Tamagui – An optimizing compiler for better React Native and Web UI

Hacker News

Trying submitting again. Previously shown here[0], Tamagui is now in beta. Tamagui is the first UI kit for React that has an optimizing compiler - one that not only extracts styles at build-time, but even evaluates inline styles and inline logic like ternaries / conditional spreads. It also flattens components (even those logic-containing ones) completely down to a div or View on Native when possible. All of this leads to amazing performance gains[1], especially on the web and low-end mobile devices. I think Tamagui is also really special outside of just the performance. It has an incredibly powerful theme system which you can truly change every aspect of every component through, it supports a `size` prop on every component that just works, and it has incredible first-class animation support. Read more on the recent Beta blog post: https://tamagui.dev/blog/tamagui-enters-beta-themes-and-anim... --- [0] https://news.ycombinator.com/item?id=29321748 [1] https://tamagui.dev/docs/intro/benchmarks

Visit product View on Hacker NewsMay 22, 2022

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, 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.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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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