Tag: Ai - Page 3

Mozilla AI surprises again: AI agents that work just by opening an HTML

A few days ago I came across a Mozilla AI project that really caught my attention: WebAssembly Agents. And after 30 years watching the industry complicate life with dependencies, installations, and configurations, seeing something that works just by “opening an HTML” made me smile.

The problem it solves (and we all know it)

How many times have you tried to test an AI project and encountered this?

After many years working with systems of all kinds, I’ve seen how information retrieval has evolved from simple databases to today’s sophisticated systems. But what Baidu researchers have just proposed has particularly caught my attention, and I believe it marks a before and after in how we think about intelligent information retrieval.

The problem we all know (but don’t always admit)

If you’ve worked with RAG (Retrieval-Augmented Generation) systems, you know they work quite well for direct questions. But when you face queries that require multiple reasoning steps, comparing information from multiple sources, or handling contradictory data… that’s where it gets complicated. And a lot.

The hype vs reality: reflections from a developer with 30 years of experience

This morning I came across a talk that made me reflect quite a bit about all this fuss surrounding AI and software development. The speaker, with a healthy dose of skepticism, does a “reality check” on all the grandiose claims we’re hearing everywhere.

The complete talk that inspired these reflections. It’s worth watching in full.

Yet another protocol promising to change everything

When IBM Research announced the Agent Communication Protocol (ACP) as part of the BeeAI project, my first reaction was the usual one: “Oh, just another universal protocol”. With nearly 30 years in this field, I’ve seen too many “definitive standards” that ended up forgotten.

But there’s something different about ACP that made me pay attention: it doesn’t promise to solve all the world’s problems. It simply focuses on one very specific thing: making AI agents from different frameworks talk to each other. And it does it in a way that really makes sense.

Confession of a converted skeptic

When Anthropic announced the Model Context Protocol (MCP) in November 2024, my first reaction was: “Ah, another protocol promising to solve all integration problems”. As a DevOps Manager who has seen dozens of “universal standards” born and die, I have reasons to be skeptical.

But after several months watching MCP be massively adopted - OpenAI integrated it in March 2025, Google DeepMind in April - I decided to investigate beyond the hype. And I have to admit something: I was wrong.

A necessary reflection on the “AI-Native Engineer”

I read Addyo’s article about the “AI-Native Software Engineer” and, as a Principal Backend Engineer who has seen technological promises come and go for years, I have quite sincere opinions about it. Not all are comfortable to hear.

I’ve seen enough “revolutions” to separate the wheat from the chaff. And there’s a lot of both here.

What’s really working (honestly)

1. AI as copilot, not as pilot

The article’s metaphor about treating AI as a “junior programmer available 24/7” is accurate. In my experience working with teams, I’ve seen developers use GitHub Copilot and Claude effectively to:

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.

“Content Independence Day”: the day the web said “enough is enough”

Today, July 1, 2025, Cloudflare officially declared “Content Independence Day” with the launch of “Pay per Crawl”, its new tool that allows website owners to charge AI crawlers for accessing their content. As a DevOps Manager who manages web infrastructures daily, I can say it was about time.

And the numbers don’t lie: while Google maintains a ratio of 18 crawls for every referral it sends (which is already brutal compared to 6:1 from six months ago), OpenAI has a ratio of 1,500:1 and Anthropic reaches 73,000:1. Basically, they’re sucking up all our content without returning even a crumb of traffic.

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.

Creating long, well-founded articles has traditionally been a complex task requiring advanced research and writing skills. Recently, researchers from Stanford presented STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking), a revolutionary system that automates the Wikipedia-style article writing process from scratch, and the results are truly impressive.

In this detailed analysis, we’ll explore how STORM is transforming the way we think about AI-assisted writing and why this approach could forever change the way we create informative content.