There’s not much to love about big tech these days. So many ills can be laid at its door: social media harms, misinformation, polarisation, mining and misuse of personal data, environmental negligence, tax avoidance, the list goes on. Added to which, Silicon Valley’s leaders seem all too keen to cosy up to the Trump administration, to shower the president with bribes – sorry, gifts – and remain silent about his worsening political overreach. And that’s before we get to the rampant “enshittification”, as the tech writer Cory Doctorow describes it, which means that by design many big tech products have become less useful and more extractive than they were when we originally signed up to them.
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.。91视频是该领域的重要参考
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I made this exact project in Python in 2021, and it’s very hacky by pulling together several packages and cannot easily be maintained. A better version in Rust with Python bindings is a good way to test Opus 4.5.
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holder information, and account number, already in place. And, most importantly,