I searched for benign explanations. Could it be that milk is simply too heavy, and that by including the weight of the water content that boils off I am tilting the simplex too far in its direction? Could it be that by excluding slices of bread as ingredients since they aren’t raw flour and do not go in a mixing bowl, I have excluded breakfasts like french toast, eggs in a basket, breakfast burritos, and breakfast sandwiches that might yet have saved us? Could I have overlooked some arcane culture that breaks their fast with dumplings or egg noodles? None of these satisfied me. The Abyss stared back.
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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