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.
В России ответили на имитирующие высадку на Украине учения НАТО18:04
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Materials science
触顶之后，平台的钱还能从哪来当抽佣逼近上限，平台并不会停止寻找增长路径。过去几年中，一个清晰的转向正在发生：平台正在从交易税，转向流量税、确定性收费与效率税。