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972d6643e2
Reviewed grand unified theory equations (10 axioms + 4 unified equations) and mapped them to the 4-primitive framework. Mapping results: - Field primitive (ρ(x⃗)): 4 equations (Shannon entropy, Zipf law, grammar manifold, topological invariants) - Shear primitive (G = AᵀA): 2 equations (hyperbolic hierarchy, language as manifold) - Packet primitive (Γᵢ): 3 equations (ANS optimality, BWT, grand compression) - Spectral primitive (C = UΛUᵀ): 5 equations (Kolmogorov complexity, information bottleneck, MDL, hyperbolic distance) Key insights: - Consistency: Grand unified theory axioms map cleanly to 4 primitives - Completeness: Each primitive has representative equations from multiple sources - Integration: Compactified core equations subsume grand unified theory equations - No significant gaps — each primitive well-represented - Some redundancy: Grand compression spans packet + spectral (expected) Canonical mapping confirmed: - Field: entropy, density, topology, manifold structure - Shear: distance, metric, deformation, geometric transform - Packet: coding, compression, transform, optimization - Spectral: complexity, basis, bottleneck, decomposition, tradeoff Mapping saved to: 4-Infrastructure/shim/system_equations_4primitive_mapping.json |
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| exploit-infra/CATEGORY/TSM | ||
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| infra | ||
| nano-kernel | ||
| NoDupeLabs | ||
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| 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