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I built Engramma to explore better approaches for design systems and improves the experience of working with them. It’s very much a work in progress with only a handful of users, so this warning felt especially surreal.

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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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宜昌人对蜡梅的喜爱,印在了年年岁岁的记忆里。作为传统的年宵花,春节前后,郊区农民剪下自家的蜡梅花枝进城出售,很快就会销售一空。捧着造型独特的蜡梅花枝回家,选个好看的花瓶插上,淡淡的清香弥散开来,新春的氛围一下子就浓了。