this post was submitted on 12 Dec 2025
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Quilter, which has raised more than $40 million from investors including Benchmark, Index Ventures, and Coatue, used its physics-driven AI to automate the design of a two-board computer system that booted successfully on its first attempt, requiring no costly revisions. The project, internally dubbed "Project Speedrun," required just 38.5 hours of human labor compared to the 428 hours that professional PCB designers quoted for the same task.

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[–] wjrii@lemmy.world 64 points 2 days ago* (last edited 2 days ago) (3 children)

"Language models don't apply to us because this is not a language problem," Nesterenko explained. "If you ask it to actually create a blueprint, it has no training data for that. It has no context for that...." Instead, Quilter built what Nesterenko describes as a "game" where the AI agent makes sequential decisions — place this component here, route this trace there — and receives feedback based on whether the resulting design satisfies electromagnetic, thermal, and manufacturing constraints.... The approach mirrors DeepMind's progression with its Go-playing systems.

This is kind of interesting and cool, and it's not a hallucinating LLM. I've designed a couple of simple circuit boards, and running traces can be sort of zen, but it is tedious and would be maddening as a job, so I can only imagine what the process must be like on complex projects from scratch. Definitely some hype levels coming from the company that give me pause, but it seems like an actual useful task for a machine learning algorithm.

[–] jacksilver@lemmy.world 11 points 2 days ago

I was going to ask how this is different than a Reinforcement Learning algorithm but then they called out Deep Minds Alpha-Go

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