Unbreakable – An AI Focus App That Blocks Based on Context, Not URLs

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hey hn, wanted to share something i've been working on for a bit. most focus apps like opal are static. they block a list of websites and that’s it. once you start a timer, you’re locked in. can’t quit, can’t delete, can’t reason with it. but real focus isn’t that binary. sometimes i actually need to use youtube or reddit for a task, like watching a physics lecture or checking a solution. blocking everything just doesn't work. also, sometimes "harmless" sites like wikipedia pages or news pages send me into rabbit holes. you can't realistically block every one of these. so i tried to build something that uses the context on your screen to figure out whether you're focused. i set a task (e.g. “studying physics – waves + optics”), describe what i’m doing, and how long i want to focus. then the app uses my browser's context to check if i'm on-task or off-task, based on what i'm doing - not just what site i'm on. if i'm off-task, blocks the site. even after it's blocked, you can explain to it why you really need the site - if the app thinks the reason's valid, it lets you in. this takes care of most false positives. still early and it's only a chrome extension, but it works and it’s helped me a lot. happy to hear feedback and would be curious to hear if people would pay for this. there's also a demo video on the extension page for those curious.

Visit product View on Hacker NewsAug 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, context · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% 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: apps, video · Missing: mobile apps, ios, personal

47%47% 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

25%25% 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

20%20% 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.

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