After this lesson, you will be able to: Understand how AI is reshaping web development today and which AI tools fit which parts of the workflow.
AI hasn't replaced developers, it has changed what their day looks like. This lesson is a tour of the AI tools (Claude Code, GitHub Copilot, Cursor, browser-based AI agents, v0) and where each one fits in the modern workflow, so you know which to pick up next.
This is a free introductory lesson. No purchase required.
Five years ago, building a website meant typing every line yourself, looking up syntax constantly, and spending half your time on configuration. Today, AI tools handle the boilerplate, generate first drafts, explain unfamiliar code, and write tests on demand. Developers still own the design and the judgment, but the typing has shrunk dramatically.
In 2025 most engineering teams expect every developer to use AI in their daily workflow, Claude, Cursor, Copilot, prompt-driven code review, AI-generated tests. The same way 'comfortable with a terminal' became baseline in the 2010s, productive AI use is now the implicit yes/no question on every junior interview. This subtrack is the practical fluency course: real workflows, real verification habits, real boundaries on where AI fits and where it doesn't. By the end you'll be able to answer the 'how do you use AI tools' interview question with the kind of judgment that makes hiring managers nod.
Claude Code, terminal-based AI agent that can read, write, and edit code across your project. GitHub Copilot, inline autocompletion inside VS Code. Cursor, a fork of VS Code with AI deeply built in (chat with your codebase, multi-file edits). Browser-based AI agents, browser-based building, with an AI agent that can scaffold whole apps from a prompt. v0 / Lovable. AI tools that generate UI from natural language (great for quick prototypes).
Planning: rubber-duck a design with Claude or ChatGPT. Scaffolding: ask Cursor or Claude Code to generate the initial files. Writing: Copilot or Cursor for inline suggestions while you code. Reviewing: AI explains unfamiliar code or proposes refactors. Debugging: paste an error and let AI suggest fixes. Testing: AI writes the first draft of unit tests, you tweak.
What can't AI do well that you need to do?