What's really interesting is that the LLMs become better and better at setting up the environments / tasks themselves. I got this surreal experience the other day where I was writing a prompt0n.md file (I try to log all my prompts in a .folder to keep track of what I prompt and the results I get), and the autocomplete in antigravity kinda sorta wrote the entire prompt by itself... Granted it had all the previous prompts in the same folder (don't know exactly what it grabs in context by itself) and I was working on the next logical step, but it kept getting the "good bits" out of them, and following the pattern quite nicely. I only edited minor things, and refused one line completion in the entire prompt.

So much this.

People make fun of prompt engineering, but I think "AI ops" will eventually become a real role at most if not all software companies. Harness Engineers and Agent Reliability Engineers will be just as important as something like DevOps is now.

don't forget the size of the search space...

this is very far from hyperparameter tuning in at least three important ways:

- it can modify code arbitrarily, the notion of a "hyperparameter" dissolves

- there is no need to run "sweeps" - this is the standard parallel process that wastes compute. because LLM agents are sequential, they can do more efficient versions such as binary search to narrow in on the right setting very quickly (usually many parameters will have a U shaped optimal setting).

- it's fully automatic, it doesn't require human in the loop to mess with the code.

You're right that many of the changes it seems to make out of the box (as I intentionally did not try to prompt engineer it too hard yet because I was curious what you get by default) seem to be tuning existing hyperparameters. not all of the changes are like that - e.g. it tried to replace the non-linearity, etc. I will say that overall (and again, out of the box) the LLM feels unwilling to creatively pursue a research direction or something like that. The models feel very "cagy" and "scared" when they are given problems that are a little too open ended. But that's just where the fun parts, e.g. I had some early successes with the idea of a "chief scientist" that was basically a never-ending plan mode that looked at what worked, didn't work, tried to find related code/papers, and created a long list of experiments to try, which it could then send to junior engineers running in tmux sessions. I think quite a few approaches are possible, so I think it's a nice canvas. The reason we're not getting "novel research" feels like half capability issue and half skill issue.

The first half of this is already happening to a certain extent. I first noticed this in a submission[1] on Dimitris Papailiopoulos' Adderboard[2], which is a code-golf competition for training the smallest transformer that can add two 10-digit numbers. Most submissions on it are fully AI generated.

The report in the linked repo is Claude Code generated.

[1]: https://github.com/rezabyt/digit-addition-491p

[2]: https://github.com/anadim/AdderBoard

Cool idea!…

That's a great idea.