Doesn’t that just mean it was trained with inputs and outputs from simple programs?
You can even combine your pseudo language with natural language. See the OP’s custom GPT and the comments here: https://news.ycombinator.com/item?id=38594521
>We provide our models with a working Python interpreter in a sandboxed, firewalled execution environment, along with some ephemeral disk space. Code run by our interpreter plugin is evaluated in a persistent session that is alive for the duration of a chat conversation (with an upper-bound timeout) and subsequent calls can build on top of each other. We support uploading files to the current conversation workspace and downloading the results of your work.
It really feels like I'm just googling for you, you had the feature name.
I’m sure there are use cases for this. But in the end it is only a simple feature added onto a—sometimes—marginally related service.
This is a lot more than a party trick. The model is able to describe the program it wants to execute and now it can accurately execute that - that it 'offloads' the work to a specialized program seems fine to me.
It's way more than a simple feature, this is enabling it to overcome one of the biggest limitations and criticisms of LLMs - it can answer questions it has never seen before.