Hacker News Summarizer (Chrome Extension)

Hacker News

Hey Hacker News community, We're excited to announce the launch of our new Chrome extension, "Hacker News Summarizer," which uses OpenAI API to summarize articles on Hacker News. It helps you quickly understand the key takeaways without reading the whole article. Plus, no login or confirmation is required to use it! Simply install the extension and click the "summary" link next to "comments" when you want to read an article. Disclaimer: Not all articles are summarised, or supported but we will change and try to support them later. Additionally, this extension is not affiliated with OpenAI or Hacker News. Chrome Extension: https://chrome.google.com/webstore/detail/hacker-news-summar... Try it out today and let us know what you think in the comments below! Thanks, The Hacker News Summarizer team

Visit product View on Hacker NewsMar 24, 2023

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Actual performance

19points

6comments

Made the leaderboard

Launch Intel predictions

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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 · Strong signals: plus · Missing: platform, intuitive, reviews

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

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% 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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