Tag: Ai

I have been using Claude Code daily for months, and there is one configuration that has completely changed how it works with my code. It is not a new plugin, a more powerful model, or a magic prompt. It is something that has existed since 2016 and that most developers use without knowing it every time they open VS Code: the Language Server Protocol (LSP).

Karan Bansal published an excellent article explaining in detail how to enable LSP in Claude Code and why it matters. After trying it, I can confirm the difference is real and significant.

Greg Brockman, President and Co-Founder of OpenAI, recently published a thread that perfectly describes the moment we’re living in software development. According to him, we’re witnessing a genuine renaissance in software development, driven by AI tools that have improved exponentially since December.

The qualitative leap

The most striking part of Brockman’s thread is how they describe the internal change at OpenAI: engineers who previously used Codex for unit tests now see the tool writing practically all code and handling a large portion of operations and debugging. This isn’t an incremental improvement, it’s a paradigm shift.

A few days ago I discovered Agent Lightning, a Microsoft project that I believe marks a before and after in how we think about AI agent orchestration. It’s not just another library; it’s a serious attempt to standardize how we build multi-agent systems.

What is Agent Lightning?

Agent Lightning is a Microsoft framework for orchestrating AI agents. It enables composition, integration, and deployment of multi-agent systems in a modular and scalable way. The premise is simple but powerful: agents should be components that can be combined, connected, and reused.

A few days ago I watched a video that has given me a lot to think about. Jeffrey Way, founder of Laracasts and one of the most influential people in the Laravel/PHP community, shared a brutally honest reflection on how artificial intelligence is transforming his business and his profession.

The video starts with a phrase that leaves you cold: “I’m done”. It’s not a goodbye to programming, but an acceptance of the reality to come.

Vercel has announced the general availability of Vercel Sandbox, an execution layer designed specifically for AI agents. But beyond the AI agent hype, there’s an interesting question: can it be useful for running code safely in different languages like PHP, Node, or Go?

What is Vercel Sandbox?

Vercel Sandbox provides on-demand Linux microVMs. Each sandbox is isolated, with its own filesystem, network, and process space. You get sudo access, package managers, and the ability to run the same commands you’d run on a Linux machine.

If you’re using tools like Claude Code, GitHub Copilot Workspace, or similar, you’ve probably noticed there’s technical jargon that goes beyond simply “chatting with AI”. I’m talking about terms like rules, commands, skills, MCP, and hooks.

These concepts are the architecture that makes AI agents truly useful for software development. They’re not just fancy marketing words — each one serves a specific function in how the agent works.

Let’s break them down one by one in a clear way.

Lately I’m seeing more and more AI models calling themselves “open source”. Llama, Mistral, Falcon… they all claim to be “open”. But are they really? How open are they actually?

I recently discovered the European Open Source AI Index (OSAI), a European initiative doing excellent work systematically evaluating how open generative AI models really are.

Recently, Addy Osmani published an article that gave me much to think about: “Self-Improving Coding Agents”. The idea is simple but powerful: agents that not only execute tasks, but improve their own performance over time.

This isn’t science fiction. It’s happening now, in 2026. And it has profound implications for the future of software development and, by extension, for all professions.

What is a Self-Improving Agent?

A self-improving agent is an AI system with the capacity to:

Lately I’ve been following a discussion that worries me quite a bit: to what extent are we delegating our thinking to AI. It’s not an abstract or philosophical question, it’s something very real I’m seeing day to day in our profession and in society in general.

Recently I read an article by Erik Johannes Husom titled “Outsourcing thinking” that, among other things, discusses the concept of “lump of cognition fallacy”. The idea is that, just as there’s an economic fallacy saying there’s a fixed amount of work to do, some believe there’s a fixed amount of thinking to do, and if machines think for us, we’ll just think about other things.

A few days ago I read news that left me thinking for a while. It’s not the first time I’ve heard about AI in space, but it is the first time I’ve read about Claude planning routes on Mars. And the best part: it worked.

NASA has been using Anthropic’s Claude to plan the Perseverance rover’s routes on Mars. Yes, you read that right: a generative language model generating navigation routes for a rover that’s 225 million kilometers away.

A few days ago I came across an article that literally left me with my mouth open. It’s about TinyEMU-Go: a RISC-V emulator written entirely in Go, ported from C using Claude. And the best part: you can run a complete Linux with a single command.

The Command Line That Gave Me Envy

go run github.com/jtolio/tinyemu-go/temubox/example@2c8151233c2d

And boom, you have a complete Linux running. No special permissions, no containers, no weird dependencies. A pure static Go binary.

Lately I’ve been closely following everything around the MCP protocol (Model Context Protocol), and recently I found a project that makes a lot of sense: MCPHero.

The reality is that although MCP is taking off, many “traditional” AI libraries like openai or google-genai still don’t have native MCP support. They only support tool/function calls. MCPHero comes to solve exactly this: make a bridge between MCP servers and these libraries.

What is MCPHero?

MCPHero is a Python library that lets you use MCP servers as tools/functions in native AI libraries. Basically, it lets you connect to any MCP server and use its tools as if they were native OpenAI or Google Gemini tools.