THE EVIDENCE

We Didn't Build a Framework and Hope It Works

We used AI to build CleanAim®—twice. These metrics come from 1,000+ production sessions building real infrastructure. Every number is auditable.

1.1M+

Lines of Production Code

Version 1: 705K lines. Version 2: 400K lines. All built with AI assistance.

98/100

Self-Audit Score

Honest calibration matters more than perfect scores.

9,309

Test Functions

Not lines of test code—actual test functions.

1,000+

Session Handoffs

Context preservation across AI coding sessions.

57,338

Genetic Patterns

Learning that compounds across sessions.

93.3%

Transfer Efficiency

Cross-model learning that survives provider switches.

BUILT TWICE

Proven Across Two Complete Builds

Version 1

Initial platform build. Proved the methodology works. Identified patterns that informed v2 architecture.

  • 705K Lines of code
  • ~600 Sessions
  • Complete Platform build

Version 2

Complete rebuild with lessons learned. Cleaner architecture. Better patterns. Faster development velocity.

  • 400K Lines of code
  • 58 PRs merged
  • Zero Architectural violations

DETAILED METRICS

The Full Picture

Session Management

  • Session handoffs completed 1,000+
  • Handoff automation rate 92%
  • Context restoration success 100%

Guardrail Enforcement

  • 'Do NOT' rules defined 515
  • Exit gate references 1,350
  • Bypass attempts logged 100%

Learning System

  • Genetic patterns captured 57,338
  • Predictions in database 14,000+
  • Prediction-outcome pairing 100%

Cross-Model Transfer

  • Transfer efficiency 93.3%
  • LLM providers supported 7
  • Frozen behavioral events 275

Architectural Integrity

  • Protocol classes tracked 416
  • Specification files 42
  • must_exist rules enforced 137

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