yberreby

Joined 10 years ago

Karma
334
Submissions
118
Member since
Mar 12
Yohaï-Eliel Berreby, PhD student in Physiology (Neuro-AI) @ McGill University and Mila (mila.quebec)

Building CanViT (Canvas Vision Transformer), and kickstarting Active-Vision Foundation Models (AVFMs).

Contact: [email protected]

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meet.hn/city/45.5031824,-73.5698065/Montreal

Socials: - linkedin.com/in/yberreby - github.com/yberreby

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Recent submissions

Comment · 7 months ago
Wouldn't be a very good look if they did anything else.
Comment · 8 months ago
The Houthis have been doing a lot of shipping lane disruption, recently. They have sunk several ships.

Iran's Islamic regime has provided material and monetary support to the Houthis.

Crippling their capabilities aligns with the goal of protecting global shipping.

Comment · 8 months ago
This applies to any US company. Have we forgotten everything we learned in 2012? If your data is shared with Google, Anthropic, Meta, Amazon, or any of their US competitors, it is within reach of the NSA. Whether or not a company provides support to the DoW is orthogonal to that fact.
Comment · 8 months ago
Sure, but aren't most people running the *Claw projects using cloud inference?
Comment · 8 months ago
Comment · 8 months ago
Since this has garnered some interest, I definitely will sit down and write a blog post when I have a little bit of time. I have upgraded the setup since that post a few days ago, and keep doing so continuously; it's always running in the background while I work. There are some rough edges, but the workflow feels like what Siri should have been.
Comment · 8 months ago
That's a fair point, and I had the exact same thought while building this. I had previously resisted the urge of integrating Claude Code with e.g. ntfy.sh for this reason. But in practice, this works for me. I end up being less likely to spend time on the computer and more likely to be doing something on my feet.

For context, I'm a PhD student. Work-life balance is already... elusive.

Comment · 8 months ago
Looks like a nice library, thanks for sharing! I know Telegram bots are very popular and that the API story is quite nice, but I have tended to avoid Telegram. My preference would be to go through Signal. I just started looking into my options on this yesterday. Any particular reason why you chose Telegram?
Comment · 8 months ago
Comment · 8 months ago
Watching the OpenClaw/Molbot craze has been entertaining. I wouldn't use it - too much code, changing too quickly, with too little regard for security - but it has inspired me.

I often have ideas while cleaning around, cooking, etc. Claude Code (with Opus 4.5) is very capable. I've long wanted to get Claude Code working hands-free.

So I took an afternoon and rolled my own STT-TTS voice stack for Claude Code. The voice stack runs locally on my M4 Pro and is extremely fast.

For Speech to Text, Parakeet v3 TDT: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3

For Text to Speech, Pocket TTS: https://github.com/kyutai-labs/pocket-tts

Custom MCP to hook this into Claude Code, with a little bit of hacking around to get my AirPods' stem click to be captured.

I'm having Claude narrate its thought process and everything it's doing in short, frequent messages, and I can interrupt it at any time with a stem click, which starts listening to me and sends the message once a sufficiently long pause is detected.

I stream the Claude Code session via AirPlay to my living room TV, so that I don't have to get close to the laptop if I need extra details about what it's doing.

Yesterday, I had it debug a custom WhatsApp integration (via [1]) hands-free while brushing my teeth. It can use `osascript` for OS integration, browse the web via Claude Code's builtin tools...

My back is thankful. This is really fun.

[1]: https://github.com/jlucaso1/whatsapp-rust

Comment · 9 months ago
Comment · 10 months ago
Would you share some additional details? CPU, amount of unified memory / VRAM? Tok/s with those?
Comment · 10 months ago
Based on what works elsewhere in deep learning, I see no reason why you couldn't train once with a randomized number of experts, then set that number during inference based on your desired compute-accuracy tradeoff. I would expect that this has been done in the literature already.

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