Most Python developers know dataclasses. Or at least… they think they do 🙂. You add @dataclass, define a few fields, and enjoy writing less boilerplate. But dataclasses hide a lot more power than most people realize and some of that power can quietly bite you if you don’t understand what’s really going on. In this week’s video, I walk through 7 lesser-known dataclass features that make your code safer, clearer, and easier to maintain. I start with common pitfalls like mutable defaults, then...
15 days ago • 1 min read
AI can write code now. But you already knew that :). If you’ve tried using AI seriously, you’ve probably noticed something: the code works (kind of) and then slowly turns into a mess. In this week’s video, I show why prompting alone isn’t enough and why software design is becoming more important, not less, in an AI-driven world. I walk through a real interaction with an AI coding assistant and show how thinking in terms of responsibilities, structure, and system boundaries completely changes...
22 days ago • 1 min read
In a recent video on refactoring complicated business logic, I managed to introduce several subtle errors. Many of you immediately spotted them in the comments. That’s exactly what my first video in 2026 is about. I walk through the mistakes I made, explain why they happened, and show how easy it is to accidentally change behavior when you refactor code. Even with tests in place and decent coverage, assumptions can sneak in, business rules can shift, and logic can quietly break without anyone...
29 days ago • 1 min read
You’ve tested the endpoint. The response looks right. No errors in the terminal. So you’re done, right? Well... not quite. In the last video of this year (time flies!) I start with a tiny FastAPI app that "works", and step by step, I turn it into something that’s actually ready for production (and that's more work than you think). That includes: Proper type usage Input validation and error handling Configuration management Rate limiting to prevent abuse ...and more! All using a real example:...
about 1 month ago • 1 min read
Sometimes your code fails, you change absolutely nothing, and on the next run everything works again. If that sounds familiar, this week’s video is for you. These kinds of failures usually have nothing to do with bad code. They come from the outside world: APIs that time out, networks that briefly misbehave, or LLMs that occasionally return something almost structured, but not quite. It’s frustrating, because the failure feels random, and those types of failures are really annoying to debug!...
about 1 month ago • 2 min read
If you’ve ever opened a function and instantly regretted it… this week’s video is for you. I’m talking about those legendary functions: ten levels of indentation, contradictory conditions, duplicate branches, mysterious try/except blocks, and business logic so unclear that nobody knows what it actually does anymore. I start with a really messy example and walk through the exact process I use to refactor it safely. You’ll see how to create characterization tests so you don’t break existing...
about 2 months ago • 1 min read
Have you ever written a Python script that felt slow, even though the logic was simple? Sometimes it’s not the efficiency of your code that's the problem, it’s when your code does its work. This week’s video is all about the Lazy Loading design pattern, and I think many developers underestimate how powerful (and practical) it is. I start with a real example: loading a huge CSV file that freezes your program for 10 seconds before doing anything useful. Then, step by step, we improve it using:...
about 2 months ago • 1 min read
If your code is hard to test, hard to reuse, and hard to change, you’re probably hardcoding your dependencies. It’s one of the most common architectural problems I see in Python code, even in production systems. In this week's video, I take a small but realistic example, a data pipeline that loads, transforms, and exports some records, and show you how Dependency Injection can turn it from a rigid mess into something clean, testable, and modular. No frameworks required. You’ll learn: Why...
2 months ago • 1 min read
Hi there, Most apps only store the final result: the current balance, the current inventory, the current status. But what if you could track every change that led to that result? That’s exactly what the event sourcing pattern allows you to do. Instead of overwriting state, you store a sequence of immutable events and derive the current state by replaying them. It’s like Git (kinda), but for your domain logic. In this week’s video, I show you how to implement event sourcing in Python from...
2 months ago • 1 min read