WaitToUnlock, a website that makes your Screen Time limits unbypassable

Hacker News

I love iOS Screen Time because app blocking happens at the operating system level. It’s built into every iPhone, so you can’t just uninstall it like a third party app. The biggest weakness is the “Ignore Limit” button. You can set a Screen Time passcode, but because you know it, you can always enter it and bypass your own limits. So I built WaitToUnlock. It generates a random Screen Time passcode and guides you through entering it in a deliberately confusing sequence (for example: enter 5, enter 8, delete it, enter 2…) so you’re very unlikely to remember it. The passcode is then locked away, and you have to wait at least 6 hours before you can retrieve it. This makes your Screen Time limits impossible to bypass. Because you no longer know the passcode, you can’t simply tap “Ignore Limit” and enter it whenever you’re tempted. If you want to remove the limits, you have to wait until the passcode becomes available again.

Visit product View on Hacker NewsJul 26, 2026

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

73%73% 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: using, code · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, 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

47%47% 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, way · Missing: mobile apps, personal, entrepreneurs

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

19%19% 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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