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.
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.
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.
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?
I'm in the process of migrating from my first POC's disgusting mess of vibe-coded Python to a cleaner (and shareable) Rust architecture. It's going well but I will wait for it to stabilize a bit before sharing.
The main non-trivial parts are proper state machine / concurrency management, and AirPods interaction; in particular, detecting a stem click while the microphone is active. I worked around this by having the mic-off-to-mic-on transition use a media player Play event, and mic-on-to-mic-off do silence detection. It's super hacky but actually works surprisingly well.
Currently looking into using `AVAudioApplication.setInputMuteStateChangeHandler(_:)`, like AirMute [1] does, so that I don't have to rely on silence detection and can manually terminate the voice command with a second click.
If you want to roll your own version of what I described today, it should be pretty easy to do so based on what I wrote if you have a Max x5-x20 plan and feed it to Opus. Bonus points, you get to customize it to your exact needs.
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.
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...
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.
Yes, this is part of the French prépa/CPGE system, which is the "standard" way for students to enter elite engineering schools. You do your first 2-3 years of undergrad in prépa.