Nicolas Chagnet's Homepage

I always love a good algorithm visualisation, and this article does a really good job at explaining the A* and Dijkstra graph exploration algorithms.

As a physicist, I have always been drawn towards philosophical works that tackle the deep questions I was trying to understand.

A well-known Python library to handle structured data with serialization/deserialization.

A very interesting sampling method specialized in fair selection of streaming events.

I'm always looking to improve my diagrams and visualisations.

Opinionated take on using Copilot.

A fun analysis of the birthday effect using actual data and thorough methodology.

This is a very useful crate providing macros to make wrapping custom/various error types in one error Enum.

The question of imbalanced classes is a source of recurring discussions within the data science community.

I've been waiting for the astral type checker for a while: mypy is just excruciatingly slow, and every astral product tends to be a superperformer.

I'm still itching to try jujutsu in more details, but every time I dive in the documentation, I feel like I'm missing a piece.

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.

Bloom filters are interesting data structures.

This is a great article presenting various polynomial bases used in mathematics (canonical, Legendre, Chebyshev) and how they can be used to fit data.

You can find all sorts of beginner "top 10 features of X" online, and most of the time, they're basic and barely interesting.

This is a very thorough and well designed visual introduction to Gaussian Processes and Bayesian optimisation.

This is a high-quality review of how visualisations shape our understanding of data.

This article shares an interesting viewpoint on the role of friction in our lives, both as a positive and negative influence.

TDD can feel daunting, and advocacy to strict adherence of TDD principles can be off-putting when you are starting with it.

A great write-up on how to write good documentation.

Adding new types to existing codebases can be daunting, but one shouldn't be shy to do what's necessary.

Read this if you want a good reality check on the current "vibe coding" trend.

It is possibly one of the best summaries out there of how LLMs function, broken down by high-level components (memory, tools), and well illustrated.

An old but fantastic reference on vector embeddings.

Some musings on the importance of good naming conventions in programming.

This is an extremely interesting take on recent global warming data and the role of sulfur in masking some of the effects.

A fantastic collection of free textbooks on algorithms for optimization, decision making and validation.

Some musings on how to use protocols and abstract classes as interfaces in Python.

During my PhD, I wrangled with beamer for important presentations, but I always yearned for a simpler markdown-based system for smaller, recurrent presentations.

Succinct data structures are clever ways to pack a lot of information in lightweight structures like bit vectors.

I've been looking for a good numpy and pytorch typing system in Python.

This is a good example that even if you understand the math behind a concept, there's nothing like good storytelling.

An interesting take on the rigidity of best practices and how much more productive we can be once we let go of them.

A very neat summary of what Markov chains are and how they work, with beautiful animations.

Another article about python's type system.

Very cool breakdown of the difference between abstract classes and protocols in python.

A simple afternoon project to familiarize myself with the Ollama API.

A good git workflow for small teams.

An interesting CLI math tool, with its own language and sane defaults.

An interesting analysis of the cost of modern logging infrastructure and its usefulness (or lack thereof).

A great introduction to maze generation algorithms with informative visual.

This is a very useful reference sheet containing major changes added by every new python version.

This article presents various pieces of advice and tips on how to efficiently write Rust code at the prototype stage.

A TL;DR version of Andrej Karpathy's "Deep dive into LLMs like ChatGPT" video.

An interesting analysis of various optimization algorithms applied to a simple dynamical programming problem.

uchū is a minimalistic color palette based on OKLCH color space.

An interesting use of deep learning to solve physics problems.

An interesting library to record ML experiments metadata through commit messages.

I've been more and more tempted by jujutsu as a drop-in replacement for git.

This is an interesting CSS framework which picks some parts out of Tailwind while also being more minimalistic.