This structure fit neatly into what we needed for the offlining work. A new serverless repo was created for generating the Native AOT DLL files. The repo contains a core project that depends on the pre-existing library projects allowing for code reuse. Individual platform projects were created for handling all the custom build and linking logic required for Windows as well as each supported console.
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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support. There is something of an inverse vertical integration penalty here:
{"role": "developer", "content": "You are a model that can do function calling..."},
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Why do people opt for a Brazilian butt-lift and is it safe?
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