algorithm, believe it or not. That's what Zs are, right? That's what Z stands for in, in computers, compression. So Z standard does, sort of excels at high compression, high compression ratios for small data. It implements something called dictionary compression. The problem that this is trying to solve is that with small data set or with, no, rather with small pieces of data, the algorithm is not able to typically to learn from what's already been compressed because there's no, by the time you've compressed one thing and you're onto the next, now you're not, you're compressing something new. You're not learning from the past. Z standard apparently has a training sort of method or a training mode where it looks at the data, all the little small bits of data and kind of makes some rules, some general rules over what can be compressed and so on. I don't know enough about compression or dictionary compression or Z standard to say how all of that magic actually happens, but that's kind of (43/45)
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