Extracted abstract or opening context
> This document tells the full story of the meta-evolution program: how disconnected evo-cube research was collapsed into a governed architecture program, why that collapse was necessary, and how the wave-based application model replaced unbounded cube generation. It is the entry point for anyone who needs to understand the program without reading the 8+ source files it synthesizes.
The meta-evolution program is the system that governs how architectural research gets generated, organized, and applied to the live codebase. It replaced an earlier mode where evo-cubes (multi-stage architecture analysis documents) were generated as isolated outputs. That earlier mode produced valuable research but no mechanism for turning research into architecture changes. The meta-evolution program exists to close that gap.
The program's control plane lives in `Desktop/evo-cube-output/meta-candidate-mining/` and `Desktop/evo-cube-output/mega-cube-registry.md`. Its operational policy is defined in `architecture-application-roadmap.md`. Its execution history is recorded in the stage files (stage0 through stage3) and the backlog directories.
The program began with an exhaustive fact inventory of the entire codebase. Stage 0 surveyed 70+ projects, 106 Prefect flows, 34 Claude hook directories, 15 MCP servers, 46+ iOS/macOS apps, 141 Supabase tables, and infrastructure spanning 5 physical machines plus a cloud VM. That survey identified 114 distinct evo-cube candidates across 14 categories: individual apps, backend services, data pipelines, infrastructure, AI/ML systems, knowledge and memory, monitoring, integration layers, revenue surfaces, creative systems, security, developer tooling, cross-system architectures, and additional high-potential threads.
The 114 candidates were not hypothetical. Each had file paths, line counts, gap analysis, and cross-system dependency notes. The survey also identified 10 critical fleet-wide gaps: no canonical source for 12+ projects, no unified event schema, no production deployment map, no continuous ML training loop, no graduation lifecycle, no cross-app progression, no flow dependency graph, no credential rotation, no knowledge graph analytics, and a 30% pane spawn miss rate.
Why this is not always a full paper yet
Corpus pages are public-safe readers for discovered workspace artifacts. They are not automatically final papers. A corpus item becomes a polished paper only after the editable source, evidence checkpoints, references, figures, render path, and release status are attached through the paper schema.