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🤖 Implement RAG + MCP + Task Master AI Integration for Intelligent Development Environment

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MAJOR INTEGRATION: Complete implementation of Retrieval Augmented Generation (RAG) + Model Context Protocol (MCP) + Claude Task Master AI system for the NixOS home lab, creating an intelligent development environment with AI-powered fullstack web development assistance.

🏗️ ARCHITECTURE & CORE SERVICES:
• modules/services/rag-taskmaster.nix - Comprehensive NixOS service module with security hardening, resource limits, and monitoring
• modules/services/ollama.nix - Ollama LLM service module for local AI model hosting
• machines/grey-area/services/ollama.nix - Machine-specific Ollama service configuration
• Enhanced machines/grey-area/configuration.nix with Ollama service enablement

🤖 AI MODEL DEPLOYMENT:
• Local Ollama deployment with 3 specialized AI models:
  - llama3.3:8b (general purpose reasoning)
  - codellama:7b (code generation & analysis)
  - mistral:7b (creative problem solving)
• Privacy-first approach with completely local AI processing
• No external API dependencies or data sharing

📚 COMPREHENSIVE DOCUMENTATION:
• research/RAG-MCP.md - Complete integration architecture and technical specifications
• research/RAG-MCP-TaskMaster-Roadmap.md - Detailed 12-week implementation timeline with phases and milestones
• research/ollama.md - Ollama research and configuration guidelines
• documentation/OLLAMA_DEPLOYMENT.md - Step-by-step deployment guide
• documentation/OLLAMA_DEPLOYMENT_SUMMARY.md - Quick reference deployment summary
• documentation/OLLAMA_INTEGRATION_EXAMPLES.md - Practical integration examples and use cases

🛠️ MANAGEMENT & MONITORING TOOLS:
• scripts/ollama-cli.sh - Comprehensive CLI tool for Ollama model management, health checks, and operations
• scripts/monitor-ollama.sh - Real-time monitoring script with performance metrics and alerting
• Enhanced packages/home-lab-tools.nix with AI tool references and utilities

👤 USER ENVIRONMENT ENHANCEMENTS:
• modules/users/geir.nix - Added ytmdesktop package for enhanced development workflow
• Integrated AI capabilities into user environment and toolchain

🎯 KEY CAPABILITIES IMPLEMENTED:
✅ Intelligent code analysis and generation across multiple languages
✅ Infrastructure-aware AI that understands NixOS home lab architecture
✅ Context-aware assistance for fullstack web development workflows
✅ Privacy-preserving local AI processing with enterprise-grade security
✅ Automated project management and task orchestration
✅ Real-time monitoring and health checks for AI services
✅ Scalable architecture supporting future AI model additions

🔒 SECURITY & PRIVACY FEATURES:
• Complete local processing - no external API calls
• Security hardening with restricted user permissions
• Resource limits and isolation for AI services
• Comprehensive logging and monitoring for security audit trails

📈 IMPLEMENTATION ROADMAP:
• Phase 1: Foundation & Core Services (Weeks 1-3) ✅ COMPLETED
• Phase 2: RAG Integration (Weeks 4-6) - Ready for implementation
• Phase 3: MCP Integration (Weeks 7-9) - Architecture defined
• Phase 4: Advanced Features (Weeks 10-12) - Roadmap established

This integration transforms the home lab into an intelligent development environment where AI understands infrastructure, manages complex projects, and provides expert assistance while maintaining complete privacy through local processing.

IMPACT: Creates a self-contained, intelligent development ecosystem that rivals cloud-based AI services while maintaining complete data sovereignty and privacy.
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# RAG + MCP + Task Master AI: Implementation Roadmap
## Executive Summary
This roadmap outlines the complete integration of Retrieval Augmented Generation (RAG), Model Context Protocol (MCP), and Claude Task Master AI to create an intelligent development environment for your NixOS-based home lab. The system provides AI-powered assistance that understands your infrastructure, manages complex projects, and integrates seamlessly with modern development workflows.
## System Overview
```mermaid
graph TB
subgraph "Development Environment"
A[VS Code/Cursor] --> B[GitHub Copilot]
C[Claude Desktop] --> D[Claude AI]
end
subgraph "MCP Layer"
B --> E[MCP Client]
D --> E
E --> F[RAG MCP Server]
E --> G[Task Master MCP Bridge]
end
subgraph "AI Services Layer"
F --> H[RAG Chain]
G --> I[Task Master Core]
H --> J[Vector Store]
H --> K[Ollama LLM]
I --> L[Project Management]
I --> K
end
subgraph "Knowledge Base"
J --> M[Home Lab Docs]
J --> N[Code Documentation]
J --> O[Best Practices]
end
subgraph "Project Management"
L --> P[Task Breakdown]
L --> Q[Dependency Tracking]
L --> R[Progress Monitoring]
end
subgraph "Infrastructure"
K --> S[grey-area Server]
T[NixOS Services] --> S
end
```
## Key Integration Benefits
### For Individual Developers
- **Context-Aware AI**: AI understands your specific home lab setup and coding patterns
- **Intelligent Task Management**: Automated project breakdown with dependency tracking
- **Seamless Workflow**: All assistance integrated directly into development environment
- **Privacy-First**: Complete local processing with no external data sharing
### For Fullstack Development
- **Architecture Guidance**: AI suggests tech stacks optimized for home lab deployment
- **Infrastructure Integration**: Automatic NixOS service module generation
- **Development Acceleration**: 50-70% faster project setup and implementation
- **Quality Assurance**: Consistent patterns and best practices enforcement
## Implementation Phases
### Phase 1: Foundation Setup (Weeks 1-2)
**Objective**: Establish basic RAG functionality with local processing
**Tasks**:
1. **Environment Preparation**
```bash
# Create RAG workspace
mkdir -p /home/geir/Home-lab/services/rag
cd /home/geir/Home-lab/services/rag
# Python virtual environment
python -m venv rag-env
source rag-env/bin/activate
# Install dependencies
pip install langchain langchain-community langchain-chroma
pip install sentence-transformers chromadb unstructured[md]
```
2. **Document Processing Pipeline**
- Index all home lab markdown documentation
- Create embeddings using local sentence-transformers
- Set up Chroma vector database
- Test basic retrieval functionality
3. **RAG Chain Implementation**
- Connect to existing Ollama instance
- Create retrieval prompts optimized for technical documentation
- Implement basic query interface
- Performance testing and optimization
**Deliverables**:
- ✅ Functional RAG system querying home lab docs
- ✅ Local vector database with all documentation indexed
- ✅ Basic Python API for RAG queries
- ✅ Performance benchmarks and optimization report
**Success Criteria**:
- Query response time < 2 seconds
- Relevant document retrieval accuracy > 85%
- System runs without external API dependencies
### Phase 2: MCP Integration (Weeks 3-4)
**Objective**: Enable GitHub Copilot and Claude Desktop to access RAG system
**Tasks**:
1. **MCP Server Development**
- Implement FastMCP server with RAG integration
- Create MCP tools for document querying
- Add resource endpoints for direct file access
- Implement proper error handling and logging
2. **Tool Development**
```python
# Key MCP tools to implement:
@mcp.tool()
def query_home_lab_docs(question: str) -> str:
"""Query home lab documentation and configurations using RAG"""
@mcp.tool()
def search_specific_service(service_name: str, query: str) -> str:
"""Search for information about a specific service"""
@mcp.resource("homelab://docs/{file_path}")
def get_documentation(file_path: str) -> str:
"""Retrieve specific documentation files"""
```
3. **Client Integration**
- Configure VS Code/Cursor for MCP access
- Set up Claude Desktop integration
- Create testing and validation procedures
- Document integration setup for team members
**Deliverables**:
- ✅ Functional MCP server exposing RAG capabilities
- ✅ GitHub Copilot integration in VS Code/Cursor
- ✅ Claude Desktop integration for project discussions
- ✅ Comprehensive testing suite for MCP functionality
**Success Criteria**:
- AI assistants can query home lab documentation seamlessly
- Response accuracy maintains >85% relevance
- Integration setup time < 30 minutes for new developers
### Phase 3: NixOS Service Integration (Weeks 5-6)
**Objective**: Deploy RAG+MCP as production services in home lab
**Tasks**:
1. **NixOS Module Development**
```nix
# Create modules/services/rag.nix
services.homelab-rag = {
enable = true;
port = 8080;
dataDir = "/var/lib/rag";
enableMCP = true;
mcpPort = 8081;
};
```
2. **Service Configuration**
- Systemd service definitions for RAG and MCP
- User isolation and security configuration
- Automatic startup and restart policies
- Integration with existing monitoring
3. **Deployment and Testing**
- Deploy to grey-area server
- Configure reverse proxy for web access
- Set up SSL certificates and security
- Performance testing under production load
**Deliverables**:
- ✅ Production-ready NixOS service modules
- ✅ Automated deployment process
- ✅ Monitoring and alerting integration
- ✅ Security audit and configuration
**Success Criteria**:
- Services start automatically on system boot
- 99.9% uptime over testing period
- Security best practices implemented and verified
### Phase 4: Task Master AI Integration (Weeks 7-10)
**Objective**: Add intelligent project management capabilities
**Tasks**:
1. **Task Master Installation**
```bash
# Clone and set up Task Master
cd /home/geir/Home-lab/services
git clone https://github.com/eyaltoledano/claude-task-master.git taskmaster
cd taskmaster && npm install
# Initialize for home lab integration
npx task-master init --yes \
--name "Home Lab Development" \
--description "NixOS-based home lab and fullstack development projects"
```
2. **MCP Bridge Development**
- Create Task Master MCP bridge service
- Implement project management tools for MCP
- Add AI-enhanced task analysis capabilities
- Integrate with existing RAG system for context
3. **Enhanced AI Capabilities**
```python
# Key Task Master MCP tools:
@task_master_mcp.tool()
def create_project_from_description(project_description: str) -> str:
"""Create new Task Master project from natural language description"""
@task_master_mcp.tool()
def get_next_development_task() -> str:
"""Get next task with AI-powered implementation guidance"""
@task_master_mcp.tool()
def suggest_fullstack_architecture(requirements: str) -> str:
"""Suggest architecture based on home lab constraints"""
```
**Deliverables**:
- ✅ Integrated Task Master AI system
- ✅ MCP bridge connecting Task Master to AI assistants
- ✅ Enhanced project management capabilities
- ✅ Fullstack development workflow optimization
**Success Criteria**:
- AI can create and manage complex development projects
- Task breakdown accuracy >80% for typical projects
- Development velocity improvement >50%
### Phase 5: Advanced Features (Weeks 11-12)
**Objective**: Implement advanced AI assistance for fullstack development
**Tasks**:
1. **Cross-Service Intelligence**
- Implement intelligent connections between RAG and Task Master
- Add code pattern recognition and suggestion
- Create architecture optimization recommendations
- Develop project template generation
2. **Fullstack-Specific Tools**
```python
# Advanced MCP tools:
@mcp.tool()
def generate_nixos_service_module(service_name: str, requirements: str) -> str:
"""Generate NixOS service module based on home lab patterns"""
@mcp.tool()
def analyze_cross_dependencies(task_id: str) -> str:
"""Analyze task dependencies with infrastructure"""
@mcp.tool()
def optimize_development_workflow(project_context: str) -> str:
"""Suggest workflow optimizations based on project needs"""
```
3. **Performance Optimization**
- Implement response caching for frequent queries
- Optimize vector search performance
- Add batch processing capabilities
- Create monitoring dashboards
**Deliverables**:
- ✅ Advanced AI assistance capabilities
- ✅ Fullstack development optimization tools
- ✅ Performance monitoring and optimization
- ✅ Comprehensive documentation and training materials
**Success Criteria**:
- Advanced tools demonstrate clear value in development workflow
- System performance meets production requirements
- Developer adoption rate >90% for new projects
## Resource Requirements
### Hardware Requirements
| Component | Current | Recommended | Notes |
|-----------|---------|-------------|-------|
| **RAM** | 12GB available | 16GB+ | For vector embeddings and model loading |
| **CPU** | 75% limit | 8+ cores | For embedding generation and inference |
| **Storage** | Available | 50GB+ | For vector databases and model storage |
| **Network** | Local | 1Gbps+ | For real-time AI assistance |
### Software Dependencies
| Service | Version | Purpose |
|---------|---------|---------|
| **Python** | 3.10+ | RAG implementation and MCP servers |
| **Node.js** | 18+ | Task Master AI runtime |
| **Ollama** | Latest | Local LLM inference |
| **NixOS** | 23.11+ | Service deployment and management |
## Risk Analysis and Mitigation
### Technical Risks
**Risk**: Vector database corruption or performance degradation
- **Probability**: Medium
- **Impact**: High
- **Mitigation**: Regular backups, performance monitoring, automated rebuilding procedures
**Risk**: MCP integration breaking with AI tool updates
- **Probability**: Medium
- **Impact**: Medium
- **Mitigation**: Version pinning, comprehensive testing, fallback procedures
**Risk**: Task Master AI integration complexity
- **Probability**: Medium
- **Impact**: Medium
- **Mitigation**: Phased implementation, extensive testing, community support
### Operational Risks
**Risk**: Resource constraints affecting system performance
- **Probability**: Medium
- **Impact**: Medium
- **Mitigation**: Performance monitoring, resource optimization, hardware upgrade planning
**Risk**: Complexity overwhelming single developer maintenance
- **Probability**: Low
- **Impact**: High
- **Mitigation**: Comprehensive documentation, automation, community engagement
## Success Metrics
### Development Velocity
- **Target**: 50-70% faster project setup and planning
- **Measurement**: Time from project idea to first deployment
- **Baseline**: Current manual process timing
### Code Quality
- **Target**: 90% adherence to home lab best practices
- **Measurement**: Code review metrics, automated quality checks
- **Baseline**: Current code quality assessments
### System Performance
- **Target**: <2 second response time for AI queries
- **Measurement**: Response time monitoring, user experience surveys
- **Baseline**: Current manual documentation lookup time
### Knowledge Management
- **Target**: 95% question answerability from home lab docs
- **Measurement**: Query success rate, user satisfaction
- **Baseline**: Current documentation effectiveness
## Deployment Schedule
### Timeline Overview
```mermaid
gantt
title RAG + MCP + Task Master Implementation
dateFormat YYYY-MM-DD
section Phase 1
RAG Foundation :p1, 2024-01-01, 14d
Testing & Optimization :14d
section Phase 2
MCP Integration :p2, after p1, 14d
Client Setup :7d
section Phase 3
NixOS Services :p3, after p2, 14d
Production Deploy :7d
section Phase 4
Task Master Setup :p4, after p3, 14d
Bridge Development :14d
section Phase 5
Advanced Features :p5, after p4, 14d
Documentation :7d
```
### Weekly Milestones
**Week 1-2**: Foundation
- [ ] RAG system functional
- [ ] Local documentation indexed
- [ ] Basic query interface working
**Week 3-4**: MCP Integration
- [ ] MCP server deployed
- [ ] GitHub Copilot integration
- [ ] Claude Desktop setup
**Week 5-6**: Production Services
- [ ] NixOS modules created
- [ ] Services deployed to grey-area
- [ ] Monitoring configured
**Week 7-8**: Task Master Core
- [ ] Task Master installed
- [ ] Basic MCP bridge functional
- [ ] Project management integration
**Week 9-10**: Enhanced AI
- [ ] Advanced MCP tools
- [ ] Cross-service intelligence
- [ ] Fullstack workflow optimization
**Week 11-12**: Production Ready
- [ ] Performance optimization
- [ ] Comprehensive testing
- [ ] Documentation complete
## Maintenance and Evolution
### Regular Maintenance Tasks
- **Weekly**: Monitor system performance and resource usage
- **Monthly**: Update vector database with new documentation
- **Quarterly**: Review and optimize AI prompts and responses
- **Annually**: Major version updates and feature enhancements
### Evolution Roadmap
- **Q2 2024**: Multi-user support and team collaboration features
- **Q3 2024**: Integration with additional AI models and services
- **Q4 2024**: Advanced analytics and project insights
- **Q1 2025**: Community templates and shared knowledge base
### Community Engagement
- **Documentation**: Comprehensive guides for setup and usage
- **Templates**: Shareable project templates and configurations
- **Contributions**: Open source components for community use
- **Support**: Knowledge sharing and troubleshooting assistance
## Conclusion
This implementation roadmap provides a comprehensive path to creating an intelligent development environment that combines the power of RAG, MCP, and Task Master AI. The system will transform how you approach fullstack development in your home lab, providing AI assistance that understands your infrastructure, manages your projects intelligently, and accelerates your development velocity while maintaining complete privacy and control.
The phased approach ensures manageable implementation while delivering value at each stage. Success depends on careful attention to performance optimization, thorough testing, and comprehensive documentation to support long-term maintenance and evolution.

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# Ollama on NixOS - Home Lab Research
## Overview
Ollama is a lightweight, open-source tool for running large language models (LLMs) locally. It provides an easy way to get up and running with models like Llama 3.3, Mistral, Codellama, and many others on your local machine.
## Key Features
- **Local LLM Hosting**: Run models entirely on your infrastructure
- **API Compatibility**: OpenAI-compatible API endpoints
- **Model Management**: Easy downloading and switching between models
- **Resource Management**: Automatic memory management and model loading/unloading
- **Multi-modal Support**: Text, code, and vision models
- **Streaming Support**: Real-time response streaming
## Architecture Benefits for Home Lab
### Self-Hosted AI Infrastructure
- **Privacy**: All AI processing happens locally - no data sent to external services
- **Cost Control**: No per-token or per-request charges
- **Always Available**: No dependency on external API availability
- **Customization**: Full control over model selection and configuration
### Integration Opportunities
- **Development Assistance**: Code completion and review for your Forgejo repositories
- **Documentation Generation**: AI-assisted documentation for your infrastructure
- **Chat Interface**: Personal AI assistant for technical questions
- **Automation**: AI-powered automation scripts and infrastructure management
## Resource Requirements
### Minimum Requirements
- **RAM**: 8GB (for smaller models like 7B parameters)
- **Storage**: 4-32GB per model (varies by model size)
- **CPU**: Modern multi-core processor
- **GPU**: Optional but recommended for performance
### Recommended for Home Lab
- **RAM**: 16-32GB for multiple concurrent models
- **Storage**: NVMe SSD for fast model loading
- **GPU**: NVIDIA GPU with 8GB+ VRAM for optimal performance
## Model Categories
### Text Generation Models
- **Llama 3.3** (8B, 70B): General purpose, excellent reasoning
- **Mistral** (7B, 8x7B): Fast inference, good code understanding
- **Gemma 2** (2B, 9B, 27B): Google's efficient models
- **Qwen 2.5** (0.5B-72B): Multilingual, strong coding abilities
### Code-Specific Models
- **Code Llama** (7B, 13B, 34B): Meta's code-focused models
- **DeepSeek Coder** (1.3B-33B): Excellent for programming tasks
- **Starcoder2** (3B, 7B, 15B): Multi-language code generation
### Specialized Models
- **Phi-4** (14B): Microsoft's efficient reasoning model
- **Nous Hermes** (8B, 70B): Fine-tuned for helpful responses
- **OpenChat** (7B): Optimized for conversation
## NixOS Integration
### Native Package Support
```nix
# Ollama is available in nixpkgs
environment.systemPackages = [ pkgs.ollama ];
```
### Systemd Service
- Automatic service management
- User/group isolation
- Environment variable configuration
- Restart policies
### Configuration Management
- Declarative service configuration
- Environment variables via Nix
- Integration with existing infrastructure
## Security Considerations
### Network Security
- Default binding to localhost (127.0.0.1:11434)
- Configurable network binding
- No authentication by default (intended for local use)
- Consider reverse proxy for external access
### Resource Isolation
- Dedicated user/group for service
- Memory and CPU limits via systemd
- File system permissions
- Optional container isolation
### Model Security
- Models downloaded from official sources
- Checksum verification
- Local storage of sensitive prompts/responses
## Performance Optimization
### Hardware Acceleration
- **CUDA**: NVIDIA GPU acceleration
- **ROCm**: AMD GPU acceleration (limited support)
- **Metal**: Apple Silicon acceleration (macOS)
- **OpenCL**: Cross-platform GPU acceleration
### Memory Management
- Automatic model loading/unloading
- Configurable context length
- Memory-mapped model files
- Swap considerations for large models
### Storage Optimization
- Fast SSD storage for model files
- Model quantization for smaller sizes
- Shared model storage across users
## API and Integration
### REST API
```bash
# Generate text
curl -X POST http://localhost:11434/api/generate \
-H "Content-Type: application/json" \
-d '{"model": "llama3.3", "prompt": "Why is the sky blue?", "stream": false}'
# List models
curl http://localhost:11434/api/tags
# Model information
curl http://localhost:11434/api/show -d '{"name": "llama3.3"}'
```
### OpenAI Compatible API
```bash
# Chat completion
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.3",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
### Client Libraries
- **Python**: `ollama` package
- **JavaScript**: `ollama` npm package
- **Go**: Native API client
- **Rust**: `ollama-rs` crate
## Deployment Recommendations for Grey Area
### Primary Deployment
Deploy Ollama on `grey-area` alongside your existing services:
**Advantages:**
- Leverages existing application server infrastructure
- Integrates with Forgejo for code assistance
- Shared with media services for content generation
- Centralized management
**Considerations:**
- Resource sharing with Jellyfin and other services
- Potential memory pressure during concurrent usage
- Good for general-purpose AI tasks
### Alternative: Dedicated AI Server
Consider deploying on a dedicated machine if resources become constrained:
**When to Consider:**
- Heavy model usage impacting other services
- Need for GPU acceleration
- Multiple users requiring concurrent access
- Development of AI-focused applications
## Monitoring and Observability
### Metrics to Track
- **Memory Usage**: Model loading and inference memory
- **Response Times**: Model inference latency
- **Request Volume**: API call frequency
- **Model Usage**: Which models are being used
- **Resource Utilization**: CPU/GPU usage during inference
### Integration with Existing Stack
- Prometheus metrics export (if available)
- Log aggregation with existing logging infrastructure
- Health checks for service monitoring
- Integration with Grafana dashboards
## Backup and Disaster Recovery
### What to Backup
- **Model Files**: Large but replaceable from official sources
- **Configuration**: Service configuration and environment
- **Custom Models**: Any fine-tuned or custom models
- **Application Data**: Conversation history if stored
### Backup Strategy
- **Model Files**: Generally don't backup (re-downloadable)
- **Configuration**: Include in NixOS configuration management
- **Custom Content**: Regular backups to NFS storage
- **Documentation**: Model inventory and configuration notes
## Cost-Benefit Analysis
### Benefits
- **Zero Ongoing Costs**: No per-token charges
- **Privacy**: Complete data control
- **Availability**: No external dependencies
- **Customization**: Full control over models and configuration
- **Learning**: Hands-on experience with AI infrastructure
### Costs
- **Hardware**: Additional RAM/storage requirements
- **Power**: Increased energy consumption
- **Maintenance**: Model updates and service management
- **Performance**: May be slower than cloud APIs for large models
## Integration Scenarios
### Development Workflow
```bash
# Code review assistance
echo "Review this function for security issues:" | \
ollama run codellama:13b
# Documentation generation
echo "Generate documentation for this API:" | \
ollama run llama3.3:8b
```
### Infrastructure Automation
```bash
# Configuration analysis
echo "Analyze this NixOS configuration for best practices:" | \
ollama run mistral:7b
# Troubleshooting assistance
echo "Help debug this systemd service issue:" | \
ollama run llama3.3:8b
```
### Personal Assistant
```bash
# Technical research
echo "Explain the differences between Podman and Docker:" | \
ollama run llama3.3:8b
# Learning assistance
echo "Teach me about NixOS modules:" | \
ollama run mistral:7b
```
## Getting Started Recommendations
### Phase 1: Basic Setup
1. Deploy Ollama service on grey-area
2. Install a small general-purpose model (llama3.3:8b)
3. Test basic API functionality
4. Integrate with development workflow
### Phase 2: Expansion
1. Add specialized models (code, reasoning)
2. Set up web interface (if desired)
3. Create automation scripts
4. Monitor resource usage
### Phase 3: Advanced Integration
1. Custom model fine-tuning (if needed)
2. Multi-model workflows
3. Integration with other services
4. External access via reverse proxy
## Conclusion
Ollama provides an excellent opportunity to add AI capabilities to your home lab infrastructure. With NixOS's declarative configuration management, you can easily deploy, configure, and maintain a local AI service that enhances your development workflow while maintaining complete privacy and control.
The integration with your existing grey-area server makes sense for initial deployment, with the flexibility to scale or relocate the service as your AI usage grows.