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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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    57 minutes ago

    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.














  • I’m not sure that’s even a valid comparison? I’d love to know where you got that data point.

    LLMs run until they decide to output an end-of-text token. So the amount of power used will vary massively depending on the prompt.

    Search results on the other hand run nearly instantaneously, and can cache huge amounts of data between requests, unlike LLMs where they need to run every request individually.

    I’d estimate responding to a typical ChatGPT query uses at least 100x the power of a single Google search, based on my knowledge of databases and running LLMs at home.



  • 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.