ColorParser – Parse any color format from messy text into clean panels

Hacker News

From working years ago with Photoshop, then Figma and more recently CSS and Tailwind, converting color formats has always been a pain. You need the exact format, find the right website, deal with comma placement and right formatting, it's annoying... So I built colorparser.com over the weekend. Basically, you paste any messy text with colors in it, and it automatically parses them and creates separate panels for each color. I know most people will have only one color to convert, but it works with many. Then you just click on whatever format you need (RGB, HSL, hex, OKLCH, etc.) to copy it. The nice thing is it handles format variations - so `hsl(200, 100, 50)`, `hsl(200 100 50)` or `hsl(200, 100%, 50%)` all work the same way. When you visit the site, it automatically reads your clipboard and parses any colors it finds, which saves time. Everything runs in the client. Currently supports hex, RGB, RGBA, HSL, HSLA, CMYK, and OKLCH. Planning to add more formats and features in the future. It's free and open source. Thought some of you might find it useful. Works best on desktop. Demo: https://qt7a9hbcr6.ufs.sh/f/fyvuhoH125pGLsvpcrgR21gHMBmdNi4y...

Visit product View on Hacker NewsJul 21, 2025

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best fitHighest predicted score across all platforms for this description.

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

39%39% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

19%19% predicted probability of success on TrustMRR, 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.

Correct prediction on native model

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