Learn to build ML systems — not by memorizing code, but by understanding the concepts well enough to direct AI tools to do it for you.
AI tools like Claude, Codex, and Cursor can write the boilerplate. What they can't do is decide what to build, debug why it's broken, or know which approach fits your problem.
"Here's how to write a for loop in Python"
"Here's what you need to understand to direct Claude to build ML systems"
We focus on the skills AI can't replace: systems understanding, debugging intuition, and domain expertise.
Concept explanation and intuition. What is this thing, why does it exist, when would you use it, and what are the mental models that make it click.
No code in this section — just understanding.
Build something real. Not a toy example — an actual useful project that applies the concepts. You follow along and see the process, not just the finished result.
This is where it becomes tangible.
Target length: 15–25 minutes per episode
Each Knowledge section is built to give you a durable mental model — something that stays useful even as tools and libraries change.
What it is — the concept in plain language, no jargon walls
Why it exists — the problem it solves, and what came before it
How it works — the core mechanism, with intuition over formulas
When to use it — practical decision-making, tradeoffs, and gotchas
Every episode has a hands-on project that applies the concepts to a real problem — not contrived exercises, but tools and systems you'd actually want to use.
Projects are grounded in real domains:
Projects build on each other — data from Episode 5 gets reused in 6, the model from 5–6 gets improved in 7. By the end, you've built a full ML pipeline.
Understand what's happening under the hood. AI writes the boilerplate — you need to know what to ask for and whether the output makes sense.
Not toy examples. Every episode builds something you'd actually use — a price predictor, a search engine, a game AI. Grounded in crypto, trading, and gaming domains.
You follow along and see the full process — including the debugging, the decisions, and the dead ends. Not line-by-line dictation, but a realistic workflow.
We focus on what AI tools can't do for you: systems thinking, debugging intuition, architecture decisions, and domain expertise. The stuff that makes you an engineer, not a typist.
Developers who want to become ML engineers — you can code, now you want to understand the ML side
Builders using AI coding tools — Claude, Codex, Cursor — who want to give better instructions and catch mistakes
People who learn by building — not by watching lectures or reading textbooks