Training Apple’s On-Device LLM: LoRA, QLoRA, and What Actually Worked
Apple’s adapter toolkit assumes a beefy GPU. I got LoRA training running on a free T4 and a Mac.
Apple’s adapter toolkit assumes a beefy GPU. I got LoRA training running on a free T4 and a Mac.
Claude Desktop and Claude Code are separate worlds. Here’s an MCP server that bridges them.
I wired Apple’s on-device 3B LLM into three zsh hooks and benchmarked multiple approaches to make it write correct shell commands.
One prompt, zero code, and a team of agents built a BLE monitoring system that exercised real judgment.
What happens when you point an AI agent at a microcontroller and say ‘deploy this ML model on the device’?
A CLI tool that converts Claude Code session logs into interactive, self-contained HTML replays you can share, embed, or host anywhere.
The same MCP servers — BLE, serial, debug probe — working across Claude Code, VS Code + Copilot, and Cursor. That’s the point of a protocol.
What happens when the AI agent can halt the CPU, set breakpoints, flash firmware, and inspect memory directly?
We learned to trust compilers. Can we learn to trust AI? The answer isn’t about the code — it’s about the controls.
What happens when the agent can see both the wireless interface and the debug console at the same time?
Serial is where embedded engineers live. What happens when the agent can see your console too?
An MCP server that lets AI agents interact with real BLE hardware — closing the loop between reasoning and physical devices.
Models became useful not by getting smarter, but by gaining ways to act on the world. MCP makes that interface sane.