This site is not a product.
It is not a demo.
It is not a framework.
It is a record of thinking under uncertainty.
The Problem
Modern software—especially AI-assisted software—produces outcomes that are:
- non-deterministic
- highly sensitive to execution paths
- difficult to evaluate after the fact
Most repos hide this reality.
They optimize for forward motion, not understanding.
The Choice
This project makes a different tradeoff:
- clarity over velocity
- evidence over momentum
- restartability over polish
That requires discipline.
Why So Much Process?
Because without it:
- failures look like bad ideas
- successes look repeatable when they aren't
- learning gets lost in filesystem noise
The structure you see exists to preserve truth, not control.
If the repository is dirty, conclusions drawn from it are invalid.
What You're Looking At
- PRDs are hypotheses
- Attempts are observations
- Evidence is first-class
- Production is explicit
- Cleanup is mandatory
Nothing here is accidental.
The Core Idea
AI can generate code quickly.
But generated code is not the same as understood code.
This project exists to answer a question:
What does it take to actually learn from AI-assisted development, rather than just produce with it?
The answer, so far:
- Treat outcomes as experiments
- Capture evidence, not just artifacts
- Reset state between attempts
- Never trust a dirty repository
Who This Is For
This is for:
- builders working with AI
- teams exploring uncertain design spaces
- people who care more about learning than shipping illusions
If that's not you, this may feel heavy.
That's okay.
What This Is Not
This is not:
- a claim that this approach is the only way
- a judgment of faster, looser workflows
- a finished system
It is a working hypothesis about how to build carefully in an era of easy generation.
Orientation
If you want to understand the philosophy:
→ Start with the ODD Manifesto
If you want to see how it's applied:
→ Browse the Attempts
If you want to understand the rules:
→ Read the Canon