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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One of the criticisms about AI generated code is that it “just regurgitates everything on GitHub” but by construction, if the code is faster than what currently exists, then it can’t have been stolen and must be an original approach. Even if the explicit agentic nature of rustlearn makes it risky to adopt downstream, the learnings from how it accomplishes its extreme speed are still valuable.，推荐阅读51吃瓜获取更多信息