Indentation markers for HN comments (CSS hack)

Hacker News

Use this add-on [1] or the userContent.css file in Firefox. That’s what it looks like: http://oi44.tinypic.com/2je5usy.jpg @-moz-document url-prefix(https://news.ycombinator.com/) { td { vertical-align: top } td > img { background: repeating-linear-gradient(to right, rgba(0,0,0,0), rgba(0,0,0,0) 38px, rgba(0,0,0,0.3) 38px, rgba(0,0,0,0.3) 40px); margin-top: 4px; height: 10px !important; } } [1] https://addons.mozilla.org/en-US/firefox/addon/generic-css-loader-2/

Visit product View on Hacker NewsJul 14, 2013

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: 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.

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.

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

39%39% 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

38%38% 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, tiny · Missing: mac, agents, macos

18%18% 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 · Missing: arr, mrr, revenue

18%18% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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