#rag

Compare 12 self-hosted Deep Research systems: GPT Researcher, Onyx, Open WebUI, Khoj, Vane and more. Architectures, local LLM, RAG, licenses. #AI Agents #RAG #LLM #AI #Self-Hosting #SelfHosting #Open Source #Ollama https://www.glukhov.org/ai-systems/comparisons/deep-research-with-ai/

🚀 Building Stoic — an AI Search & Answer Platform

Stoic is an AI-powered search and answer platform I’m building to turn documents, web content, images, and other information into useful, grounded answers. It uses RAG, embeddings, semantic search, and vector databases to retrieve relevant context before generating an answer. The backend is built with Python and FastAPI, with Supabase + pgvector for vector storage and similarity search. I’m also experimenting with LLM model routing, streaming responses, multimodal processing, and LLM-as-a-judge evaluation. The goal is to make AI search more reliable by focusing not only on generation, but also on retrieval quality and evaluation. I’m building Stoic as a hands-on learning project and continuously improving the architecture, performance, and user experience. If you're interested in AI engineering, RAG, LLMs, semantic search, or building AI products, I’d love to connect and hear your thoughts. 🤝

🔗 Try Stoic: https://stoic-app.vercel.app 💻 GitHub: https://github.com/Rakesh051204/cloud9-frontend

#AI #GenerativeAI #LLM #RAG #MachineLearning #AIEngineering #Python #BuildInPublic

🔍 skaldlabs/skald Context layer platform in your infrastructure Provides a production-ready RAG system with plug-and-play API for document parsing, vector storage, and chat with configurable retrieval and evaluation tools ⭐ Stars: 563 📅 Last Update: Jul 13, 2026 https://github.com/skaldlabs/skald #selfhosted #homelab #selfhost #selfhosting #opensource #rag #api

fly51fly (@fly51fly) 에이전트 네이티브 메모리 시스템이 준비됐는지 점검하는 연구. 장기 기억, 상태 관리, 컨텍스트 유지가 필요한 에이전트 아키텍처 설계에 직접 연결되는 주제로, RAG·메모리 모듈·에이전트 운영 관점에서 유의미하다. https://x.com/fly51fly/status/2070981480860504220 #agent #memory #rag #llm #architecture