I built an open-source collection of fancy and useless react components

Hacker News

Hi there, friends! I built a fun side-project called Fancy Components to explore and recreate some of the more unique, funky, unconventional, and sometimes useless UI designs & microinteractions you might see around the web. Built (mostly) with React, TypeScript, TailwindCSS, and Motion (formerly Framer Motion). Just copy the source code into your project, and you’re good to go. Each component includes examples, demos and also a documentation to help you understand how it works and built. It’s 100% free and open-source!

Visit product View on Hacker NewsDec 30, 2024

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

88%88% 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

73%73% 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

52%52% 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 · Missing: plus, platform, intuitive

41%41% 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

34%34% 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

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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