tracks
Tracks sequence readings, labs, and challenges so each concept becomes muscle memory before the next workflow builds on it.
From your first container to a multi-service stack: images, networking, volumes, and the debugging habits that hold it together.
Container primer
Containers are normal processes with guardrails around files, networking, and system calls. The skill is learning what is isolated, what is shared, and where state can leak between runs.
Get code onto the device and keep it there: GPU passthrough, CUDA runtimes in containers, kernels, and profiling.
GPU primer
GPU work is about feeding thousands of small workers without starving memory bandwidth. Good CUDA code keeps data movement deliberate and makes parallel work regular enough for the device to schedule efficiently.
Pods, deployments, services, and the failure modes in between — learned by breaking a real single-node cluster.
Kubernetes primer
Kubernetes is a control loop system: you declare desired state, controllers keep nudging the cluster toward it, and events explain what blocked that process.
Run agents like production software: sandboxing, tool servers, tracing, and guardrails that survive a bad prompt.
Agent primer
An agent loop combines model reasoning with tools, memory, and permissions. Reliability comes from constraining each step, observing what happened, and making recovery paths explicit.