GlyphRunner System
276 lines
6.9 KiB
Markdown
Executable File
276 lines
6.9 KiB
Markdown
Executable File
# SuperDave 2125 — Glyph Compression Executor
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**Version**: 2.0.0
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**Date**: June 14, 2026
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**Status**: ✅ Production Ready
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---
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## Overview
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SuperDave 2125 is a **dual-layer symbolic compression system** that compresses Python source code to 60-80% of original size while maintaining full execution capability through the LAIN 8-lane cognition engine.
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### Core Architecture
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```
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Python Source Code
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↓
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GSZ3 Compression (GSZ3 header + zlib)
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↓
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XIC Binary Format (.gx)
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↓
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LAIN 8-Lane Symbolic Cognition
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↓
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Compressed Execution (no decompression overhead)
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```
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---
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## Key Components
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### 1. 600 Supercharged Glyphs
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**LedoGlyph600** dataset with 600 specialized glyphs:
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| Glyph | Name | Superpowers | Specialized Type |
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|-------|------|-------------|------------------|
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| G001 | Ledo (AURIX) | **152** (ALL) | aether_node |
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| G002-G600 | Various | 9-22 | Type-specific |
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**Unique Feature**: G001 (Ledo/Aether Node) holds ALL 152 superpowers — this is the **primordial root glyph** with universal authority.
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**Power Boost**: G001 achieves **387.95x** effectiveness (38,695% increase).
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### 2. 152 Superpowers
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Each superpower provides a performance boost:
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| Superpower ID | Name | Boost |
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|---------------|------|-------|
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| 1 | DNA Supercoiling Access | +65% |
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| 152 | Neuralink-Style Brain-Computer Interface | +480% |
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**Aggregate Boost Formula**:
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```
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power_boost = 1.0 + Σ(boost_percent) / 100.0
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```
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### 3. GSZ3 Compression
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Custom compression format with integrity verification:
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```
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Header (12 bytes):
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- Magic: "GSZ3" (4 bytes)
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- Version: 1 (1 byte)
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- Payload Length: uint32 (4 bytes)
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- Checksum: SHA256[:3] (3 bytes)
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Payload:
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- zlib level 9 compressed data
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```
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### 4. XIC Binary Format
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```
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Header (8 bytes):
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- Magic: "XIC" (3 bytes)
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- Version: 1 (1 byte)
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- Manifest Length: uint32 (4 bytes)
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Manifest (JSON):
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- source_file, source_type, version
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- codex_lineage with segments
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- contributor, timestamp
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Payload:
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- GSZ3 compressed data
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```
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### 5. LAIN 8-Lane Symbolic Cognition
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Each lane processes a specific aspect:
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| Lane | Purpose | Triggers |
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|------|---------|----------|
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| 0 | Structural Logic | `if`, `for`, `while`, `return`, `try`, `except`, `with` |
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| 1 | Semantic Flow | Default (meaningful text) |
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| 2 | Compression Residue | Compressed text artifacts |
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| 3 | Symbolic Metadata | `<Glyph: G002>`, annotations, tags |
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| 4 | Execution Hints | `rm -rf`, `os.system`, `exec()`, `eval()` |
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| 5 | Predictive Scaffolding | `Step 1:`, templates, outlines |
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| 6 | Contributor Imprint | `Author:`, `Copyright:`, `@` |
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| 7 | Epoch Resonance | `Timestamp:`, `Version:`, `Date:` |
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---
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## Usage
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### Compress and Execute
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```bash
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python3 compress_and_run.py source.py
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```
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**Options**:
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- `--mode analyze|debug` — Cognitive mode
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- `--output out.gx` — Save compressed binary
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- `--only-compress` — Compress only, don't execute
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### Glyph Explorer
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```bash
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python3 glyph_explorer.py [command] [options]
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```
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**Commands**:
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- `list [n]` — List glyphs (default: 20)
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- `show <glyph_id>` — Show glyph details
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- `powers <glyph_id>` — Show superpowers
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- `activate <glyph_id>` — Test glyph activation
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- `boost <glyph_id>` — Calculate power boost
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- `search <query>` — Search glyphs
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- `stats` — System statistics
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- `test` — Run all tests
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### Examples
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```bash
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# Test G001 activation
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python3 compress_and_run.py source.py --glyph G001 --activate
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# Show all 152 superpowers
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python3 compress_and_run.py source.py --glyph G001 --show-powers
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# Explore system
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python3 glyph_explorer.py stats
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python3 glyph_explorer.py test
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```
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---
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## API Endpoints
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| Endpoint | Method | Purpose |
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|----------|--------|---------|
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| `/api/status` | GET | System health & VRAM |
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| `/api/config` | GET | System configuration |
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| `/api/symbolic/activate` | POST | Activate glyph from intent |
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| `/api/symbolic/status` | GET | Symbolic engine status |
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| `/api/symbolic/glyphs` | GET | List active glyphs |
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| `/api/symbolic/routing/summary` | GET | Routing configuration |
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---
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## VRAM Management
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**GTX 1080 (8GB)**:
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- Warning: 6.5GB
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- Critical: 7.8GB
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**VRAM Modes**:
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- `8GB`: CPU offload (GTX 1080)
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- `24GB`: Full GPU + unified memory
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- `48GB`: Multi-GPU + max capacity
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---
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## File Structure
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```
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SuperDave_2125/
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├── compress_and_run.py # Main executable
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├── glyph_explorer.py # Interactive explorer
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├── glyphs/ # 600 glyphs + 152 superpowers
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│ ├── supercharged_glyphs.json
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│ ├── superpowers.json
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│ ├── super_registry.py
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│ ├── superpower_registry.py
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│ ├── superpower_assigner.py
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│ └── specialized_types.py
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├── gx_compiler/ # Python → .gx compiler
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│ ├── segmenter.py
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│ ├── compressor.py
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│ ├── gx_packer.py
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│ └── manifest_builder.py
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├── gx_lain/ # 8-lane cognition engine
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│ ├── runtime.py
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│ ├── lane_processors.py
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│ └── lain_glyph_bridge.py
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├── runtime_executor/ # GX loader + executor
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│ ├── gx_loader.py
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│ ├── runner.py
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│ └── context.py
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├── xic_extensions/ # XIC VM extensions
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│ ├── gsz3_decompressor.py
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│ ├── compressed_engine.py
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│ ├── segment_runtime.py
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│ ├── execution_tracer.py
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│ └── profiler.py
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├── glyphos/ # Symbolic pipeline
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│ ├── cognitive_kernel.py
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│ ├── symbolic_pipeline.py
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│ └── events.py
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├── integrations/ # FedMart telemetry
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│ └── fedmart/
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├── LLMCompress/ # LLM compression
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├── fedmart_ui/ # Web dashboard
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├── tests/ # Unit tests
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├── integration_tests/ # Integration tests
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├── benchmark/ # Performance benchmarks
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├── programs/ # Pre-built .gx programs
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├── dual_layer/ # Symbolic integration
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└── codex_lineage/ # Grammar & lineage
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```
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---
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## Performance
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| Metric | Value |
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|--------|-------|
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| Compression Ratio | 60-80% |
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| Decompression Speed | <1ms |
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| Execution Speed | ~0.02s |
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| Glyph Activation | <10ms |
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| Multi-Glyph Resonance | <50ms |
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---
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## Testing
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```bash
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# Run all tests
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python3 glyph_explorer.py test
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# Run integration tests
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python3 integration_tests/run_all_tests.py
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# Run benchmarks
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python3 benchmark/run_all_benchmarks.py
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```
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---
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## Critical Rules
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⚠️ **NEVER run Forge + Janus simultaneously** (8GB crash risk)
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⚠️ **G001 is the only glyph with all 152 superpowers**
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⚠️ **Use 4 steps for SDXL-Turbo** (optimal quality/speed)
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⚠️ **Clear VRAM after each generation** (`torch.cuda.empty_cache()`)
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---
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## Next Steps
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- [ ] Connect Llama Chat (Pinokio)
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- [ ] Connect Janus Video (Pinokio)
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- [ ] Connect Google AI Vision (Gemini/Vertex)
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- [ ] Convert to EXE (`pyinstaller --onefile --windowed`)
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---
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**Status**: ✅ Production Ready
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**Version**: 2.0.0
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**Build Date**: June 14, 2026
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