Engineering insights, model updates, and technical deep-dives from the Bee team.
Why we built a small, specialized LLM with LoRA adapters instead of training another massive model. Architecture decisions, trade-offs, and the road ahead.
Read moreInside the evolutionary search system that generates, evaluates, and mutates attention mechanisms, compression schemes, and memory protocols autonomously.
Read moreBee has engines for invention, self-coding, self-healing, and compression — but they were disconnected. Here is how the EvolutionOrchestrator ties them into an autonomous self-evolution cycle.
Read moreBee's compression engine uses vector-quantized autoencoders to compress token representations at 2x, 4x, and 8x ratios while preserving semantic content.
Read moreHow Bee's FAISS-based retrieval pipeline chunks, embeds, indexes, and retrieves relevant document sections for every query — with zero hallucination on known material.
Read moreGradient explosion, loss spikes, NaN activations — Bee's self-heal module monitors training health and applies automatic interventions before damage occurs.
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