HNLikes – The most frequently posted links in Hacker News comments

Hacker News

I’m addicted to reading Hacker News comments and watching YouTube videos, so I wanted to see which videos the HN crowd posts most often in the comments. Along the way I also thought to pull out the most posted links to papers on arXiv, projects from GitHub, items from Amazon, articles from Wikipedia and comics from xkcd. (I’m actually pulling all links in all comments and filtering to these at the end. If anyone is interested in another site I can add those.) The result is HNLikes where you can browse all those links and filter by the date of the most recent post to get kind of an idea of what’s trending. I’m not a professional programmer and this is the first website I’ve done so would love feedback if anyone is interested. I describe how I went about making this in the About page.

Visit product View on Hacker NewsMay 26, 2023

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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: hacker news, 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.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal

32%32% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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.

Incorrect prediction on native model

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