DevOps · SRE · Platform engineering
Compute Central
Practical, lab-tested guides for the infrastructure you actually run — Linux, networking, containers, Kubernetes, cloud, automation, CI/CD, reliability, and security, explained from first principles all the way to production.
- Free to read
- Hands-on labs and case studies
- Updated September 2026
$ docker compose up -d✔ Container caddylab-db-1 Healthy✔ Container caddylab-api-1 Started✔ Container caddylab-caddy-1 Started$ curl -s localhost/api/todos | jq length30 Guides and lab-tested chapters
0 Learning tracks, foundations to production
0 Hands-on labs and case studies
2016 Notes and lessons collected since
Learning tracks¶
Pick a track, or filter by what you're working on. Every track stands on its own.
The path through this site¶
Most readers move left to right — but jump in wherever your work is today.
- 1FoundationsLinux, networking, Git, scripting
- 2ContainersHow Docker really works
- 3KubernetesOrchestrate workloads
- 4Cloud & IaCAWS, Terraform, Ansible
- 5DeliveryCI/CD and code quality
- 6ReliabilityObservability and SRE
- 7SecurityDevSecOps and zero trust
What's new¶
Learning paths by goal¶
Pick the goal closest to yours — each tab is a short, ordered route through the material.
Start with Linux for DevOps and Networking, then Git, Shell Scripting, and Python Automation.
Start with the Docker and Linux Containers course, then move to Kubernetes core concepts.
Try the Minikube lab, the kind lab, or the Podman lab.
Set up accounts and IAM, design a VPC, run containers on ECS or EKS, and provision it all with Terraform.
Provision with Terraform, configure hosts with Ansible, and glue it together with production-ready scripts.
Start with branching strategies and the CI/CD overview, add SonarQube quality gates, then wire in Kubernetes CI/CD.
Stand up the monitoring lab, define SLOs, alert on burn rate, and practice incident response.
Threat model with STRIDE, centralize secrets with Vault, and sign what you ship with Sigstore.
Start with the AI Engineering Guide, then LLM fundamentals and AI evaluation.
How to use this site¶
Start with the topic you need, then follow the examples in a local or test environment before using them in production. Most pages explain the reason behind each step, not just the command.
Best way to learn
Read the short explanation first, run the example safely, then write down what changed and why it worked.
Production reminder
Always review commands, credentials, namespaces, and environment names before running anything against shared or production systems.
Browse every section
- Foundations — Overview, Linux, Shell Scripting, Networking, Git, Python Automation
- Docker — Course index, Dockerfiles, Compose, quick reference
- Kubernetes — Core Concepts, labs, OpenShift, troubleshooting, quick reference
- Cloud — Overview, AWS, IAM, VPC networking, cost optimization
- Terraform — Overview, first project, state and backends, modules, testing and CI/CD, interview questions
- Ansible — Overview, Getting Started, Core Concepts, Modules, Case Studies, Troubleshooting, Interview Prep
- CI/CD Pipelines — Overview, GitHub Actions, GitLab CI/CD, ArgoCD, Jenkins
- Code Quality — Overview, open-source tools, SonarQube installation, quality gates, pipeline examples
- Monitoring — Stack overview, monitoring lab, Prometheus, Grafana, Alertmanager
- SRE — Overview, SLOs, alerting, incident response, postmortems, on-call
- Security — Overview, threat modeling, Vault, supply chain, scanning, zero trust
- AI Engineering — Guide, LLM fundamentals, AI agents, evaluation
About¶
I build and run reliable, automated, observable, and secure systems — and write down what I learn along the way. Compute Central brings a decade of notes, experiments, and real-world lessons into one organized place.
- Infrastructure design
- Deployment automation
- Monitoring
- Incident response
- Platform operations