krasikra

The WebRTC + LiveKit suggestion is spot on. Full-duplex speech-to-speech at the edge is where this gets really interesting — the latency budget for conversational AI basically demands local inference. Running a 7B model on-device with sub-200ms round-trip changes what you can build compared to cloud endpoints where you are fighting network jitter on top of inference time.

This is essentially the same "lost wakeup" problem you see with condition variables in pthreads, but asyncio makes it more insidious because the single-threaded event loop gives you a false sense of safety. The per-consumer queue approach is really just reinventing what Go channels and Rust's tokio::sync::watch do at the primitive level — buffer transitions so observers never miss intermediate states.

The code review bottleneck is even worse on edge devices. We ship Jetson-based systems running local AI agents, and the 'cheap code' problem compounds when you factor in ARM-specific bugs and thermal constraints that don't show up in testing. Learned the hard way: test coverage on x86 means nothing when your agent hits OOM on a Xavier at 3AM because someone let the LLM generate unbounded batch sizes.

Fine-tuned Qwen models run surprisingly well on NVIDIA Jetson hardware. We've deployed several 7B variants for edge AI tasks where latency matters more than raw accuracy – think industrial inspection, retail analytics where you can't rely on cloud connectivity. The key is LoRA fine-tuning keeps the model small enough to fit in unified memory while still hitting production-grade inference speeds. Biggest surprise was power efficiency; a Jetson Orin can run continuous inference at under 15W while a cloud round-trip burns way more energy at scale.