Given that the most important feature for agent performance is the popularity of the language, ie, the amount of training data, (https://danluu.com/pl-tokens/), why would you cause problems for yourself by using Lisp rather than Python/Javascript if you care mainly about results fast, or C/C++/Rust if you care about performance too?
Feel free to do your own analysis -- my informal experiments backs this up, though. I see worse results when I try to do anything in an unpopular language.
It makes sense, needing to train the model on things that aren't already in its weighs takes up valuable context. Until we have models that update their weights based on what they've seen in their recent sessions and learn like people, this will be a problem.
For now, though, between the results I'm seeing here, and the lack of need to look at code, I think this kills off any reason for me to use less popular languages.
Your attempt was probably pretty, ahem, weak. How much support would you say you gave your goes, before you threw in the towel?
I don't think there's really ever a downside to leaning in and making use of a language or system that works for you. Trying to tell people they should just use the popular thing is, imo, bad advice to turn hackers and experimenters into boring people.
A day or so for each of the oddball languages; again, I'm still waiting for an argument on why I'd bother, since the entire point of an agentic system like this is that I don't have to read the code. Experiment with the AI, sure, but you've got a pretty high burden of proof to show that AI is going to pick it up without a high per-prompt token cost.
AI changes the constraints here for now, since it can't permanently learn things. I'm waiting until that changes, but right now it's better to use what it knows out of the box if you want good results.
A better language doesn't buy me anything other than performance; the reason to stick an AI in here is to remove interactions with the code. I don't care what the AI chooses to use, as long as it gets results.
This is an interesting idea. I was thinking about something similar in Smalltalk or Erlang the other day. Mostly building on the actor/object+mailbox concept.
I didn’t actually do anything with that idea yet but may look at the idea in Elixir this weekend.
This is explicitly called out as only weakly supported in that blog post:
It makes sense, needing to train the model on things that aren't already in its weighs takes up valuable context. Until we have models that update their weights based on what they've seen in their recent sessions and learn like people, this will be a problem.
For now, though, between the results I'm seeing here, and the lack of need to look at code, I think this kills off any reason for me to use less popular languages.
I don't think there's really ever a downside to leaning in and making use of a language or system that works for you. Trying to tell people they should just use the popular thing is, imo, bad advice to turn hackers and experimenters into boring people.
AI changes the constraints here for now, since it can't permanently learn things. I'm waiting until that changes, but right now it's better to use what it knows out of the box if you want good results.
A better language doesn't buy me anything other than performance; the reason to stick an AI in here is to remove interactions with the code. I don't care what the AI chooses to use, as long as it gets results.
I didn’t actually do anything with that idea yet but may look at the idea in Elixir this weekend.