Privacy is no longer just a feature; it's a human right—especially when it comes to mental health. Imagine a Cognitive Behavioral Therapy (CBT) assistant that lives entirely in your browser, never sends a single byte of your conversation to a remote server, and runs at lightning speed.
Thanks to the explosion of Edge AI and the maturing WebGPU standard, this isn't science fiction anymore. In this tutorial, we will explore how to use WebLLM, TVM Unity, and React to build a high-performance, offline-capable mental health assistant. By leveraging WebLLM and Local-first AI principles, we can provide low-latency support while keeping sensitive user data exactly where it belongs: on the user's device.
Why Local AI for Mental Health?
Mental health data is incredibly sensitive. Using traditional LLM APIs (like OpenAI or Claude) means sending private thoughts to the cloud. By using WebGPU acceleration, we can run models like Llama 3 or Mistral directly on the client's GPU via the browser.
The Architecture: From Weights to WebGPU
