VectorAI DB Doesn't Embed Your Text, and That's the Point
Building a small local knowledge base on top of Actian VectorAI DB, the actual challenge wasn't the database. It was easy to accidentally fake seman…
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Using SynapCores as a LlamaIndex Vector Store + Property Graph Store
Most LlamaIndex setups end up with two separate backends once you go beyond plain vector search: a vector store for VectorStoreIndex , and a separat…
jina-embeddings-v4 as an OpenAI-Compatible Embeddings Server
jina-embeddings-v4 is a self-hosted server for the jina-embeddings-v4 embedding model with an OpenAI-compatible /v1/embeddings endpoint. It runs on…
Scaling RAG Systems: Production Architecture, Performance, and Cost Optimization
The first three parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything…
Rebuilding the Cerebras Knowledge Base: Results Appendix (P1–P4)
This is the data appendix for Posts 1–4 . The narrative and takeaways live in the main posts. This page is pure measurement. Eval set: 22 questions…
My Favorite Constant in Retrieval Is 60. Nobody Tunes It. That's the Point.
Reciprocal rank fusion merges two ranked lists — say, BM25 results and vector-search results over your chargeback cases — into one. It has exactly o…
Why AI Agents Lose Their Memory And How MemoFS Solves It
Whether you are using off-the-shelf AI coding tools like Claude Code and Cursor or building custom autonomous AI agents with TypeScript and LLM APIs…
Kdrant: an idiomatic, coroutine-first Kotlin client for Qdrant
If you build on the JVM and want to use Qdrant , the official client is io.qdrant:client — and it's built for Java. Every call returns a ListenableF…
Can ClickHouse Replace a Vector Database?
Vector databases like Pinecone, Weaviate, and Milvus exist for one job: store embeddings and find the nearest ones to a query vector, fast. ClickHou…
How Vector Databases Search a Million Vectors Without Checking a Million
Take the word "king." Your database does not store the word. It stores a vector: a list of 768 numbers that place king at a point in space, where wo…
Vector Databases, Deep Indexing & Token Economics: The Complete Story (phase 3)
From "we have vectors" to "this actually scales, and doesn't bankrupt us." The Story Starts: Two Questions in One 👦 Nephew: Uncle, Phase 2 is done.…
Quantified Self 2.0: Stop Guessing Your Health History—Build a Personal Medical Vector Database
Let's be real: our personal medical history is a mess. It’s a chaotic mix of PDF lab results, grainy scans of prescriptions, and cryptic Electronic…
Vector databases explained
Behind almost every "AI-powered search" and RAG feature sits the same quiet workhorse: similarity search over vectors. If you're building anything t…
The Markdown File That Beat a $50M Vector Database: Separating Storage and Search in Agent Memory
In the rush to build AI agents, we defaulted to complex vector databases. But high-traffic platforms are converging on a simpler, more robust founda…
Building a cross-user review graph with pgvector on Amazon Aurora
Every review app is a silo. Yelp reviews places, Amazon reviews its own catalog, Letterboxd reviews film. I wanted to build the opposite: one place…
Pinecone vs Weaviate vs Milvus vs Qdrant: Which Vector DB in 2026?
I have helped a number of teams pick a vector database in the last year. The conversation always starts the same way: four logos, one Slack message,…
Embeddings Magic
Transforming language into geometry. Introduction Embeddings are one of the most important building blocks of modern AI applications, yet they're of…
# Vector Search and RAG: A Primer
A short learning path from a weekend project: I indexed my personal markdown notes (~800 chunks), tried a few local embedding models, stored the sam…
Vector Databases Are Not Magic, Here's What's Actually Happening Under the Hood
You've seen the tutorials. Spin up Pinecone, call .upsert() , do a similarity search, ship it. Everyone claps. The demo works. Then you take it to p…
milvuslite-kit configuration over code for vector search and rag workflows
Built a small framework called MilvusLite Kit to make vector search and RAG workflows more configuration-driven and less code-heavy. Instead of writ…
I Built a Python Agent That Uses a Vector DB as Memory, Not Retrieval
Vector databases are almost always talked about in the context of RAG. Store your documents, embed them, retrieve the relevant chunks at inference t…
Your vector memory database remembers everything. That’s exactly the issue.
There is a design assumption baked into almost every vector database and AI memory implementation that sounds reasonable until you watch it grow nod…
Why Your Vector Database Is Overpriced: Lucene's 32x Compression and Serverless Economics
Why Your Vector Database Is Overpriced: Lucene's 32x Compression and Serverless Economics In 2026, the boundary between "search engine" and "AI infr…
The challenges of creating a semantic memory layer on Cloudflare Workers, D1, and Vectorize.
The main concept is straightforward: embed text, store the vector, and query it later. The time-consuming part was everything else. I created a memo…
Enterprises Are Quietly Moving Their AI Back On-Premises. Here Is Why.
42% of companies are considering moving workloads off the cloud. For AI infrastructure specifically, the reasons are more urgent than cost. The Tren…
Your AI Agent Craves Curation. Here’s the FADEMEM Memory Architecture That Delivers It.
You have explained your tech stack to your coding agent four times this month. You mentioned your preferred approach to a problem in January, and yo…
Getting Started with Vector Databases Using Amazon Aurora PostgreSQL + pgvector
Hello! I'm Satoshi Kaneyasu, DevOps engineer at Serverworks. In this article, I'll introduce the basic concepts and terminology of vector databases…
Progressive Distillation
Now that almost everyone has thought about or is actively integrating AI workflows into their projects, some might ask is this all worth the cost? M…
How We Reduced LLM Costs Without Touching Model Quality
How We Reduced LLM Costs Without Touching Model Quality One of the fastest ways to destroy an AI system in production is uncontrolled token growth.…
Three Design Decisions That Shaped the Enterprise RAG Retrieval Pipeline
Enterprise RAG — A practitioner's build log | Post 3 of 6 A retrieval pipeline has more design surface than it appears. The technology choices — vec…