#AI

Model context protocol, agent to agent standards, and a dozen new specs are the loudest infra story in the room right now. Here is a plain language map of what goes where, which layer actually matters for lock in, and the one mistake that will cost you more than picking the wrong protocol.

We accepted that private work had to run on a stranger's computer. Local models just got good enough to change that. Here is why the cloud was a convenience trade disguised as a requirement, and what happens when inference moves back onto the device.

Frontier intelligence got cheap while nobody watched. The model was never the moat, and open weights proved it. A plain look at where the value moved, and the one test that tells you whether your company still has an edge.

Teams are shipping AI-generated code faster than they can understand it, and the bill is coming due. Here is how code you never traced quietly becomes code you do not own, and what a studio built on root cause thinking does about it.

Every observability tool for agents can show you a thousand spans. Not one of them can point at the sentence that caused the failure. This post is about the missing discipline of failure attribution.

Every team is shipping an AI feature right now. Most of them are treating a symptom while the real problem sits underneath. This is how a founder tells the difference, and why the teams that trace first build the things that last.

AI agents fail all the time. The tools we built can show you what they did. Almost none of them can tell you why. Here is the debugging gap nobody has fixed, and how a root cause trace closes it.