Раскрыты подробности похищения ребенка в Смоленске09:27
For more than two decades, Emil Michael has operated at the fault line between Silicon Valley ambition and American geopolitical power, helping scale one of tech’s most disruptive companies before returning to government to shape how artificial intelligence will be used in war. Self-proclaimed “one of the best deal guys” Michael has now become the Pentagon’s most aggressive public combatant in its escalating standoff with Anthropic.
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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.