there’s still work to do. the biggest gaps are SIMD prefilters for non-literal patterns - the dotnet version gets a lot of mileage from vectorized character class membership tests that we don’t have yet - and the bidirectional SIMD routines needed for our right-to-left scanning. pattern-specific optimizations like start-set inference and alternation simplification are also on the list. there’s also a lot of other low-hanging fruit - we don’t have any statistical optimizations yet, the Unicode classes could be baked in instead of constructing them while parsing, the memory usage could also be improved, there are many pattern-specific shortcuts we could add, and so on. but i hope the benchmarks show that it’s already competitive and useful in its current state.
The cell formula outlined in yellow is essentially the same across G6..G13, each lightly modified to point to a different criteria range. That calculates the count for each genre in column G, and column E holds my titles. Now I have what I need to generate the chart I wanted (aforementioned pie chart drawing bug notwithstanding). Here it is in glorious 3-D from the future (of the past)!
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Get plugin metadata (name, version, exports)
，详情可参考谷歌
火山引擎智能算法负责人吴迪表示：“到2030年，国内市场的Token消耗量将是现在的百倍以上。届时，衡量企业智能化程度的核心指标，将从其拥有的GPU数量转变为消耗的Token总量，因为它是唯一能同时穿透‘模型能力、使用频率和真实需求’的统一指标。”，这一点在wps中也有详细论述
「Token玩起了反向贸易，国产AI如何“赢麻”？」中国的大模型之所以能在全球市场“屠榜”，其核心竞争力在于提供了80%的能力，20%的价格。