https://grandpacad.com - AI modeling for 3D printing
Been at it for about year and a half.
Really exciting stuff is happening literally every month, because underlying models are getting better and better. When I started it was pretty basic: “make a cube with a hole through it”. Now it’s at the point “make a raspberry pi 4 case” and the agent searches, builds, verifies…
What surprised me in this process is how little meaning AI benchmarks have. Pareto frontier for my use case looks completely different than any other benchmark portrays.
Same here. 5.5 would sometimes oneshot relatively complex models even without MCP. Opus, however, even with Fusion 360 MCP access would usually make comically bad models. It seemed that Opus tried to reason through the problem while GPT would come up with a general plan of using whatever tools were available to solve the problem. In chat, it would use the available Python and trimesh. It was also the only time I had ChatGPT work on something for over 30 min without giving up.
100%, but this means we are going to move to stateful APIs on the AI provider's end (like OpenAI already does with Codex and Responses API) to make this work.
GrandpaCAD - AI 3D modeling software focused on simplicity. Made it so even my grandpa could model.
He’s been asking me for years when will I teach him how to 3D model. I tried, we failed and then I seen him use ChatGPT so I knew there was a better way than traditional CAD tools.
Recently we also got European funding and the project got some traction. Very exciting times ahead.
- Does it support layers / assembling structures? This would allow to combine multiple parts into a product (don't know hoe that is named exactly in english)
- Awesome you support not only subscriptions but also a credit-based approach. I would definetly be a credit-customer, as I may have usage a couple of times a year, bot not continouous!
You are right, relatively to other llm providers this is not slow. But if you think what is possible when you have 1000t/s a sec you might find it slow.
I agree with the conclusion and am happy to see this blog post, but this killed a bit of credibility for me:
> Using a single LLM judge for scoring. Every score here comes from one model, GPT-5.6 Luna, looking at one image at a time. I didn’t do much alignment and didn’t check how often it agrees with itself on a re-run.
Having used a similar setup (with previous gen LLMs) to evaluate the 3D models that my product[0] generates, it turned out there was no correlation at all. LLM judgments were very much random and I assume judging SVGs is not that far from judging 3D models. I guess I have to re-test this with current gen.
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