A few weeks ago I published a post about building ncmds — a zero-configuration, Markdown-driven documentation site builder I was running on Flask + Python. The post got some traction, a handful of stars on GitHub, and a few contributors poking around the code. That was great.

Then I quietly rewrote the entire backend in TypeScript.

This post is the honest story of why.

There is a very specific kind of tension that builds in a progressive house track — the kind that grabs your chest somewhere around bar 28 of the break, when the filter opens halfway and the reverb tail of the lead swells. Producers like Alesso, Martin Garrix, and Axwell /\ Ingrosso have turned that tension into a craft. I have spent years studying it, replicating it, and eventually producing it myself under my alias EyeMad.

But recently, as an AI engineer, I started asking a different question: could an AI music generation model — without fine-tuning, using only prompt engineering — accurately describe, architect, and guide the creation of those layered progressive house lead sounds that define the genre's signature drops?

This article is my attempt to answer that question. It is long, technical, and deeply personal. It lives at the intersection of two things I love most.

A few months ago I found myself in a familiar situation: I needed to document a side project quickly. I had used Docusaurus before, loved it in theory, but every time I bootstrapped a new doc site I ended up fighting Node.js toolchains, React component boilerplate, and config files before I had written a single line of actual documentation. MkDocs was simpler, but it felt dated and lacked the AI-native features I increasingly wanted in my workflow.

So I did what every engineer does when the existing tools annoy them enough — I built my own. The result is ncmds (No Code Markdown Documentation Sites), and this post walks through the thinking, the architecture, and the lessons learned.

After months of deep dives into AI music systems, research papers, and hands-on experimentation, I decided to consolidate everything I learned into a comprehensive open-source educational resource: Neural Audio Theory.

This project explains how modern AI music systems are actually built — from the fundamentals of digital audio processing all the way to transformer architectures and diffusion model training.

I built a terminal-first notebook backend engine for C, and the results honestly surprised me.
The project started as a proof-of-concept, but it quickly became a strong validation that notebook-style workflows can work for low-level languages too.

Creating modern, fast, and well-structured websites in a fast way using Markdown or MDX and documentation is a crucial part of any tech project. Two standout frameworks that developers gravitate towards are Nextra and Docusaurus. While they share similarities, they are designed with different goals in mind. This article dives into their core differences, strengths, and ideal use cases to help you pick the right one for your project.