A simple web app to combat phone addiction

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When I'm stuck on coding something, I find myself reaching for my phone even if I don't have any particular reason to do so. Inspired by Calm's DoNothingFor2Minutes.com which launched on HN 13 years ago [1], I made this simple webapp to see if my friends and I could go an hour without touching our phones. It is surprisingly difficult. According to a 2022 survey [2], the average US adult picks up their phone 352 times per day, or approximately once every 2m43s while they're awake. On browsers that support it (iOS 16.4+, most versions of Android Chrome), it uses the Screen Wake Lock API [3] to keep the page open, and falls back to nosleep.js [4] otherwise. From testing on my iPhone 14 Pro Max running iOS 16.6, battery life only went down 3 or 4 percentage points after an hour with the wake lock. Made this as a web app as a quick demo to be compatible across all mobile devices. As an app, we can probably save more on battery + not have the screen on. One caveat is that on iOS this will actually increase your Screen Time (although hopefully reduce your other category usage). I currently only track time on page through Google Analytics 4. No other calls are made to a server, although if we actually wanted to verify that you kept the page open vs. javascript/inspector-system clock-fu, we could add a verified mode that pings the server every X minutes. As a PWA, possibly due to an iOS/Mobile Safari quirk/bug [5], neither wake lock nor nosleep.js appear to work . [1] https://news.ycombinator.com/item?id=2124106 [2] https://www.asurion.com/connect/news/tech-usage/ [3] https://developer.mozilla.org/en-US/docs/Web/API/Screen_Wake... [4] https://github.com/richtr/NoSleep.js [5] https://bugs.webkit.org/show_bug.cgi?id=254545

Visit product View on Hacker NewsAug 24, 2023

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

92%92% 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: google, new, coding · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, google · Missing: mobile apps, personal, entrepreneurs

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

51%51% 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 · Strong signals: calls · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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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