HN Insights – HN front page summaries

Hacker News

Hi HN, Sharing HN Insights, a webapp I built that highlights trending themes and summarizes discussion threads from the front page. This started earlier this week as a toy project to test out Gemini 3 Pro in aistudio. I found the POC useful, so I decided to productionize it. I've included the original seed prompt below: > Create an app that creates a summary of the comment threads for hacker news front page. The UX should be similar, but clicking the comments instead opens a summary. The summary is generated when clicked so it can gather new threads. To productionize, I used Claude Code and heavy use of Agent SOPs ( https://news.ycombinator.com/item?id=45998644 ).

Visit product View on Hacker NewsNov 22, 2025

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Actual performance

8points

1comments

Made the leaderboard

Launch Intel predictions

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

65%65% 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 NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

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 · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: started, gemini · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% 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

17%17% 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.

Incorrect prediction on native model

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