Learn Modern AI
A beautifully clear, high-contrast guide to the technical breakthroughs, architectural changes, and key ideas behind modern artificial intelligence.
Understanding Modern AI
Artificial Intelligence has moved at a breakneck speed since the introduction of the Transformer architecture. This site acts as an editorial textbook designed to explain the key engineering ideas behind these models without the math-heavy clutter. It is specifically optimized to read cleanly on Amazon Kindle and other e-ink displays, as well as modern screens.
Select a chapter below to begin reading:
The Transformer Core
Understand self-attention, the Query-Key-Value mechanism, and how it replaced recurrent networks to form the bedrock of generative AI.
LLM Training & Alignment
Deep dive into pre-training, fine-tuning, and alignment mechanisms like RLHF and DPO that make AI models helpful and controllable.
RAG & Context Windows
Explore how models fetch real-time data using Vector Databases, semantic search, and the mechanics behind ultra-long context windows.
Scaling Efficiency: MoE & Quantization
How Mixture of Experts (MoE) keeps models fast by only activating subset networks, and how quantization compresses parameters for consumer hardware.
Diffusion & Generative Media
Learn how diffusion models generate images and videos by systematically removing random noise, and how Latent Diffusion speeds this up.
Agentic AI & Reasoning
Step into loop-based reasoning, tool usage (function calling), and System 2 thinking paradigms where models reason before responding.
Future Frontiers & Physical AI
Analyze native multimodality, synthetic data constraints, and the next physical frontiers for AI integration.
Play Doom
Take a break — play a fully functional raycasting FPS built in pure JavaScript. Kindle-friendly touch controls included.