Technology Documentation Hub

A reference library for DevOps, cloud, data, and modern software infrastructure

This section is a reference library for the software delivery stack. It starts with the network, storage, and security foundations an application depends on, moves through the version control, build, and CI/CD tooling that ships it, and ends with the containers, orchestration, infrastructure-as-code, and cloud platforms that run it in production. The pages are written as references rather than tutorials: they explain how each system works, give current commands and configuration, and compare the alternatives where there is a real choice to make.

Topics that span several technologies, such as distributed systems, API design, observability, and testing, have their own hubs, listed under Related sections.

How the topics fit together

flowchart LR
    subgraph Build["Build and ship"]
        GIT["Git"] --> CI["CI/CD"]
        PLZ["Build systems<br/>(Please)"] --> CI
    end
    subgraph Package["Package"]
        IMG["Docker images"]
        RT["Container runtimes"]
    end
    subgraph Run["Run"]
        K8S["Kubernetes"]
        CLOUD["AWS"]
        TF["Terraform"]
    end
    subgraph Found["Foundations"]
        NET["Networking"]
        DB["Databases"]
        SEC["Security"]
    end
    CI --> IMG --> RT --> K8S --> CLOUD
    TF -->|"provisions"| CLOUD
    TF -->|"provisions"| K8S
    Found -.->|"underpin every layer"| Run

Code lives in Git. A CI/CD pipeline, often driven by a build system, tests it and packages it as a container image. A container runtime executes the image, Kubernetes schedules containers across machines, and Terraform provisions the AWS infrastructure underneath. Networking, databases, and security apply at every layer.

Foundations

Topic Covers
Networking The TCP/IP stack, transport protocols (TCP, QUIC, HTTP/3), routing, congestion control, wireless, cloud networking, and programmable networks
Database Crash Course Relational concepts and SQL essentials: the quick on-ramp
Database Design Modeling and normalization, indexing and query execution, transactions and concurrency, storage internals, replication, distributed SQL, and NoSQL data models
Cybersecurity Applied cryptography, attack techniques and defenses, application and cloud security, security operations, incident response, compliance, and privacy engineering

Version control and delivery

Topic Covers
Git Crash Course The fastest route to working with Git: the everyday workflow and the mental model behind it
Git Internals The object model, the commit DAG, merge and rebase algorithms, transfer protocols, and recovery
Git Command Reference Lookup tables for common commands, with syntax and examples
Git Branching Strategies GitHub Flow, GitLab Flow, Git Flow, and trunk-based development compared, with a decision guide
Advanced Branching Techniques Feature flags, rulesets and merge queues, stacked pull requests, and release and hotfix branches
CI/CD Pipeline design, test stages, deployment strategies (blue-green, canary), GitOps, and pipeline security
Please Build A Bazel-style build system for polyglot monorepos: build graph, caching, remote execution, and querying

Containers and infrastructure

Topic Covers
Docker Images, Dockerfiles, networking, storage, registries, security, and design patterns
Docker Essentials A command cheat sheet for running, building, Compose, networking, and cleanup
Container Runtimes The layers beneath Docker: OCI specifications, runc and crun, containerd and CRI-O, gVisor, Kata, Firecracker, and WebAssembly
Kubernetes Cluster architecture, workloads, networking, storage, resource management, and day-2 operations
Terraform Infrastructure as code: core workflow, state and modules, patterns, and advanced usage (including OpenTofu)
AWS Compute, storage, databases, networking, security, monitoring, cost management, and reference architectures

AI and emerging technology

Topic Covers
AI Fundamentals (Simplified) A plain-language introduction to AI, machine learning, and large language models, with no mathematics
AI Deep Dive How transformers and LLMs work: attention, pre-training and post-training, inference, retrieval, agents, and LLM security
AI & Machine Learning The full technical treatment: ML foundations, deep learning architectures and theory, generative models, reinforcement learning, and fine-tuning
Quantum Computing Qubits and gates, the main algorithms and their real speedups, error correction, hardware platforms, and programming today’s machines
Unreal Engine Real-time 3D with UE5: Nanite, Lumen, the gameplay framework, and Blueprints

Multi-page topics (Docker, Kubernetes, Terraform, AWS, CI/CD, Git internals, networking, databases, cybersecurity, and AI) live in their own subdirectories, and the links above go to each section’s landing page.

Learning paths

Goal Suggested order
New to software infrastructure Networking → Database Crash Course → Git Crash Course → Docker
DevOps / platform engineering Git → Branching Strategies → CI/CD → Docker → Kubernetes → Terraform
Cloud architecture Networking → AWS → Terraform → Kubernetes, with Cybersecurity throughout
Backend and data Database Design → Distributed Systems → API Design
Large codebases Git Internals → Monorepos → Please Build → CI/CD
AI AI Fundamentals → AI Deep Dive → AI & Machine Learning → Generative AI hub