I failed to recreate the 1996 Space Jam website with Claude

Claude/LLMs in general are still pretty bad at the intricate details of layouts and visual things. There are a lot of problems that are easy to get right for a junior web dev but impossible for an LLM. On the other hand, I was able to write a C program that added gamma color profile support to linux compositors that don't support it (in my case Hyprland) within a few minutes! A - for me - seemingly hard task, which would have taken me at least a day or more if I didn't let Claude write the code. With one prompt Claude generated C code that compiled on first try that:

- Read an .icc file from disk

- parsed the file and extracted the VCGT (video card gamma table)

- wrote the VCGT to the video card for a specified display via amdgpu driver APIs

The only thing I had to fix was the ICC parsing, where it would parse header strings in the wrong byte-order (they are big-endian).

Claude didn't write that code. Someone else did and Claude took that code without credit to the original author(s), adapted it to your use case and then presented it as its own creation to you and you accepted this. If a human did this we probably would have a word for them.

That's an interesting hypothesis : that LLM are fundamentally unable to produce original code.

Do you have papers to back this up ? That was also my reaction when i saw some really crazy accurate comments on some vibe coded piece of code, but i couldn't prove it, and thinking about it now i think my intuition was wrong (ie : LLMs do produce original complex code).

We can solve that question in an intuitive way: if human input is not what is driving the output then it would be sufficient to present it with a fraction of the current inputs, say everything up to 1970 and have it generate all of the input data from 1970 onwards as output.

If that does not work then the moment you introduce AI you cap their capabilities unless humans continue to create original works to feed the AI. The conclusion - to me, at least - is that these pieces of software regurgitate their inputs, they are effectively whitewashing plagiarism, or, alternatively, their ability to generate new content is capped by some arbitrary limit relative to the inputs.

This is known as the data processing inequality. Non-invertible functions can not create more information than what is available in their inputs: https://blog.blackhc.net/2023/08/sdpi_fsvi/. Whatever arithmetic operations are involved in laundering the inputs by stripping original sources & references can not lead to novelty that wasn't already available in some combination of the inputs.

Neural networks can at best uncover latent correlations that were already available in the inputs. Expecting anything more is basically just wishful thinking.