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Rewind-UI – A ReactJS/TailwindCSS component library

Hacker News

Rewind-UI – A ReactJS/TailwindCSS component library

I recently decided to document and open-source the component library that I've been using internally on all my react-tailwindcss projects and share it. Rewind-UI uses only TailwindCSS classes, which can be overridden using the className prop on each component or can be fully customized using a theme provider. It is built on top of the CVA (class-variance-authority - https://cva.style/docs ) library, so if anyone needs to adjust the theme, will just need to edit the CVA files. Furthermore it must be noted that you can add either all the style files in your tailwind.config.js file, or only those that you are going to use, so that you can avoid having a bloated css file. You can check out the repository here: https://github.com/rewindui/rewindui Even though it is still in beta I would love to hear your feedback on it.

Visit product View on Hacker NewsJun 9, 2023

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

56%56% 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.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% 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

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

12%12% 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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