Local RAG app for question answering from PDFs, fully offline
A Flask-based local RAG application that lets users upload PDFs, chunk and embed them, then ask questions answered solely from the document content. It uses Ollama for chat and embedding models, ChromaDB as vector store, and runs entirely offline, solving privacy and data control needs for users who want to query their own documents without sending data to external services.
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Offline PDF chat with RAG using local LLMs and vector search
A production-oriented Retrieval-Augmented Generation (RAG) system for chatting with PDFs. It uses local LLMs via Ollama, ChromaDB for vector search, and LangChain to provide grounded answers from uploaded documents, fully offline. Aimed at users needing private, local document Q&A.
LocalEyes gives blind LLMs vision via local Ollama models
LocalEyes is a new tool that enables text-only LLMs like DeepSeek, CodeLlama, and Qwen-Coder to process images locally using an Ollama vision model. It supports screen capture, clipboard reading, and image file analysis without cloud uploads or API keys, offering a private, fast, and free solution for developers using Claude Code.
Wigolo: MCP server giving LLM coding tools live web access with crawling and caching
Wigolo is an open-source MCP server that runs locally and provides LLM coding tools (Claude Code, Cursor, Codex, Gemini CLI) with ten web tools: multi-engine search with reranking, page fetching for JavaScript-heavy sites and PDFs, whole-docs-site crawling, structured extraction, and persistent local caching. It solves the problem of LLMs relying on stale training data by giving them real-time web access.
Founder runs full multi-agent content pipeline offline on local laptop
A founder demonstrates a multi-agent pipeline for content research, drafting, and editing that runs entirely on a local laptop with no internet connection, using models stored on the hard drive. This showcases the growing capability of local AI to replace cloud-dependent workflows, eliminating API costs and data privacy concerns.
LLM hardware recipe database with filters and community usage tracking
A community-driven database that lists which LLM models run on which hardware, with performance details. Users can filter by hardware, submit new recipes, and mark which recipes they actively use to show popularity. It solves the problem of finding compatible model-hardware combinations for LLM deployment.