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A handy color picker for programmers and web designers

Hacker News

A handy color picker for programmers and web designers

This is a handy dandy color picker utility program, written as a web app. It is designed for laptop/desktop use, doesn't fit on a mobile device. You can select a starting color by either selecting from one of the named web colors, or one of the 700 Mark Ferrari superstar pixel artist color swatches, then you can explore nearby colors which are arranged in a novel hexagonal grid that lets you explore in 12 different directions at once. When you select a color, it copies the color name to the clipboard so you can paste it into what you are working on. There are multiple output formats you can choose from, like web hex #AABBCC, or rgb(1,2,3) for example. Free. The 1400 lines of code is posted on GitHub, if you are curious as to how it was made. There are some clever tricks in the code regarding how to sort colors properly.

Visit productView on Hacker NewsAug 1, 2022
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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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48%48% 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
35%35% 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
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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