You've watched an AI produce website code. Now you need to know whether it's any good. This is a beginner's guide to the fundamentals that make that judgment possible. Across fifteen chapters you build one project - a Community Event Board - starting from an empty folder and ending with a published, verified site. You write every part yourself and learn to check every part yourself: reading a diff before accepting it, tracing a value in the debugger, running a test that can genuinely fail, and telling the difference between "it looks right" and "I have evidence." What the book covers: - The web platform from the ground up - semantic HTML, a responsive stylesheet with design tokens, and browser JavaScript, each verified as you go - Debugging as a method - reproduce, isolate, hypothesize, repair minimally, confirm, using browser DevTools on defects whose cause isn't obvious - Version control and publishing - Git history you can read and recover, a GitHub repository, and a GitHub Pages deployment you verify rather than assume - Automated checks - HTML validation, unit tests for pure logic, and Playwright Chromium tests covering focus, storage failure, and bounded accessibility checks - Life after launch - a dated maintenance review, a rehearsed rollback, and an honest statement of what the finished project can and cannot do An AI assistant is optional. Where one appears, the exercise is reviewing its output - spotting invented packages, unrequested edits, deleted tests, and confident explanations that contradict what you can observe. No framework, backend, database, authentication, or paid service is required. The book makes no career or income promises, claims no universal accessibility or security result, and never treats generated code as correct because it reads well. Companion checkpoint files and a public repository accompany every chapter.
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