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Tailcolors, a TailwindCSS Color Palette

Hacker News

Tailcolors, a TailwindCSS Color Palette

Hey HN, I built tailcolors.com — a TailwindCSS color palette where you can click to copy any Tailwind color class. I'm a longtime Tailwind user and I've tried some similar tools, but none supported v4 colors or the new oklch variants. Tailcolors has both! You can easily copy the 50-950 variants of each color by clicking on them, plus optionally toggle between copying the raw Tailwind class, a hex code, or the oklch property. Enjoy!

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Actual performance

12points
4comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% 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.
AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
43%43% 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 · 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
18%18% 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
17%17% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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