Coding with AI: Getting Started with AI Assistants

I’m going to save your engineering team 10 hours this week — and I’m not exaggerating. If your developers write code and aren’t using an AI coding assistant yet, they’re leaving 30-50% productivity on the table. Not on the creative architecture stuff — on the tedious stuff that eats their day.

The big three:

• GitHub Copilot — Lives in your existing IDE (VS Code, JetBrains, etc.). Autocompletes code as you type. Broad language support. Best for: developers who want AI help without changing their setup.
• Cursor — A full IDE built around AI. Great for editing entire files, explaining unfamiliar code, and refactoring. Best for: developers who want the deepest AI integration.
• Claude Code — Command-line agent that works across multiple files. Excellent for architecture-level tasks, writing tests, and debugging. Best for: developers comfortable in the terminal who want AI that thinks about the whole project.

Where they absolutely crush it: writing tests (seriously, this alone is worth it), generating boilerplate, documenting code, refactoring, debugging error messages, writing regex, and explaining unfamiliar codebases. These are the tasks developers spend hours on that don’t require creative thinking — just time.

Where to be careful: complex architecture decisions, security-critical code, novel algorithms. The AI will produce something that compiles and looks reasonable — but for high-stakes decisions, a human architect still needs to drive.

The real skill shift: coding with AI isn’t about typing less. It’s about shifting from “write every line” to “review and direct AI-generated code.” You become the architect and reviewer, not the typist.