{"slug": "qwen-cloud-hackathon-1-technical-deep-dive-on-what-i-am-building", "title": "QWEN Cloud Hackathon - #1 Technical Deep dive on what I am building", "summary": "A developer built Smriti, a 4-agent elderly care system using QwenVL, MemoAssistant, and persevere thinking to address dementia patient needs. The system includes a Vision Agent for recognition and hazard detection, a Memory Agent for persistent storage, a Guardrail Agent for hallucination blocking, and a Caregiver Agent for human notification. The agents communicate in under 2 seconds to provide real-time monitoring and alerts.", "body_md": "The Problem: A week ago I went to my grandmother who is suffering from demantia. At first I thought it was normal at this second but i wanted to know the cause so i looked up in google and saw that over 55 million patentis are navigating a world where they wake up and cannot recognize their own children's faces. They miss critical life spanning medications and suffer catastrophic falls when unmonitored.\n\nWe cannot solve a dynamic, high stakes human crisis with a single, static LLM prompt or a standard chatbot wrapper. If a healthcare AI hallucinates a medication dosage or fails to recognize a family caregiver, the consequences are life threatening.\n\nThat's why we are introducing Smriti - Building a 4-Agent Elderly Care system with QwenVL, MemoAssistant and persevere thinking.\n\nThe Solution - 4 Specialized QwenNative Agents working together:\n\n**Agent1:** - Vision Agent (Qwen-VL) -> Responsible For Recognition, medicine label reading, hazard detection.It is optimized to perform high speed facial detection, medicine label reading, and spatial hazard analysis (such as identifying a water spill on the floor or an open stove burner).\n\n**Agent2:** - Memory Agent(MemoAssistant): Persistent KeyValue Storage Across sessions.The Memory Agent uses MemoAssistant to manage persistent key-value storage across sessions. It caches family profiles, authorized medical staff, and historical routines so the system doesn't have to re-evaluate static profiles on every frame loop.\n\n**Agent3:** - Guardial Agent(Qwen 3.6 Max + Persevere Thinking) - Hallunication blocking with reasoning traces.It utilizes Qwen's trillion-parameter MoE architecture and forces a reasoning trace calculation using the preserve_thinking parameter. This layer acts as a strict hallucination blocker by forcing the model to explicitly evaluate safety weights and confidence before executing actions.\n\n**Internal Reasoning for Guardrail Agent:**\n\n**Agent4:** - When the Guardrail Agent detects an anomaly or drops below the 85% confidence score, it forwards the state payload to the Caregiver Agent via the Model Context Protocol (MCP). This pushes live notifications, image clips, and the system's reasoning logs directly to a web-based dashboard for immediate human approval.\n\nHow Agents Communicate - Vision -> Memory -> Guardrail -> Response in under 2 seconds.\n\nOne of the reason to build this - Only in reddit there are like 70K poeple in the subreddit channel of dementia.", "url": "https://wpnews.pro/news/qwen-cloud-hackathon-1-technical-deep-dive-on-what-i-am-building", "canonical_source": "https://dev.to/siddhartha_bhattarai_a084/qwen-cloud-hackathon-1-technical-deep-dive-on-what-i-am-building-16aj", "published_at": "2026-06-14 18:07:41+00:00", "updated_at": "2026-06-14 18:10:39.959481+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-agents", "computer-vision"], "entities": ["Qwen", "QwenVL", "MemoAssistant", "Smriti", "Model Context Protocol", "MCP"], "alternates": {"html": "https://wpnews.pro/news/qwen-cloud-hackathon-1-technical-deep-dive-on-what-i-am-building", "markdown": "https://wpnews.pro/news/qwen-cloud-hackathon-1-technical-deep-dive-on-what-i-am-building.md", "text": "https://wpnews.pro/news/qwen-cloud-hackathon-1-technical-deep-dive-on-what-i-am-building.txt", "jsonld": "https://wpnews.pro/news/qwen-cloud-hackathon-1-technical-deep-dive-on-what-i-am-building.jsonld"}}