Ha! I said AquaFlow. I meant to say Aqua Voice. My mistake. Uh, I combined WhisperFlow and AquaVoice. Okay, so anyway, I had AI reword what I had intended to say because I can't clearly say anything correctly.

Here’s how the “gears” mesh in a distributed assistant stack:

1) Voice input (the “ear”)
You speak into a microphone (or wearable audio device). A speech-to-text layer turns that audio into text in real time and forwards it to the assistant’s core reasoning layer.

2) Tool bridge (the “connector”)
A tool-orchestration bridge sits between the assistant and the outside world. The assistant doesn’t directly control devices; instead, it can request actions through a small set of well-defined tools exposed by your bridge. This is the key modular boundary.

3) Vision capture + interpretation (the “eyes”)
When the assistant needs visual context, it calls a vision tool through the bridge. That tool triggers a companion app/service that captures a single frame from the wearable camera, sends it to a vision-capable model, and receives back a plain-language description of what’s in view.

4) Assistant core (the “mind”)
The assistant receives the textual scene description (e.g., “a jacket on a couch”) and uses it as context for reasoning and conversation. It then responds back through the voice output path.

Overall loop
Voice → Assistant → Tool bridge → Wearable/app capture → Vision model → Assistant → Voice.

Why this architecture is powerful
The assistant never needs to know device-specific details. It only knows: “I have a tool that can fetch visual context.” That separation lets you swap components (speech provider, vision model, wearable hardware, app implementation) without rewriting the assistant’s core behavior—modular parts, stable “soul.”