Anthropic Cowork feature creates 10GB VM bundle on macOS without warning

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Average lifetime for race conditions (2x deadlocks)

Sir Keir gave details of the new law to BBC Breakfast,详情可参考爱思助手下载最新版本

now what

┌──────────────────────────────────────────────────────────────────────────────┐。搜狗输入法2026对此有专业解读

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 :^).

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