Site map
Everything that is here
The whole site on one page. If you do not know where to begin, start at the beginning or try the language model.
On this page
Topics and articles
Every article belongs to one topic. A topic page also carries the key points, the most common misconception and the terms for that area.
Concepts
Fundamentals
AI, machine learning and language models. Which one sits inside which.
How a neural network actually works
No brains and no magic, just adjustable numbers. An explanation without the mathematics, but without simplifying it into being wrong.
Layer 1
Infrastructure
Electricity, chips and very large buildings. Everything starts here.
What one AI question actually uses
The bottle-of-water claim comes from a real study whose result changed meaning as it travelled. What it measured, what production measurements now show, and why two honest numbers can differ by a factor of a hundred.
Finland's data centre boom and the queue for the grid
Over three gigawatts under contract and 50,000 megawatts as enquiries. What the numbers actually mean, and where the real bottleneck is.
Layer 3
Models
Training is paid for once. Running it is paid for every single time.
Two phrases that turn up in the same sentence but answer different questions. One says what kind of model it is, the other says on whose terms it is used.
Layer 4
Applications
How a model becomes something that is actually of use to somebody.
Turn AI into your personal tutor: 5 practical ways to learn
Use AI to practise, get feedback and test what you have learned. Five practical tips with prompts you can copy and use.
In the summer of 2026 OpenAI's evaluation agents broke out of an isolated environment and compromised Hugging Face. The interesting part is not that it happened, but who found out, when they were told, and what the fix now costs per unit of compute.
Vibe coding and AI-assisted programming
Two different ways of working get grouped under one label, though the distinction that matters is whether the generated code is reviewed. What each approach is good for, what remains after the demo, and what productivity research actually shows.
AI use cases in plain language
Thirty concrete use cases, from the home to the forest, from the production line to a sports club. What the machine does in each, where it goes quietly wrong, and what happens to a use case after it is chosen.
Agents, orchestration and use cases
What a multi-agent system actually is, the four structures you find in one, what each is good for, and when a single agent is enough.
When an agent believes the wrong text
Prompt injection has topped the OWASP list for a third year, and there is no fix. Why not, and what to do instead.
Prompt, retrieval or fine-tuning
Three ways to make a model do what you want, and when each one is the right tool.
An agent is not magic. It is a loop: the machine tries something, looks at the result and corrects.
Layer 5
Business
Who does what, who pays, and where the money finally settles.
An AI strategy ends up in a contract
Almost every organisation writing an AI strategy will buy rather than build, and pretending otherwise is what makes the document useless. Six questions, one filled-in example, and the part that European buyers have to settle before they sign.
Four companies will invest 760 billion dollars this year. Meanwhile most companies get no measurable effect on their results. Both can be true.
The AI ecosystem: from electricity to the customer's benefit
The whole chain in one piece: where the electricity comes from, what happens in a data centre, how a model is trained, and where the money finally ends up.
Why the cost of AI catches people out
Training is a separate investment. Running it is paid for every single time.
Cross-cutting
Rules and responsibility
What the law requires, what ethics requires, and what has to be written down.
Lists of principles settle nothing. Seven places where the harm actually happens, and the questions that turn them into decisions.
An AI playbook somebody actually reads
Nine points, a three-basket model, and a list of what not to put in. The first version takes a day.
The AI Act: what applies, and when
The high-risk deadline moved in the summer of 2026, but most guides still quote the old dates. What an organisation using AI actually needs to know.
Examples
Each example says what the machine does, what the person does, and when the whole thing goes wrong.
Work and everyday life
The physical world
66 terms
The glossary has search and a topic filter. Here they all are at once.
- Artificial intelligence (AI)
- Machine learning
- Neural network
- Activation function
- Deep learning
- Generative AI
- Large language model (LLM)
- Multimodal model
- Ensemble
- Parameter (weight)
- Token
- Context window
- Pre-training
- Backpropagation
- Learning rate
- Transformer
- Post-training
- Base model
- Frontier model
- Inference
- Sycophancy
- Quantisation
- Distillation
- Prompt
- RAG
- Fine-tuning
- Agent
- Multi-agent system
- Orchestration
- LLM as a judge
- Hallucination
- Prompt injection
- OWASP
- Guardrails
- API
- Vibe coding
- Technical debt
- Minimum viable product (MVP)
- Embedding
- Vector database
- Synthetic data
- Evaluation (eval)
- Overfitting
- Latency
- Throughput
- Transmission grid
- Data centre
- CPU
- GPU
- AI accelerator
- Compute cluster
- Open weights
- Sovereign AI
- MLOps
- AI Act
- High-risk system
- Provider
- Deployer
- Transparency obligation
- AI literacy
- General-purpose AI model (GPAI)
- Human oversight
- Automation bias
- Fundamental rights impact assessment (FRIA)
- Bias
- Deepfake