Lightweight and robust CLI color converting utility

Hacker News

I started this project because I found myself converting between Hex and RGB a lot. As I was frequently visiting various color picker websites, I began thinking more and more about how color conversions even work in the first place. What is a color space? Using any random color-picker online to go from RBG<->Hex works plenty fine, but, I think CLI tools are cool. I'm still pretty new to the Linux scene, and I really enjoy finding random command line programs to play with. I searched around a bit on Github and the AUR and couldn't find any CLI color conversion utilities, which I honestly found surprising (maybe I didn't look hard enough). I decided I'd really enjoy trying to make a simple command line program for RGB<->Hex, and the scope slowly expanded. I could see myself expanding it (maybe including more color formats, suggesting complementary colors) but for now, unless people discover ways to break it or offer any pull requests, I think I'll be moving on to other projects. It has no dependencies besides Python, and can be easily installed on mac/linux (see the README for more). I'm not certain how you'd set it up as a command line program on Windows, but you can just use Python in cmd to use the script anyway!

Visit product View on Hacker NewsFeb 14, 2025

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3points

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

66%66% 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: mac, new, using · Missing: agents, macos, agent

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

14%14% 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.

Correct prediction on native model

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