I wanted a reliable way to generate all legal K+R vs K chess positions with White to move for AI training data.
After exploring several constraint programming tools (MiniZinc, OR-Tools, and Prolog variants), I ended up using Scryer Prolog, embedded directly from Rust.

In the end, I realized that I didn’t even need constraint programming (CLP) at all — plain Prolog was enough.
The constraints were simple, the domain small, and performance was already instant.

Understanding Tensors in AI: A Journey Through Physics and Mathematics

When exploring the world of Artificial Intelligence, one frequently encounters the term tensor. In practice, a tensor is often treated as just a multi-dimensional array — a generalization of scalars, vectors, and matrices. However, this modern usage is a far cry from its deeper mathematical and physical origins. To understand what a tensor really is, it’s helpful to trace its meaning through physics and mathematics before returning to AI.

The generated image is based on the simple prompt: "Create a humorous image highlighting the difficulty of programming in RUST." Even if the result is not perfect, I find it very relevant, and it made me laugh. I think every RUST programmer has struggled with the borrow checker.

I have developed a chess game in RUST offering a UCI allowing engine tournament. The link is the following: https://github.com/christophelc/chess_rust

The program is based on RUST and Actix actors.

I see several advantages to learning Rust. 

Technically speaking:

I used to write a POC in Rust in 2020. At this time there was no LLM to help us learn a new language. My first steps were not so easy. At this time, I discovered the concept of borrower checking and ownership. Coming from Scala, I was used to full immutability too. In Rust, we have to choose even if the default is immutability. And after 3 months, I stopped using Rust.