3 posts tagged with "AI"

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.