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Combines ALL theories from first portion to now: - Density field encoding (semantic manifolds, Morse-Smale complex) - GCCL-GEC (glyph packets, chirality, typebook, eigenbook) - OAC (observer-admissible cavities, S3C shells, spherion shaping) - Hypercube-rhomboid (shear matrix, Gram matrix, geometric compression) - Radius-ratio motif compression (local admissibility quantization) - Maximum math density (custom logographic notation, full Unicode) - Hippocampus tabula plena (full slate initialization, FAMM pruning) - Engram consolidation (neuron dropout, pattern separation) - FAMM delay lines (preshaped delays, Q16.16 fixed-point) - S3C shells (multi-scale coordinate encoding) - PIST n-D bundle (perturbation encoding) - erans (enumerative rANS entropy coding) Core synthesis: Start tabula plena (full Unicode 1,114,112 codepoints + custom glyphs + omniversal chirality) → represent as semantic density field → extract Morse-Smale topological skeleton → apply shear matrix (orthogonal hypercube → correlated rhomboid) → FAMM consolidation (uniform → sparse structured delays based on eigenvalue spectra) → S3C shell coordinates → radius-ratio quantization → logographic glyph selection → GCCL packet construction → OAC speculative manifestation → gain test filtering → math notation eigenvector encoding → repeat position encoding → PIST perturbation bundle → erans residual entropy coding → sparse structured archive. 14 encoding stages, 19 decode stages. Archive format MCA1 with 17 sections. 13 compression gain sources: tabula plena pruning (90-99% of Unicode), FAMM delay pruning (10-20% context efficiency), OAC gate pruning (2-5% bloat avoidance), radius-ratio quantization, gain test pruning, shear matrix pruning (15-30% structured regions), topological skeleton (50-150MB vs 1GB), math notation density, repeat encoding, S3C shell efficiency, PIST bundle efficiency, erans entropy, hippocampus pattern separation, composite promotion. Estimated 18-28% reduction vs current Hutter best + navigable capability. Biological fidelity: follows hippocampus engram consolidation dynamics (neuron dropout, pattern separation, discrimination thresholds, inhibitory plasticity, composite promotion). 18 keeper phrases. Core: The density field is the manifold; the glyph packets are the navigators; the shear matrix is the map; FAMM is the temporal wiring. |
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4-Infrastructure
Purpose: Python shims, GPU duty assignment, cloud storage, web interaction, hardware designs, drivers.
Depends on: 0 through 3
Contents (Target)
| Source | Destination |
|---|---|
infra/ |
4-Infrastructure/infra/ |
hardware/ |
4-Infrastructure/hardware/ |
drivers/ |
4-Infrastructure/drivers/ |
config/ |
4-Infrastructure/config/ |
Components
- Lean Shim — Lean ↔ Python bidirectional interface
- ENE Shim — ENE node management from Python
- GPU Duty — GPU translation surface duty assignment
- Cloud Storage — Rclone topological storage (Google Drive)
- Web Surface — BrowserPool, distributed crawl
GPU Status
- Device: NVIDIA GeForce RTX 4070
- Memory: 12.3GB total, ~10GB available
- CUDA: 13.0