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Taildrops – Free Tailwind CSS 4 code snippets

Hacker News

Taildrops – Free Tailwind CSS 4 code snippets

Free Tailwind CSS 4 Components — and this is just the beginning! I’ve been sharing a bunch of free Tailwind CSS components on X, but honestly, they just keep getting buried in the timeline. It’s super frustrating when something you put effort into disappears so quickly. That’s why I decided to put everything on a website. Now you can easily find all the components I’ve shared in one place, and I’ll keep adding any new ones I create. It feels good to have a space where they won’t get lost. Check them out if you’re interested — I’d love to hear what you think!

Visit product View on Hacker NewsMar 26, 2025

Actual performance

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

34%34% 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

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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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