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Vertical Spacing Between Upvote and Downvote

Hacker News

Vertical Spacing Between Upvote and Downvote

If you don't like the fact that the upvote and downvote buttons are really close together (and have actually accidentally clicked downvote instead of up or vise versa) this simple css hack will let you have an extra 10px margin between the two buttons. img[src="http://ycombinator.com/images/graydown.gif"]{margin-top:10px;} The code is located as a css file at: http://cssfiler.googlecode.com/svn/trunk/hn.css or https://cssfiler.googlecode.com/svn/trunk/hn.css if you're using secure HN. You can test the css by making a bookmark on your browser and putting this as the link: javascript:(function(){var s=document.createElement('link'); s.rel='stylesheet';s.type='text/css';s.href='http://cssfiler.googlecode.com/svn/trunk/hn.css'; document.body.appendChild(s);})(); It would be really cool if someone could make an extension that just loads this css. Sorry, I really don't have the time right now. But maybe, one of you could do it.

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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
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, using, code · Missing: mac, agents, macos
34%34% 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
23%23% 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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