Enabling humanity to coordinate at the speed of thought
Humanity's greatest challenges aren't knowledge problems—they're coordination problems.
We have climate scientists who know solutions, engineers who can build them, communities who need them, and resources scattered globally. But we can't coordinate action at the speed and scale required.
Substrate solves this.
It's a platform that enables transparent, verifiable coordination between humans with complementary capabilities. Every AI decision is explainable, every match is verifiable, and human agency is preserved throughout.
Unlike black-box AI systems, Substrate shows its work:
- Every decision has a complete provenance graph
- Users can verify AI reasoning step-by-step
- Confidence is calibrated and uncertainty is quantified
- Alternative options are always shown
- Sensitive data never leaves your device
- Local AI understands your context privately
- Only anonymized, shareable profiles go to cloud
- You control what's shared, always
- AI suggests, humans decide
- Enables rather than controls
- Respects individual agency
- Builds trust through verification
- Value grows exponentially with users
- Learning from successful coordinations
- Cross-domain discoveries
- Solving real problems at scale
┌─────────────────────────────────────────────────────────────┐
│ SUBSTRATE │
├─────────────────────────────────────────────────────────────┤
│ │
│ LOCAL (Your Device) CLOUD (Coordination) WEB (UI) │
│ ───────────────── ────────────────── ──────── │
│ • Privacy engine • Matching engine • Interface │
│ • Local AI reasoning • Team optimization • Collab │
│ • Knowledge graph • Simulation • Trust │
│ • Your data stays • Transparency • Verify │
│ │
└─────────────────────────────────────────────────────────────┘
LOCAL LAYER - Runs on your device
- Understands your skills, resources, goals
- Analyzes problems and identifies needs
- Maintains complete privacy
- Generates shareable profiles
CLOUD LAYER - Global coordination
- Matches complementary capabilities
- Optimizes team compositions
- Simulates collaboration outcomes
- Provides complete transparency
WEB LAYER - Enables action
- Collaboration workspaces
- Trust and reputation systems
- Real-world integrations
- Verification tools
# The demo shows the entire system in action
python demo/substrate_demo.pyThis demonstrates:
- 3 users with complementary skills (computational, hardware, AI/ML)
- Problem analysis with local AI
- Capability matching with complete transparency
- Identification of coordination opportunities
- Full explanations and verification protocols
🎯 Match Score: 0.78
Confidence: 0.64
Complementarity: 0.60
💡 Why this match?
User needs: Hardware/sensor expertise
Match has: PCB Design & Electronics (0.95 proficiency)
📊 DETAILED EXPLANATION:
Step 1: Semantic similarity computed (0.80 confidence)
Step 2: Complementarity analyzed (0.70 confidence)
Step 3: Feasibility assessed (0.76 confidence)
✓ Full verification protocol provided
from substrate.local.reasoning.engine import LocalReasoningEngine
# Runs entirely on your device
engine = LocalReasoningEngine()
# Analyze a problem
need = engine.analyze_problem(
problem_description="Need help integrating sensors...",
user_context={"budget": 10000, "timeline": "3 months"}
)
# Identify your capabilities
capabilities = engine.identify_user_capabilities(your_data)
# Generate privacy-preserving profile
shareable = engine.generate_shareable_profile(profile, privacy_prefs)from substrate.cloud.matching.engine import CapabilityMatcher
# Find complementary capabilities
matcher = CapabilityMatcher()
# Index user profiles
matcher.index_user_profile(user_profile)
# Find matches for a need
matches = matcher.find_matches(need, user_profile, max_results=10)
# Each match includes:
# - Match score with confidence intervals
# - Complete provenance of reasoning
# - Evidence and verification methods
# - Uncertainty factorsfrom substrate.cloud.transparency.engine import TransparencyEngine
# Generate explanations for any decision
transparency = TransparencyEngine()
# Explain a match
explanation = transparency.explain_match(
match,
include_reasoning=True,
include_alternatives=True,
include_verification=True
)
# Get verification protocol
protocol = transparency.generate_verification_protocol(match, "match")Every decision includes complete reasoning history:
provenance = ProvenanceGraph(decision_type="capability_match")
provenance.add_step(ProvenanceStep(
operation="semantic_similarity",
inputs={"need": "...", "capability": "..."},
outputs={"score": 0.78},
reasoning="Computed similarity using embeddings",
confidence=0.85,
alternatives_considered=[...]
))- Sensitive data stays local
- Only shareable profiles sent to cloud
- Homomorphic encryption for similarity computation (roadmap)
- Zero-knowledge proofs for verification (roadmap)
- Not just a score, but uncertainty quantification
- Confidence intervals for predictions
- Identification of factors that affect certainty
- Honest about what we don't know
- System actively identifies what could go wrong
- Users given tools to verify claims
- External validation suggested
- Red flags highlighted
Problem: Researcher needs crystallization expertise Solution: Substrate matches with expert in Japan Result: Problem solved in weeks instead of months
Problem: Strong in software, need hardware integration Solution: Match with hardware engineer Result: Both problems solved, joint project potential
Problem: Know how to build solar solutions, need deployment expertise Solution: Match with field deployment experts Result: Faster clean energy deployment
Problem: Project needs specific expertise Solution: Find contributors with needed skills Result: Better projects, faster progress
- Core architecture design
- Local AI reasoning engine
- Capability matching algorithm
- Transparency engine
- Working demo
- Documentation
- Homomorphic encryption for matching
- Zero-knowledge proofs
- Differential privacy guarantees
- Security audit
- Production-ready local LLM integration
- Vector database for semantic search
- Learning from collaboration outcomes
- Advanced team optimization
- Multi-region deployment
- Real-world integrations
- Web interface and collaboration tools
- Trust and reputation systems
- Cross-domain discoveries
- Open beta with 1000+ users
- Measurable impact on coordination problems
Language: Python 3.11+
Core:
- Local AI: Llama.cpp integration ready
- Matching: Custom graph-based algorithms
- Storage: In-memory (transitioning to persistent)
Dependencies:
- torch, transformers (for LLM)
- networkx (for graphs)
- numpy, scipy (for math)
Local:
- Llama 3.1 70B (quantized) or Mistral
- ChromaDB for vector storage
- SQLite for local data
Cloud:
- Python/FastAPI for API
- PostgreSQL for persistence
- Redis for caching
- Kubernetes for orchestration
Web:
- React with TypeScript
- WebSockets for real-time
- D3.js for visualizations
Coordination is genuinely hard. This addresses a universal need.
Value grows super-linearly with users. Early adopters see benefits immediately.
Users can verify everything. No blind faith required.
Data stays local. Users maintain control.
Clear success metrics: time to team formation, project success rate, problems solved.
- Human agency preserved
- AI enables rather than controls
- Open and verifiable
- Designed for good
Substrate is being built in the open. We believe transparency in our development process mirrors transparency in the system itself.
- AI/ML: Improve matching algorithms, add semantic search
- Security: Implement privacy-preserving protocols
- Frontend: Build collaboration interfaces
- Domain Expertise: Apply to specific fields (climate, health, etc.)
- Testing: Real-world validation and feedback
- Star the repo
- Run the demo
- Open issues for bugs or ideas
- Submit PRs for improvements
- Join the community (coming soon)
- Matching latency: ~1 second (Python, unoptimized)
- Supported users: 1000
- Match quality: 0.7+ average score
- Matching latency: <100ms
- Supported users: 100K+
- Match quality: 0.8+ with calibrated confidence
- Success rate: 60%+ of matches lead to collaboration
- Matching latency: <50ms globally
- Supported users: 1M+
- Match quality: 0.85+ with learning
- Success rate: 75%+ with network effects
- Measurable impact: 10K+ problems solved
GerdsenAI-Frontier/
├── substrate/
│ ├── local/ # Local layer (runs on device)
│ │ ├── reasoning/ # AI reasoning engine
│ │ ├── knowledge_graph/# Personal knowledge graph
│ │ └── privacy/ # Privacy controller
│ ├── cloud/ # Cloud layer (coordination)
│ │ ├── matching/ # Capability matching
│ │ ├── optimization/ # Team optimization
│ │ ├── simulation/ # Outcome simulation
│ │ └── transparency/ # Transparency engine
│ ├── web/ # Web layer (interface)
│ │ ├── frontend/ # React UI
│ │ ├── backend/ # FastAPI server
│ │ └── integrations/ # External integrations
│ └── shared/ # Shared models and utilities
│ ├── models/ # Core data models
│ ├── utils/ # Utility functions
│ └── protocols/ # Communication protocols
├── demo/ # Working demonstrations
│ └── substrate_demo.py # Complete system demo
├── docs/ # Documentation
│ ├── ARCHITECTURE.md # Technical architecture
│ ├── SUBSTRATE_VISION.md # Vision and principles
│ └── examples/ # Usage examples
└── README.md # This file
- SUBSTRATE_VISION.md - The vision and mission
- ARCHITECTURE.md - Technical architecture details
- Demo Guide - How to run and understand the demo
MIT License - see LICENSE file
Built with ❤️ and genuine hope for better human coordination.
If you use Substrate in research:
@software{substrate2025,
title={Substrate: The Transparent Coordination Engine},
author={GerdsenAI},
year={2025},
url={https://github.com/GerdsenAI-Admin/GerdsenAI-Frontier}
}- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: [coming soon]
- Discord: [coming soon]
Every day, somewhere in the world:
- A researcher struggles with a problem someone else has solved
- An engineer needs expertise that exists but can't be found
- A community needs solutions that exist but aren't deployed
- Resources sit idle while needs go unmet
Substrate changes this.
By enabling transparent, verifiable coordination at scale, we can:
- Accelerate scientific discovery
- Deploy solutions faster
- Reduce wasted effort
- Amplify human potential
This isn't about making AI smarter. It's about making humanity more coordinated.
Every coordination we enable is:
- A problem solved
- A discovery made
- A life improved
- Progress accelerated
This is the frontier. This is Substrate. This is just the beginning.
"The best way to predict the future is to build it." - Alan Kay
"But the best way to build the future is to coordinate." - Substrate