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NFAs are cheaper to construct, but have a O(n*m) matching time, where n is the size of the input and m is the size of the state graph. NFAs are often seen as the reasonable middle ground, but i disagree and will argue that NFAs are worse than the other two. they are theoretically “linear”, but in practice they do not perform as well as DFAs (in the average case they are also much slower than backtracking). they spend the complexity in the wrong place - why would i want matching to be slow?! that’s where most of the time is spent. the problem is that m can be arbitrarily large, and putting a large constant of let’s say 1000 on top of n will make matching 1000x slower. just not acceptable for real workloads, the benchmarks speak for themselves here.
If you’ve been following my recent posts on Metaduck, you know I spend my days building infrastructure for AI agents and wrangling LLMs into production. As software engineers, we’re used to treating neural networks as massive, static mathematical graphs powered by backpropagation and matrix multiplication running on beefy GPUs (e.g. with PyTorch or similar).，详情可参考WPS官方版本下载
With assistance from LLMs, I wrote this code that emits the Shave and A Haircut melody on a 1000 KHz carrier. Here’s a basic AM radio picking up the audio:，更多细节参见体育直播
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