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An OKLCH-based perceptually uniform color system/theme builder

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An OKLCH-based perceptually uniform color system/theme builder

I've been using a version of this internally for a few months but decided to polish it a little to finally deploy it. It's a color system generator that creates accessible, perceptually uniform color palettes using the OKLCH space. It takes one seed (primary) color, generates relative key colors from multiple color harmony schemes (analogous, complementary, etc) that are then used to create 26-step color ramps each. Shades from the ramps are then used to generate semantic color roles, or can be used for creating custom palettes. All colors are gamut-mapped to the sRGB gamut with chroma reduction, essentially preserving lightness and hue values while finding the maximum in-gamut chroma for each step. There are obvious similarities to Material Design Themes, mostly because I'm visually pretty comfortable with it. Plus, I started this project back when some of the colors generated by Material could be a little dull and I wanted to learn/build something like this from the ground up. There are a couple of improvements I would like to make to this in the near future. The first one is a dynamic chroma curve (the chroma falloffs for the ramps are on a bell curve). At the moment, the chroma curve peaks at L ~0.55 for all hue ranges, which works good enough but isn't ideal for a few reasons. The second one would be adding seed color extraction from images. And maybe a built-in contrast checker. If you find the tool helpful and/or have any feedback or suggestions, let me know.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
70%70% 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: visual, using · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: plus, builder · Missing: platform, intuitive, reviews
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
41%41% 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
21%21% 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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