He

Hedge – An AI powered distraction blocking browser extension

Hacker News

Hedge – An AI powered distraction blocking browser extension

Hello HN! We developed a browser extension that blocks distracting sites. The killer feature is that we use LLMs and user-defined goals to provide the best possible blocking experience. The decision-making process is not binary, we have multiple categories, allowing for more nuanced user interactions. This makes the browsing experience much less frustrating compared to other website blockers. Sites like Reddit or YouTube are not always bad, and Wikipedia or other educational resources are not always good. By considering what the user is working on, we are able to make more informed and effective decisions. Other blockers rely on pre-made blocklists for distracting websites. This approach is clearly insufficient for most use cases, as curating such lists is tedious—whether for the developer or the user. We've also added quality-of-life features, including whitelists, blacklists, active hours, temporary unblocking, nuclear mode and a well-integrated (and optional) Pomodoro timer. Learnings: Initially, we scraped page content to make decisions. However, this approach posed significant privacy concerns. Now, all our decisions are based on tab metadata, ensuring a much better level of privacy and much better performance. No page content is transferred to the server. Data collection: We don't store browsing data unless the user explicitly reports incorrect decisions, BYOB model version will be released soon as well based on user demand. Links: [1] https://chromewebstore.google.com/detail/hedge-ai-powered-di... [2] https://addons.mozilla.org/en-US/firefox/addon/hedge-ai-dist...

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · 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: model, google, user · Missing: mac, agents, macos
74%74% 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, including · Missing: https docs, excited, just released
48%48% 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: google, way, education · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
10%10% 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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