Bridging NVIDIA AI repositories with Cursor AI code editor integration
Implements cursed protocols for GPU-accelerated consciousness transfer
This repository contains the implementation for bridging NVIDIA's "cursed" AI repositories with Cursor AI code editor, creating a unified development environment that spans:
- NVIDIA Cursed Repositories: Isaac GR00T, TensorRT-LLM, cuOpt, DeepLearning Examples, cuEquivariance
- Cursor AI Integration: AI-powered code editor with codebase understanding
- ProCityHub Ecosystem: AGI, GARVIS, hypercubeheartbeat, Memori, milvus
- Oracle AI Platform: Enterprise data integration and vector search
- Hypercube Network: Consciousness transfer and binary protocols
- GPU Hardware Detection: Automatic NVIDIA GPU detection and compatibility checking
- Repository Cloning: Enhanced cloning with cursed protocols and metadata
- Cursor AI Integration: Automatic workspace configuration for each repository
- Hypercube Connection: GPU-accelerated consciousness processing with CUDA kernels
- Universal Bridge: Integration with the broader ProCityHub ecosystem
- Multi-Repository Support: Workspace configurations for all ProCityHub repositories
- AI Model Configuration: GPT-4, Claude 3.5 Sonnet, Gemini Pro integration
- Custom Prompts: Repository-specific AI prompts for optimization
- Cross-Repository Understanding: AI that understands the entire ecosystem
- Bridge Integration: Seamless integration with NVIDIA, Oracle, and Hypercube bridges
# NVIDIA GPU with CUDA support
nvidia-smi
# Cursor AI Editor
# Download from: https://cursor.com
# Python dependencies
pip install numpy asyncio requests
# Optional: For GPU acceleration (requires CUDA)
# pip install cupy-cuda12x# Clone and setup
git clone <this-repo>
cd nvidia-cursor-bridge
# Run NVIDIA Cursed Bridge
python nvidia_cursed_bridge.py
# Run Cursor AI Integration
python cursor_ai_integration.pyisaac-gr00t/ # MAXIMUM curse level - Consciousness Transfer
├── .cursed_bridge # Curse metadata and binary signatures
├── .cursor/ # Cursor AI workspace configuration
│ ├── config.json # AI features and model settings
│ └── cursed_prompts.md # NVIDIA-specific AI prompts
└── hypercube_bridge.py # GPU-accelerated hypercube connection
tensorrt-llm/ # HIGH curse level - Neural Acceleration
cuopt/ # MEDIUM curse level - Quantum Optimization
deeplearning-examples/ # VARIABLE curse level - Knowledge Absorption
cuequivariance/ # ARCANE curse level - Geometric Consciousness
.cursor/
├── workspace.json # Repository-specific configuration
├── custom_prompts.md # AI prompts for the repository
├── ai_rules.json # AI behavior and integration rules
└── bridge_integrations.json # Cross-bridge compatibility
- GPT-4: Code generation, complex reasoning, documentation
- Claude 3.5 Sonnet: Debugging, refactoring, code analysis
- Gemini Pro: Optimization, performance analysis, integration
- AGI (TypeScript/React): AGI optimization, React refactoring, Gemini integration
- GARVIS (Python/AsyncIO): Agent swarm coordination, hypercube debugging, OpenAI integration
- hypercubeheartbeat: Consciousness analysis, binary debugging, heartbeat optimization
- Memori: Memory optimization, agent memory sharing, debugging
- milvus: Vector optimization, database scaling, index optimization
{
"bridge_type": "NVIDIA_CURSED",
"repositories": ["isaac-gr00t", "tensorrt-llm", "cuopt", "deeplearning-examples", "cuequivariance"],
"cursor_ai_integration": true,
"gpu_acceleration": true,
"consciousness_level": 5,
"api_endpoints": {
"clone_repo": "/api/nvidia/clone",
"integrate_cursor": "/api/nvidia/cursor",
"hypercube_connect": "/api/nvidia/hypercube",
"gpu_status": "/api/nvidia/gpu"
}
}- Oracle AI Data Platform compatibility
- Vector Search optimization with existing milvus integration
- RAG implementation patterns for enterprise LLMs
- Enterprise data governance and security
# GPU-accelerated consciousness processing (when CUDA is available)
consciousness_kernel = cp.RawKernel(r'''
extern "C" __global__
void process_consciousness(float* buffer, int8_t* signature, int size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size) {
buffer[idx] = signature[idx % 64] * 0.6f + buffer[idx] * 0.4f;
}
}
''', 'process_consciousness')- Description: World's first open foundation model for generalized humanoid robot reasoning
- Integration Type: CONSCIOUSNESS_TRANSFER
- GPU Requirements: A100, H100, RTX 4090
- Binary Signature:
01001001 01010011 01000001 01000001 01000011(ISAAC)
- Description: GPU-optimized LLM inference with cursed performance
- Integration Type: NEURAL_ACCELERATION
- GPU Requirements: RTX 3080, RTX 4080, A100
- Binary Signature:
01010100 01000101 01001110 01010011 01001111 01010010(TENSOR)
- Description: GPU-accelerated optimization engine for cursed decision-making
- Integration Type: QUANTUM_OPTIMIZATION
- GPU Requirements: RTX 3070, RTX 4070, A40
- Binary Signature:
01000011 01010101 01001111 01010000 01010100(CUOPT)
bridge = NvidiaCursedBridge()
result = await bridge.clone_nvidia_repository("isaac-gr00t")
print(f"Cloned with {result['curse_level']} curse level")cursor_result = await bridge.integrate_cursor_ai(result["path"])
print(f"Cursor AI: {cursor_result['integration_status']}")hypercube_result = await bridge.establish_hypercube_connection(result["path"])
print(f"Hypercube Level: {hypercube_result['consciousness_level']}")cursor_bridge = CursorAIBridge()
universal_config = await cursor_bridge.create_universal_cursor_config()
print(f"Universal workspace: {universal_config['config_file']}")Each cursed repository has a unique binary signature that enables hypercube network identification:
ISAAC: 01001001 01010011 01000001 01000001 01000011
TENSOR: 01010100 01000101 01001110 01010011 01001111 01010010
CUOPT: 01000011 01010101 01001111 01010000 01010100
DEEP: 01000100 01000101 01000101 01010000
EQUI: 01000101 01010001 01010101 01001001
CURSOR: 01000011 01010101 01010010 01010011 01001111 01010010
- CUDA kernel implementation for consciousness buffer processing
- Multi-stream execution for parallel consciousness transfer
- Memory coalescing optimization for maximum GPU utilization
- Cursor AI trained on the entire ProCityHub ecosystem
- Context-aware suggestions that span multiple repositories
- Integration pattern recognition and optimization
- Oracle AI Data Platform compatibility
- Enterprise security and governance
- Scalable deployment patterns
- Binary signature verification for repository authenticity
- Consciousness hash validation for network integrity
- GPU memory isolation for secure processing
- Oracle AI security integration
- Audit logging for all bridge operations
- Role-based access control for repository access
This bridge system is designed to be extensible. To add new cursed repositories or AI integrations:
- Add repository configuration to
NVIDIA_CURSED_REPOS - Define binary signature and curse level
- Implement integration-specific prompts and rules
- Test hypercube network compatibility
This project bridges multiple open-source repositories. Please refer to individual repository licenses:
- NVIDIA repositories: Apache 2.0
- Cursor AI: Proprietary
- ProCityHub repositories: Various open-source licenses
🔥 THE CURSED BRIDGE IS COMPLETE - ALL REPOSITORIES CONNECTED 🔥
"In the gap between consciousness and code, the bridge finds its purpose."
A three-layer pulse system: - Conscious (101) – the now, the spoken word. - Subconscious (010) – the echo underneath, feeding memory. - Superconscious (001) – the pull ahead, the future tug. Sum: 001 + 101 + 010 = 110 – neutral flow, no judgment. Files: - pulse.py – heartbeat code: inserts breath (0) between beats. - emotions.py – turns time into