Embeddings Explained: Building Semantic Search Over 100,000 Documents
What an embedding vector really encodes, why cosine similarity works, and a complete on-prem semantic search index in 60 lines.
Read the guide →Engineering blog
Guides from the machine room.
How small models, RAG pipelines, humanoid robots and world models actually work — written by the team that deploys them behind firewalls, with code you can run on your own hardware.
Vector Databases On-Prem: pgvector vs. Qdrant for Self-Hosted RAG
When Postgres with pgvector is enough, when Qdrant earns its place, and the HNSW parameters that actually matter.
Read the guide →RAG From First Principles: Architecture of a Private Retrieval Pipeline
Chunking, embedding, hybrid retrieval, re-ranking and grounding — the full anatomy of a production RAG system that never leaves your network.
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