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P-Hacker – group/analyze HN trends by topic (not just keywords)

P-Hacker – group/analyze HN trends by topic (not just keywords)

by morland·May 20, 2026·2 points·0 comments

AI Analysis

●●SolidBig BrainNiche Gem

ML clustering beats n-gram keyword matching for finding real HN trends.

Strengths
  • Statistical significance testing (q-values) filters noise better than raw volume counts.
  • Topic clustering handles semantic variations that strict keyword search misses.
  • Side-by-side comparison view helps contextualize trend magnitude.
Weaknesses
  • Two-year data limit restricts historical context for long-term trend analysis.
  • Nightly updates mean trends aren't truly real-time for fast-moving discussions.
Category
Target Audience

Data analysts, HN power users, trend watchers

Similar To

Hacker News Trends · GrowthHackers · Exploding Topics

Post Description

I'd seen various HN trends tools over the years ([1] [2]), but they all used strict keyword (n-gram) matching. That limited a) how sophisticated any trend-surfacing could be and b) the depth with which you could explore the full discussion around any topic. How can you tell what people are saying about, e.g., agentic coding when you have to search for a dozen applicable keywords at once?

So I built p-Hacker, which uses a ML pipeline to cluster the HN corpus into distinct topics and spot current trends beyond simple keyword frequencies (though it does that, too). You can search for full topics or simple keywords, and compare them side-by-side.

I started off running it on my laptop just to work out the mechanics, but after lots of iteration thought maybe I should put it online publicly. There's lots of room for improvement (the data only goes back about two years, nightly updates take a couple of hours, item topic membership could be improved).

Feedback welcome.

[1] https://news.ycombinator.com/item?id=22233457 [2] https://hntrends.net/

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