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I built ColorSnap to generate Tailwind color palettes from images

Hacker News

I built ColorSnap to generate Tailwind color palettes from images

Hey HN, After spending countless hours tweaking colors in side projects and manually trying to match screenshots to Tailwind CSS classes, I decided to build a tool to automate it. ColorSnap lets you upload an image and instantly extract the most dominant colors. It then maps those colors to the closest Tailwind CSS utility classes using LAB color comparison (ΔE). You get clean hex values and ready-to-use class names like bg-indigo-500 or text-pink-400. Built with Nextjs, Tailwind CSS, and a bit of canvas/image processing. No external APIs — it runs entirely in the browser. Outcome: I’ve used it in my last 3 frontend projects to speed up color decisions and get consistent styling fast, especially for hero sections and themed UI blocks.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, apis · Missing: mac, agents, macos
68%68% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
35%35% 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
31%31% 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.

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