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iceberg1d ago · 2 min read

Introducing Iceberg DB: India's Serverless Vector Database & AI Agent Memory Engine (<20ms Latency)

While building RAG pipelines and autonomous AI agents in India, I kept hitting the same three walls with US-hosted vector databases: 200ms+ Network Latency: Every vector search from India to us-east-

icebergdb.hashnode.dev

Praise James3d ago · 13 min read

Build an offline visual defect search system with CLIP and VectorAI DB

This article was originally published on the Actian blog. Manufacturing plants often hold years of defect photographs with no practical way to search them. An engineer looking for earlier cases of a

1 actiandev.hashnode.dev

Waseem Anjum4d ago · 15 min read

pgvector vs Pinecone vs Qdrant: Benchmarked for RAG in 2026

TL;DR Start with pgvector if you already run Postgres: on 100,000 OpenAI embeddings it hit 0.948 recall@10 at 1.45 ms with defaults and 0.988 at 4.0 ms with ef_search 200. Qdrant 1.19 hit 0.985 at 2.6

fabwaseem.hashnode.dev

Huzaifa Khan6d ago · 20 min read

A $0 RAG chatbot for your personal site: Qdrant vs Upstash Vector

Say you want a chatbot on your personal site: one that answers visitors' questions from your own pages and links to the page behind each answer. The usual options both cost money every month: A hoste

2 itshuzaifa.hashnode.dev

a21aiOct 15 · 9 min read

Vector Database Security and Embedding Inversion Defense in 2026: Protecting Enterprise RAG from Vec2Text Reconstruction and Index Poisoning

In 2026, Chief Information Security Officers (CISOs), Chief Data Officers (CDOs), and enterprise infrastructure architects face a dangerous architectural misconception across generative AI stacks: the

a21ai.hashnode.dev

Praise JamesOct 11 · 12 min read

A Practical Guide to AI Agent Memory Architecture

This article was originally published by Actian for Developers on DEV. An LLM application holds state in exactly one place by default, the context window. That window is rebuilt from scratch on every

actiandev.hashnode.dev

Alexey PolevOct 8 · 13 min read

How semantic search works (and why keyword search fails)

TL;DR Semantic search works by converting text into high-dimensional vectors (embeddings) and finding nearby vectors instead of matching words. This solves the vocabulary mismatch problem — a query fo

savedthat.hashnode.dev

Praise JamesOct 6 · 20 min read

Build a self-improving GPT-6 Astra coding agent with codebase memory

This article was originally published on the Actian blog. Read the original here. GPT-6 Astra keeps context inside a response chain. In the Responses API, previous_response_id links each response t

actiandev.hashnode.dev

Patrick KearnsSep 30 · 21 min read

Deleting Personal Data from a RAG System in .NET

A customer asks you to remove their personal data. You delete their database record, remove the uploaded PDF and close the request. Later that afternoon, your AI assistant answers a question using a p

15 dotnetdigest.com

Dan WeaverOct 1 · 11 min read

Hybrid code search on Azure SQL and SQL Server 2025, end to end

Azure SQL and SQL Server 2025 both have a native VECTOR type, a DiskANN index, and VECTOR_SEARCH. The documentation covers the syntax for each one. It does not cover what happens when you put them tog

danweaver.hashnode.dev

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Trending tags this week

Introducing Iceberg DB: India's Serverless Vector Database & AI Agent Memory Engine (<20ms Latency)

Build an offline visual defect search system with CLIP and VectorAI DB

pgvector vs Pinecone vs Qdrant: Benchmarked for RAG in 2026

A $0 RAG chatbot for your personal site: Qdrant vs Upstash Vector

Vector Database Security and Embedding Inversion Defense in 2026: Protecting Enterprise RAG from Vec2Text Reconstruction and Index Poisoning

A Practical Guide to AI Agent Memory Architecture

How semantic search works (and why keyword search fails)

Build a self-improving GPT-6 Astra coding agent with codebase memory

Deleting Personal Data from a RAG System in .NET

Hybrid code search on Azure SQL and SQL Server 2025, end to end