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Thoughts on cloud infrastructure, AI workloads, Kubernetes, and developer tools.

Four shapes, one cluster, very different operational headaches.
“Agent” is doing some heroic heavy lifting as a word right now.
Depending on who you ask, an agent might be:
- A python script firing off 10,000 parallel …
Two weeks ago, Model Context Protocol shipped its 2026-07-28 specification revision.
The biggest updates in this release are: no more stateful initialize handshake, Mcp-Session-Id headers have been retired, and every request …
I created a skill to save users 40-60% on token costs when migrating AI inference workloads from a serverless platform (like Cloud Run or Gemini Enterprise Agent Platform) to GKE. So what is it? What isn’t it? Why a …
Introduction to Distributed RL Sandboxing on GKE
Reinforcement Learning (RL) is the cornerstone of modern AI training. Rather than train a model to produce an expected output, we verify if it has achieved a particular outcome. This is …

GKE for your AI agents
Ok, so you’re writing an AI agent to automate some task (who isn’t these days?). But where does it live? That largely depends on what you want it to do.

Get in builder, we're writing …
XPK Cluster Create
Simplifying AI Infra Management
You’ve probably used terraform and kubectl to set up infrastructure and deploy code to it (if you haven’t, thanks for stopping by, but this post might not be super interesting …
The Am Dash And Discerning Human Writing from AI
Was This Post Written by Gemini?*
The other day I was listening to one of my favorite podcasts talking about AI’s writing style. And of course, they were talking about the Em dash. I’ll let …
I’ve been a big fan of Gemini CLI since I was first lucky enough to test an early version internally it before it launched publicly. It’s become an integral part of my workflow, and MCP servers only expand the world of …