The Intelligence
Engine
Self-verifying, self-evolving AI that runs on your hardware. Five specialized domains. Quantum-enhanced reasoning. Train it with the Hive. No GPU required.
Capabilities
Not a chatbot. Infrastructure.
Production-grade AI with domain adapters, neural compression, autonomous invention, and self-healing training.
Multi-Domain LoRA
5 specialized adapters — programming, cybersecurity, quantum, fintech, general. Switch domains with a single parameter. Each adapter is independently trainable.
Neural Compression
VQ-VAE hierarchical autoencoders compress hidden states at 2x/4x/8x. Process longer contexts with the same memory footprint.
RAG + Document Grounding
FAISS + sentence-transformers retrieve and inject relevant chunks from your documents. Every answer is grounded. Zero hallucination on known material.
Self-Verification
Adaptive router estimates difficulty, routes queries, and self-verifies outputs. Every response is checked before delivery. 100% verification pass rate.
Autonomous Invention
Evolutionary search discovers novel algorithms — attention mechanisms, compression schemes, state-space models. Bee writes its own improvements.
Self-Healing Training
Monitors gradient health, detects training anomalies, auto-adjusts learning rate, rolls back to stable checkpoints. Training never crashes.
API
OpenAI-compatible.
Zero lock-in.
Drop-in replacement for OpenAI. REST + WebSocket streaming. RAG ingestion. Human-in-the-loop feedback. Bearer auth with request tracing.
# One line to switch from OpenAI
curl -X POST http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "bee",
"messages": [{
"role": "user",
"content": "Explain quantum entanglement"
}],
"domain": "quantum"
}'Bee Hive
Distributed training.
Anyone can contribute.
Run one command on any machine. Your compute trains Bee. Validated adapters push to the community automatically. The longer it runs, the smarter Bee gets.
Install
pip install bee-engine
Train
python -m bee.hive
Contribute
Adapters auto-push to Hub
# Works on MacBook, Linux, Colab, Kaggle — any hardware
$ python -m bee.hive --device auto
> Detected Apple M4 Max (MPS)
> Loading base model: SmolLM2-360M-Instruct
> Training domain: programming (LoRA r=16, α=32)
> Eval loss improved: 2.41 → 2.18 ✓
> Pushing adapter to cuilabs/bee-hive-programming
# Cycle complete. Starting next domain...Runs everywhere
Intelligence should
be free.
Run locally. Train with the Hive. Use the API. No subscriptions. No vendor lock-in. Just intelligence.