A developer has identified a persistent security threat to AI agents called memory poisoning, where malicious instructions stored in an agent's memory can influence all future interactions indefinitel…
A developer has built a multi-agent AI customer support system using Make.com, Groq, Pinecone, and Airtable that replaces traditional chatbots with specialized agents for sales, support, and general i…
Pinecone announced a new integration between its Nexus knowledge engine and Microsoft OneLake at Microsoft Build 2026, enabling enterprise AI agents to query corporate data through pre-built knowledge…
A developer built a semantic search service using FastAPI and Pinecone, demonstrating how to wire a FastAPI service to the Pinecone vector database for embedding generation and similarity lookup. The …
A developer built Vektr, a custom RAG engine implementing HNSW graphs from scratch, achieving recall@10 of 0.984 — meaning 98.4% of queries returned all 10 true nearest neighbors in the top results. T…
A developer has built a production-grade AI trading bot for Polymarket that operates autonomously with persistent memory, server-side event loops, and native tool integration. The system uses a multi-…
A developer's team spent two months building an AI chatbot for internal documentation using retrieval-augmented generation, encountering high costs with GPT-4 and embedding generation on 50,000 pages …
A developer built a production-grade Medical Retrieval-Augmented Generation (RAG) system that cures LLM hallucinations by grounding clinical AI responses in peer-reviewed evidence from the PubMed API.…
A developer built a Retrieval-Augmented Generation (RAG) pipeline for a documentation Q&A bot after multiple failed attempts, including token limits, high costs, and hallucination issues with direct L…
AI search systems that understand intent and context are emerging as a major SaaS opportunity, transforming how businesses access operational data. By using embedding engines and vector databases, the…
SynapCores has launched an AI-native database that unifies graph traversal, vector similarity, and LLM inference into a single execution engine, eliminating the need for multiple systems and round-tri…
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…
To build a production-ready Retrieval-Augmented Generation (RAG) pipeline in Python using BM25 retrieval via Meilisearch instead of a vector database, arguing that BM25 achieves 85–95% of the recall o…
"RAG Readiness," a tool designed to eliminate the vague "it depends" answers that plague Retrieval-Augmented Generation (RAG) architecture decisions. Instead of providing comparison tables, the tool u…
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…