Search, Retrieval & Recommendation .

My own HNSW index in C++, faster than the libraries everyone imports.

Everyone imports a vector search library. I wrote my own in C++ and made it win. It answers nearest neighbor queries 1.8 times faster than hnswlib and 2.5 times faster than FAISS, at 0.999 recall, on a single thread.

Runs the real HNSW algorithm on points you can see and replays each query hop by hop.

Vector search does not scan your data, it walks a graph. This runs the real HNSW algorithm on points you can see and replays the walk hop by hop, so you can watch a query fall through the layer stack and land on its neighbors. Click to move the query, drag the sliders, break it on purpose.

A semantic cache: prompts are matched by meaning through Proxima, not by exact text.

A gateway that remembers what your model already said. It matches prompts by meaning, not exact text, so 78 percent of requests skip the model entirely and the typical answer comes back in 44 milliseconds. The memory runs on Proxima.

Retrieve, then rank: a two-tower model pulls candidates through Proxima and a neural ranker reorders them.

A two-stage hybrid movie recommender, built the way production systems are. A two-tower neural model retrieves candidates through Proxima, my own C++ vector search engine; a neural ranker reorders them. It answers free-text requests like 'a slow-burn sci-fi like Arrival but funnier' and includes movies in theaters this week.

Retrieval over vector embeddings grounds every answer in real source material.

A study assistant that refuses to make things up. Every answer is pulled from real source material and arrives with citations, and it stays quiet when it genuinely does not know.