WebLLM is a high-performance in-browser LLM inference engine that brings language model inference directly onto web browsers with hardware acceleration. Everything runs inside the browser with no server support and is accelerated with WebGPU.

NVIDIA Personal AI Router (PAIR) is a local inference router for a group of compatible computers on the same network. It discovers participating nodes, manages supported inference engines, and presents Ollama-compatible and OpenAI-compatible proxy endpoints to applications and agents. Independent requests can be routed to eligible nodes according to engine availability, model availability, and current workload.

Agent skill for beautiful, verifiable architecture, workflow, sequence, data-flow, and lifecycle diagrams—self-contained HTML with motion and crisp export.

For eight years I ran the SRE team behind a storage system measured in exabytes. Over time, the dashboard I checked every morning shrank to a handful of numbers. These are the seven that told me whether the service was healthy.

At this scale, even minor overloads aren't isolated events, they cascade. A brief spike in one part of the system can ripple outward: downstream services time out, retries pile up, and degradation amplifies into broader failure. This blog shares how we built an intelligent load manager that detects overload from multiple signals to keep our databases stable and fair under pressure.

Kubernetes has matured into a stable platform, while innovation is increasingly happening in the tools around it.

We recently started migrating Kafka clusters from EC2 to EKS using Strimzi. As soon as we moved the first cluster, we saw persistent disk reads across the brokers and higher latency than we expected on comparable hardware.

Picture this. A developer asks an AI assistant to "scale down staging to save costs." The AI, helpful as always, executes: kubectl scale deployment critical-api --replicas=0 -n production. Wrong namespace. Right outcome, wrong cluster. The API is down.

I'm currently working with a client to build out an agent-based automation system designed to reduce the manual labor associated with weekly, monthly, and ad-hoc operational activities.

In part 1 I covered k6's philosophy and the anatomy of a first test. This post is where things get real — a production-grade test suite running against a live microservices app on a homelab Kubernetes cluster, including what went wrong on the first run and how I debugged it.