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The Model Context Protocol (MCP) is genuinely useful. It gives people who develop AI tools a standardized way to call functions and access data from external systems. Instead of building custom integrations for each data source, you can expose databases, APIs, and internal tools through a common protocol that any AI can understand. However, I’ve […]
Following the publication of his new book, Building Applications with AI Agents, I chatted with author Michael Albada about his experience writing the book and his thoughts on the field of AI agents. Michael’s a machine learning engineer with nine years of experience designing, building, and deploying large-scale machine learning solutions at companies such as
The following article originally appeared on Medium and is being republished here with the author’s permission. This post is a follow-up to a post from last week on the progress of logging. A colleague pushed back on the idea that we’d soon be running code we don’t fully understand. He was skeptical: “We’ll still be
Quantum computing (QC) and AI have one thing in common: They make mistakes. There are two keys to handling mistakes in QC: We’ve made tremendous progress in error correction in the last year. And QC focuses on problems where generating a solution is extremely difficult, but verifying it is easy. Think about factoring 2048-bit prime
The market is betting that AI is an unprecedented technology breakthrough, valuing Sam Altman and Jensen Huang like demigods already astride the world. The slow progress of enterprise AI adoption from pilot to production, however, still suggests at least the possibility of a less earthshaking future. Which is right? At O’Reilly, we don’t believe in
In 2025 AI reshaped how teams think, build, and deliver software. We’re now at a point where “AI coding assistants have quickly moved from novelty to necessity [with] up to 90% of software engineers us[ing] some kind of AI for coding,” Addy Osmani writes. That’s a very different world to the one we were in
When AI systems were just a single model behind an API, life felt simpler. You trained, deployed, and maybe fine-tuned a few hyperparameters. But that world’s gone. Today, AI feels less like a single engine and more like a busy city—a network of small, specialized agents constantly talking to each other, calling APIs, automating workflows,
The debate about open source AI has largely featured open weight models. But that’s a bit like arguing that in the PC era, the most important goal would have been to have Intel open source its chip designs. That might have been useful to some people, but it wouldn’t have created Linux, Apache, or the
What MCP and Claude Skills Teach Us About Open Source for AI Read More »
The debate about open source AI has largely featured open weight models. But that’s a bit like arguing that in the PC era, the most important goal would have been to have Intel open source its chip designs. That might have been useful to some people, but it wouldn’t have created Linux, Apache, or the
What MCP and Claude Skills Teach Us About Open Source for AI Read More »
The conversation around artificial intelligence is shifting dramatically.We’ve evolved beyond the binary narrative of humans versus machines. The new paradigm is human–AI orchestration, led by a new generation of Agentic AI systems — autonomous, reasoning, goal-driven agents that collaborate with humans instead of replacing them. Welcome to the era of Human-in-the-Loop 2.0 — a world
The Future of Work: How Agentic AI Is Powering the Human-in-the-Loop 2.0 Revolution Read More »
