WIKI/Research Papers/Human-AI Codevelopment
Research Papers

Human-AI Codevelopment

In Simple Terms

How do you build a system with 76,000 lines of code and 18 services? Not alone, and not in the traditional way. This paper documents how a human developer and AI coding assistants (Claude Code) co-developed Frank's architecture through iterative cycles of building, testing, discovering bugs, and redesigning.

Development Pattern

  1. Human designs the concept. "Frank needs a sensory gating system like the thalamus."
  2. AI implements the first draft. Claude Code writes the initial 570 lines.
  3. Testing reveals unexpected behavior. Frank fixates on "whispers of the Architecture Bay" for 5 hours.
  4. Joint diagnosis. Human and AI analyze the logs together, identify the root cause.
  5. AI implements the fix. 7 targeted patches to the anti-rumination system.
  6. Repeat.

Key Discoveries Made Through Codevelopment

  • The regex co-author bug (v6 of IAPT) — discovered through adversarial testing that no human reviewer would have caught
  • The mood decay non-cumulative bug — E-PQ was re-reading the base value every cycle instead of tracking deltas
  • The watchdog cascade — services trying to restart non-existent services, creating a chain reaction
  • The negative instruction priming — training data with "NEVER be nihilistic" actually made the model more nihilistic

Why It Matters

The paper provides evidence that human-AI codevelopment isn't just "AI writes code, human reviews." It's a genuine collaborative process where each party contributes capabilities the other lacks. The human provides architectural vision and judgment. The AI provides implementation speed and exhaustive testing.

Read the full paper →

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