Tag: Automation

A few days ago, working with Claude Code, I came across a tool that’s been around in the Docker ecosystem for a while but that I didn’t know about: docker pushrm. And the truth is it surprised me how useful it is for something as simple as keeping your container repository documentation synchronized.

The problem it solves

Anyone who has worked with Docker Hub, Quay, or Harbor knows the typical flow: you update your project’s README on GitHub, build and push your image, but… the container registry’s README is still outdated. You have to manually go to the browser, copy and paste the content, and do the update manually.

After my previous article about agent-centric programming, I’ve been researching more advanced techniques for using Claude Code really productively. As a programmer with 30 years of experience, I’ve seen many promising tools that ultimately didn’t deliver on their promises. But Claude Code, when used correctly, is becoming a real game-changer.

Beyond the basics: The difference between playing and working seriously

One thing is using Claude Code for experiments or personal projects, and another very different thing is integrating it into a professional workflow. For serious projects, you need a different approach:

A few days ago I came across a very interesting stream where someone showed their setup for agentic programming using Claude Code. After years developing “the old-fashioned way,” I have to admit that I’ve found this revealing.

What is Agentic Programming?

For those not familiar with the term, agentic programming is basically letting an AI agent (in this case Claude) write code for you. But I’m not talking about asking it to generate a snippet, but giving it full access to your system so it can read, write, execute, and debug code autonomously.

Lately, there’s been talk of AI agents everywhere. Every company has their roadmap full of “agents that will revolutionize this and that,” but when you scratch a little, you realize few have actually managed to build something useful that works in production.

Recently I read a very interesting article by LangChain about how to build agents in a practical way, and it seems to me a very sensible approach I wanted to share with you. I’ve adapted it with my own reflections after having banged my head more than once trying to implement “intelligent” systems that weren’t really that intelligent.

The Problem We All Have (But Solve Poorly)

As a DevOps Manager, I spend more time than I should configuring ways for the team to show their development work. Client demos, webhooks for testing, temporary APIs for integrations… we always need to expose localhost to the world.

Traditional options are a pain:

• ngrok: Works, but ugly URLs, limits on free plan, and every restart generates a new URL
• localtunnel: Unstable, URLs that expire, and often blocked by corporate firewalls
• SSH tunneling: Requires your own servers, manual configuration, and networking knowledge
• Manual Cloudflare Tunnels: Powerful but… God, the manual configuration is hellish

And then I discovered Moley.

With the constant evolution of AI-powered development tools, Claude Code has introduced a revolutionary feature: Hooks. This feature allows developers to customize and automate specific behaviors in the Claude Code lifecycle, transforming suggestions into executable code that works deterministically.

Hooks represent a qualitative leap in the customization of AI development tools, allowing each team and developer to adapt Claude Code to their specific needs and project standards.

What are Claude Code Hooks?

Claude Code Hooks are user-defined shell commands that execute automatically at various specific points in the Claude Code lifecycle. Unlike prompting instructions, hooks guarantee that certain actions always occur, providing deterministic control over the tool’s behavior.

The future of shopping is here, and Walmart is leading a quiet revolution that will forever change how we interact with retail. While many companies are still experimenting with ChatGPT and basic generative AI tools, the Arkansas giant has taken a quantum leap toward Agentic AI, developing autonomous systems that not only recommend products but act, decide, and execute complete tasks on their own.

In this deep analysis, we’ll explore how Walmart is building a future where AI agents don’t just assist humans but operate as true autonomous collaborators, transforming from the shopping experience to the most complex internal operations.

Daily life has kept me from doing many things, including keeping this blog updated, but there’s no time for everything, and in the end fatigue forces me to reorganize priorities.

In recent months, I’ve been working on implementing RedHat CloudForms, Ansible Tower, and more, so I’ve had to work much more with Ansible. It was already a habit, but lately it’s been much more intensive.

So I’ve earned a few RedHat “certifications”: