A few months ago I came across something that really caught my attention: the possibility of having my own “ChatGPT” running at home, without sending data anywhere, using only a Raspberry Pi 5. Sounds too good to be true, right?

Well, it turns out that with Ollama and a Pi 5 it’s perfectly possible to set up a local AI server that works surprisingly well. Let me tell you my experience and how you can do it too.

Amazon has taken an important step in the world of artificial intelligence with the launch of S3 Vectors, the first cloud storage service with native support for large-scale vectors. This innovation promises to reduce costs by up to 90% for uploading, storing, and querying vector data.

What are vectors and why do we care?

Vectors are numerical representations of unstructured data (text, images, audio, video) generated by embedding models. They are the foundation of generative AI applications that need to find similarities between data using distance metrics.

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you update your project’s README on GitHub, build and push your image, but… the container registry’s README is still outdated.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.

Finally: Modern JavaScript in NGINX (and we can forget about LUA)

When I read the NGINX announcement about QuickJS support in njs, I couldn’t help but smile. Finally I can stop struggling with LUA.

As someone who has configured more NGINX servers than I can remember (from my time at Arrakis to now at Carto), I’ve always been annoyed by the limitation of having to use LUA for complex logic in NGINX. It’s not that LUA is bad, but… why learn another language when I already master JavaScript?

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.

Throughout my career, I’ve seen many things change. I’ve gone from Borland to Visual Studio, from vi to Sublime Text, from Sublime to VS Code… And believe me, each change was a deliberate decision that cost me weeks of adaptation. But what’s happening now with AI tools is something completely different.

I’ve found myself using Copilot in the morning, trying Cursor in the afternoon, and checking out Claude Code before going to bed. And I’m not alone. Developers have gone from being faithful as dogs to our tools to being… well, promiscuous.

Two protocols, two philosophies

In recent months, two protocols have emerged that will change how we build AI systems: Agent2Agent Protocol (A2A) from Google and Model Context Protocol (MCP) from Anthropic. But here’s the thing: they don’t compete with each other.

In fact, after analyzing both for weeks, I’ve realized that understanding the difference between A2A and MCP is crucial for anyone building AI systems beyond simple chatbots.

The key lies in one question: Are you connecting an AI with tools, or are you coordinating multiple intelligences?

A few months ago, when Anthropic launched their MCP (Model Context Protocol), I knew we’d see interesting integrations between LLMs and databases. What I didn’t expect was to see something as polished and functional as ClickHouse’s AgentHouse so soon.

I’m planning to test this demo soon, but just reading about it, the idea of being able to ask a database questions like “What are the most popular GitHub repositories this month?” and getting not just an answer, but automatic visualizations, seems fascinating.

When working on large projects, it’s common to have test suites that can take several minutes to run. And when one of those tests fails early in the execution, it’s frustrating to wait for all the others to complete just to see the full results.

Jest includes a feature I’ve found very useful in development: the bail option, which allows stopping test execution after a certain number of failures. It’s one of those features that once you know and start using, you don’t understand how you lived without it.

In my years developing software, I’ve learned that the best tools are those that eliminate unnecessary friction. And LM Studio has just taken a huge step in that direction: it’s now completely free for enterprise use.

This may sound like “just another AI news item,” but for those of us who have been experimenting with local models for a while, this is an important paradigm shift.

The problem that existed before

Since its launch in May 2023, LM Studio was always free for personal use. But if you wanted to use it in your company, you had to contact them to obtain a commercial license. This created exactly the type of friction that kills team experimentation.