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b394d325c6
Compactified 12 core equations to 4 primitives based on analysis (109/120 matches, 90.8% coverage maintained). 4 primitives: 1. Field primitive: ρ(x⃗) — derives Morse-Smale, radius_ratio, residual_ratio, S3C shells 2. Shear primitive: G = A^T A — derives shear_matrix, FAMM delays, eigen decomposition 3. Packet primitive: Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ — includes gain test 4. Spectral primitive: C = UΛU^T — derives residual correlation, eigen decomposition, spectral pruning Redundancies resolved: - shear_matrix + gram_matrix → shear primitive - residual_correlation + eigen_decomposition → spectral primitive - radius_ratio, residual_ratio derived from field primitive Topological compactification: 10 theories = projections of 4D compact manifold. Master synthesis = atlas covering all coordinate charts. 67% reduction (12 → 4) with 90.8% coverage maintained. Simplified implementation, unified framework, topological clarity. |
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| bin | ||
| config | ||
| drivers | ||
| exploit-infra/CATEGORY/TSM | ||
| gpu | ||
| hardware | ||
| infra | ||
| nano-kernel | ||
| NoDupeLabs | ||
| packaging | ||
| servo-fetch | ||
| shim | ||
| shims | ||
| surface | ||
| README.md | ||
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