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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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These debt-funded projects create “indirect but potentially material” exposure to fiscal risk, the analysts wrote. Beyond infrastructure, governments will have to plan for labor disruptions and related social support. The IMF estimates 40% of global jobs—and 60% in advanced economies—are exposed to AI, particularly high-skill roles, potentially eroding payroll taxes while spiking demand for reskilling and safety nets.