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architecture

AI vs Machine Learning vs Deep Learning vs LLMs — the actual differences

AI vs Machine Learning vs Deep Learning vs LLMs — the actual differences

Four layers everyone conflates — AI, Machine Learning, Deep Learning, and Large Language Models. What defines each, what each can and cannot do, where production systems use them in 2026, and how to tell which layer a system actually is. With a paired lab.

What is artificial intelligence? A complete intuitive guide

What is artificial intelligence? A complete intuitive guide

A complete intuitive guide to what artificial intelligence actually is — the nested AI/ML/DL/LLM hierarchy, the one idea that drives modern AI, narrow vs general vs super, five myths the news still gets wrong, and a 60-second tour from 1956 to 2026. With an interactive lab.

Three cards labeled QUERY, KEY, VALUE — the three projections every transformer attention layer computes.

What is attention? The mechanism behind every modern LLM

The mechanism every modern LLM is built on, explained from scratch. Q/K/V, the scaled dot-product formula walked through term by term, multi-head attention, positional encoding, and the engineering variants that show up in 2026. With a live interactive lab.

Diagram contrasting total parameters with active parameters in a mixture-of-experts AI model.

What is mixture-of-experts? The architecture behind cheaper trillion-parameter AI

Mixture-of-experts is how a 671-billion-parameter model can cost less per token than a 70-billion-parameter one. A plain-English guide to MoE, active vs total parameters, routers and experts, and why open-weights frontier labs picked the architecture first.