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The AI engineering library
Handwritten notes across 16 categories. Preview the first five pages of any note free.
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Python for AI Engineering
Python Foundations for AI Engineering
Everything a working developer needs in Python for AI: environments, NumPy, data pipelines, notebook workflows and the libraries the AI stack actually uses.
RAG and Vector Databases
RAG Systems: The Retrieval Blueprint
End-to-end retrieval-augmented generation: chunking strategies, embeddings, hybrid search, reranking and evaluation — with full pipeline diagrams.
Large Language Models
LLM Fundamentals: Tokens to Alignment
How large language models really work: tokenisation, pretraining objectives, instruction tuning, RLHF and decoding strategies, explained visually.
Transformers and Attention
Transformer Architecture, Explained by Hand
The transformer block, self-attention and multi-head attention drawn and annotated page by page — the clearest way to internalise the architecture behind modern LLMs.
AI Agents and Agentic AI
AI Agents and Agentic Workflows
Tool use, planning loops, memory and multi-agent orchestration — a developer's field guide to building reliable agentic systems.
GenAI Interview Preparation
GenAI Interview Guide: 120 Questions Solved
The questions GenAI interviewers actually ask — with handwritten explanations, diagrams and code for every answer.
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