Here’s a question worth more than most of the AI hype takes clogging your feed: what happens to your velocity the day your frontier provider changes the deal? Raises the price. Deprecates the model you tuned your whole workflow around. Throttles you at the worst possible moment. Or just decides your use case, your industry, or your country isn’t one they want to serve anymore. If your honest answer is “I’d be dead in the water,” then you don’t have a strategy — you have a dependency. And it’s time to look hard at running some inference on your own metal.
XDA ran a piece that stopped me mid-scroll: a 27B open-weights model reverse-engineered a commercial application’s license check — recovered a deliberately obscured crypto key out of ARM64 assembly, caught and corrected its own mistake without being told, and produced a working bypass PoC. In about thirty minutes. On a desktop box. I have (a version of) that box. So I went and set it up.
I have two 128 GB unified-memory machines on my desk. Day job gear, not mine. One is an ASUS Ascent GX10 — an NVIDIA GB10 Grace Blackwell appliance half the size of a hardback book. The other is an HP ZBook Ultra G1a, a 14" mobile workstation running AMD’s Ryzen AI MAX+ PRO 395 (“Strix Halo”). Both hold an 80B coding model entirely in memory. How do they compare for agentic coding?
Right now, as an industry, we are running almost entirely on COGS and calling it innovation. Kelsey Hightower gave a talk at PlatformCon called Zero Token Architecture, and the whole thing compresses down to one sentence: infer once, export, run without inference. He’s right. I want to give you the vocabulary that makes it land in a budget meeting, because I work at a company that ships hardware, and we already have words for this.
In my previous post about Claude Code, I talked about using VS Code devcontainers to safely run Claude in “dangerous mode.” That was great for a typical software project. But what about something harder? What about porting a complex hardware-interfacing library from Python to Go, where you need to deeply understand USB protocols, radio registers, and firmware internals?
That’s exactly what I did with gocat – a Go library for controlling the YardStick One sub-GHz RF transceiver. And Claude Code was instrumental in making it happen.
I’m building a board for robotics, but it’s also useful for IoT and home automation. What’s it do? What problem does it solve? It’s basically a WiFi interface to a variety of I2C-driven devices (motors or sensors). The I2C side is Engineered to be highly robust and reliable.