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 :^).
News of OpenAI’s Codex’s growth also comes amid reports of surging business adoption for Anthropic’s products. Data released by Ramp, a software company that handles expense management, show that Anthropic’s marketshare of business AI chatbot invoices has climbed to more than 60% in February, from just over 10% a year earlier. Meanwhile, Ramp’s figures showed OpenAI’s business marketshare falling to about 35%, down from almost 90% the year before. Anthropic CEO Dario Amodei also told a conference this week that his company was operating at a $19 billion annualized revenue run rate, a figure that climbed by $6 billion in February.
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// 易错点2：遍历结束后k仍0 → 栈是递增的，末尾数字更大，移除末尾k位，详情可参考同城约会
Анастасия Волова (редактор отдела оперативной информации)