My base layout is very close to Halmak. I modified a couple of characters in the center columns to better align frequency with ease of reach on a columnar layout. I use home row mods for my home keys, and all of my thumb keys are dual-function keys with layer switching hold actions.
backpressure: 'strict' // or 'block', 'drop-oldest', 'drop-newest'
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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.
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