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I analyzed 297k HN posts – your fate is sealed in 2 hours

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I analyzed 297k HN posts – your fate is sealed in 2 hours

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=5910263 Code + data: github.com/philippdubach/hn-archiver

Visit productView on Hacker NewsDec 30, 2025
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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 · Missing: supports, reddit linkedin, podcasting
79%79% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: code, open · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% 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
21%21% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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