As promised, let's deep dive into the learnings from my text-to-3D agent project. The goal was to go beyond simple shapes and see if an AI agent could generate complex 3D models using Blender's Python API.
The short answer: yes, but the architecture is everything.
The Core Challenge: Reasoning vs. Syntax
Most LLMs can write a simple Blender script for a cube. But a "low poly city block"? That requires planning, iteration, and self-correction—tasks that push models to their limits. This isn't just a coding problem; it's a reasoning problem.
My Approach: A Hybrid Agent Architecture 🧠
I hypothesized that no single model could do it all. So, I designed a hybrid system that splits the work:
A "Thinker" LLM (SOTA models): Responsible for high-level reasoning, planning the steps, and generating initial code.
Responsible for high-level reasoning, planning the steps, and generating initial code. A "Doer" LLM (Specialized Coder models): Responsible for refining, debugging, and ensuring syntactical correctness of the code.
I tested three architectures on tasks of varying difficulty:
Homogeneous SOTA: A large model doing everything. Homogeneous Small: A small coder model doing everything. Hybrid: The "Thinker" + "Doer" approach.
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