Insights.hn – Real-time Hacker News posts and comments analytics

Hacker News

Hi HN, I'm excited to share a project I've been working on for the past few months: https://insights.hn/ , a real-time analytics platform for Hacker News posts and comments. Why did I build this project? I built this project to help me understand the trends and patterns in the HN community. I've always been fascinated by the discussions and insights shared on HN, and I wanted to create a tool that could help me explore and analyze this data in real-time. How it works? The platform collects data from the Hacker News API and processes it in ClickHouse database to provide insights on the top posts, comments, and users on HN. The data is updated in nearly real time, so you can see the latest trends and discussions as they happen. I hope you find this project useful and interesting. I would love to hear your feedback and suggestions for how I can improve it further. Previous similar HN threads: - https://news.ycombinator.com/item?id=3745965 - https://news.ycombinator.com/item?id=34110321

Visit product View on Hacker NewsSep 24, 2024

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4points

9comments

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: excited, hacker news, ide · Missing: https docs, just released, exist

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal

42%42% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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