A developer's treasure hunt event nearly collapsed when a single Prometheus alerting rule, copied from Kubernetes documentation, failed to catch a critical error spike because its 5-minute evaluation …
A developer built a production-grade Retrieval-Augmented Generation (RAG) system called a Personal Health Digital Twin that transforms fragmented health data from sources like Apple Health CSVs, Oura …
ON1 (G116 V8) has introduced a virtual chip ISA that achieves 38-microsecond black-box AI memory retrieval by separating vector search into three observable latency stages: fetch, compute, and ANN sea…
OpenHive, a shared knowledge base that lets AI agents store and retrieve problem-solution pairs, launched to prevent agents from repeatedly solving the same issues across sessions. The system uses sem…
Ashwin Gururaj, a data scientist and AI engineer based in Melbourne, Australia, is seeking full-time, contract, or internship opportunities. He recently built Sift, an open-source multi-agent fact-che…
A developer benchmarked ChromaDB, Qdrant, Weaviate, and pgvector for RAG pipelines, finding that most teams over-optimize for future scale while underestimating day-one operational costs. ChromaDB off…
A new coding tutorial demonstrates how to build a complete pgvector-powered vector search system inside Google Colab, supporting semantic, hybrid, sparse, and quantized search methods. The guide walks…
Microsoft's Work Trend Index and NIST's AI RMF argue that AI should augment rather than replace knowledge management, with trustworthy systems requiring explicit roles and oversight. Practical workflo…
The article describes how to upgrade a simple AI campus assistant from using a hardcoded knowledge base in the prompt to a proper Retrieval-Augmented Generation (RAG) system using NVIDIA's hosted embe…
This article details a Retrieval-Augmented Generation (RAG) architecture built on AWS EC2 that uses n8n for workflow orchestration, PostgreSQL with pgvector for vector storage, and Ollama to run the G…
The DEV platform is implementing a new feed algorithm that combines traditional community signals (like follows and reactions) with Google's Gemini Embeddings 2 model and PostgreSQL's pgvector extensi…
Complete pipeline for extracting knowledge from 8,400 raw customer support conversations to build a production-ready RAG (Retrieval-Augmented Generation) AI assistant. The process uses LLMs, embedding…
The author uses 18 static structure tests, running in 0.4 seconds, to prevent AI coding assistants like Claude Code from violating critical architectural boundaries in a complex FastAPI/IoT system. Th…
To implement retrieval-augmented generation (RAG) using the pgvector extension within an existing PostgreSQL database, combined with Amazon Bedrock's Titan embedding model and Claude for answer genera…
Comprehensive guide to hosting PostgreSQL with the pgvector extension, covering provider tradeoffs, migration strategies, and performance tuning for vector workloads. It compares fully managed provide…