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The Little Learner is a great book. I tried rewriting all the code in Python/JAX while following the Scheme code style as closely as possible, and it worked out great.

The appendix on autodiff is a bit rushed, in my opinion. But in all fairness, the number of pages would probably need to be doubled to give a proper exposition of autodiff.


Yeah, it's the easiest way to get a beachhead in deep learning and then expand from there. I dislike their heavy use of currying, it's elegant in theory but bad error messages make it confusing and inconvenient in practice. But it's a small tradeoff for an otherwise excellent book.

Best decision I made this year.


It's subsystem maintainers asking Linus to pull from their branches, e.g. https://lkml.org/lkml/2025/7/29/291


May I introduce to you

https://huggingface.co/canopylabs/orpheus-3b-0.1-ft

(no affiliation)

it's English only afaics.


The sample sounds impressive, but based on their claim -- 'Streaming inference is faster than playback even on an A100 40GB for the 3 billion parameter model' -- I don't think this could run on a standard laptop.


"In 2021: No (IPT) cases were found in favour of the complainant": https://en.wikipedia.org/wiki/Investigatory_Powers_Tribunal#...


This sounds like something Douglas Adams would have written about.


you have no freedom of speech to publish opinions opposing (as decided by me) freedom of speech.


That's how freedom of speech works. It is free from government persecution.




Parent is quite possibly talking about arrow lake and not lunar lake which is a mobile only part.



There is FxHashMap (https://github.com/rust-lang/rustc-hash) which is faster than std::collections::HashMap.

With ~100 static entries, o1hash (https://github.com/rurban/smhasher/blob/master/o1hash.h) should also work.


Note FxHashMap is just the std HashMap with a different hashing algorithm.


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