MCP vs Skills

A common question that I still see flying around is that: will skills replace MCP? I understand why people ask. Both can extend what an AI agent can do. A skill can include scripts that call APIs, while an MCP server can expose tools that call those same APIs. They can look like two ways … Continue reading MCP vs Skills →

How I run AI coding agents as a team with AI DevKit

I spend most of my time in the AI DevKit agent console. I start one agent as the manager, usually Codex, and brainstorm with it. Once an idea becomes concrete enough to execute, I ask the manager to create another agent and hand off the work. That executor might run in Codex, Claude Code, Pi, … Continue reading How I run AI coding agents as a team with AI DevKit →

AI adoption is not AI maturity

I used to think the hard part was getting people to use AI. Now I think that is only the first step. AI is becoming part of how we write, build, research, design, analyze, and operate. That is a good thing. I use AI heavily myself, and I believe it can meaningfully increase the leverage … Continue reading AI adoption is not AI maturity →

Why does MCP matter? A Deep Dive for Engineers

Everyone's talking about MCP (Model Context Protocol) and how it's supercharging workflows in tools like Claude Desktop, Cursor, and other desktop apps. And yeah, the integrations with desktop environments are cool. But honestly, MCP is way more than just these integrations for desktop applications. You’ve probably seen folks compare MCP to USB-C for AI. It’s … Continue reading Why does MCP matter? A Deep Dive for Engineers →

The Turning Point in AI

Artificial Intelligence (AI) has been evolving for decades, but we’ve now hit an inflection point where AI is moving from research labs into everyday tools, and everyone now talks about AI. Just as electricity transformed every industry a century ago, AI is in a position to have a similar sweeping impact. In particular, the emergence … Continue reading The Turning Point in AI →