HN comment previews with Gemini 1.5 Flash-8B

Hacker News

I was inspired by the notification summaries on iOS 18 and really liked the idea of AI giving you a quick preview of the content before going to the content. I applied that to HN and built a Chrome extension that summarizes the sentiment and content of HN comments and previews it right next to the stories on the homepage. Makes it so much easier to decide whether I want to delve into the HN comments of a story. It gave me a chance to play with Gemini 1.5 Flash-8B which has been great. Super fast, super cheap, huge context and does a great john on basic tasks like summarizing comment threads. Get it here: https://github.com/StreakYC/hn-summarizer/ You’ll also need a Gemini API key from: https://aistudio.google.com/ to make the extension work.

Visit product View on Hacker NewsOct 23, 2024

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios, gemini · 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.

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

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

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, 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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% 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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