I built a 1:1 coaching service to reduce my screen time

Hacker News

Hi HN! I'm a former Facebook engineer who has struggled with smartphone / social media usage (ironic) for many years. The accountability of a personal trainer really worked for me to start a consistent fitness routine, so I've spent the past two years building out a low cost, lightweight coaching service (with real human coaches!) to help people with everyday habits -- screen time, fitness, sleep, diet, hobbies, personal hygiene. We recently launched a dedicated screen time coaching service for iPhone users, which integrates with the iOS Screen Time API to block problematic apps. Instead of most screen time apps where your lizard brain can just keep ignoring / overriding your limits, if you want to override your limit in Zabit you have to write a 25+ character justification to your coach. Combined with removing all social apps and setting a 30 daily minute limit on Safari, I've cut my phone usage to around an hour per day. We're currently offering 2 weeks of free coaching for new users to try out the service. Check it out!

Visit product View on Hacker NewsFeb 28, 2025

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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: apps, user, new · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

65%65% predicted probability of success on TrustMRR, 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

42%42% 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

35%35% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

32%32% 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.

Incorrect prediction on native model

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