I've got a lot of color background[0]. This is a cool approach for sure, but one thing I would caution anyone on doing is only relying on math or an algorithm t

I've got a lot of color background[0]. This is a cool approach for sure, but one thing I would caution anyone on doing is only relying on math or an algorithm to choose colors. The author makes a great point about halfway through - that it's extremely hard to algorithmically define "nice looking". Human eyes are biological constructs. Mapping how they see the world to an algorithm that's working with RGB colors is a half baked effort at best. Use algorithms as a starting point, then refine afterwards. The author's approach feels like it didn't include that final refinement process. IMO their approach fell apart a little bit in the chart of age distribution of users. While the even color distribution does prohibit a bias of the data, it's also using colors that don't feel harmonious with each other at all. The luminousity of the colors differs heavily, making some feel more bold than others. A better approach here would be to isolate specific hues that match luminousity, then filter those based on color blindness perception, rather than just tossing each of the hues palettes into a simulated annealing process. A final nitpick, if you are doing color theory maths, avoid WCAG 2's contrast algorithms. They're fairly outdated, and should only be used if you have an enterprise requirement to use WCAG 2 specifically. APCA[1] is a better algorithm for color contrast, and is ADA/EAA compatible (it's also the proposed contrast algorithm for WCAG 3). [0] Most recenly was in charge of Figma's update to accessible colors and their migration to dark mode. Previously worked on Atlassian's color palettes. [1] https://www.myndex.com/APCA https://www.myndex.com/APCA