FerrellSyntheticIntelligence/queen-bee-v5

Queen Bee Swarm - State-of-the-Art Edge Coder

"The needs of the many outweigh the needs of the few, or the one." - Spock

A paradigm-shifting approach to edge AI coding assistants, inspired by honeybee colony intelligence.

Architecture

Queen Bee (Orchestrator)

• Size: 230M-1.2B parameters (using LFM2.5)
• Role: Routes tasks, spawns specialists, maintains colony state
• Inspiration: Queen bee produces pheromones that organize colony behavior

Nanobots (Specialists)

• Size: 1K-10K parameters each
• Role: Handle specific coding tasks
• Inspiration: Worker bees with age-based specialization

Key Innovations

1. Dynamic Spawning - New specialists created on-demand
2. Knowledge Crystallization - Specialists freeze after training
3. Biological Swarm Intelligence - Decentralized routing
4. Edge-First Design - Runs on ARM CPUs with minimal RAM

Quick Start

# Install dependencies
pip install -r requirements.txt

# Run demo
python demo.py

# Interactive mode
python main.py --interactive

# Test suite
python main.py --test

Components

Code Generator Nanobot

Generates code from natural language descriptions. Supports:

• Python, JavaScript, TypeScript, Java, C++, Rust, Go, SQL, HTML, Bash
• Functions, classes, algorithms, data structures

Terminal Commander Nanobot

Generates terminal commands from descriptions. Supports:

• File operations, process management, networking
• Git, Docker, system administration
• Safety checks for dangerous commands

Debugger Nanobot

Analyzes code and finds issues. Supports:

• Syntax error detection
• Common bug patterns
• Style recommendations
• Automatic fixes

Spock Persona

The system speaks with Spock's logical, analytical voice:

• "Fascinating. Your code has interesting properties."
• "The logic is sound. Proceeding with implementation."
• "I find this most interesting. Allow me to explain."

Training

Knowledge Distillation

Train from larger teacher models:

swarm = QueenBeeSwarm()
swarm.train(teacher_model, training_texts)

Specialist Training

Spawn and train specialized nanobots:

nanobot = swarm.trainer.spawn_and_train_nanobot(
    name="python_expert",
    task_type="code_generation",
    training_data=[...]
)

Technical Details

Based on Liquid AI LFM2.5

• Hybrid architecture: Gated convolutions + GQA
• Not transformers - more efficient for edge
• 230M-1.2B parameter range
• Runs in <1GB RAM

Edge Optimization

• INT4 quantization
• Minimal memory footprint
• ARM NEON SIMD support
• CPU-only inference

Philosophy

"I do not teach. I merely provide the environment in which you can learn." - Spock

This system is designed to think, not memorize:

• Understanding over recall
• Reasoning over pattern matching
• Adaptation over rigidity

License

MIT License - Built with logic and purpose.