Sharing a case study from building HUMMBL—a systematic mental models framework for complex problem-solving.

The meta-recursive twist: I used the framework's own models to architect, validate, and deploy it.

Key technical decisions:

1. 6 transformations, not categories: Perspective, Inversion, Composition, Decomposition, Recursion, Systems. Every model maps to exactly one.

2. Base-N scaling: Base6 for literacy, Base42 for wicked problems, Base120 for pedagogical completeness. Match complexity to problem tier.

3. Quantitative wickedness scoring: 5-question rubric (variables, stakeholders, predictability, interdependencies, reversibility) replacing subjective tier assignment.

4. Multi-agent development: Treated Claude, ChatGPT, Windsurf, and Cursor as team members with defined roles. SITREP protocol for coordination. 4x parallel execution.

Results: - 120 models, 9.2/10 quality score - 140 chaos tests, 100% pass rate - MCP server for Claude Desktop - 22 months, solo founder

Tech stack: React, Cloudflare Workers, D1, TypeScript

Links: - Live: hummbl.io - MCP: npm @hummbl/mcp-server - Case study: https://github.com/hummbl-dev/mcp-server/blob/main/docs/case...

Would love feedback on the framework architecture and multi-agent coordination approach. AMA about the development process.