>I still occasionally hand write code in NeoVim on the bits I care the most about (CSS, design and early architecture like API patterns)
I find it amazing how people's opinions differ here. This is the first stuff I'd trust to Claude and co. because it is very much in-distribution for training data. Now if I had sensitive backend code or a framework/language/library that is pretty new or updated frequently, I'd be much more cautious about trusting LLMs or at least I would want to understand every bit of the code.
Just yesterday I used Claude to great effect in FreeCAD to model a church tower. The tower has a square base and an octagonal top, but connecting the two by creating a loft using the GUI in FreeCAD results in a wrong and ugly abomination.
Claude understood the problem and produced elegant Python code that worked perfectly the first time.
So I continued and described the other features of the tower to Claude, who coded them.
It's sometimes difficult to properly describe what you want in English, and Claude does a lot of thinking, and sometimes goes deep into a wrong direction of which it won't come out easily; but in the end the result is almost perfect.
Great article. I've been trying to achieve something similar with a Revit. It's an old CAD application for Windows which means there's a few additional hurdles in exposing a cli interface that allows the LLM to drive it. However, once that is done, the loop of "write code, take a screenshot, repeat" works pretty well.
> Instead of expecting it to understand my requests, I almost always build tooling first to give us a shared language to discuss the project.
This is probably the key. I’ve found this to be true in general. Building simple tools that the model can use help frame the problem in a very useful way.
Honestly understanding and applying 3d transformations should be a new LLM benchmark. Three.js, OpenSCAD, even Nano Banano prompts. The moment you add that extra dimension any semblance of ‘intelligence’ goes right out the window. Every model out there seems to spin themselves in circles trying to logic through it with no success.
Gemini's best ability is it's 3d spatial reasoning. It's downright terrible at a lot of things (toolcalling is an absolute nightmare), but it consistently wins in stuff like 3d modeling, reasoning through 3d problems, and even 2d layout and animation tasks like the infamous pelican riding a bycicle benchmark
I find it amazing how people's opinions differ here. This is the first stuff I'd trust to Claude and co. because it is very much in-distribution for training data. Now if I had sensitive backend code or a framework/language/library that is pretty new or updated frequently, I'd be much more cautious about trusting LLMs or at least I would want to understand every bit of the code.
If you setup your skeleton in a way it is familiar to you, reviewing new features afterwards is easier.
If you let the LLM start with the skeleton, they may use different patterns and in the long run it's harder to keep track of it.
Claude understood the problem and produced elegant Python code that worked perfectly the first time.
So I continued and described the other features of the tower to Claude, who coded them.
It's sometimes difficult to properly describe what you want in English, and Claude does a lot of thinking, and sometimes goes deep into a wrong direction of which it won't come out easily; but in the end the result is almost perfect.
> Instead of expecting it to understand my requests, I almost always build tooling first to give us a shared language to discuss the project.
This is probably the key. I’ve found this to be true in general. Building simple tools that the model can use help frame the problem in a very useful way.
all i wanted was some opinions on if my bad idea would work, but it instead wrote me files for making my own sony earphones in 3ish parts.
and when i sewed it together, it worked!
that said, it did have full access to a mini CAD app, but i think it wrote all its own calculations inline