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New here? The Start here page walks the five foundations in order. This page is the full map: every long-form guide, grouped so you can find the part you actually want.

The foundations

The core ideas, in the order they build on each other.

• What is artificial intelligence? — the big picture, and why the definition keeps moving.
• AI vs machine learning vs deep learning — three circles, not three rivals.
• What is machine learning? — learning patterns from data instead of coding rules.
• Supervised vs unsupervised learning — the four types, and the one that broke the textbook split.
• How neural networks work — the idea underneath all of it.
• What is deep learning? — the engine of the current era.
• What is a large language model? — the technology behind the chatbots.
• What is generative AI? — learning a distribution, then sampling from it.
• What is AGI? — the goal nobody can define.

How language models work

From your prompt to the answer, one layer at a time.

• How a sentence becomes an answer — the whole pipeline end to end.
• What is a token? — how models read text in chunks.
• What are embeddings? — turning meaning into geometry.
• How transformers work — the architecture that ate AI.
• Why AI gives different answers — sampling and temperature.
• How LLM inference works — why it is bound by memory, not compute.
• What is a context window? — the model's working memory.
• How AI memory works — and why the model remembers nothing.

Training, scaling, and efficiency

How models are built, made larger, and then made cheaper.

• How AI models are trained — pretraining, fine-tuning, and RLHF.
• AI scaling laws — why bigger wins, and whether it is ending.
• What is fine-tuning? — specialising a model for your task.
• What is reinforcement learning? — learning from reward.
• What is synthetic data? — training AI on AI-made data.
• Knowledge distillation — how small models learn from big ones.
• What is quantization? — shrinking models without breaking them.
• What is a mixture of experts? — bigger and cheaper at once.
• Speculative decoding — faster generation, identical output.

Building with AI

Retrieval, agents, tools, and prompts: the practical layer.

• How RAG actually works in 2026 — beyond vector search.
• RAG vs fine-tuning — the decision, honestly.
• Your RAG system isn't hallucinating — it never found the answer.
• Do you need a vector database? — often not.
• How AI agents actually work — the loop behind the hype.
• Why AI agents fail — the seven failure modes.
• What is MCP? — the standard that wired AI into everything.
• Prompt engineering in 2026 — what still works.
• Context engineering — the skill that replaced prompting.
• Open weights vs open source AI — what you are actually given.

Beyond text

Images, video, sound, and the fields AI grew out of.

• How AI generates images — from noise to a picture.
• How AI generates video — from noise to motion.
• What is multimodal AI? — one model that sees, reads, and hears.
• What is computer vision? — turning pixels into meaning.
• What is natural language processing? — the field LLMs came from.

Safety, security, and failure

Where these systems break, and why the breaks are structural.

• AI alignment and safety — without the hype or dismissal.
• AI bias and fairness — why "fair" has no single answer.
• Will AI take my job? — what the evidence actually shows.
• AI and copyright — the three questions people confuse.
• Why AI hallucinates — the confident lie is a feature, not a bug.
• How to reduce AI hallucinations — what actually works.
• What is AI jailbreaking? — why safety can be talked around.
• What is prompt injection? — and why it is unsolved.
• How to secure an LLM application — risks and defenses.
• What is AI sycophancy? — why AI tells you what you want.
• Why AI models get worse — forgetting, collapse, and drift.

Measurement and evidence

How progress is claimed, and how much of it to believe.

• How do we measure AI progress? — the benchmark problem.
• Why AI benchmarks mislead — contamination, gaming, saturation.
• How to tell if your AI actually works — evaluating your own system.
• How to read a model release — decoding the announcement.
• Do AI detectors work? — accuracy, bias, and false positives.

The physical layer

The hardware and energy underneath the software.

• Why does AI need GPUs? — parallelism and the memory wall.
• How much energy does AI use? — training vs inference.
• Run an LLM locally — how much VRAM you actually need.

Open questions

The parts nobody has finished working out.

• Why does deep learning work? — the generalization mystery.
• Mechanistic interpretability — opening the black box.
• How reasoning models work — AI that thinks before answering.
• Why long context windows fail — the lost-in-the-middle problem.
• Does AI actually understand? — why the debate is stuck.
• What can AI do, and what can't it? — the four properties that predict it.
• What is the Turing test? — and why it stopped mattering.

Other ways in

• The concept map — 255 concepts and how they connect.
• The glossary — every term, defined in a line.
• A timeline of AI — how the field got here.