Every explainer on the site, grouped by topic. Written to stay useful as products change: we explain how to judge a tool rather than ranking this month's leader.
A plain-English tour of tokens, training, context and hallucination — what a language model is really doing when it answers you.
Clear definitions of the terms you actually encounter — LLM, token, context window, RAG, fine-tuning, agent, multimodal — and what each one means for choosing a tool.
A practical framework for choosing between the major AI assistants based on your actual work, rather than on benchmark scores or marketing claims.
Seven prompting patterns that reliably improve AI assistant output, with before-and-after examples you can adapt to your own work.
A practical map of where AI writing tools genuinely help — drafting, editing, and summarizing — and how to judge any tool for your own work.
The common tells that make AI-assisted prose recognisable, and an editing workflow that removes them while keeping the time savings.
A practical framework for comparing AI image tools by output style, control, workflow fit and licensing, rather than by whichever model launched most recently.
How to describe an image so a generative model produces what you actually had in mind, and how to steer it there over a handful of revisions.
A grounded look at where AI coding tools genuinely save time, where they quietly create work, and the review habits that keep the difference in your favour.
AI tutors can make learning to program dramatically less lonely, but only if you use them in a way that builds your skill rather than substituting for it.
A grounded look at the everyday workflows where AI tools actually save a small business time, and the ones where they quietly create more work.
How token-based API billing actually works, how it differs from flat monthly plans, and a simple method for working out which one fits your usage.