Topic Map
How the core topics build on one another, and reading paths through them.
This page shows how the site’s topics depend on one another. The interactive map below covers the core topics in technology, AI, and physics; the prerequisite graphs that follow add the architecture and theory sections; and reading paths by role turn the graphs into ordered lists of pages. For a flat listing of every page, see the documentation index.
Knowledge Map
Legend
Reading the interactive map
Each circle is a topic. Clicking one highlights its direct connections and opens a panel with a short description, its related topics, and a link to the page.
| Element | Meaning |
|---|---|
| Node color and letter (B, I, A) | Level: beginner, intermediate, or advanced (see the legend in the map) |
| Solid line | Progression: the source topic is a natural prerequisite for the target |
| Dashed line | Related: the topics share ideas but neither is required for the other, often across domains |
| Control | Action |
|---|---|
| Drag a node | Reposition it; the layout settles around it |
| Scroll or pinch | Zoom in and out |
| Drag the background | Pan |
| Filter by level | Show only beginner, intermediate, or advanced topics |
| Reset View / Fullscreen | Restore the default zoom, or expand the map to fill the screen |
The map works best on a wide screen. On a phone, the static graphs below carry the same information.
Prerequisite graph
A static view of the main dependencies, one diagram per area. Arrows point from a prerequisite to the topic that builds on it.
Technology and architecture
flowchart LR
GIT["Git"] --> CICD["CI/CD"] --> K8S["Kubernetes"]
DOCK["Docker"] --> K8S
NET["Networking"] --> K8S
NET --> SEC["Cybersecurity"]
TF["Terraform"] --> AWS["AWS"]
NET --> AWS
DB["Databases"] --> DS["Distributed systems"]
NET --> DS
API["API design"] --> EDA["Event-driven systems"]
DS --> EDA
K8S --> OBS["Observability"]
DS --> OBS
DS --> DST["Distributed systems theory"]
AI and machine learning
flowchart LR
AIB["AI fundamentals"] --> MLF["ML foundations"] --> DLA["Deep learning architectures"] --> GEN["Generative models"]
GEN --> SD["Stable Diffusion"] --> TOOLS["ComfyUI, ControlNet, LoRA"]
DLA --> FT["Fine-tuning"] --> MLOPS["MLOps"]
MLF --> AIM["AI mathematics"]
Physics
flowchart LR
CM["Classical mechanics"] --> LH["Lagrangian & Hamiltonian"] --> QM["Quantum mechanics"] --> QFT["Quantum field theory"] --> ST["String theory"]
CM --> SR["Special relativity"] --> GR["General relativity"] --> COS["Cosmology & black holes"]
SR --> QFT
TH["Thermodynamics"] --> SM["Statistical mechanics"] --> CMP["Condensed matter"]
QM --> CMP
QM --> QC["Quantum computing"]
Theory and research
flowchart LR
AUT["Automata & formal languages"] --> CX["Complexity theory"] --> APX["Approximation algorithms"]
CX --> CRY["Cryptography"]
INF["Information & coding theory"] --> CRY
CX --> QA["Quantum algorithms research"]
Several links cross these areas: statistical mechanics supplies much of the vocabulary of machine learning (energy-based models, entropy, free energy); quantum mechanics and complexity theory together underpin quantum algorithms; and applied cybersecurity leads naturally into the theory of cryptography.
Reading paths by role
Each path is an ordered list; read left to right, and branch into a topic’s sub-pages once its overview makes sense. The Getting Started page gives a shorter first step for each role.