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I built a website blocker with a dynamic timeout that actually works

Hacker News

I built a website blocker with a dynamic timeout that actually works

Like many of us, I struggle with staying focused and avoiding distractions while browsing the web. I’ve tried all the website blockers out there – Blocksite, Freedom, you name it. Most of these tools failed for one of two reasons: they were too strict, so I ended up disabling them permanently, or they were too lenient, so I would quickly find a way around them. To solve this for myself, I built DecayBlock, a browser extension based on the principle of adaptive friction. The mechanism is simple: 1. When you visit a site on your distraction list, a brief timeout is applied before the page loads. 2. This timeout accumulates with each visit, so repeated or habitual access becomes progressively more difficult. 3. Over time, the accumulated timeout decays with a configurable half-life when you stay away from those sites. The goal isn't to enforce a ban (although you can permanently block websites if you choose), but to introduce a small, increasing cost to the act of procrastination. That brief pause is often enough to break the subconscious loop of opening a distracting tab, without ever fully locking you out. You can configure the timeout growth rate and decay half-life to match your own habits. I've been using this system daily for several months, and it has been significantly more effective for me than any other tool. DecayBlock is available now on both the Chrome and Firefox web extension stores. Currently it’s only on desktop but I’m hoping to release a mobile version at some point in the future. Chrome: https://chromewebstore.google.com/detail/decayblock/lpljcnal... Firefox: https://addons.mozilla.org/en-GB/firefox/addon/decayblock/ I'm here to answer any questions and would love any feedback!

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best fitHighest predicted score across all platforms for this description.
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72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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nativeThis product was originally launched on this platform.
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20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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