Llm

What this is: the implementation guide for a LAN-only family voice assistant. The client is a thin browser page: at home it runs on a wall tablet, and the same page works on any phone or laptop. The server is one Python file: a hand-rolled WebSocket endpoint that does voice activity detection, streaming speech to text, deterministic routers for facts, a scoped local brain for the rest, and text to speech through a three-layer voice chain that ends in an OmniVoice model fine-tuned on 213 clean voice lines of Reach Cortana from my own legally-owned MCC install. No cloud speech service, no cloud brain, no monthly fee. ...

What this is: the implementation guide for my local LLM box, the hardware and the serving stack behind it. A retired Dell Precision T7910 workstation got two new CPUs and three used datacenter GPUs, then power, cooling, RAM, and software fights followed. The box now serves a 180B-parameter Qwen hybrid at 524,288 tokens of context across two 262K slots, and a 284B DeepSeek, both through llama.cpp. Environment used here: Windows Server 2022 and Ubuntu on the same T7910, dual Xeon E5-2697A v4, three Tesla V100 32 GB (PG500-216, TCC mode), llama.cpp forks compiled from source against CUDA 12.8. Windows Server is the reference side and where both forks were first built; since 2026-09-08 production serving runs on the Ubuntu side of the dual boot, from .sh launchers under transient systemd units, with logs going to journald instead of a file. Where a figure came from one side and not the other, the text says which. Build window: about nine weeks, mid-June to September 2026. ...