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Identified 12 Erdős problems amenable to 4-primitive framework approach. Primitive distribution: - Packet: 6 problems (50%) - encoding/witness problems dominate - Field: 2 problems (16.7%) - density/distribution problems - Shear: 2 problems (16.7%) - extremal/metric problems - Spectral: 2 problems (16.7%) - eigenvalue problems High priority problems: - Erdős–Rényi Random Graph Model (SPECTRAL) - eigenvalue distribution, VERY HIGH feasibility - Erdős–Turán Conjecture (FIELD) - additive basis density, HIGH feasibility - Erdős–Straus Conjecture (PACKET) - Egyptian fraction encoding, HIGH feasibility - Erdős Conjecture on Arithmetic Progressions (FIELD) - density implies structure, HIGH feasibility Recommended approach order: 1. Erdős–Rényi (validation point, spectral methods standard) 2. Erdős–Turán (additive basis density) 3. Erdős–Straus (Diophantine encoding) 4. Erdős Conjecture on APs (density implies structure) Key insight: Packet primitive dominates - many Erdős problems are about encodings/witness structures. All primitives represented - framework covers diverse Erdős problem types. Mapping saved to: 4-Infrastructure/shim/erdos_problems_4primitive_mapping.json |
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| exploit-infra/CATEGORY/TSM | ||
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| hardware | ||
| 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