OfficeCLI is the first and best Office suite purpose-built for AI agents to read, edit, and automate Word, Excel, and PowerPoint files. Free, open-source, single binary, no Office installation required.

A native macOS UI for Apple's container platform — think OrbStack/Docker Desktop, but for the Linux-containers-as-lightweight-VMs stack Apple ships for Apple silicon.

A powerful and flexible Kubernetes Model Context Protocol (MCP) server implementation with support for Kubernetes and OpenShift.

ZeroFS serves S3-compatible buckets as POSIX filesystems over NFS and 9P, and as raw block devices over NBD. All three servers run in a single userspace process. Data is compressed and encrypted before upload.

Harmful workload generator for PostgreSQL.

A few weeks after publishing the 1M token/s post, I spent a weekend helping my good friend Milko Ilari set up vLLM on his shiny new DGX Spark with Gemma 4. My first in-person reaction was “It’s Champagne” (from the old days of PC Perspective) The Spark is a wild little machine — 128 GB of unified memory in a box you can hold with one hand, running the same Blackwell architecture as the datacenter B200s. But its memory bandwidth is 273 GB/s. The B200s in our cluster do 8,000 GB/s. Almost 30x less.

Watching the numbers on that tiny machine got me thinking. The benchmark I ran on GKE Autopilot with 96 B200 GPUs had reported 4.4% FLOPS utilization. 10.9% memory bandwidth. Tensor cores active 1.5% of the time. The GPUs looked almost idle while pushing a million tokens per second. Was something wrong?

No. And honestly, figuring out why turned out to be more interesting than the benchmark itself.

That first post covers the journey — every optimization, and many failure 🫠. This one covers the physics.

GKE pod IP exhaustion is one of the few failure modes that gives you no warning before it goes terminal. I recently stepped into a war room where a client’s primary scaling group had flatlined — workloads cordoned, deployments stuck in Pending, and the estimated cost of the stall nearing $15k per hour in lost transaction volume. The culprit wasn’t traffic. It was a /20 subnet that had quietly run out of address space, and a set of GKE allocation defaults nobody had questioned at design time.

For the last decade, Kubernetes has been the backdrop to most of my work: operating clusters, helping build hosted Kubernetes, and writing Kubernetes operators. At PlanetScale, that now means running stateful systems like Postgres and MySQL in production. Kubernetes has many faces, but here I want to talk about one face only: why it is so good at running workloads at scale.

People ask me what an operator actually does. The canonical answer is: "it reconciles desired state." This is correct, but it also tells you almost nothing.

An operator is a feedback controller. It's the same closed loop that runs a thermostat or keeps your car at a fixed speed on cruise control. In our case, the thing being controlled is a database. I have been building these loops for years, and the best way I know to make them click is to ignore Kubernetes at the beginning. Kubernetes is full of control theory, even if we don't call it that in the day-to-day.

Before we look at a single line of Kubernetes, we're going to run a production database by hand and slowly let the feedback loop appear on its own. Then we'll map that loop to Kubernetes, with the pieces production needs: a store, watches, queues, retries, and more. At the end, we'll look at what one of these loops looks like in a real operator.

This article documents a real production investigation into latency variance in Python-based microservices running on Kubernetes, revealing how CPU throttling amplifies GIL contention into unpredictable response time spikes.

KubeUser is a kubernetes-native operator that turns user management into a declarative code experience. No more manual certificate juggling — just apply a YAML file, and the operator handles the rest.

There is a lot of noise right now about letting AI "fix" your infrastructure. When production is acting up, you need to maintain a complete mental model of the system. If you let the AI be the driving force, you lose the overview.