TL;DR for every comment on HN

Hacker News

Hi HN! I love reading the comments under HN submissions but I often get overwhelmed when I'm skimming threads to find interesting ones. To make my life easier, I built a Chrome extension that generates small summaries for each comment in a thread. When you load an HN submission page, the extension reads comments (with their context), pushes them to gpt-4o-mini for summarization, and inserts the resulting summaries into the page as a heading for each comment. It makes it easy to quickly see where a thread is headed and focus on the ones that interest you most. The result looks something like this: https://github.com/jnnnthnn/hn-comment-summaries/raw/v1.0/sc... The extension requires you to provide an OpenAI API key, which is then used to make requests directly from your browser. I've been using the extension for a few days now and it's only cost me a couple cents/day, making it well worth it. I hope you'll find it useful! Feedback and questions welcome! Jonathan PS: You can install it directly from the Chrome Web Store at https://chromewebstore.google.com/detail/hacker-news-comment...

Visit product View on Hacker NewsAug 29, 2024

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

64%64% 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, context · 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: ide, io · Missing: https docs, excited, just released

43%43% 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 · Missing: plus, platform, intuitive

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 · Missing: mobile apps, ios, personal

37%37% 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

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

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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