Goldman agreed with Slok at least that the vibes are pretty freaked out, titling its report “AI-nxiety,” and highlighting how corporate chatter has far outpaced tangible implementation. A record 70% of S&P 500 management teams discussed AI on their quarterly calls, with 54% specifically framing the technology around productivity and efficiency. Yet when it came to providing hard numbers, the narrative faltered, lending support to the research of Wharton management professor Peter Cappelli, who has embedded with several firms attempting AI adoption and previously told Fortune that the productivity gains are real, but getting there is really hard work and quite expensive to implement.
At 11:49 a.m. GMT (6:49 a.m. ET), Anthropic posted on its Claude status page that it was "currently investigating this issue." Later updates said the issues were "related to Claude.ai and with the login/logout paths, and that the team had "discovered that some API methods are not working."
。关于这个话题，91视频提供了深入分析
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 :^).
当然，技术端的降本革新也在同步进行：通用型/异体疗法通过“现货”供应，大幅降低生产成本；体内CAR-T则通过简化流程，避免个体化制备，带来成本下降的想象空间。
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