Your comprehensive guide to understanding artificial intelligence. From benchmarks and techniques to research and the mathematics that power modern AI systems.
The full 11-chapter pipeline that frontier labs use: data, architecture, pretraining, RLVR, reasoning, agents, multimodal, inference, infrastructure, and evaluation and safety. Updated October 2026, with references to 100+ papers from 2017 to 2026.
How frontier models are evaluated in late 2026, now that MMLU, GPQA, SWE-bench Verified and ARC-AGI-2 are saturated. Covers Humanity's Last Exam, ARC-AGI-3, Terminal-Bench 4.0, METR time horizons, arena.ai ratings, and how to read leaderboards critically.
Master the methods to enhance AI model performance. From prompt engineering to RAG, fine-tuning, and RLHF—learn when and how to apply each optimization strategy.
The ways AI perceives, generates and acts. Covers text, vision and audio; omni models such as Gemini Omni; world models such as Genie 3 and AMI Labs' JEPA; robot foundation models such as Gemini Robotics 2 and π0.7; and future senses like touch.
The foundational and cutting-edge research shaping AI, from "Attention Is All You Need" to the latest 2026 breakthroughs in efficient architectures, training and reasoning.
The labs, money and people driving AI in 2026: Anthropic, OpenAI and Google DeepMind's latest flagships, xAI inside SpaceX, Meta's turn to closed Muse models, China's open-weight labs, new startups like AMI Labs, and the year's people moves.
Build the mathematical foundation for understanding AI. Linear algebra, calculus, probability, and optimization—the essential concepts behind machine learning.
New to AI? Follow this recommended progression to build your understanding from the ground up.
Start with the mathematical foundations. Understanding linear algebra, calculus, and probability is essential for grasping how AI models work.
Read the foundational papers like "Attention Is All You Need" to understand the transformer architecture that powers modern AI.
Learn how AI models are evaluated and compared. Understanding benchmarks helps you assess model capabilities for your use cases.
Master practical methods like RAG, fine-tuning, RLVR, and MCP tool-use for your specific applications.
Capstone. Walk through the actual pipeline a frontier lab uses — data, architecture, pretraining, RLVR, reasoning, agents, multimodal, inference, infrastructure, and safety.
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