A web extension to control distracting feeds on popular websites

Hacker News

Hi Hacker News! Throughout the summer, @paul_designs, @avi_dave, and I were creating Grey Software's first public product: Focused Browsing. We would love for you to try it out! https://grey.software/focused-browsing Like 200,000 other people, we used News Feed Eradicator (NFE) to control the detrimental effects of consuming feeds. https://chrome.google.com/webstore/detail/news-feed-eradicator/fjcldmjmjhkklehbacihaiopjklihlgg We loved the extension, but we had a few problems with it. - It was tedious to toggle between focused and unfocused modes. You had to open the extension's settings page, and click on the website that you want to enable/disable. - The codebase had gained bloat and complexity over the years. Things like using 2 Redux stores made it difficult to reason about the program flow, so we anticipated that adding new functionality like keyboard shortcuts and blocking promoted posts would be tricky. Our extension recently went into Beta and we're eager to hear everyone's feedback and find any edge cases we haven't covered. Thank You!

Visit product View on Hacker NewsSep 22, 2021

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

88%88% 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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, 000, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

55%55% 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

30%30% 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

16%16% 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.

Incorrect prediction on native model

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