Calliagnosia – 50 lines of CSS to hide karma on HN and more

Hacker News

https://pastebin.com/raw/4NJKDeZj I'm probably not the first one to do this. I wrote some CSS that hides all instances of popularity (i.e. karma and comment counts) on HN, lobste.rs, and old.reddit . What motivated this? It was my tendency to ignore possibly-bespoke submissions simply because they had less than X points. Hiding karma might not be for everyone, but I'd encourage giving it a go and seeing how your behavior changes. Here's how HN front looks for me: https://i.imgur.com/AKmc6Ak.png I named this user style after the beauty-recognition-disabling tech from a Ted Chiang story: "Think of calliagnosia as a kind of assisted maturity. It lets you do what you should: ignore the surface, so you can look deeper." - from the short story "Liking What You See", by Ted Chiang Currently I load the CSS at document-start using Greasemonkey on Firefox, though pasting it on any user-CSS browser extension would do (like Stylish, Styler, etc).

Visit product View on Hacker NewsJan 13, 2020

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

67%67% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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