00🧭
Meet AI
Build the big picture: AI history, the evolution from symbolic systems to deep learning, and why modern LLMs work.
Your personal AI learning platform · 2026
Understand AI.
Build what comes next.
Explore the complete journey behind modern AI — from the foundations and architecture that make a model possible, through training and inference, to complete AI systems and production engineering.
The curriculum
Eight connected learning tracks
One primary home for each concept. Learn the fundamentals, understand the model, then build real systems.
The dashboard and detailed roadmap share one numbering system: 00–07. Career growth and live labs are supporting resources across the tracks.
01∑
Foundations
Build the mathematical and programming foundations needed to understand learning, vectors, optimization and neural networks.
02⌘
Language & Model Architecture
Follow text into tokens, vectors and embeddings; then understand recurrent models, attention, QKV, Transformers and GPT.
03⚙️
Model Training Pipeline
Understand how raw data becomes a trained, aligned and deployable language model across 14 connected stages.
04✧
Model Inference Pipeline
Trace a user question through tokenization, embeddings, Transformer computation, next-token prediction and response delivery.
05🕸️
AI Systems Engineering
Connect models to external knowledge, context, tools and actions with RAG, vector databases, agents and MCP.
06🚀
Production & Optimization
Move from experiments to reliable AI services with evaluation, safety, efficient models, optimized inference and production operations.
07✦
Advanced AI & Frontier
Explore reasoning models, multimodal architectures, diffusion, Vision Transformers, Mixture of Experts and open research questions.
Your track progress
0 / 8Completion is saved in this browser.
Parts A, B and C
The complete GPT lifecycle
Training creates the model. Inference uses the trained model to produce an answer.
PART A · MODEL CREATION
Model Training
How the model learns patterns, acquires capabilities and has its weights updated.
Data preparationArchitecturePretrainingSFTPreference alignmentSafety & evaluationOptimization & deployment
Output: trained model, tokenizer and deployable artifacts
Explore 14 training stages →→
PART B · USER INTERACTION
Model Inference
How a deployed model processes a prompt and generates a response using fixed model weights.
User inputTokenizationEmbeddingsTransformerPredictionDecodingOutput
Output: response delivered to the user
Open interactive inference lab →The connection: model training learns the parameters. Deployment packages and serves the trained model. Inference runs forward computation with those parameters. Tokenization is used in both training and inference.
Part D
Training vs inference
Two different operating modes of the same model lifecycle.
| Dimension | Model training | Model inference |
|---|---|---|
| Purpose | Learn patterns and adjust model parameters. | Use learned parameters to process an input and produce output. |
| Input | Prepared training examples and targets. | User prompt, instructions, history and other context. |
| Computation | Forward pass, loss, backpropagation and optimizer updates. | Forward computation and next-token probability calculation. |
| Weights | Updated during training. | Normally fixed for a deployed model version. |
| Output | Trained model checkpoint / artifacts. | Generated response. |
| Frequency | Development or post-training runs. | Every request / generation session. |
Learn by building
Labs, experiments & projects
Practice the concepts in working visualizations and small implementations.
🔤Tokenization LabText → tokens → IDs
🔭Attention LabToken relationships
▦Transformer LabFollow the signal
🧪MiniLLM LabBuild a small language model
📚RAG Learning SpaceBuild retrieval systems
🗺️Roadmap MindmapExplore topic connections
◷Study StatisticsTrack focused learning
✦Frontier WatchEmerging AI topics
CURRICULUM RULEOne concept, one primary home
Shared concepts such as tokenization are taught once, then referenced from training and inference.
BUILD IN PARALLELLearn → visualize → code → test
Every important topic should lead to a tangible experiment, not just a reading checklist.
PRESERVE EXISTING WORKKeep your current labs
The inference walkthrough and existing topic lessons remain intact while the curriculum is reorganized.