I

I made React with a faster Virtual DOM

Hacker News

I made React with a faster Virtual DOM

Hi! I made a React compatibility library for a Virtual DOM library ( https://github.com/aidenybai/million ). The idea is to have much faster rendering (a compiler optimizes virtual DOM beforehand) while ensuring the same developer experience React provides. This is very, VERY early stage, so be prepared for weird bugs / plugin incompatibility / etc. If you have any suggestions, I'd be more than happy if you replied in a comment with it! You can spin up the demo here >> https://stackblitz.com/github/aidenybai/million-react-compat

Visit product View on Hacker NewsJun 1, 2022

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88comments

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

53%53% 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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

28%28% 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

26%26% 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

10%10% 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

8%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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