Developer-depth guides for coding with AI assistants — prompting that lands, review that catches real bugs, building LLM features, and shipping safely. Written by engineers, for engineers.
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Work out what an AI model actually costs per month from your token usage, and compare the major models side by side.
Free toolPaste any text to estimate how many tokens it uses, and see what that text would cost to send to each major model.
Free toolCompare a flat monthly chat subscription against the equivalent API usage and find the break-even point where one overtakes the other.
Free toolFour questions about your task, budget and experience — then a recommendation and the guides to go with it.
How AI coding assistants really work — context, retrieval, models — and how to set up a workflow that gets good code.
3 guidesPrompting that produces correct, idiomatic code, feeding the AI real codebase context, and debugging without loops.
3 guidesUsing AI for code review that finds real bugs, tests worth keeping, and docs and commit messages that stay useful.
3 guidesAdding LLM features to an app, a hype-free take on RAG, and when an agent beats a single well-crafted prompt.
3 guidesEvaluating Copilot, Cursor, Claude Code and the rest for your workflow — and the limits worth knowing.
3 guidesSecurity-reviewing AI-written code, the licensing and IP questions, and managing tech debt and over-reliance.
A developer-level look at how coding assistants assemble context, retrieve code, and why they confidently call functions that don't exist.
The difference between usable output and a rewrite is usually the constraints you set before the model writes a line.
The specific vulnerability classes that show up disproportionately in model output, and how to catch them in review.
A practical rubric for testing AI coding tools on the work you actually do, instead of trusting leaderboards and demos.
Good code from an AI depends less on the question and more on which files, types, and rules you put in front of it.
How to add a genuinely useful model-backed feature to an existing codebase while keeping it boring, testable, and cheap to remove.
Most writing about AI coding is a demo video or a hot take. It looks great until you point the tool at a real codebase with real constraints, and then the advice runs out.
We write for that codebase: how context and retrieval actually work, how to prompt for correct code, how to review what the model produces, and how to ship it without introducing a vulnerability or a pile of tech debt. Where we cite figures like model prices, they live in one date-stamped table you can verify on the model pricing page (last checked 20 July 2026).
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