A color name API that maps hex to the closest human-readable name

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I built this API to return the closest named color for any hex value—using curated lists like my own [1], XKCD [2], and others. I made it from scratch without Express or any frameworks because: - I’m a frontend/interaction dev and wanted to learn how to build an API from the ground up. - Existing APIs didn’t guarantee unique names per color—mine does. - It also supports WebSocket updates, gzip responses, and multiple name sets. I’ve been collecting color names for over 10 years [1]. With ~30,000 entries, bundling them into every color-related project became excessive. This API keeps things lightweight—for me and hopefully for others too. GitHub: https://github.com/meodai/color-name-api Would love feedback on naming logic, accuracy, performance, or backend best practices I might’ve missed. [1] Large Color Name List: https://github.com/meodai/color-names [2] XKCD color survey results: https://xkcd.com/color/rgb/

Visit product View on Hacker NewsJun 19, 2025

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

78%78% 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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, 000 · Missing: https docs, excited, just released

57%57% 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 HuntOn track for Day 1 leaderboard · Strong signals: using, apis · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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