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Swapin Vidya
Swapin Vidya

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PeachBot Deploy: A Prototype Deployment Layer for Edge AI Systems (How It Works + How to Run)

#peachbotai #edge #ai #machinelearning

⚠️ Scope & Status

This repository represents an early-stage deployment and execution prototype for the PeachBot system.

  • Built for testing and validation
  • Not production-ready
  • Under active development

The goal is to explore how edge-based AI systems can be initialized, executed, and monitored with minimal infrastructure.

What This Repo Does

PeachBot Deploy provides a structured way to run an edge AI system using:

  • Script-based setup
  • Configuration-driven execution
  • CLI-based monitoring

It focuses on making systems easy to start, inspect, and iterate on.

CLI Interface (Demo)

The system runs through a lightweight CLI menu where you can:

  • Run the framework
  • Replay logs
  • Configure the system

Live Telemetry View

Once running, the system displays:

  • Node values and decisions
  • Status indicators
  • Signal trends
  • Anomaly timeline

This demonstrates a working execution loop with observable outputs.

Installation & Initialization

1. Setup Environment

Windows 

scripts\start.bat
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Linux / macOS 

chmod +x scripts/setup.sh
chmod +x scripts/start.sh

./scripts/setup.sh
./scripts/start.sh
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2. What These Scripts Do

setup.sh

  • Creates Python virtual environment
  • Installs dependencies
  • Installs PeachBot in editable mode

start.sh / start.bat

  • Activates virtual environment
  • Launches PeachBot system

Optional: Manual Setup (Advanced Users)

If you prefer manual control:

# Create virtual environment
python -m venv venv
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Activate Environment 

# Windows
venv\Scripts\activate

# Linux/macOS
source venv/bin/activate
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Install Dependencies 

pip install -r requirements.txt
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Run the System 

python -m launcher.system
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Execution Flow (What Happens Internally)

  1. Environment is initialized
  2. Configuration is loaded
  3. System modules are started
  4. Signals are processed
  5. State is updated
  6. Decisions are generated
  7. Telemetry is displayed

What Currently Works

  • Script-based setup and startup
  • CLI-driven system control
  • Real-time telemetry display
  • Deterministic signal processing
  • Structured execution flow

Current Limitations

  • Limited real-world deployment
  • No large-scale benchmarking
  • Some modules are still evolving
  • Hardware integration is partial

Design Approach

This system is built with focus on:

  • Simplicity (easy to run)
  • Deterministic behavior
  • Edge compatibility
  • Observable system state

Where This Can Be Used (Exploratory)

  • Environmental monitoring
  • Edge analytics pipelines
  • Real-time alert systems

(These are exploratory directions, not production claims.)

Future Improvements

  • CLI → Web interface
  • Better configuration management
  • Integration with PeachBot Core
  • Deployment automation
  • Real hardware validation

Contributing

If you're interested in:

  • Edge AI systems
  • Deployment pipelines
  • Real-time processing

👉 https://github.com/peachbotAI/peachbot-deploy

Final Note

This project focuses on a simple question:

How do we reliably start, run, and observe AI systems in constrained environments?

This repository is one step toward answering that.

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