Hey HN! I built this because I was tired of waiting 10 seconds for Ollama's 680MB binary to start just to run a 4GB model locally. Quick demo - working VSCode

Hey HN! I built this because I was tired of waiting 10 seconds for Ollama's 680MB binary to start just to run a 4GB model locally. Quick demo - working VSCode + local AI in 30 seconds: curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy https://github.com/Michael-A-Kuykendall/shimmy/releases/late... ./shimmy serve # Point VSCode/Cursor to localhost:11435 The technical achievement: Got it down to 5.1MB by stripping everything except pure inference. Written in Rust, uses llama.cpp's engine. One feature I'm excited about: You can use LoRA adapters directly without converting them. Just point to your .gguf base model and .gguf LoRA - it handles the merge at runtime. Makes iterating on fine-tuned models much faster since there's no conversion step. Your data never leaves your machine. No telemetry. No accounts. Just a tiny binary that makes GGUF models work with your AI coding tools. Would love feedback on the auto-discovery feature - it finds your models automatically so you don't need any configuration. What's your local LLM setup? Are you using LoRA adapters for anything specific?