Knowledge Map
The knowledge map places articles, projects, downloads, and learning routes in one relationship graph. It answers, “What should I read next from this page?” Nodes are organized by technical dependency, experiment material, and topic boundary so a reader can move from a concept to code and then to verification material.
| Node type | Example | Why it connects | How to use it |
|---|---|---|---|
| Topic page | Machine learning, deep learning, networking, AI security. | Entry point for a related group of articles and labs. | Start here to establish the direction of a topic. |
| Article | K-means, eight queens, TLS, Transformer math. | Concept dependency, code reuse, or experiment verification. | Find prerequisites and the next runnable material. |
| Resource | CSV files, C/Python source, diagrams, experiment archives. | Data, code, or visual evidence used by the article. | Download and reproduce the article steps. |
| Learning route | AI foundations, algorithm visualization, networking lab. | A curated order from simple to complex. | Avoid random jumps between unrelated pages. |
Knowledge map
Connect posts, routes, and resources
Drag the map like a landscape canvas, click nodes to reveal nearby content, and use the detail panel below for summaries, project context, and reading links.
Drag the canvas, click nodes to expand
Current node
Pick a node
The map shows how projects, posts, resources, and learning paths connect.
Related nodes
- Related content appears here after selection.