Phase 2: Static Analysis & Automated Gates · 50 min · Python · Ruff · mypy

Linting & Static Analysis Basics

The linter never trusts the AI. Neither should you.

Real-world application: Engineers who can configure strict linters that catch AI-specific anti-patterns (unused variables, repetitive logic, missing type annotations) demonstrate the ability to build automated quality gates that scale across 75+ AI-assisted PRs per day.

What you will learn

Configure Ruff with strict rulesets that catch AI-introduced anti-patterns
Set up mypy in strict mode to enforce type safety on AI-generated code
Integrate linters into pre-commit hooks for real-time feedback
Measure and interpret alert density differences between AI and human code

Introduction

Linting & Static Analysis Basics

Objective

Configure strict Ruff and mypy rulesets for AI-generated code, run the static analyzer on provided AI and human code samples, and produce an alert density comparison report.

The linter never trusts the AI. Neither should you.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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