Latest Posts
Can AI Replace Your Terraform Modules? Infrastructure’s New Future
My AI agent just failed to create a database. It forgot the resource group, messed up the credentials, and made three attempts before getting it right.
But here’s the plot twist: That’s the best thing that could have happened.
Today, I’m going to show you why AI agents might make your carefully crafted golden paths obsolete. Why those Terraform modules or similar abstractions you spent months building might be holding AI back, and why letting AI fail and learn might be the future of infrastructure management.
The End of Infrastructure-as-Code: AI Changes Everything
Here’s something that might shock you: every Infrastructure-as-Code tool you’re using today will be dead in upcoming years. Terraform, Pulumi; all of them. And the killer? AI agents that don’t need the abstractions we’ve built our entire industry around.
I know this sounds crazy. You’re probably thinking, “But we just invested years learning these tools!” Here’s the thing though - AI agents don’t operate like humans. They don’t get overwhelmed by complexity in the same way we do. They don’t need user-friendly abstractions. And that changes everything.
By the end of this video, you’ll see exactly why the tools we consider essential today are actually obstacles for AI agents, and what’s coming to replace them. But first, let me show you how we got here…
AI vs Developer: Can GitHub Copilot or Claude Replace My Job?
I just gave two AI agents the same complex coding task. One completely failed. The other… honestly shocked me.
But here’s the twist: I’m not sure if I want the better one to succeed. Because if these autonomous coding agents can really implement full features from scratch, write tests, and handle edge cases like an experienced developer… well, we might all be looking for new careers sooner than we think.
Today, I’m putting GitHub Copilot’s new coding agent head-to-head with Claude’s autonomous agent. Same PRD, same requirements, zero hand-holding. And the results? Let’s just say one of these agents just changed everything I thought I knew about AI coding.
Vibe Coding Explained: AI Coding Best Practices
Vibe coding is probably the hottest trend right now in the software industry. Instead of manually writing every line of code, we can now describe what we need in plain language and let AI agents generate code, run tests, perform some, if not all, SDLC operations, and whatever else we might need.
That’s great since it means that now it is socially acceptable to talk to your computer. “Hello, precious. Can you write a Web app based on the OpenAI schema defined over there?” “Please be so kind as to fix this issue for me and make a patch release.” “Would you mind working on that problem for a while longer?”
There’s even a button now in VS Code that enables us to literally talk to AI through a microphone.
Vibe Coding is, in a way, still coding. We generate instructions for machines and those instructions are interpreted into binary code. The major difference is in the language we use. With “normal” coding, we do not become proficient just by writing code. We become proficient once we learn the rules, understand best practices, and practice (a lot). It’s the same with Vibe Coding. The more we practice it and the better we understand the rules, the more proficient we become.
So, today I want to share my best practices for vibe coding. They are mine. They work well for me, and I am very curious to hear your thoughts.
Better Code Reviews with AI? GitHub Copilot and Qodo Merge Tested
Second opinions are important. We get them them from doctors, as well as from software engineers. We want “stuff” to be reviewed and we want feedback. Today, however, we will not talk about second opinions and suggestions from doctors and software engineers. We’ll talk about one AI reviewing work of another AI, with us being managers of both.
Today we’ll explore the possibilities of using a few AI agents to do code review. We’ll see how they integrate into pull requests in GitHub, whether they can find issues in code written by a different AI in an IDE, and how we can incorporate those reviews into our development workflow.
My Workflow With AI: How I Code, Test, and Deploy Faster Than Ever
Today I want you share my development workflow with AI. I want to share how I start working on a new feature, how I manage product requirement documents, or PRDs, how I write code and test it, and how I move through the development lifecycle. The way I approach all that today is very different from the way I did all that in the past. There is a whole team working on each feature, with me being the only human involved.
Kubernetes AI: The Good, The Bad, and The Disappointing (kubectl-ai)
I will make an assumption by saying that you work, in some capacity or another, with Kubernetes and that you are interested in making management of your clusters much easier, better, and faster with AI. If that’s the case, I have a treat for you. We’ll explore how to do just that. We’ll take a look at an AI agent specialized in management of Kubernetes clusters. An agent that comes from the company that made Kubernetes. An agent that is open source. An agent that has the potential to be one of the most important tools in your toolbelt.