• 7 Posts
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Joined 1 year ago
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Cake day: July 1st, 2023

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  • You often need to be pretty good at math. But not because you’re “doing math” to write the code.

    In real world software systems, you need to handle monitoring and alerting. To properly do this, you need to understand stats, rolling averages, percentiles, probability distributions, and significance testing. At least at a basic level. Enough to know how to recognize these problems and where to look when you run into them.

    For being a better coder, you need to understand mathematical logic, proofs, algebra/symbolic logic, etc in order to reason your way through tricky edge cases.

    To do AI/ML, you need to know a shitton of calculus and diff eqs, plus numerical algorithms concepts like numerical stability. This is kinda a niche (but rapidly growing) engineering field.

    The same thing about AI also applies to any other domain where the thing being computed is fundamentally a math or logic solution. This is somewhat common in backend engineering.

    I’m not “doing math” with pen and paper at work, but I do use all of these mathematical skills all. the. time.

    I am an SRE on a ML serving platform.












  • Video codecs mostly work by tracking movement, predicting which pixels will change, and striving to only encode the pixels that actually change or change dramatically. In other words, compression looks for patterns.

    All of that goes out the window when you try to compress static. There are no patterns. It simply can’t be compressed. This isn’t a matter of the algorithms not being good enough. It’s a fundamental limit of information theory.

    Anything fancier amounts to embedding the intro into the compressor as a well-known pattern. And at that point, you’re better off just caching a 4K version of the intro as a standalone video file directly in the app.