🚀 Electron-based LLM App RepoCross-platform app built with Electron. Deploy LLMs locally, create intelligent Agents, and use MCP for multi-agent collaboration. Integrated with system-level APIs for extended functionality. Ideal for devs exploring LLM use cases or users wanting personalized AI. Star, fork, and contribute! 🌟​

🤖 AI Game Master Assistant - Professional RPG content generator using Google Gemini AI. Instantly create NPCs, locations, plots, encounters, and items. Premium UI with multiple RPG system support. Perfect for DMs needing creative inspiration. Fast, reliable, beautifully designed. 🎮✨

A web-based RAG tool that recommends SHL assessments using natural language queries. It combines semantic search over the product catalog with LLMs to generate personalized recommendations for hiring, skills, or development use cases. Ideal for HR and talent professionals.

🧠 A beginner-friendly notebook to explore and compare popular LLMs using a simple personalized career advice use case.

Using on-prem LLMs (llama2 in this case) for function calling and using Advance Rag to reduce hallucinations and personalize.

Interview Practice Platform: A real-time, voice-based AI coding interview system using LiveKit, Deepgram STT, Cartesia TTS, and Groq LLM, with an agent dispatch architecture across Python, Node.js, and Next.js, and post-interview LLM evaluation that analyzes code quality, communication, and edge cases to generate personalized feedback.

An open-source AI assistant that merges LLMs with RAG techniques. Ragbot.AI processes user prompts alongside custom instructions and curated datasets, enabling personalized and context-aware interactions. It supports multiple use cases, including personal assistance, professional tasks, educational support, and project-specific help.

This Project is an intelligent farming/horticulture app that creates personalized cultivation roadmaps. It uses a hybrid system, combining a rules engine for common cases with a large language model (LLM) for custom scenarios. This allows it to analyze environmental data and produce dynamic, step-by-step plans for a successful harvest.