发布仅两周的 MiniMax M2.5 模型以 4.55 万亿 Token 的调用量位列月度第一；月之暗面的 Kimi K2.5 以 4.02 万亿 Token 排名第二。谷歌 Gemini 3 Flash Preview、DeepSeek V3.2 与 Anthropic Claude Sonnet 4.5 分列其后。
这些公司有一个共同点：它们没有在造更聪明的模型，它们在解决数据从混乱到有序的问题。
，更多细节参见WPS下载最新地址
But that’s unironically a good idea so I decided to try and do it anyways. With the use of agents, I am now developing rustlearn (extreme placeholder name), a Rust crate that implements not only the fast implementations of the standard machine learning algorithms such as logistic regression and k-means clustering, but also includes the fast implementations of the algorithms above: the same three step pipeline I describe above still works even with the more simple algorithms to beat scikit-learn’s implementations. This crate can therefore receive Python bindings and even expand to the Web/JavaScript and beyond. This also gives me the oppertunity to add quality-of-life features to resolve grievances I’ve had to work around as a data scientist, such as model serialization and native integration with pandas/polars DataFrames. I hope this use case is considered to be more practical and complex than making a ball physics terminal app.
þone will require some grammar, though I could have just said "the" and it would have made sense