So that’s +=, -=, etc.
So far we’ve just dipped our toes into the techniques that we can use for building anonymous credentials. This tour has been extremely shallow: we haven’t talked about how to build any of the pieces we need to make them work. We also haven’t addressed tough real-world questions like: where are these digital identity certificates coming from, and what do we actually use them for?
。关于这个话题，快连下载安装提供了深入分析
Anthropic called for "very adaptable regulation" instead of SB-1047. As Zvi Mowshowitz said, "this request is not inherently compatible with this level of paranoia about how things will adapt. SB 1047 is about as flexible as I can imagine a law being here, while simultaneously being this hard to implement in a damaging fashion".
we assign a minterm id to each of these classes (e.g., 1 for letters, 0 for non-letters), and then compute derivatives based on these ids instead of characters. this is a huge win for performance and results in an absolutely enormous compression of memory, especially with large character classes like \w for word-characters in unicode, which would otherwise require tens of thousands of transitions alone (there’s a LOT of dotted umlauted squiggly characters in unicode). we show this in numbers as well, on the word counting \b\w{12,}\b benchmark, RE# is over 7x faster than the second-best engine thanks to minterm compressionremark here i’d like to correct, the second place already uses minterm compression, the rest are far behind. the reason we’re 7x faster than the second place is in the \b lookarounds :^).