StayedCSS – A CSS Library for Next.js App Router

Hacker News

Hello, HN! I'm excited to introduce StayedCSS, a static CSS library built specifically for the Next.js App Router. StayedCSS fully supports server and client components using a syntax inspired by pure CSS. I created this library to meet the need for efficient styling in both SSR and 'use client' environments. With features like media queries, pseudo-elements, pseudo-classes, combinators, and smooth dark mode transitions, StayedCSS is designed to elevate your Next.js projects. Currently in beta, StayedCSS is being actively optimized and improved. Your feedback is incredibly valuable, so feel free to share your thoughts. Thank you!

Visit product View on Hacker NewsDec 16, 2024

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1points

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

76%76% 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: using · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, io · Missing: https docs, just released, exist

49%49% 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: efficient · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Strong signals: introduce · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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