PeachBot Core: An Early-Stage Edge AI System Prototype (How It Works + How to Run)

⚠️ 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)

1. Input signals are generated (or simulated)
2. Signals are structured
3. Knowledge layer enriches context
4. State is updated
5. Decision logic is triggered
6. 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.