Week 1: Foundations of Working With Machines

This lesson resets the role of AI in your career. The model is an amplifier for judgment, not a substitute for technical taste, system literacy, or the ability to verify behavior.

In production, weak fundamentals turn every AI-generated answer into a liability. If you cannot read stack traces, inspect data flow, and reason about interfaces, you will ship impressive-looking nonsense.

Treat AI as a junior-but-fast collaborator embedded inside a real software system. Your value is in defining the constraints, judging outputs, and spotting when the collaborator is confidently wrong.

This lesson teaches a boundary that every serious SaaS depends on: proving who the caller is versus deciding what that caller may do.

Production systems fail quietly when auth and authorization are mixed. You either block valid work or, worse, allow privileged actions to untrusted users because “the user is logged in” felt sufficient.

Authentication is identity. Authorization is policy. API handlers should first establish principal, then evaluate policy, then perform business logic.

This lesson gives you the operator posture behind all later weeks: terminal comfort, basic network reasoning, and a debugging loop that resists random guessing.

AI accelerates troubleshooting suggestions, but it also accelerates thrashing when you do not know how to isolate a fault. Production work rewards diagnosis, not frantic patch generation.

Debugging is controlled narrowing. Start with the symptom, identify the smallest reproducible path, check assumptions layer by layer, and only then change code or config.