⚠️ Scope & Status
This repository represents an early-stage prototype of an edge-native AI system.
- Not production-ready
- Not fully benchmarked
- Under active development
The goal is to explore how state-driven, edge-first systems can operate in real-world environments with constraints like limited connectivity and latency.
What This Repo Is
PeachBot Core is a Python-based system prototype focused on:
- Processing signals locally (edge-first)
- Maintaining structured system state
- Running iterative decision loops
It is designed as a system architecture experiment, not a finished AI product.
Why This Exists
Many AI systems today depend on:
- Cloud inference
- Stateless input → output pipelines
This repo explores an alternative:
How can systems operate locally, maintain context, and make decisions continuously?
Core Idea (Simplified)
Instead of:
input → model → output
This system follows:
signals → state → evaluation → decision → update
This allows:
- Context retention
- Continuous updates
- Local decision-making
Architecture Overview
From the current implementation , the system is structured into layers:
1. Interface Layer
- Converts raw inputs into structured signals
2. Knowledge Layer
- Lightweight rules / domain knowledge
- Helps interpret signals
3. Edge Intelligence Layer (SBC Engine)
- Maintains system state
- Updates state based on signals
- Drives decisions
4. Coordination Layer
- Logging
- Policy checks
- Session handling
5. Optional Aggregation (FILA concept)
- Structured outputs can be shared
- No raw data transfer required
Repository Structure
core/ → core system logic (SBC, coordination)
interfaces/ → input handling
knowledge/ → rules / structured knowledge
models/ → signal processing / edge models
deployment/ → configs and setup
tests/ → basic testing
docs/ → architecture notes
What Currently Works
At this stage, the repo includes:
- Basic signal → state processing
- Structured decision loop
- Modular architecture for extension
- Simulated input scenarios
Current Limitations
Important to be transparent:
- No large-scale dataset validation
- Limited real-world deployment
- No performance benchmarking yet
- Some modules are placeholders or evolving
How to Run (Basic Setup)
1. Clone the Repository
git clone https://github.com/peachbotAI/peachbot-core.git
cd peachbot-core
2. Create Virtual Environment
python -m venv venv
source venv/bin/activate # Mac/Linux
# or
venv\Scripts\activate # Windows
3. Install Dependencies
If requirements file exists:
pip install -r requirements.txt
If not (early-stage repo), install basics:
pip install numpy pandas
4. Run a Basic Module
Depending on structure (example):
python -m core.main
or:
python core/run.py
(Check /core or /deployment folder for actual entry point — this may evolve.)
5. Run Tests (Optional)
pytest
Example Flow (What Happens When You Run It)
- Input signals are generated (or simulated)
- Signals are structured
- Knowledge layer enriches context
- State is updated
- Decision logic is triggered
- Output/log is generated
Where This Could Be Applied (Exploratory)
This prototype is being explored for:
- Environmental monitoring
- Edge-based analytics
- Low-connectivity systems
These are experimental directions, not production deployments.
Engineering Direction
The system is being developed with focus on:
- Edge-first execution
- Modular architecture
- State-based reasoning
- Compatibility with constrained hardware
Future Work
Planned improvements include:
- Clear execution entry points
- Better documentation and examples
- Real-world datasets
- Benchmarking vs existing approaches
- Integration with graph-based models (Edge-GNN direction)
Contributing
If you're interested in:
- Edge AI systems
- Distributed architectures
- Real-time processing
Feel free to explore and contribute.
👉 https://github.com/peachbotAI/peachbot-core
Final Note
This is not a finished system.
It is an engineering exploration into how AI systems can operate reliably outside ideal conditions.
Feedback is welcome.