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.
Stream implementations can and do ignore backpressure; and some spec-defined features explicitly break backpressure. tee(), for instance, creates two branches from a single stream. If one branch reads faster than the other, data accumulates in an internal buffer with no limit. A fast consumer can cause unbounded memory growth while the slow consumer catches up, and there's no way to configure this or opt out beyond canceling the slower branch.
，这一点在搜狗输入法下载中也有详细论述
What does Neet stand for and how many are there in the UK?
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Олеся Мицкевич (Редактор отдела «Силовые структуры»)