%17% 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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