HN
HN Zeitgeist – what 40M HN comments reveal about 20 years of tech
Hacker NewsHN Zeitgeist – what 40M HN comments reveal about 20 years of tech
Hi HN! I analyzed 40M comments from 2006-2026 and organized them into ~10k topics using AI. You can explore: - Rising/falling trends over time - Individual topics with sample comments + links to original threads - Deep-dive reports (Bitcoin, Nvidia, self-driving, etc.) Built in about a week. Feedback welcome!
Share cardActual performance
4points
Did not reach leaderboard
Launch Intel predictions
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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released46%46% 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, intuitive30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue19%19% 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, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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