I built Run for Fun to stop me from doomscrolling until I exercise

Hacker News

Some of you might know me as the developer of HACK, the iOS and Android apps for Hacker News. I spend a lot of time doomscrolling on Reddit, YouTube, and here at Hacker News. I also enjoy Powerlifting. So, I wondered whether I could use exercising as a way to reduce doomscrolling. So, I built Run for Fun. It helps you reduce screen addiction, end doomscrolling, practice self-discipline, and get healthy. You can privately and securely limit the usage of addictive social media apps and websites until you perform physical activity. When you walk, run, bike, climb stairs, and exercise, you gain screen time points to use those apps. You can control how easy or hard it is to gain those points. You can choose which weekdays and between what time period restrictions apply. You can snooze restrictions, choose how long to snooze for, or disable the snooze button altogether. I built it in Swift and according to Apple, is only 860 kB to download and 1.8 MB to install. I pride myself in keeping downloads small. It uses Apple's Screen Time and HealthKit APIs. The physical activity and app usage data stays private and is not transmitted to me, the developer. There are no ads and no subscriptions. Free version is limited to restricting 1 app, 1 website, 1 category and all-day everyday schedule. A one time only in app purchase gets you lifetime access to restricting unlimited apps, websites, and categories, restrictions scheduled on specific weekdays and time, and custom screen time points calculation and snooze times. Is this something you may find useful?

Visit product View on Hacker NewsApr 21, 2025

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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, new · Missing: mac, agents, macos

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% 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.

Incorrect prediction on native model

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