I listed all component libraries for any frameworks

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I quit my dev job 2 years ago to work on projects I actually care about. I built a CRM for philanthropy as a nonprofit, and on the side, I tinker with fun projects to learn new languages and sharpen my skills. Recently, I was looking for new component libraries to use, but I realized… there’s no single platform showcasing ALL component libraries across frameworks—especially independent, bootstrapped ones. No wonder so many projects default to MUI, DaisyUI, and other mainstream options. So, I decided to fix that. I collected 75+ libraries for React, Vue, Angular, Svelte, Rails, Nuxt, and more, then categorized them by features like Dark Mode support, Accessibility, and Customizability. If you've built a component library, you can submit it for free. After launch, users requested more frameworks—so I automated the process with Make, letting me add new libraries in under an hour. Hope you'll find new libraries to try on for your next projects and let me know if you know more or want to see new frameworks added to it

Visit product View on Hacker NewsFeb 17, 2025

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

77%77% 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.

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

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

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.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: profit, bootstrapped · Missing: arr, mrr, revenue

19%19% 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.

Incorrect prediction on native model

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