PCBlarp

What they're building

PCBlarp is an AI agent that designs printed circuit boards for robots from a plain-English spec. You tell it what the robot has to do (motors, sensors, microcontroller, power budget, form factor) and it runs the full design loop: picks real components, writes the schematic and netlist, lays out the board, runs design-rule checks, and exports manufacturable files plus a bill of materials. Every part is grounded in a real datasheet, so the output is buildable, not plausible-looking. It turns a blank EDA canvas, normally days of an EE's time, into a conversation. What it uses: OpenClaw runs the agent loop and ships to Nebius Serverless in one command. Nebius Token Factory powers inference for the reasoning and design steps. Tavily pulls datasheets, footprints, and in-stock part availability so the agent designs around components that actually ship today. Composio wires the actions: pushing the project to GitHub, pulling reference designs, and chaining the EDA and sourcing steps. Why it matters: PCB design is the slow, expensive bottleneck between a robotics idea and a working prototype. Teams pay EE contractors weeks for the first pass. PCBlarp does it in minutes, grounded in real parts, so a founder, a lab, or a serious hobbyist gets a manufacturable starting point instead of a blank file. What hurt: getting a model to output electrically valid designs instead of confident-sounding nonsense. Netlist correctness, footprint-to-symbol alignment, and grounding every part against a real datasheet ate most of the build. Closing the loop from spec to manufacturable files, without a human patching the netlist by hand, was the real fight.