5 min read
Two ways to run keyes.ai services
The same engine — Vector, Memory, GitDB — runs as a managed service or inside your own cloud account. A short note on what each shape is for, and what's actually built.
Tag
#vector
6 posts tagged vector.
- 7 min read
What 100% recall could mean for AI search engines
AI-native search is a real category now — Exa alone is a $700M business — and the open technical question is the same one we keep running into. The retrieval layer either finds the right document or it doesn't, and at scale most stacks settle for "approximately".
- 7 min read
Why we don't use brute-force or mmap for 100% recall
The standard paths to 100% vector recall are GPU brute-force and mmap-backed indexes. Both work; both have a property that ruled them out for the workloads we kept seeing. A look at the design space and where we landed.
- 5 min read
Vector databases in 2026 — where we fit beside Pinecone, Qdrant, and Milvus
Three production vector databases dominate the category. A neutral look at what each one does, what we do, and how the trade-offs compare.
- 4 min read
3072-dim is the new hard mode for vector search
text-embedding-3-large produces 3,072-dim vectors, and most vector pipelines truncate them to stay performant. Here's what happened when we benchmarked the full dimension end-to-end.
- 6 min read
100% recall, on disk, no GPU
The conventional wisdom on vector search is that you have to pick two of three — recall, speed, footprint. Here's what our engine does on the dbpedia-openai benchmark.