The ideas underneath: shared vocabulary, the math, and how to choose between models.
In 1992 Bucharest, a student bluffed his way through a math exam with the only theorem he knew. Thirty years later, that theorem turned out to be a structural pillar of modern AI. A personal account of the Cauchy-Bunyakovsky-Schwarz inequality — why it governs how AI understands similarity, attention, and language — and what a wise professor's five out of ten can mean across a lifetime.
Part 2 completes the AI Periodic Table framework covering production Systems (agents, knowledge bases, thinking models, frameworks, observability, red teaming, RLHF) and Emerging elements (multi-agent, synthetic data, diffusion LLMs, interpretability, alignment, A2A). Shows how elements combine into chemical reactions — from RAG chatbots to full multi-agent pipelines — and explains the deliberate gaps that predict what AI still needs to solve.
AI has a vocabulary problem — everyone says "agent," nobody means the same thing. Part 1 proposes a solution borrowed from chemistry: a periodic table of AI elements organized by abstraction level and functional concern. Covers the Architecture of the Table, why Context is not an element, and all elements from Infrastructure through Compositions — tokenization, attention, embeddings, prompts, RAG, MCP, LoRA, guardrails, and more.
Whether you're a developer seeking the best coding assistant, a researcher requiring advanced reasoning capabilities, or a budget-conscious user looking for maximum value, this guide provides the detailed analysis you need to choose your ideal AI companion for 2026.
