Hacker News dark mode with uBlockOrigin / AdGuard cosmetic filters

Hacker News

Cosmetic filters are usually used to hide elements, but they can also be used to add CSS styles to the matching elements. Inspired by https://news.ycombinator.com/item?id=32719488, here are two rules to implement a dark mode on HN: news.ycombinator.com##html:style(filter:invert(100%) hue-rotate(180deg)) news.ycombinator.com##body:style(background: white) The first rule inverts all colors (white to black, orange to blue), then returns blues to oranges with a hue rotation. The second rule adds a black border in the margins around the main table (white + invert(100%) = black). To use these rules: - uBlockOrigin: open the preferences (right-click on the toolbar icon and cog icon, or via the extension list), and copy these in the "My filters" tab - AdGuard: open the settings and copy these in the "User rules" section

Visit product View on Hacker NewsSep 11, 2022

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Actual performance

3points

1comments

Did not reach leaderboard

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

61%61% 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 · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, open · Missing: mac, agents, macos

23%23% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: margin, margins · Missing: arr, mrr, revenue

19%19% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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