AI Coding for Real Engineers by Matt Pocock

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ABOUT THE COURSE:

A two-week intensive course for developers who want to implement AI tools in real production: from context management and architectural planning to autonomous agents and effective code review.

Are you using AI correctly?

I am. And daily development with the help of neural networks has completely changed my perspective on modern engineering work.

The main thing I realized is that AI is both hype and a powerful tool. But the potential is unlocked only for those who possess the skill of systematic design.

Inexperienced use of AI (for example, Claude Code) creates technical debt and chaotic code. Developers usually fall into one of two traps:

Delegate too much. Fall into a mindless generative flow, creating spaghetti code.

Delegate nothing. Fear AI, keep everything in their head, and quickly burn out.

We propose an engineering path—a balanced working model where AI becomes a predictable tool, not a chaotic generator.

Course Program: From "Vibe-Coder" to AI‑Hero

In 2 weeks, you will go from chaotic requests to systematic automation. The program is divided into logical modules:

Course Modules

Pre-course: Rapid immersion into Claude Code, basics of LLM and the Explore/Build/Clear cycle.

Context Management: Working with configuration (AGENTS.md), custom skills, and targeted content delivery.

Architecture and Planning: PRD, multi-step plans, tracer bullets for solution testing.

Feedback Loops: How to build a constraint system ensuring the quality of output code.

Autonomous Work (AFK): Launching agents through Ralph loops with progress monitoring.

Product Design: How to combine research, prototyping, and engineering practices for mature IT solutions.

What will change in your work?

Before the Course

"YOLO" mode and chaotic code generation.

Confused codebase without architecture.

Either hundreds of useless tests or none at all.

Loss of understanding of one's own system.

AI that needs constant oversight.

After the Course

Sandboxes, constraints, and predictability of AI behavior.

Architecturally clean codebase with deep structure.

Testing at key boundaries of the system.

Control of structure and logic without overload.

A reliable AI agent to whom tasks can be delegated.

Why This Methodology Works

Engineering is a mindset, not an IDE. Tools change, but the foundation remains: communication, decomposition, systematic planning, and skill in managing complexity. These skills are equally effectively applied to working with both people and AI agents.

The Author's Personal Experience

Using Claude Code and established automation cycles, I single-handedly created a professional video editor and CMS on TypeScript/Effect.ts—over 1000 commits and 500+ tasks without sacrificing work and personal life.

Now, I want to share this superpower with you.