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HTML/Functional CSS Performance Test of HN Homepage

Hacker News

HTML/Functional CSS Performance Test of HN Homepage

This project was made to demonstrate the power of the functional CSS and proper HTML techniques on Hacker News frontpage. Everything started with this post: https://news.ycombinator.com/item?id=20854214 that talks about how good is that HN wasn't redesigned since the OP was browsing it with slow connection. I though I could make HN even faster without changing the essence of the design too much. So I decided to re-do Hacker News using pure functional css (https://github.com/meerita/utilcss) and some tweaks to the code to increase the performance and test both performance gains in HN on a local server. Code, notes and results available here https://github.com/meerita/hnredone

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Actual performance

6points
2comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
74%74% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, using, notes · Missing: mac, agents, macos
34%34% 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
15%15% 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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