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The Approach

IKEA instructions for the Zero Vector workflow.

00

This Is Not Vibe Coding

Vibe coding is pointing an AI at a screenshot and saying "make this." No architecture. No systems thinking. No intention beyond "it looks right." Vibe coding produces trinkets — pretty, fragile, disposable things that break the moment you need them to scale, adapt, or survive contact with real users.

The Zero Vector approach is the opposite. It is intentional creation at full velocity. You bring the systems thinking, the architecture, the understanding of what good looks like. AI extends your reach, not your judgment. You are not removed from the process — you are finally, fully in it.

Speed without intention is just faster failure. Speed with intention is leverage.

The Mental Model

Every project follows the same loop. You understand what you are building and why. You describe it precisely. The AI builds it. You verify the result against your intent. You refine. The quality of the output is directly proportional to the quality of your input — and the curriculum teaches you what quality input looks like.

The Zero Vector Loop
1
Understand
Know what you are building and why. PRD, use cases, architecture.
2
Describe
Tell the AI precisely what to build. Context, intent, constraints.
3
Build
The AI writes the code. You review what it wrote against your intent.
4
Verify
Test it. Does it match the plan? Does it work? Commit or refine.
Refine and repeat. Each cycle produces working software.

What Makes This Different

Vibe Coding
"Make me a website"
Accept whatever the AI produces
No architecture, no plan
Cannot debug what breaks
Cannot extend or maintain it
The AI is in charge
Zero Vector
"Build this component with these specs"
Review every change against intent
PRD, use cases, CLAUDE.md
Systematic debugging workflow
Clean architecture, clear ownership
You are the auteur

The Knowledge Stack

The Approach guides assume you have — or are building — the foundational knowledge from the Curriculum. Not because you need to memorize everything before you start, but because every instruction you give an AI is only as good as your understanding of what you are asking for.

Approach
What to do — step-by-step walkthroughs
Curriculum
What to know — concepts, patterns, principles
Intent
What to build — your vision, your standards, your craft

When you understand systems thinking, your architecture instructions are better. When you understand data modeling, your database prompts are precise. When you understand the pipeline, you know which phase you are in and what questions to ask. The curriculum is the vocabulary. The approach is the conversation.

The friction was never the point. The handoffs were never inevitable. The translation layer between what you imagine and what gets built was a limitation of the tools, not a feature of the process.