Yes! It started out by me building/designing it with my own blood sweat and tears (this is probably the 3rd or 4th serious attempt at a language), until I started toying around with Codex (uggh), then eventually Claude Code. I used the language for the first time to solve many of 2026 Advent of Code puzzles for the first time last year.

At this point, it's mostly for fun, educational purposes and an attempt to build the smallest, but generally useful language with a good balance of dynamic performance, native code compilation, self hosting and a language that is both easy to learn and easy to understand it's design and implementation.

I call it Mu (µ) lang. You can find it at https://mu-lang.dev

Essentially µ is a language that has a dual runtime, Interpreter + Native code.

I've also experimented with building a full UNIX(ish) OS and Kernel in Mu itself. So that means, about ~2k Assembly, the rest in Mu. It's at about a 80/20 (Mu/Asm) and works fairly well!

I once made a program for calculations in engineering, but it wasn't general enough to be practical, so I thought of creating a simple coding language to script it. After the first day, I realized that creating a language was far more complicated than I had ever expected and would be a maintenance hell. So, in the end, I exposed the program routines to Python and made a GUI around it, so it could be scripted with Python.

Today, with AI and LLVM, creating a language is probably an easier task, but I still think it's smarter to do what I did. Any language you create will necessarily be mediocre and full of bugs. Don't reinvent the wheel.

Yes, I am. I’ve been working on a language for what I think the future of programming might look like under fast and intelligent LLMs. In my own projects and at work it’s already getting close to where the inner loop of checking, linking, compiling and running is slower than the LLMs thinking and edits. I’ve been working on a language that makes it fast to run large programs without sacrificing lots of runtime performance. There are some other tools and features to hopefully keep the inner loop tight.

Additionally, I’ve been really fascinated by the idea of a language that makes LLM observation and rewrites at runtime possible. There is an interface to get profile data and replace functions at runtime based on the analysis of an LLM.

Lastly, I’ve always wanted a language that could model more of the external environment and make those easier/safer to interact with. I think of libraries like Kysely as an example of making that easier. It’s a really cool time to be playing around with these.