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New Hacker News posts and comments in realtime

Hacker News

New Hacker News posts and comments in realtime

Hi HN, I made a live feed for viewing all of the new items on Hacker News in almost-realtime. It is a single, modern HTML file that doesn't use polling. It works by establishing a websocket connection to the Hacker News Firebase database, to receive updates every time the HN server updates Firebase, which is about once every 30 seconds. This is very efficient, putting no load on HN's server and using minimal bandwidth. To make the feed continuous despite the delay, it waits to display each item until exactly 30 seconds before displaying it. I think this is a fair tradeoff, it gives you a sense of how active HN is. For comparision, there are something like 6-7 thousand tweets every second . Official HN Firebase API: https://github.com/HackerNews/API Source code: https://github.com/jerbear2008/hn-live/blob/main/index.html

Visit productView on Hacker NewsFeb 26, 2024
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Actual performance

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1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
18%18% 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.

Incorrect prediction on native model

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