Let's Block It – Custom uBlock Origin Filters Made Easy

Hacker News

uBlock Origin is more than an ad-blocker, it's a general purpose content filter that can be leveraged to hide low-quality content from pages you browse. While the main filter lists can remove mailing list popups and obvious nags, the definition of low-quality content is personal, so one size cannot fit all. I used to have an ad-hoc script to render and publish a personal uBlock Origin filter list, added to all my browsers. The goal of this project is to enable more people to build such a list custom list to filter out low-quality content and nags. Chose from a list of community-maintained templates, set your options, add your custom rules, and get your personal filter list. Code and content are licensed under the Apache 2.0 license and hosted on https://github.com/xvello/letsblockit . The project is still pretty young and needs more filter templates, and lots of frontend improvements (my last web project was in 2005, this is not my forte). Any feedback is welcome!

Visit product View on Hacker NewsJan 24, 2022

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

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, 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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, 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.

Incorrect prediction on native model

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