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 Me…
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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 tha…
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 foundati…
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 wh…
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, o…
Embeddings Magic
Transforming language into geometry. Introduction Embeddings are one of the most important building blocks of modern AI applications, yet they're ofte…
# 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 same …
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 pro…
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 writin…
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 tim…
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 nodes…
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 infras…
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 memory…
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 Trend …
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 your…
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 fo…
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? Man…
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. Mo…
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 — vecto…
The Whitepaper Thunderdome: NeuSymMS vs. State Contamination
One paper builds the vault. The other paper proves the vault is already on fire. 12 min read · 4 parts · Published by Vektor Memory Part 1: Two Tribes…
5 Best Python Vector Database Libraries
Most comparisons of Python vector database libraries focus on retrieval speed, indexing algorithms, or benchmark results. These metrics matter, but pr…
Can AI in Manufacturing Work Without the Cloud? A Guide
Keeping external traffic out of operational networks is a best practice that most manufacturing facilities build into their architecture from the grou…
The Whitepaper Thunderdome: HAGE vs Storage Is Not Memory
Two papers. One ring. No referees. Popcorn mandatory. 12 min read · 4 parts · Published by Vektor Memory Press enter or click to view image in full si…
Your OpenClaw Bill Is Bleeding Tokens. Here’s What We Measured — and How to Fix It.
Memory bloat, compaction loss, and a retrieval-first path: ~32% less token spend on the AppWorld dev split — without dumbing the agent down. Developer…
Building a Safety-First RAG Triage Agent in 24 Hours
Last weekend, I participated in HackerRank Orchestrate 2026 — a 24-hour hackathon where the challenge was deceptively simple: build a terminal-based s…
How to get your AI to finally stop repeating itself…
How we spent three hours chasing a bug through five layers of Node.js to teach Vektor Memory that time moves forward. Ask your AI assistant what kind …
Vector Databases Explained: What They Don’t Tell You
Everyone working in AI reaches a moment where they search a document and get back something that looks right but means nothing — or searches for a con…