CSS-powered hover-animations library

Hacker News

Hello, I've created a CSS-library called sunset.css. If offers a collection of (hopefully) useful CSS-powered hover-animations and also some animations that react to clicks instead. It's my first open-source project so I try to make everything right: * I created a npm-project so it is available on npmjs and CDNs * I created a public github-repository with a wiki for documentation * I created a landing page to show off the animations I'd love to here some feedback about all of this! :) Here are the links: https://github.com/philippwilhelm/sunset.css https://sunsetcss.com -------------- I have hidden the original post, because it didn't follow the rule to add "Show HN".

Visit product View on Hacker NewsJan 6, 2021

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6points

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Did not reach leaderboard

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

65%65% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

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

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

11%11% 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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