Instead of tee() with its hidden unbounded buffer, you get explicit multi-consumer primitives. Stream.share() is pull-based: consumers pull from a shared source, and you configure the buffer limits and backpressure policy upfront.
kern_return_t kr;
，更多细节参见新收录的资料
i ran some comparisons on state representation width - 16-bit state IDs fit noticeably better into CPU cache than wider ones, and if you’re hitting 64K+ states you’re probably better off splitting into two simpler patterns anyway. one design decision i’m happy with is that when the engine hits a limit - state capacity, lookahead context distance - it returns an error instead of silently falling back to a slower algorithm. as the benchmarks above show, “falling back” can mean a 1000x+ slowdown, and i’d rather you know about it than discover it in production. RE# will either give you fast matching or tell you it can’t.
But just two years into her stint at the U.S.-based international law firm, Brown decided it was time to do her own thing. In June of last year, she founded Soxton: an AI-powered legal services business serving startups.
(Presumably there’re also CRLs 118, 120, etc.)