Nicolas Chagnet's Homepage

I've been following this advice and I must say in the age of agentic coding it's really useful!

A simple illustration of how TypeSafe's Jev works using a homemade code-quality CLI tool.

Where trauma dumping and programming meet.

Not a recent entry but a classic in its own right.

Everything you'll ever need to know about SSH, and more!

My homemade recipe for enabling pre-push hooks with Jujutsu.

See various optimization algorithms live.

An interesting project to visualize historical figures place in history.

Incredible article about compression algorithms, with insane visualizations.

A very rich blog post on dynamic programming, very pedagogical too!

A bit different from the usual content I share, but this is webapp provides the best TV experience I've ever seen online.

There is a fine line between dedication and obsessiln, and this post toys with it.

This post is a very well written explanation on whar mathematical conditions a loss function should obey so that gradient descent is effective on it.

The typestate pattern is a very powerful way to handle state within the type system in Rust.

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

This website is a treasure trove of gorgeous, in-depth articles and interactive visualizations of systems engineering.

This blog has really cool interactive visualizations of complex concepts like weighted random sampling or load balancing.

Yet another great article about jujutsu, this time it's about the megamerge workflow.

A guide for data scientists on how to handle the deployment of your models, and how to maintain them in production.

What a poignant read.

With this variable font, you can make inline charts (rough but simple) from raw data.

A beautiful color palette with a realistic paper-like feel to it.

Rediscovering frontend work in a world of React and Tailwind.

A quick project for a long weekend.

Stumbled upon this rather promising diff and merging tool (which can be integrated within git) to do semantic merges.

If you've ever wondered how Shazam-like programs work, then read this.

An interesting secrets manager from one of the devs behind mise.

A very cool (and thorough) explanation of Kalman filters with great visuals.

Due to the curse of dimensionality, a person perfectly average is quite rare, so what does a typical person look like?

Finally I have my own space!

A lengthy introduction to the concept of entropy in physics, from thermodynamics to statistical physics and quantum mechanics.

This post describes how the concept of entropy in statistics can be related to a well-defined notion of surprise, and how this can make entropy more intuitive.

I like the "surprise" interpretation of the KL divergence presented in this post.

This is a really cool explanation of Gaussian Processes from a more theoretical perspective.

Another deep connection between physics and "AI": this time it's reinforcement learning (perhaps the original "AI") expressed as a Hamiltonian flow.

There are all sorts of applications of differential geometry to ML, and this article does a great job of introducing some key concepts.

A rather strong argument that Kimball's dimensional modelling is still relevant these days and provides both storage savings and more efficient queries compared to OBT.

An interesting perspective on the abstraction behind the dataframe algebra.

Modeling holiday effects in timeseries forecasting is no easy feat, and this article shows how bayesian methods can really help with that.

A really great explanation of the Dijkstra algorithm with clear visualizations.

This website contains very high quality visualizations of common machine learning concepts, presented in a very pedagogical manner.

How to choose a random element using weighted sampling.

A gentle introduction to Bayesian statistics for data scientists who, like me, are confused by it.

Candle is a very interesting pytorch alternative in Rust.

This article highlights a very interesting aspect of analysing historical data.

I've been on a "declarative" kick recently, and this sounds a really nice way to do this for environment files.

If like me you feel late to the dbt party, we're not alone!

And this is a thorough list of resources to learn more about RL.

This is a very interesting dive into practical applications of reinforcement learning to optimization problems.