You're right that physical context is crucial. However, mathematically, PCB routing is a Graph Theory optimization problem that falls into the NP-hard complexity class (related to the Steiner Tree problem).
Traditional solvers rely on heuristics (like A* or annealing) because finding an exact mathematical solution is computationally intractable for complex boards.
My point about AI—specifically Graph Neural Networks (GNNs)—isn't about bypassing the learning process. It's about using statistical approximation to handle these high-dimensional topological constraints more effectively than human intuition or standard heuristics can. It's not 'lazy'; it's applying a modern computational lens to an unsolvable deterministic problem.
This is awesome! I'm in the industrial software / CAD space, so I have a rough idea of how painful and complex these engineering workflows can be, even though I don't design hardware myself.
You might find it interesting to look into AI-powered PCB design tools for your next iteration. There are some cool tools popping up that can use AI to assist with the trace routing and layout, which could make the process much less tedious for you.
I built this because I saw how painful it was for makers to manually draw puzzle paths in Illustrator/Inkscape—it literally takes hours to get the interlocking tabs right. I wanted a tool that could do this in seconds.
Tech Stack: It's a native macOS/iPadOS app written in Swift and C++.
Core Algorithm: The puzzle generation is powered by a custom C++ engine using Voronoi diagrams and Bezier optimization to create organic, non-repetitive shapes.
Rendering: Highly optimized generic C++ geometry converted to native SVG paths.
Output: The generated SVGs are strictly optimized for laser cutters (LightBurn, Glowforge) with clean, continuous paths to minimize cutting time.
I'd love to hear your feedback, especially if you are into laser cutting or puzzle design! Happy to answer any technical questions.