Legend-State 1.0 – The fastest React state library

Hacker News

After almost a year of development and iterating, we just released Legend-State 1.0. It's the fastest React state library and is very easy to use, based on Observables (Signals) with fine-grained reactivity and built-in persistence. I'd love to know what you think, and I'm also happy to answer any general JavaScript performance questions if you want since I've gone very deep into optimizing . https://github.com/LegendApp/legend-state

Visit product View on Hacker NewsMay 15, 2023

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

67%67% 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

67%67% 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

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

54%54% 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

40%40% 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 · 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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