Linklog

If you need to profile some Rust crates, this might make your life easier.

If instead you prefer your error-handling to be more manual, the `derive_more` crate is a great way to remove boilerplate when defining enum wrappers for custom Errors.

This is an interesting error-handling crate mixing the good parts from `thiserror` and `anyhow`.

Useful Rust crate to work with complex Enums and automatically build a 'Kind' Enum.

This is a very useful crate providing macros to make wrapping custom/various error types in one error Enum. Importantly, it is equivalent to using the standard library and does not introduce any custom error handling, just reducing boilerplate.

I've been working a lot on our data pipelines at work, switching to polars mostly for performance and introducing rigorous checks and validations of data at various stages. I haven't yet used dataframely, but its principle really resonates with my use case, so I recommend checking it out.

I've been looking for a good numpy and pytorch typing system in Python. Initially written for Jax, this library looks like exactly what I wanted.

An interesting library to record ML experiments metadata through commit messages. Even better, it supports a query language to find which commit satisfies a given criterion.

A useful Python library to sample custom probability distributions. Looks useful if the PDF is expensive to compute.

A Python library for timeseries forecasting with very extensive features. The documentation also features some in-depth pedagogical explanations of how to properly forecast data and what methods can be used to improve results.

Bring some sanity to Python and remove null checks. Clearly inspired by Haskell's Maybe or Rust's Option type. I am mostly familiar with the latter, and I often wish it existed in Python, and now it does.

Useful library to estimate feature importance of machine learning models, based on game theory principles. The main idea is to estimate the importance of each feature to take a sample from the mean prediction value to a given prediction value. It can also be aggregated over samples to understand global feature importance, conditional on feature value.

Natural language processing toolkit optimized for CPU hardware. I haven't tested it yet but it looks really useful for quick clustering, deduplication, similarity search, etc...

A minimalistic take on CSS frameworks which is simple and lightweight. Hopefully I one day have the time to rewrite this blog with it. Update: it looks semi-abandoned, but some forks are keeping the torch alive.

Library to make great-looking tables from Polars dataframes. It works with Pandas too but there you can just generate HTML directly, while Polars currently does not have many more options.