HackYourNews – AI summaries of the top HN stories

Hacker News

Hey there HN! I wanted to share a pet project of mine. I built HackYourNews [1] to scratch a personal itch: Knowing which stories to focus on while browsing aimlessly (though there is a certain joy in that, as well!) HackYourNews uses OpenAI's gpt-3.5-turbo to summarize the destination article as well as the comments section. Summarization of the article is always cached, while summaries of the comments are regenerated if the comments count is >10% (or >10 comments) different. While I styled the homepage to welcome HNers, my preferred view is the Mobile view, accessed from the navbar. This no-frills view honors OS-level dark mode and is easy to skim on any device. Tried to keep the site minimal. The only JS is Cloudflare's privacy-preserving analytics [2], just to gauge interest. This is the first time I'm releasing something to the wild. Hope you find this useful! The frontend is pure HTML+CSS. The backend is NodeJS (Puppeteer) + Python with the excellent Microsoft Guidance [3] library to interface to OpenAI's API. [1] https://hackyournews.com/ [2] https://www.cloudflare.com/web-analytics/ [3] https://github.com/guidance-ai/guidance

Visit product View on Hacker NewsSep 7, 2023

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Actual performance

335points

164comments

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

74%74% 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.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% 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

13%13% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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