Projects

Multi-model LLM debate and second-opinion validation. Broadcasts a prompt to several models — HTTP providers or local CLIs — has them argue over N rounds, and returns a synthesized answer plus an agreement/disagreement report.

Agentic memory implemented natively in PostgreSQL — the episodic, relational, time-anchored memory layer agents actually forget, kept in the database you already run.

GraphRAG that runs entirely in PostgreSQL — pgvector for vectors, recursive CTEs for graph traversal, tsvector BM25 for keyword search. No graph database, no second backup strategy, no data sync.

Sub-millisecond extractive summarization with byte-identical Python and Rust implementations. The preprocessor that sits in front of the LLM call.

Standalone ingest-to-pgvector with a built-in chunker × embedder bakeoff. One YAML config = one end-to-end ingest cell. Python + Rust at parity.

Scripts to test and benchmark JSON functionality across MySQL, PostgreSQL, and MongoDB — and generate simulated load.

Local-first MCP server that indexes code and docs into Postgres + pgvector for hybrid retrieval by LLM coding clients.

A top-like live terminal dashboard for monitoring LLM inference servers on NVIDIA DGX Spark.

Capture, replay, and compare PostgreSQL workloads. Validate config changes, migrations, and capacity plans with confidence.