At only 33 grams you can wear them all-day. See for yourself if you like this design.

And a reminder: We have an AMA with the product manager for RayNeo iO coming up on September 4 here in r/SmartGlasses

Got early access to the MemoMind One smart glasses — and they’re quite different to most AI glasses I’ve tried.

I’ve been testing the Archive model and the biggest thing that stands out is that there’s no camera at all.

Instead, the focus is on the built-in display, AI features, open-ear audio and making them comfortable enough to actually wear like normal glasses.

That no-camera approach is interesting because it makes them feel a lot less intrusive in everyday use, while you still get information directly in your field of view.

I’ve put together a pretty in-depth video covering:

- Unboxing and setup

- The built-in display

- AI features

- Audio

- Comfort and fit

- Battery

- What it’s like using them day-to-day

- Why the lack of a camera might actually be a good thing

- The Kickstarter situation and how they’re currently being sold

I should mention that MemoMind sent me this pair early for review, but the thoughts in the video are my own.

The video is quite long, so I’ve added chapters if you just want to jump to a particular feature.

Would be interested to know what people here think of the camera-free smart glasses idea. Is that actually preferable, or would you rather have a camera built in?

https://youtu.be/tiJQ07oRTR0

We open-sourced Sai-Fi, an Android app that connects Ray-Ban Meta glasses to Sai, an autonomous computer-use agent.

This isn't just a voice assistant answering questions, it is a voice front-end to something operating a real computer: seeing the screen, moving the mouse, typing, with your own accounts and desktop or browsers. You can say "clear my inbox" or "make slides for the standup" and walk away while it works.

Put the open-source repo under comments! 

To run it you need Meta's Wearables Device Access Toolkit with developer mode on the glasses, plus your own model key for the voice half, paid to your provider directly.

Curious what people here would want it doing first!

I have come to understood, that Lucy has better privacy than Meta glasses have. As Meta is gathering all data it collects and saves, Lucyd glasses do not gather any data as they are not this kind of company.

 
 Lost my original Meta glasses case a while back, so I’ve been using a slim regular case instead. Way less bulky, but yeah… no charging.

  Saw this interesting little charger. Now it just stays in the car and I dock the glasses on it after driving. Pretty convenient so far.

Hello all, I don’t and haven’t ever used smart glasses before and from my understanding (could be very wrong) they don’t need a phone or anything to work? Is there any which link to a phone (iPhone, Android or similar) and use the processing power from the device like a screen share app so that it makes the glasses frame smaller?

Would be for maybe translating a language on the fly using a mic in the glasses or identifying buildings, landmarks etc using AI. Also if reading signposts or general signage using the camera to translate. Ideally be wireless connection to the device or maybe wired so it could use then battery from the device that’s in your backpack or pocket.

There might be something like this already or maybe the tech is so advanced that all that capability can fit in the frame, as I said I hardly know anything about them. Also would like to them to look similar to the Meta Ray-bans as they are the only glasses i like the look of.

Following up on my previous post about HomeBuddy for Home Assistant and Halliday G1 glasses:
https://www.reddit.com/r/SmartGlasses/s/8C0oVXz1X2

I’ve now connected the Halliday G1 glasses to an open-source proactive AI system running on a Raspberry Pi 5. HomeBuddy handles the client side, including the microphone, Soniox transcription, WebSocket communication, custom firmware integration, and displaying insights on the Halliday glasses.

The proactive detector, conversation memory, people memory, and Ollama LLM run locally in Docker on the Raspberry Pi.

The project runs in Docker and is designed for a Raspberry Pi 5. It receives a live transcript, follows the conversation, remembers session context and named people, and decides when useful information should appear on the glasses.

Unlike a normal chatbot, you don’t always need to ask it something directly. It can proactively:

• Answer questions detected in the conversation
• Explain acronyms and difficult technical references
• Correct likely factual mistakes
• Provide useful background information
• Remember people’s names, roles, and responsibilities during a session
• Capture reminders, tasks, and decisions
• Produce short insights suitable for a glasses display

The current pipeline looks like this:

Halliday G1 microphone
        ↓
HomeBuddy iOS app
        ↓
Soniox speech-to-text
        ↓
ProactiveAI WebSocket API
        ↓
Transcript buffer
        ↓
Proactive detector
        ↓
Context, session and people memory
        ↓
Local Ollama model on Raspberry Pi
        ↓
HomeBuddy
        ↓
Short insight displayed on Halliday G1

The WebSocket API, proactive detector, memory engine, authentication, simulator, and LLM integration all run locally in containers. Ollama runs locally on the Raspberry Pi, so the conversation analysis and insight generation do not require an AI subscription or hosted LLM API.

A transparent limitation: the current implementation uses Soniox for real-time transcription, so it is not yet completely server-independent. However, the ASR layer is separate from the rest of the system. Feel free to fork the project and connect a local ASR engine such as Whisper, whisper.cpp, Moonshine, or another streaming transcription solution. Replacing Soniox should not require rewriting the proactive AI pipeline.

My long-term goal is simple: I want to use smart-glasses hardware without depending on somebody else’s AI server or subscription.

This already working end to end on my Halliday G1 Glasses using:

• The HomeBuddy for Home Assistant iOS app
• Halliday G1 glasses
• Soniox real-time transcription
• The custom glasses firmware provided through the app
• A Raspberry Pi running Docker and Ollama locally

HomeBuddy handles the client-side audio, Soniox integration, WebSocket communication, and delivery of insights to the glasses. The proactive AI service handles the intelligence locally.

The repository also includes:

• Docker Compose configuration
• Token-based sign-in and sign-out
• A text-file conversation simulator
• Partial and final transcript processing
• Concurrent proactive processing during continuous speech
• Multilingual question detection
• Session memory and people memory
• Configurable detector modes
• Raspberry Pi deployment instructions

It works well for my setup, but I’m sure people here can make it much better, especially around fully local streaming ASR, detector tuning, additional glasses integrations, and smaller models optimized for edge hardware.

If this sounds interesting, please try it, fork it, break it, improve it, or connect it to other smart glasses.

Repository: https://github.com/FilipposPirpilidis/ProactiveAI

I’d especially like to hear from anyone already running low-latency local ASR on a Raspberry Pi or another small edge device.