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Tamagui Beta

Hacker News

Tamagui Beta

Previously shown here[0], Tamagui is now in beta. It’s a UI kit for React Native & Web with an optimizing compiler. It's quite different from a typical UI kit in that it supports the compiler not only extracts styles at build-time, but also extracts inline styles, even if they use logic like ternaries or conditional spreads. Further, the compiler flattens any styled component completely a div (or View on Native) when it can. I've found that about 20% of our views get flattened completely, leading to much flatter trees, which means really significant performance improvements[1]. This is important not just for performance, but also code-sharing. Here's the why: If you just took a React Native Web app today and made it responsive using the typical way, hooks, you'd end up with a very janky web app. I know this because Tamagui was born out of an app that did exactly that, and resizing frequently took 300ms+ per frame. Themes today also have the same failure mode - without a compiler, you thread values through context. That means lots more JS to run, and whole-tree re-rendering on every change. Even beyond themes and responsivity, we found ourselves having to really limit our ambition especially for Native - lists that contained many items slowed down quickly after a certain depth. So Tamagui started solving for themes and media queries and hopefully a bit of tree depth, and it really succeeded at that. To release the alpha I wanted feature parity with modern UI kits, so I added a theme creator, token system, inline token props, media query props, shorthand props (ala tailwind), fonts, and more. Now over the past 6 months its matured immeasurably with components for gradients, images, inputs, labels, switches, shapes, popovers, and tooltips. Animations just landed, which power many of the new components. "npx create-tamagui-app@latest" to help get started fast. Hundreds of bugfixes and better docs. And one thing I'm really proud of: huge TypeScript type quality and performance improvements. [0] https://news.ycombinator.com/item?id=29321748 [1] https://tamagui.dev/docs/intro/benchmarks

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
92%92% 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, context, using · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
65%65% 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 · Strong signals: month, way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, 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.
Acquire.comPre-revenue stage for this audience · 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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