One Core, Two Interfaces, No Rewrites

When building applications, I always build the core first, then the interfaces. It was no different with Ask the Canon: a uv run main.py ask "..." CLI for quick iteration and validation, then the web app for MVP. Search, ranking, citations, all using the same engine.

There Is No Magic: An AI Agent in 60 Lines of Python

Everybody talks about agents, and a lot of people assume they're some new kind of model. They aren't. An agent is a small amount of plumbing around an LLM you already understand. Let's build one from scratch in Python and see exactly what that plumbing is.

AI Is an Accelerator, Not a Compass

A Reddit thread caught my eye this week. A developer spent three days with AI building out an authentication system, then realized the entire user flow was wrong. Not because the code was bad. Because nobody had mapped out what the system was actually supposed to do before line one.

That's what happens when speed gets decoupled from direction.

Building an AI Agent in 6 Weeks (and Finally Understanding How They Work)

Jeff Haemer has written software since he was teaching it at the University of Colorado in the early 1980s. But he felt he needed to brush up his Python, and above all get a grounding in AI.

In his words AI was "a big undifferentiated cloud of things I didn't know." Time to change that.

AI Human-in-the-loop: News Digest Triage Telegram Bot

In my trend digest article I shared a quick tool to keep on top of tech trends, but it's a one-way street: the model gives information, but I still have to decide what to do with it. Let's build the second half: a Telegram bot that shows me each story, guesses a tag, and lets me confirm or overrule it with one tap.

The control layer is the product, not the model

Gary Bernhardt posted something this week that names a phenomenon we're teaching in our agentic AI cohort:

Everyone seems fixated on the models, but I think there's so much low-hanging fruit in the control layer above the model. "Agent" and "harness" sell that layer short. There's so much more that we can do beyond "read input, send to model, run commands it returns."

What production AI agents actually require

Most "AI agents" shipping right now are demos wearing production paint. They answer questions fluently and break the moment they touch a workflow with money, state, or consequences.

Learn agentic AI in Python with 10 small exercises

Most "build an AI agent" tutorials hand you a framework and skip the part where you actually understand what it's doing under the hood. When the abstraction breaks, you can't debug it because you never built the layer underneath. Juanjo and I think that gap is worth closing.

Coding exercises that run in the browser with Pyodide

I've built coding-exercise platforms before (Python, Rust). AWS API Gateway + Lambda, Docker, etc. It works great, but that's a lot of infrastructure to teach someone a four-line function.

Build the data layer before you touch the LLM

Every AI tutorial I've seen opens the same way: client.chat.completions.create(...). Within ten lines you have a response. Within twenty you feel like you're building something.

What you're actually building is a demo.

How an AI expense agent is actually structured

Most AI agent tutorials show you the LLM call. They skip the part where you have to do something useful with the response.