Stop Sending Health Data to the Cloud! Build a Privacy-First Symptom Checker with WebGPU

Privacy is the ultimate luxury in the age of AI. When it comes to health data, the stakes are even higher. Users are increasingly wary of sending sensitive symptoms to a remote server. This is where Edge AI and In-browser inference change the game. By leveraging WebGPU and Transformer models , we can build a medical symptom checker that runs entirely on the user's hardware—meaning zero server costs for you and total privacy for them. In this tutorial, we’ll explore how to use the WebGPU API, ONNX Runtime , and WebLLM to deploy a lightweight Transformer engine directly in the browser. We will focus on creating a high-performance, privacy-first AI solution that bypasses the cloud entirely. If you've been looking for a way to implement On-device LLMs using TypeScript , you're in the right place. The Architecture: How It Works Traditional AI apps send a request to a Python backend. Our Wasm-Med architecture keeps everything in the client-side sandbox. graph TD A[User Input: 'I have a dry c