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Combines maximum math density + unified compression architecture + hippocampus engram consolidation (Tomé 2024) + tabula plena insight (Live Science 2024: hippocampus starts full slate, prunes to sparse). Key insight: hippocampus starts tabula plena (densely wired, hyperconnected) and prunes to sparse structured during maturation. Compression does the same: start with maximum math density (full Unicode 1,114,112 codepoints + custom glyphs + omniversal chirality) and prune via FAMM delays, OAC gates, radius-ratio quantization, gain tests to minimal representation. FAMM pruning model: uniform delays (young hippocampus) → preshaped delays based on eigenvalue spectra → sparse structured delays (mature hippocampus). 10 compression gain sources: tabula plena pruning (90-99% of Unicode unused), 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, erans entropy. Estimated 15-25% reduction vs current Hutter best + navigable capability. 14 encoding/decode stages. Archive format HFC1 with 16 sections. 13 keeper phrases. Core: Don't start blank. Start full, then prune. |
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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