Made an open-source Lego AI generator

Hi there :-) New on HN, first time posting.Past year, around December, I started experimenting with making ChatGPT and Claude generate source code in LDraw language.This LDraw is literally an "assembly" language, a low-level programming language that describes how to assemble LEGO pieces together into models, one placement instruction at a time.When executed by specific tools, like e.g. LDView, LeoCAD, Studio... these instructions become LEGO CAD models, that can be interacted with, modified, etc.Or, in other words: one LDraw source file in .mpd or .ldr format is equivalent to one LEGO CAD model.So, the idea I had was: if I manage for maybe ChatGPT or Claude to generate high-quality LDraw source files... then, they would actually be generating high-quality LEGO CAD models, right?Then, after months of iterations and trying one thing after the other... it worked!!!Long story short: using GPT-6 Astra and Opus 5.5, I've managed to create a python toolset, instructions, and docs for agents in general. Now, these can be used by them to generate LDraw models.I've packed it all as a dockerized web app for others to try and experiment, with several providers (and agents) to choose from: OpenAI, Claude and OpenRouter.Here's a bunch of exmaples: https://anteloc.github.io/index-samples.html.If you are curious about the internals of an .mpd model, the "what was the agent picturing on its mind", open the .mpd file that got your attention on a text editor, and read the first line under the ones starting with "0 FILE".I'd really appreciate feedback and comments, let's see where this goes =)

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An open-source AI tool that generates LEGO CAD models by producing LDraw source code.

The pain

LEGO CAD modeling is complex and time-consuming, and there is no easy way to generate models automatically using AI.

The gap

No existing tools combine AI with LEGO CAD generation in an accessible, open-source format for hobbyists and developers.

Build angle

The creator has built a working prototype with multiple AI backends and a dockerized web app, demonstrating technical feasibility.

Strengths

  • Unique combination of AI and LEGO CAD modeling
  • Open-source and multi-provider support
  • Technical depth and working prototype

Risks

  • Niche market with uncertain willingness to pay
  • Limited clarity on how to scale or monetize
  • Potential complexity for average users

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