Get the latest tech news

State of Show HN: 2025

Macroeconomics, DIY Hardware and AI-Driven Voting Pools I downloaded every Show HN post since the site was launched and ran them through a hierarchical topic model. I set out to discover what the Hacker News community finds interesting but in the process I ended up uncovering macro-economic trends, evidence of voting rings/fraud, and subtle shifts in behavior over the lifespan of the post What Does Hacker News Like? Reproducible Code # pip install sturdy-stats-sdk plotly from sturdystats import Index from plotly import express as px = Index(id="index_f2ffe2f7901845c59c15aab45685fa3c") index = index.queryMeta(""" df WITH t AS ( SELECT UNNEST(sum_topic_counts_vals) as topic_vals, UNNEST(sum_topic_counts_inds) - 1 as topic_id, -- duckdb is 1 indexed year(published::DATE) as year, score FROM doc ) SELECT topic_id, year, count(*) as n_posts, avg(score) as avg_score, avg((score > 10)::int) as p10, avg((score > 100)::int) as p100, FROM t WHERE topic_vals > 5 GROUP BY topic_id, year ORDER BY topic_id, year """, paginate=True) = index.topicSearch(limit=500).set_index("topic_id").to_dict() tmp "topic"] = (df.topic_id).apply(tmp["short_title"].get) df["topic_group"] = (df.topic_id).apply(tmp["topic_group_short_title"].get) df[= df.dropna().copy() df 5) df.sample( ## Add a prior to require a higher standard of evidence "P(score>100)"] = df.apply(lambda x: (x["p100"]*x["n_posts"]+ 2)/ (x["n_posts"]+50), axis=1) df[ "title"] = "Show HN<br>Performance" df[= px.treemap(df, path=["title", "year", "topic_group", "topic"], fig ="n_posts", color_continuous_scale="greens", values="P(score>100)", height=600, ) color If a picture is a thousand words, I like to think this interactive treemap is at least one thousand and one.

None

Get the Android app