Nicheloom

Market intelligence for builders — see what's gaining traction before it's crowded.

I analyzed 297k HN posts

your fate is sealed in 2 hours

Details

External ID
46434575
Source
HN
Company
—
Product
I analyzed 297k HN posts
Website domain
ssrn.com
Launched
Dec. 30, 2025
Cohort
—
Upvotes
5
Upvotes percentile
0.10400763358778627
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

I built an archiver that tracked every HN item for 27 days (Dec 3-30, 2025) and captured 72k temporal snapshots to see how posts gain and lose attention.Key findings:- Early velocity (points/hour in first 2 hours) predicts final score with ρ=0.82. A simple classifier achieves 98.4% precision for viral prediction.- No Matthew effect: high-scoring posts don't get more upvotes per hour than low-scoring ones (ρ=-0.04). HN's gravity penalty actually works.- Yet extreme inequality persists: Gini=0.89, meaning bottom 80% of posts get <10% of total upvotes. Inequality without cumulative advantage.- Attention decays as a power law (α=0.52), slower than exponential; quality content has longer tails.Paper (open access): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5910263Code + data: github.com/philippdubach/hn-archiver

Enrichment

Theme
Hacker News clients, datasets, and tools
Vertical
Horizontal
Function
Analytics & BI
Audience
Developer
AI stance
AI feature
Project type
Hobby / open-source project
Normalized one-liner
analysis of 297k hacker news posts
Manually corrected
False

Could you build this?

Yes This is a data scraper/archiver using the Hacker News Firebase API and basic statistical/correlation analysis in Python or R.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one — 95 launches clear the similarity bar, closest 8 shown.

Attention rank: #92 of 96 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 58 days after the earliest competitor.

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