A developer built a production-ready Retrieval-Augmented Generation (RAG) application using LangChain, pgvector, and Google's Gemini models. The application ingests PDF documents, splits them into chu…
Cloudflare announced that starting next month, users will be able to create and bill PlanetScale databases directly from the Cloudflare dashboard, integrating Postgres and MySQL databases with Workers…
A developer built ArchLens, a boilerplate for production-grade RAG systems over codebases, addressing common pitfalls like embedding dimension mismatches, LLM provider coupling, and multitenancy. The …
Independent researcher Sujan Lamichhane from Nepal seeks an arXiv endorsement to publish a paper on his local-first AI platform, Jarvis, which orchestrates local LLMs using Java 21, Spring Boot 4, and…
LangChain agents built on LangGraph use a tool-calling loop until a final answer is returned. The high-level createAgent function accepts a model, tools defined with tool(), and an optional system pro…
A developer built Trigix, an open-source (MIT), self-hostable workflow automation platform with a Rust execution engine and AI nodes that can run against local models. The platform features ~180 node …
PostgreSQL has evolved into a versatile data platform that can replace multiple specialized databases, handling relational storage, full-text search, vector AI workloads, geospatial queries, and event…
Batta, an open-source security review tool for AI coding agents, launches with plan-phase reviews that catch design flaws before code is written. The tool indexes a system's architecture—services, dat…
O'Reilly published "Hands-On RAG for Production" by Ofer Mendelevitch and Forrest Sheng Bao in June 2026, a 358-page guide covering the full RAG pipeline from document parsing to LLM integration with …
A developer explains that RAG (Retrieval-Augmented Generation) reduces LLM hallucinations by fetching relevant document chunks at query time and instructing the model to answer using only that context…
A developer built a semantic search engine in about 150 lines of code using the all-MiniLM-L6-v2 embedding model and pgvector on Postgres, requiring no API keys. The system matches queries like "anima…
A developer advocates for query rewriting before retrieval to improve search accuracy in support bots, using cheap LLM calls to generate multiple query variants or step-back abstractions. The techniqu…
A developer argues that the first retrieval layer for operational AI workflows should start in Postgres with pgvector, not in a standalone vector database, prioritizing control, review, and schema cla…
Metadata filtering before vector search is a cheap but often overlooked recall win. By applying a hard predicate on metadata like customer_id, the search space shrinks from millions to hundreds of chu…
Jeroen Herczeg has launched a Leanpub book titled 'Retrieval-Augmented Generation: An Engineer's Guide to Building RAG Systems with Your Own Data', aimed at engineers building production RAG systems. …
Microsoft has open-sourced pg_durable, a PostgreSQL extension that brings durable execution directly into the database by checkpointing SQL workflow steps so they survive crashes, restarts, and failur…
A developer built an AI-powered log analysis platform for Spring Boot applications that uses retrieval-augmented generation (RAG) with Ollama models to parse logs, detect exceptions, and explain root …
Satoshi Kaneyasu, a DevOps engineer at Serverworks, has published a guide explaining vector databases and their implementation using Amazon Aurora PostgreSQL with the pgvector extension. The tutorial …
A developer demonstrated a complete Retrieval-Augmented Generation (RAG) pipeline using OpenAI embeddings, PostgreSQL with pgvector, and LangChain for document chunking. The implementation stores know…
Kartik Shukla is building Nudge, an AI-powered runtime agent that replaces traditional product onboarding walkthroughs with a dynamic, free-form goal system. The zero-dependency Node package embeds wi…