Hi there, You’ve probably noticed this by now: LLM prototypes are easy to build. But as soon as you try to turn them into actual production LLM systems… a lot more goes into it. In this week’s video, I walk through how to structure AI agents in Python using design patterns like Chain of Responsibility, Observer, and Strategy. I’ll build a fully functional travel assistant using Pydantic AI and explore how to make your agent pipelines more maintainable, testable, and modular. Hope you enjoy...

Hi there, I have to admit, I often forget how powerful the Python standard library is. Whenever I'm working on some project, I have to remind myself to consider whether something I need is already there, ready for me to use. So, in this week’s video, I’m looking at 10 of the most useful and underrated modules in Python’s standard library, including pathlib, heapq, graphlib, and more. These tools can help you write cleaner, faster, and more maintainable code without adding a single dependency....

Hi there, If you’ve been experimenting with trying to integrate AI into your code, you’ve probably run into the same problem I have: unstructured, unpredictable output. And the other way around: how do you let AI agents interact with your own code? That’s where Pydantic AI comes in. In this week's video, I show how to use Pydantic AI to embed an LLM agent directly into your Python app: with structured input, validated output, and access to real business logic. What I love about this approach...