ResonanceOS v6 is a revolutionary AI system that generates content with quantifiable human resonance metrics. Unlike traditional AI writers that focus solely on semantic coherence, ResonanceOS leverages Human-Resonant Value (HRV) vectors to ensure content aligns with human engagement patterns.
- 🧠 Human-Resonant Value (HRV) System: 8-dimensional vectors measuring textual resonance
- 👥 Multi-Tenant Profile Management: Support for multiple clients/brands with distinct HRV profiles
- ⚡ Real-Time Feedback Loop: Paragraph-level resonance optimization during generation
- 🔧 Modular Architecture: Clean separation between planning, generation, and refinement layers
- 📊 Zero Dependencies: Core functionality uses only Python standard library
- 🌐 REST API: Complete API for integration with external systems
- 💻 CLI Interface: Command-line tools for content generation and management
- 📈 Analytics Dashboard: Real-time performance and quality monitoring
resonance_os/
├── core/ # Core constants and types
│ ├── hrv_constants.py # HRV dimension definitions
│ └── hrv_types.py # Type definitions
├── generation/ # Content generation pipeline
│ ├── human_resonant_writer.py # Main generation engine
│ ├── planner_layer.py # Strategic content planning
│ ├── sentence_layer.py # Sentence-level generation
│ ├── refiner_layer.py # Content refinement
│ └── hrf_model.py # Human resonance feedback model
├── profiles/ # Profile management
│ ├── hrv_extractor.py # HRV vector extraction
│ └── multi_tenant_hr_profiles.py # Profile storage
├── api/ # REST API
│ └── hr_server.py # FastAPI server
└── cli/ # Command line interface
└── hr_main.py # CLI entry point
The Human-Resonant Value (HRV) system measures 8 key dimensions:
- Sentence Variance: Cadence and rhythm variation
- Emotional Valence: Positive/negative sentiment
- Emotional Intensity: Strength of emotion
- Assertiveness Index: Authoritative tone
- Curiosity Index: Intrigue and engagement potential
- Metaphor Density: Metaphoric richness
- Storytelling Index: Narrative engagement
- Active Voice Ratio: Active vs. passive sentence ratio
# Clone or download the project
cd resonance_os
# Run complete setup (recommended)
python setup.py
# Or check existing installation
python setup.py --check-only# Run comprehensive system check
python system_runner.py --all
# Run tests
python test_runner.py
# Start API server
python system_runner.py --serve# Generate content with default settings
python resonance_os/cli/hr_main --prompt "The future of renewable energy"
# With specific tenant and profile
python resonance_os/cli/hr_main --prompt "AI in healthcare" --tenant "healthcare" --profile "medical"
# Batch generation
python resonance_os/cli/hr_main --batch --input prompts.txt --output results.jsonfrom resonance_os.api.hr_server import SimpleRequest, hr_generate
# Create generation request
request = SimpleRequest(prompt="Benefits of machine learning")
response = hr_generate(request)
print(response.article) # Generated content
print(response.hrv_feedback) # HRV resonance scoresfrom resonance_os.generation.human_resonant_writer import HumanResonantWriter
from resonance_os.profiles.hrv_extractor import HRVExtractor
# Generate content
writer = HumanResonantWriter()
article = writer.generate("The impact of climate change")
# Extract HRV vectors
extractor = HRVExtractor()
hrv_vector = extractor.extract(article)# Getting started tutorial
python examples/tutorials/getting_started.py
# Profile mastery
python examples/tutorials/profile_mastery.py
# Business scenarios
python examples/business_scenarios/content_marketing.py
# Creative writing
python examples/creative_applications/story_generation.py- Python 3.8+ (recommended 3.10+)
- Standard library modules only (for core functionality)
- Optional: FastAPI, uvicorn for API deployment
- Memory: 512MB minimum, 2GB recommended
- Storage: 100MB minimum for full installation
# Run all tests
python test_runner.py
# Run specific test categories
python test_runner.py --basic
python test_runner.py --integration
python test_runner.py --performance
python test_runner.py --unit
# Run with verbose output
python test_runner.py --verbose# System diagnostics
python system_runner.py --diagnostics
# Performance benchmarks
python system_runner.py --benchmark
# Generate system report
python system_runner.py --report
# Complete system check
python system_runner.py --allresonance_os/
├── 📁 data/ # Data directory
│ ├── config/ # System configurations
│ ├── profiles/ # HRV profiles
│ ├── corpora/ # Training data
│ ├── models/ # ML models
│ ├── scripts/ # Utility scripts
│ ├── logs/ # System logs
│ └── exports/ # Analytics & reports
├── 📁 examples/ # Usage examples
│ ├── basic_usage/ # Getting started
│ ├── advanced_usage/ # Advanced features
│ ├── business_scenarios/ # Real-world applications
│ ├── creative_applications/ # Creative writing
│ └── tutorials/ # Learning guides
├── 📁 tests/ # Test suite
├── 📄 setup.py # System setup script
├── 📄 system_runner.py # System management
├── 📄 test_runner.py # Test runner
└── 📄 README.md # This file
Environment variables:
API_HOST: API server host (default: 0.0.0.0)API_PORT: API server port (default: 8000)HRV_TENANT_DIR: Directory for HRV profiles (default: ./profiles/hr_profiles)
✅ Core Functionality: All modules operational
✅ CLI Interface: Fully functional
✅ API Layer: Basic implementation ready
✅ Profile Management: Multi-tenant support
✅ Testing: Comprehensive test coverage
- Enhanced ML models for HRF prediction
- Transformer-based text generation
- Advanced resonance optimization algorithms
- Web dashboard for resonance analytics
- Integration with external content management systems
This project represents cutting-edge research in human-AI content resonance. See LICENSE file for details.