Version: v0.1.0
PeachBot Core is the foundational system layer of the PeachBot platform, designed as a distributed, edge-first intelligence architecture.
The system enables:
- Real-time intelligence at the point of data generation
- Structured local intelligence generation with system-level consistency
- Deployment in constrained and latency-sensitive environments
PeachBot Core represents a shift from:
Centralized AI Systems
→ cloud-dependent, data aggregation heavy
to
Distributed Edge Intelligence Systems
→ localized intelligence generation, federated coordination, governed aggregation
PeachBot Core follows an edge-first, state-centric architecture composed of four interacting layers:
- Real-world signal acquisition (clinical, environmental, agricultural, biological)
- Converts raw inputs into structured system signals
- Domain-specific knowledge (clinical, environmental, agricultural, biological)
- Lightweight rule-based and structured knowledge sources
- Local, edge-deployable (no cloud dependency)
Function:
- Enrich incoming signals before SBC processing
- Provide Day-1 decision capability
- Synthetic Biological Computation (SBC) engine
- State-centric computation model
- Signal-driven state updates with:
- temporal decay
- weighted influence
- multi-signal relationships
- Priority-driven decision influence
- Policy enforcement and safety evaluation
- Session management and audit logging
- Distributed orchestration across nodes (FILA-ready)
- Aggregation of structured edge outputs
- Model validation and registry
- Controlled redistribution of updates
All primary computation, state evolution, and decision-making occur at the edge. The cloud performs coordination, not centralized training.
- Local intelligence generation and state evolution at edge nodes
- Structured session-level exports to aggregation layer
- Aggregation without raw data transfer
- Registry-driven validation and coordination
SBC defines the core computational model of PeachBot Core.
Unlike traditional AI systems that rely on stateless inference, SBC operates as a state-centric engine where system behavior emerges through evolving structured state.
Key properties:
-
State-centric computation
→ system intelligence is represented as evolving structured state -
Temporal dynamics
→ signals decay over time, preserving recency relevance -
Weighted signal influence
→ higher-intensity signals exert stronger impact on system behavior -
Multi-signal interaction
→ relationships between signals are tracked, forming a graph-like structure -
Priority-driven decision model
→ decisions are influenced by aggregate system state rather than isolated rules
This model enables adaptive, context-aware behavior aligned with real-world biological and environmental systems.
SBC operates in conjunction with the Knowledge Layer, enabling systems to combine predefined domain knowledge with evolving state-driven behavior.
- Hardware–software co-design
- Hardware-aware execution design
- Compatibility with embedded AI accelerators
- Optimized real-time inference pathways
- Edge-first execution
- Distributed intelligence coordination
- Real-time adaptive decision-making
- Minimal data centralization
- Deployment-oriented system design
docs/ → System architecture, research, compliance, IP
core/ → FILA, SBC, orchestration frameworks
interfaces/ → Domain-specific integrations
models/ → Edge AI and signal processing models
deployment/ → Infrastructure and configurationThe SBC framework produces a structured representation of system state and signal relationships that is inherently graph-compatible.
- Signals → nodes
- Relationships → edges
- Weights → node/edge attributes
This enables seamless integration with graph-based learning systems such as Edge-GNN.
The Edge-GNN framework introduces constraint-aware graph learning optimized for resource-constrained environments, balancing predictive performance with computational efficiency.
This alignment allows PeachBot Core to:
- Transition from rule-based evaluation to graph-based learning
- Operate within CPU-only and edge-class hardware constraints
- Maintain deployability across heterogeneous environments
- Bio module provides primary structured input for graph-based workflows
Edge-GNN provides the learning layer, while SBC provides the structured state representation.
Together, they form a unified edge-native intelligence system.
PeachBot Core is currently in:
Validated System Development → Deployment Transition
- Core architecture implemented and documented
- Edge intelligence modules under active development
- Initial deployment scenarios simulated and validated in controlled environments (environmental systems)
- Ongoing integration across clinical, environmental, agricultural, and biological domains
PeachBot Core supports:
- Clinical intelligence systems (MedAI+)
- Environmental monitoring networks (Eco)
- Agricultural intelligence systems (AgriAI)
- Biological intelligence systems (Bio)
PeachBot Core operates using an edge-first, state-driven execution model:
- Inputs are captured as structured signals
- Signals are enriched using Knowledge Layer
- Enriched signals update the SBC state engine
- State evolves through temporal decay and interaction
- System priority is computed
- Decisions are generated
- Structured outputs are optionally exported via FILA
This model ensures:
- Low-latency operation
- Context-aware decision-making
- Minimal data centralization
- Compatibility with edge-constrained environments
- Edge-first system design
- Federated learning (FILA)
- Biologically-inspired computation (SBC)
- Hardware–software co-design
- Deployment-oriented architecture
PeachBot Core is built on the following principles:
-
Edge-first intelligence
→ computation occurs at the point of data generation -
Distributed intelligence coordination
→ intelligence emerges across nodes, not from a central model -
Minimal data centralization
→ raw data remains local wherever possible -
System-level integration
→ hardware, models, and orchestration are co-designed -
Deployment-oriented engineering
→ systems are designed for real-world constraints, not ideal environments -
State-centric computation (SBC)
→ intelligence evolves through structured state transitions rather than isolated predictions
PeachBot Core is designed to support:
- Clinical intelligence systems (MedAI+)
- Environmental monitoring networks (Eco)
- Agricultural intelligence platforms (AgriAI)
- Computational biology and bioinformatics workflows (Bio)
These systems operate in:
- Low-connectivity environments
- Resource-constrained hardware settings
- Real-time decision-making scenarios
- Heterogeneous edge environments (microcontrollers, SBCs, and PC-based systems)
PeachBot Core combines:
- Edge-first execution (where computation happens)
- Synthetic Biological Computation (how intelligence emerges)
- Federated Intelligence & Learning Architecture (how systems coordinate)
This results in a distributed, adaptive intelligence system capable of operating across heterogeneous edge environments.
Contributions are welcome in areas including:
- Edge AI systems
- Distributed learning architectures
- Biological intelligence modeling
- Deployment infrastructure
Please refer to CONTRIBUTING.md for details.
This repository reflects an active system under development.
Documentation and modules will evolve with ongoing deployment and validation efforts.