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 developer-level look at how coding assistants assemble context, retrieve code, and why they confidently call functions that don't exist.
A concrete setup for editor, agent, context files, and guardrails so AI speeds you up instead of quietly adding risk.
How to match model tiers to real coding work — completion, refactors, agentic loops — without overpaying or waiting on the wrong tool.
The difference between usable output and a rewrite is usually the constraints you set before the model writes a line.
Good code from an AI depends less on the question and more on which files, types, and rules you put in front of it.
AI debugging fails when you outsource the thinking; it works when you drive a tight hypothesis-and-evidence loop.
How to prompt AI reviewers so they catch concurrency races, off-by-one errors, and broken invariants instead of restyling your braces.
Most AI-generated tests assert that the code does what it does. Here's how to get tests that pin behavior and fail for the right reasons.
AI is great at describing what code does and terrible at explaining why. Here's how to get documentation and commits that answer the question that matters.
How to add a genuinely useful model-backed feature to an existing codebase while keeping it boring, testable, and cheap to remove.
A grounded look at retrieval-augmented generation for developers: when it earns its keep, how the pieces fit, and where it quietly breaks.
A concrete decision framework for choosing between a single well-crafted prompt and a multi-step agent, with the trade-offs each direction costs you.
A practical rubric for testing AI coding tools on the work you actually do, instead of trusting leaderboards and demos.
Concrete techniques for getting reliable help from AI assistants inside big, inconsistent, poorly documented legacy systems.
The tasks where AI assistants quietly cost you more than they save, and how to notice you're in one.
The specific vulnerability classes that show up disproportionately in model output, and how to catch them in review.
Copyright status, license contamination, indemnity fine print, and the controls that keep AI-assisted code shippable.
How over-reliance on coding assistants degrades a codebase and a team's skills, and the practices that hold the line.