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.

A Race Condition Rust Wouldn't Have Let Me Write

A two-agent Python service ran fine in tests. Two concurrent users hit it and one user's search results showed up in the other user's response. The pattern looked safe. The Rust port doesn't compile.

Build a daily AI digest in 200 lines of Python (no framework)

One command and get the AI stories worth reading today, ranked by an LLM loaded from config. Two hundred lines of Python, no framework, no venv juggling thanks to PEP 723 (inline script metadata). Below are four patterns I explored.

The Rust Compiler as an AI Coding Agent Guardrail

AI agents write code now. What they can't do is decide what's correct. That gap is where a compiler becomes your best friend. Rust's compiler is famously strict, and that's exactly why it's a powerful tool for working with AI tools. It offers a fast feedback loop, catching errors early and enforcing good practices. This gives you more confidence in the code AI produces.

Event Sourcing in Python: Get More Insights Into Your Data

Chris May knew about event sourcing for over a decade before he used it for real. Once he tried it in production, he was sold. After hearing him describe the pattern, I can see why, so in this article I'll walk you through what event sourcing is and why it changes what questions your data can answer.

Python to Rust: A JSON Parser in 6 Weeks

Vikas Zingade is a lead ML engineer who builds complex systems in Python every day. He'd read about Rust, understanding it in theory, but he’d never actually had the borrow checker accept his code.

Six weeks and six PRs later, he has a fully compliant JSON parser with 210 passing tests that runs 12x faster than simplejson. His confidence in starting a new Rust project from scratch is a solid 7 out of 10.

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.

Vibe Coding is Easy, Owning the Architecture Isn't

These days you can prompt Claude to generate a working feature in twenty minutes. File uploads, database queries, API calls; the code appears, it runs, it looks right. But if you've never owned a mature architecture, AI-generated code becomes a liability you don't know how to manage.

From Hobby Code to SaaS to Orchestrating AI Agents

Ryan Austin runs a payroll SaaS solution in the Bahamas. He uses AI agents for customer support triage, feature scaffolding, and automated issue resolution. He has even implemented a rating system for issues, allowing agents to autonomously tackle specific tasks based on my available token budget.

Four years ago, none of this would have been possible.