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Cake day: July 9th, 2023

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  • xthexder@l.sw0.comtoScience Memes@mander.xyzCursed wretched marketing
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    22 hours ago

    You can’t use a color picker see color fringing due to subpixel rendering. (There’s tons of info about this for font rendering). Your display doesn’t map pixels 1-to-1 in most cases. But like I said in my edit, I’m fairly sure that part is irrelevant here.

    The blue/gold dress was not related to screenshotting and compression. People were arguing about the color even when looking at the exact same image. It all depends on which color temperature the dress was lit with. Noone can know for sure, and your brain just picks one (maybe depending on the room you’re in).

    It’s the same sort of deal as those rotating optical illusions. It’s possible to see it both ways, but your brain usually picks one and it’s hard to switch.





  • I think what we actually need is someone to take a picture of their screen with a microscope while the image is zoomed out.

    Based on some comments I’ve seen, it seems likely this is just an artifact of how the red/green/blue pixel layouts work when drawing the edges of white things.

    Edit: I don’t have something to check the actual display pixels, but I realized I could just rotate the image and see if the colors change, which they don’t. So this definitely seems like more of a white balance effect, similar to that old Gold/Blue Dress meme.









  • A quadratic function is just one possible polynomial. They’re also not really related to big-O complexity, where you mostly just care about what the highest exponent is: O(n^2) vs O(n^3).

    For most short programs it’s fairly easy to determine the complexity. Just count how many nested loops you have. If there’s no loops, it’s probably O(1) unless you’re calling other functions that hide the complexity.

    If there’s one loop that runs N times, it’s O(n), and if you have a nested loop, it’s likely O(n^2).

    You throw out any constant-time portion, so your function’s actual runtime might be the polynomial: 5n^3 + 2n^2 + 6n + 20. But the big-O notation would simply be O(n^3) in that case.

    I’m simplifying a little, but that’s the overview. I think a lot of people just memorize that certain algorithms have a certain complexity, like binary search being O(log n) for example.