text-to-cad

s are great at: • Manipulating complex geometry • Producing high resolution, scaled CAD models • Boolean / offset operations But they are really hard for humans to intuit, which is why we prefer vanilla CAD that works more like LEGO: faces, edges, holes, cuts, fillets, etc., with human-readable topology. Agents, on the other hand, are very, very good at coding and math — the two key ingredients for implicits. The third ingredient is visual / spatial reasoning. While lacking today, I believe for many reasons (including robotics) this will be the next frontier that labs pursue. To caveat: agents aren’t great at implicits today. Frontier models are still pretty blind, so implicits break down on complex parts/assemblies with visual feedback (code → model → screenshot → repeat). There is also no easy toolchain to inspect implicits because they lack topology. That said, 6 months ago the models couldn’t write reliable code. Things are changing fast, and I think 2026 will be the year the models solve 3D design. I also think it’s possible (and even probable) that implicit CAD becomes the preferred way for models to do 3D design. Special thanks to Blake Courter and the Gradient Control team for teaching me about implicits and sharing the Python primitives used to build this library. Blake is the GOAT of implicit CAD and gave an excellent talk on this exact topic at Kinetic a few weeks ago.