Ever opened a technical knowledge base and felt lost staring at a dense list of concepts, wondering where to begin? AI now has hundreds of terms—LLM, RAG, LoRA, RLHF, Transformer, Agent—each with its own manuals, tutorials, and papers. But what matters most is how they relate and what to learn next. Easy AI's newly launched concept map tackles exactly this problem.
A Metro Map That Solves Three Core Questions
The design goal, stated clearly in the project docs, is to answer three questions: where to start, how to learn, and what you'll gain. Instead of a dictionary-style alphabetical list, the map works like a subway diagram: pick a line and travel station by station.
The entire Easy AI knowledge base is organized into 7 color-coded learning lines, each a complete path from beginner to advanced. For example, the prompt engineering line runs Prompt → System Prompt → Few-shot → Chain of Thought—a closed learning loop from basics to depth.
Boustrophedon Layout: Fitting an Endless Line on a Finite Screen
The most interesting technical piece is the layout algorithm. A learning line with 20 concepts can't fit left-to-right in one row. Easy AI's solution is snake-folding (boustrophedon)—from Greek, "moving like plowing a field."
- Each row holds at most 6 stations.
- When a row fills, the line folds to the next row, but in the reverse direction: row 1 left-to-right, row 2 right-to-left, row 3 left-to-right again.
- Every turn connects with smooth arcs, so the whole line looks like a coiled snake—compact but readable.
- Prerequisite (green) — understand A before B
- Mechanism (coral) — A is an internal part of B
- Parallel (purple dashed) — same-level concepts for side-by-side comparison
- Solution (dark green) — A solves B's problem
- Contrast (orange dashed) — alternatives or commonly confused
- Cross-topic bridge (blue dashed) — links two major themes
The algorithm is implemented entirely with hand-written SVG + React, with no third-party graphics libraries, keeping it performance-friendly.
Six Relationship Types Connecting Concepts
Horizontal relationships between concepts also need visualization: you need LoRA before understanding LoRA rank; quantization is a compression step before deployment. The map uses 6 colored edge types:
Hub Nodes: Major Gates vs. Minor Stops
Some concepts are drawn larger—LLM, Transformer, Agent, LoRA, quantization. These hub nodes are the field's core entrances, like major subway transfer stations that almost every line passes through.
A thoughtful touch: the LLM hub carries a glowing "recommended starting point" hint, drastically reducing decision cost for complete beginners.
Why This Matters
AI knowledge is exploding faster than linear reading can keep up. Facing a flat, unordered list imposes heavy cognitive load. A structured route map helps learners build a cognitive map—knowing not just the facts, but where each fact sits in the knowledge system. It's like geography: you can memorize city names, but only a map showing mountains, rivers, and oceans reveals how the world actually fits together.
Equally notable is the clean data design. Node data derives from a single source, while edges are maintained separately in dedicated files. Adding a concept means adding an entry, then connecting edges. Dev-mode validation automatically checks: any concept missing from a learning line? Any edge referencing a nonexistent concept? All violations raise console warnings.
This means the concept map stays in sync automatically as the Easy AI knowledge base grows—it won't become an outdated map.
Final Thoughts
Easy AI has been doing something simple yet hard: explaining complex AI concepts in ways ordinary people can understand. The concept map is the next step—not just explaining concepts, but showing where they are. If you're learning AI, open the map, find your starting point, and follow a line. Knowledge isn't a well; it's a web. Knowing your position in the web matters more than memorizing more facts.