Focal – A Focus tool that dynamically blocks distractions as you browse

Hacker News

Focal is a browser extension that helps you stay focused while browsing. You state your intent when you browse, and focal will dynamically block distracting content by analyzing webpages using AI. Traditional website blockers that use URL block lists do not work for me, and I felt with recent progress in AI, something better can be built. Here is mainly why: - All or nothing approach: The classic example I think of is youtube. There is a lot of brain rot on youtube and it is a classic url thrown in block lists. It also has a lot of educational content. I want to see my educational content and not the brain rot. With current website blockers, the only solution is to block youtube completely. Focal solves this, so that you can dynamically block content on websites instead of blocking the whole website. - Distraction is contextual: My sources of distraction are not only from the typical sources like social media, news, etc. and usually contextual to what I’m doing. If I intend to write documentation for an afternoon, then working on developing a new feature is considered a distraction in that context. Focal solves this by letting you setting an intent when you browse and then using that as the context to determine what is a distraction. - Managing a block list: I don’t want to manage a block list, and have the mental burden of determining whether a website is considered distracting or not. - Some remarks: - I do not obfuscate the extension code so anyone can inspect it in the browser if they have privacy concerns. - The extension by default does not run on a bunch of websites that may contain sensitive info (banks, email, etc.). Full list is stated here: https://www.focalbrowse.com/privacy - I’m considering open sourcing it fully but the code is full on fettuccine at this point so need to clean things up. Hoping to get feedback on this new intention based browsing with the usage of AI. The extension is still in an early stage so there is still lots to work on. If you try it out, would love to hear your thoughts and experiences!

Visit product View on Hacker NewsNov 17, 2024

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

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

72%72% predicted probability of success on Indie Hackers, 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

44%44% 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: education · Missing: mobile apps, ios, personal

41%41% 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

40%40% 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

13%13% 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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