LLM Inevitabilism | slacker news

In the 90s a friend told me about the internet. And that he knows someone who is in a university and has access to it and can show us. An hour later, we were sitting in front of a computer in that university and watched his friend surfing the web. Clicking on links, receiving pages of text. Faster than one could read. In a nice layout. Even with images. And links to other pages. We were shocked. No printing, no shipping, no waiting. This was the future. It was inevitable.

Yesterday I wanted to rewrite a program to use a large library that would have required me to dive deep down into the documentation or read its code to tackle my use case. As a first try, I just copy+pasted the whole library and my whole program into GPT 4.1 and told it to rewrite it using the library. It succeeded at the first attempt. The rewrite itself was small enough that I could read all code changes in 15 minutes and make a few stylistic changes. Done. Hours of time saved. This is the future. It is inevitable.

PS: Most replies seem to compare my experience to experiences that the responders have with agentic coding, where the developer is iteratively changing the code by chatting with an LLM. I am not doing that. I use a "One prompt one file. No code edits." approach, which I describe here:

https://www.gibney.org/prompt_coding

> As a first try, I just copy+pasted the whole library and my whole program into GPT 4.1 and told it to rewrite it using the library.

That's a translation task. Transformer models are excellent at translation tasks (and, for the same reasons, half-decent at fuzzy search and compression), and that's basically all they can do, but generative models tend to be worse at translation tasks than seq2seq models.

So the fact that a GPT model can one-shot this correspondence, given a description of the library, suggests there's a better way to wire a transformer model up that'd be way more powerful. Unfortunately, this isn't my field, so I'm not familiar with the literature and don't know what approaches would be promising.