🧠 AI Explainer
Paths, chapters, and simulators: fundamentals and AI agents.
📘 Paths
🎮 Simulators
All simulators →Hands-on demos: tokenization, embeddings, temperature, RAG, MCP flow, and more.
Tokenization
Type text and see how it breaks into tokens and IDs.
🧬Embeddings & Similarity
Closest-word graph in embedding space.
📐Cosine vs Distance & 3D
Compare cosine similarity vs Euclidean distance; 3D embedding space.
🔄Cleaning → Embedding → Graph
Pipeline visual: raw text to 2D embedding graph.
🌡️Temperature & Sampling
See how temperature shapes next-token probabilities.
⚖️Training Data & Weights
How data mix affects output quality.
🧹Normalization
Before/after text cleaning (lowercase, punctuation).
💰Token Cost
Compare verbose vs concise prompts and token count.
🔍RAG Pipeline
Documents → embeddings → query → top-k → LLM.
🎯Mental Model
Full stack: text → normalization → tokens → vectors → search → generation.
🪟Context Window
See how a fixed token limit fills up; overflow is cut off.
📡MCP Flow
Step through how app, LLM, and MCP server interact.
🧩Agent Components
Memory, prompting, tools, resources at a glance.