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Quickly scan HN for new articles

Hacker News

Quickly scan HN for new articles

I have a dedicated HN tab that I come back to and reload multiple times a day to see whats new and hot. I wrote a Chrome Extension to: - quickly scan new articles since my last reload - quickly scan highly discussed articles - open actual articles and discussions in a new tab This links to the source code. The chrome extension is linked at the bottom of the github page. Note: - This extension only operates on the HN front page ( https://news.ycombinator.com/news ) - The first time you reload the HN front page after installing the extension all articles will get highlighted.

Visit product View on Hacker NewsJun 14, 2024

Actual performance

62points

23comments

Made the leaderboard

Launch Intel predictions

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

71%71% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

49%49% 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

41%41% 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

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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

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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