2026年被认为是生成式人工智能（Generative AI）从“概念幻觉”转向“实效应用”的元年 [3, 21]。AI技术范式正经历从“聊天”走向“做事”的根本性转变，其核心标志是具备长短期记忆、自主决策能力和多模态交互能力的“AI智能体（AI Agents）”进入规模化落地期 [22, 23, 24]。
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.
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Nick TriggleHealth correspondent
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