Uisual – Free Tailwind CSS Components and Templates (No JavaScript)

Hacker News

All the components and templates can be copied for free for personal or commercial projects. No sign-up, no attribution required. Simply copy the component and paste it into your Tailwind CSS project, or copy the template to get started. I'm updating the library every week, so new free Tailwind CSS components and templates will be dropping next week. They're all designed with simplicity in mind, so you can easily customize them yourself. No JS, just plain HTML and Tailwind CSS. P.S.: I'm a designer, decided to learn Vue JS a month ago with the help of ChatGPT, and this is the result.

Visit product View on Hacker NewsAug 19, 2024

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Actual performance

19points

7comments

Made the leaderboard

Launch Intel predictions

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

67%67% 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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, chatgpt, plain · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

30%30% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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