"Conviviality matters for science for multiple reasons. One of them is epistemic: if you want to derive knowledge from your work, you need to know exactly what you are doing, and that includes a detailed understanding of your tools. Moreover, research is much facilitated if you also have the inverse: the ability to create a tool that does exactly what you want to do. And since science is a collective activity, in which participants critique and build on each other's work, the understanding of tools needs to be shared inside a discipline." (also discusses the Python 3 transition as an example of a loss of conviviality)
presents "a different approach concentrating on the migration from Python to Numpy through vectorization..." with "a lot of techniques that you don't find in books and such techniques are mostly learned through experience"
"a Python compiler that aims to provide optimized machine code by compiling type-annotated Python code. It offers several backends, including LLVM, C, C++, and WASM, which allow it to generate code into multiple target languages simultaneously."
could be fun to play with. "With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process."
"a Machine Learning (ML) library for building neat pipelines, providing the right abstractions to both ease research, development, and deployment of your ML applications. [...] [T]he optimizer is a model itself that maps features of datasets and features of the hyperparameter space to a guessed performance score to predict the best hyperparameters."