Recursively apply patterns for pathfinding
I've been begrudgingly working on autorouters for 2 years, looking for new techniques or modern methods that might allow AI to create circuit boards. One of the biggest problems in my view for training an AI to do autorouting is the traditional grid-based representation of autorouting problems which challenges spatial understanding. But we know that vision models are very good at classifying, so I wondered if we could train a model to output a path as a classification. But then how do you represent the path? This lead me down the track of trying to build an autorouter that represented paths…
Recursively apply patterns for pathfinding launched on February 24, 2026 with 26 votes, #694 of 16,628 launches that month and more than 95% of that year's launches.
In plain words
This is an AI-based autorouting tool designed to help create circuit board layouts. It addresses traditional grid-based autorouting limitations by using vision models to represent paths as patterns, enabling better spatial understanding for AI training. The approach treats pathfinding as a classification task rather than conventional grid navigation, offering a novel technique for automating circuit board design that the maker has developed over two years of research.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I've been begrudgingly working on autorouters for 2 years, looking for new techniques or modern methods that might allow AI to create circuit boards. One of the biggest problems in my view for training an AI to do autorouting is the traditional grid-based representation of autorouting problems which challenges…
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