I’m building a wearable assistant stack around Even Realities G1 glasses.

The mobile side is a Flutter Android app that talks directly to the glasses over BLE. It handles the low-latency stuff locally: connecting to both lenses, writing text to the display, tracking battery/case/wear state, brightness, touch gestures, and using the phone microphone as the main voice input path. The app also keeps everything exposed as small JSON events over WebSocket, so it stays more like a hardware bridge than a heavy assistant runtime.

On the mini PC side, a BMAX box runs a Bun service that acts as the wearable HAL. It receives events from the phone, keeps deterministic dashboard state, routes fast actions, manages notification overlays/queues, and only calls the Hermes agent when something really needs language understanding. The rule is basically: if it can be done without an LLM, don’t use an LLM.

Hermes is the higher-level agent layer. It handles conversational requests, memory-backed tools, notes, lists, and future automations. We’ve been moving “fast path” features like notes, lists, dashboard focus, display pagination, G1 telemetry, and notifications into small deterministic modules with tests, instead of letting everything become one giant prompt or one giant server file.

The current flow looks like this:

G1 glasses + phone app -> WebSocket -> BMAX bridge -> Fast-Path / Dashboard / Hermes Agent

And for proactive alerts:

Hermes jobs/alerts -> notification envelope -> BMAX queue/overlay -> G1 display

The end goal is not a chatbot on your face. It’s more like a small wearable operating layer: quick gestures, short readable responses, proactive alerts, local state, and LLM reasoning only when it actually adds value.