I made Macaron – Type-safe CSS-in-JS with zero-runtime and colocation

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Hey HN, my name is Mokshit and I'm really interested in web-performance and web-development. At the time of writing this, I'm a junior in high school. Macaron is a typesafe CSS-in-JS library with zero runtime, colocation, maximum safety and productivity. The motivation behind it is that runtime CSS-in-JS libraries, that is most of the CSS-in-JS libs, add alot of bloat to web apps and have a considerable performance drop with a bundle size increase. Alot of them also don't support more performant ways of building web apps like streaming markup. Most of the current compile-time CSS-in-JS libraries try to fix this, but have some or the other drawback like not offering colocation, or not being type-safe. Today, Macaron does all this with a compiler that extracts all the style declarations at compile-time and converts them to static css files while also letting you author the type-safe styles in the same file as your application code. It supports both - a vanilla styling API and a styled-component API with support for multiple frameworks like SolidJS and React and build tools like Vite and Esbuild. Macaron is currently missing a webpack plugin, but that will also be published soon, making it compatible with almost all bundlers. There's a ton of room for improvement in this space, and I feel like macaron is a step in that direction. Github - https://github.com/macaron-css/macaron

Visit product View on Hacker NewsJan 13, 2023

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78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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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.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal

38%38% 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

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