Hide top story in HN by clicking a bookmarklet once you're done with it

Hacker News

1) Create a bookmark in your browser 2) Edit the URL and put this simple script in it : javascript:(function(){[...document.querySelectorAll('a')].filter((e)=>{return (e.textContent==="hide")})[0].click()})() 3) Chose a title for the bookmark, something simple like "Hide First HN story" or an emoji like ️"️" (HN doesn't support emojis) check https://emojipedia.org/heavy-check-mark/ to see the emoji I'm talking about 4) Test if it works 5) Save time, Stop searching for the hide button with your mouse, just click a button that doesn't change it's position for every new top story in the list 6) (optional) Enjoy 7) (optional) Upvote and share with fellow hackers

Visit product View on Hacker NewsSep 7, 2019

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Actual performance

1points

3comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

42%42% 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

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

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

29%29% 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 · Missing: arr, mrr, revenue

15%15% 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.

Correct prediction on native model

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