knowledge-graph

AI coding agents make the same mistakes over and over. We built a learning system that captures failures, stores corrections, and feeds them back into future sessions — turning every error into institutional memory.

Builds on Why Graph Embeddings Matter — the deterministic engine that makes "remember this correction forever" cheap. Apply the pattern in your own project via the Command Rewriting How-to.

You know what is embarrassing? Making the same mistake for the tenth time. Last week, I typed docker-compose up instead of docker compose up. The command failed. I sighed. I corrected it. Three days later? Same thing. Same sigh. Same correction.

Builds on Why Graph Embeddings Matter — the deterministic engine that lets Terraphim store and replay corrections in microseconds. Apply the pattern in your own project via the Command Rewriting How-to.

When it comes to semantic search, there are fundamentally different architectural approaches. Alibaba's zvec and Terraphim represent two distinct philosophies: neural embeddings vs. knowledge graphs, scale vs. interpretability, dense vectors vs. co-occurrence relationships.

How we use Aho-Corasick automata and knowledge graphs to automatically enforce coding standards across AI coding agents like Claude Code, Cursor, and Aider.

New: see Why Graph Embeddings Matter for the underlying engine that makes these hooks possible — sub-millisecond, deterministic, fully explainable.