Artificial Intelligence

Vibe coding stopped feeling like an experiment and became something... else. Many people using it couldn’t really explain what they’ve built, just that it works. Until it doesn’t.

I was at the MCP Dev Summit North America and heard from its co-creator, David Soria Parra, that the question is no longer how to use MCP, but what breaks when you try to scale it.

I admitted it: AI first sounded like coffee talk, not daily work, but I had to push through to help my teams.

If AI can do the coding and speed isn’t an issue, what do developers actually bring to the table now?

92% of developers use AI coding tools, but productivity has barely moved - stuck at 10%. Here’s why using AI doesn’t automatically mean getting more done.

As AI tools become part of developers’ everyday workflows, a lot of engineering leaders assume that getting started is just a matter of buying the right software.

What if I told you that understanding AI is a bit like juggling knowledge about Marvel, DC, Matrix, Harry Potter, Lord of the Rings, and Pokémon franchises? Crazy, right? But hear me out.

Mackenzie Jackson, security researcher and advocate, told me that AI can’t catch the bugs, but it knows which ones actually matter and provides the context teams need.

At the Pragmatic Summit, I heard firsthand that Uber engineers aren’t just using AI to write code anymore, they’re assigning it work. Let’s see how that plays out.

A canary died in the open source coal mine and a hundred people showed up to argue about the autopsy.

I often hear AI terms used loosely, so I put together this guide to explain key concepts like agents, tools, and LLMs clearly.

I was at Pragmatic Summit when Chip Huyen reframed the AI conversation - if any product can be generated from a clear description, code isn’t the constraint, and true value lies elsewhere.

After spending time with OpenClaw and seeing how it actually works, I’m convinced the hype is real. It shows that autonomous AI agents are finally living up to their promise.