[mfabbri77]

The point was to answer the question: "Can every piece of software be viewed as a permutation of software that has already been developed?" In my opinion, an email client is a more favorable example than a 3D engine. In fields where it is necessary to differentiate, improve, or innovate at the algorithmic level, where research and development play a fundamental role, it is not simply a matter of permuting software or leveraging existing software components by simply assembling them more effectively.

[Archer6621]

Actually, in the specific case of a 3D program it's the current generation of LLM's complete lack of ability in spatial reasoning that prevents them from "understanding" what you want when you ask it to e.g. "make a camera that flies in the direction you are looking at".

It necessarily has to derive it from examples of cameras that fly forward that it knows about, without understanding the exact mathematical underpinnings that allow you to rotate a 3D perspective camera and move along its local coordinate system, let alone knowing how to verify whether its implementation functions as desired, often resulting in dysfunctional garbage. Even with a human in the loop that provides it with feedback and grounds it (I tried), it can't figure this out, and that's just a tiny example.

Math is precise, and an LLM's fuzzy approach is therefore a bad fit for it. It will need an obscene amount of examples to reliably "parrot" mathematical constructs.

[debugnik]

> "make a camera that flies in the direction you are looking at"

That's not the task of a renderer though, but its client, so you're talking past your parent comment. And given that I've seen peers one-shot tiny Unity prototypes with agents, I don't really believe they're that bad at taking an educated guess at such a simple prompt, as much as I wish it were true.

[Archer6621]

You're right. My point was more that LLMs are bad at (3D) math and spatial reasoning, which applies to renderers. Since Unity neatly abstracts the complexity away of this through an API that corresponds well to spoken language, and is quite popular, that same example and similar prototypes should have a higher success rate.

I guess the less detailed a spec has to be thanks to the tooling, the more likely it is that the LLM will come up with something usable. But it's unclear to me whether that is because of more examples existing due to higher user adoption, or because of fewer decisions/predictions having to be made by the LLM. Maybe it is a bit of both.