Database

Teaches effective AI pair programming techniques for tools like Claude Code, Cursor, and Copilot. Covers when to lead versus follow the AI, providing persistent context through CLAUDE.md and .cursorrules files, breaking complex tasks into AI-manageable pieces, using git strategically with frequent commits as checkpoints, and recognizing when the AI is stuck in a loop. Use when working alongside AI coding tools in a collaborative development workflow.

Teaches how to debug code generated by AI tools, covering the unique failure modes of AI-generated code including hallucinated APIs, version mismatches, circular logic, and phantom dependencies. Explains how to read error messages back to the AI effectively, provide minimal reproductions, diagnose when the AI is giving bad fixes, and use systematic debugging approaches on codebases you did not write by hand. Use when AI-generated code is not working and you need to find and fix the issue.

Covers the unique challenges of maintaining codebases built primarily through AI code generation. Addresses inconsistent patterns across AI-generated files, refactoring AI sprawl, establishing coding conventions after the code already exists, documentation strategies for AI-built projects, and managing the specific forms of technical debt that AI tools create. Use when a vibe-coded project needs ongoing maintenance or has grown unwieldy.

Guides the complete journey from an idea to a working application using AI code generation tools. Covers writing effective app specifications, choosing the right tool for the job (Claude Code, Cursor, Bolt, v0, Lovable, Replit Agent), the spec-first approach, iterating on generated code without losing coherence, and managing scope creep during AI-assisted development. Use when someone wants to build an app from scratch using vibe coding.

Teaches how to review, audit, and evaluate AI-generated code effectively. Covers common AI code smells like over-engineering, dead code, wrong abstractions, and hallucinated APIs. Includes security review checklists, dependency auditing, performance review techniques, and strategies for catching the subtle bugs that AI confidently introduces. Use when reviewing code produced by any AI coding tool.

Guides the transition from a vibe-coded prototype to a production-grade application. Covers identifying when the project has outgrown pure vibe coding, refactoring AI-generated code for production reliability, adding tests retroactively to an untested codebase, introducing CI/CD pipelines, establishing code ownership and review processes, and building the engineering practices needed to sustain a growing application. Use when a vibe-coded project is succeeding and needs to become a real product.

Covers architecture decisions optimized for AI-assisted development. Teaches how to choose frameworks and structures that AI tools work well with, why monolith-first is the right default for vibe coding, how to organize files so AI can navigate them, which abstraction patterns help versus hinder AI code generation, and how to keep complexity within the bounds of what AI can reason about. Use when making technology and architecture choices for a vibe-coded project.

Teaches the foundations of vibe coding — the 2025-2026 paradigm of building software primarily through AI prompting. Covers what vibe coding actually is, the core prompting loop, when it works well (prototyping, MVPs, CRUD apps, internal tools) versus when it fails (distributed systems, real-time, safety-critical), how to manage context windows effectively, and when to drop out of the AI loop and take manual control. Use when someone is new to vibe coding or wants to improve their fundamentals.