Better HN on Mobile Browser

Hacker News

I got sick of looking at the crowded mobile look of HN. It needed breathing room. Plus I wanted to use iOS’s system font (ditto on Android). Here’s a CSS stylesheet you can paste into a Safari extension like Makeover (free). body, table, td, span {font-family:system-ui !important;font-size:1rem !important;} #hnmain > tbody > tr > td > table:not(.itemlist) > tbody > tr > td {padding:0.5rem !important;} table.itemlist {margin:1.2rem !important;} td.votelinks {padding-left:0.9rem !important;padding-right:0.6rem !important;} div.votearrow {margin-top:0.38rem !important;} div.votearrow::before {content:'' !important; display:inline-block !important; width:1rem !important; height:1rem !important; background: rgba(0,0,0,0.05) !important; border:solid 1px rgba(0,0,0,0.1) !important; border-radius:100px !important; margin-left:-0.256rem !important; margin-top:-0.13rem !important;} td.title {line-height:1.5 !important;} td.subtext {padding-top:0.25rem !important; line-height:1.5 !important;} tr.spacer {height:1.2rem !important;} table.fatitem {margin-top:0.5rem !important; margin-bottom:-2.5rem !important;max-width:78vw !important; overflow-wrap:break-word !important;} table.fatitem .toptext {max-width:78vw !important; overflow-wrap:break-word !important;} table.comment-tree {max-width:97vw !important; overflow-wrap:break-word !important;}

Visit product View on Hacker NewsNov 8, 2022

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Actual performance

1points

1comments

Did not reach leaderboard

Launch Intel predictions

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

53%53% 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.

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

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

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

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

26%26% 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.

Correct prediction on native model

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