就在谷歌发布会前后，OpenAI 公共 Codex 代码库中出现了针对 gpt-5.4-ab-arm1-1020-1p-codexswic-ev3 等部署端点的代码合并请求，随后被火速撤回。带有浓厚“ DevOps 持续部署”色彩的动作表明，AI 模型的迭代已经从一年一度的宏大发布，转变为高频的灰度测试与静默上线。
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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.
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