Users notice slow requests; even if 99 % finish quickly, that 1 % “long‑tail” latency can make your app feel sluggish. Request hedging solves this by speculatively firing a second duplicate after a short delay, racing to beat out outliers before they ever impact the UI. Why the slowest 1 % of requests matter The time it takes […]
Matthew Aberham
Matthew Aberham is a solutions architect, and full-stack engineer focused on building scalable web platforms and intuitive front-end experiences. He works at the intersection of performance engineering, interface design, and applied AI systems.
Blogs from this Author
Tool‑Augmented RAG Chatbot: GPT‑4, pgVector & Next.js
This is Part 3 of a three-part series (links at the bottom). In Part Two, we moved from concept to execution by building the foundation of a Retrieval‑Augmented Generation (RAG) system. We set up a Postgres database with pgvector, defined a schema, wrote a script to embed and chunk text, and validated vector search with cosine similarity. In […]
Postgres RAG Stack: Embedding, Chunking & Vector Search
This is Part 2 of a three-part series (links at the bottom). The GitHub repo can be checked out here. Postgres RAG Stack brings together Postgres, pgVector, and TypeScript to power fast, semantic search. In Part One, we covered the theory behind semantic search: how embeddings convert meaning into vectors, how vector databases and indexes enable […]
Vector Search Embeddings and Retrieval-Augmented Generation
This is Part 1 of a three-part series (links at the bottom). Traditional search engines and databases match based on keywords. These systems are fine when you’re looking for an exact or partial string match but fail when the goal is to find content that’s conceptually similar, not just textually identical. Vector search bridges this […]