I didn’t actually do anything with that idea yet but may look at the idea in Elixir this weekend.
I was thinking about Smalltalk as well before I made Autolith. I ended up going with Common Lisp because I know Lisp much better (last time I used smalltalk was like, whew, 2014 or so) and because it has better platform support and ecosystem (at least in my experience).
I think Elixir could be great, I knew a guy who was trying to do an agent in Elixir, but sadly didn't get far.
Keep me posted if you get anywhere! And if you'd like to try Autolith, I am happy to help with issues/questions on our Zulip, haha
I see there is a section on RLMs; have you ran Autolith via agentic benchmarks? I would love to see comparisons with Prime Agent.
In Autolith, the top level agent is traditional, but has RLM tools which it can use for the things RLM is good at, namely exploratory work, processing a lot of files at once, backward context research and so on.
This is explicitly called out as only weakly supported in that blog post:
- You should use a popular language
- There's weak support for this statementIt 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.
You can’t tell that with a few uncontrolled runs
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.
coincidentally, "good code" in popular lang is rarely directly attributed to only that part; and it's also about the underlying principles it tries to follow in the code... another example; is it typescript that's good, or are "types" inherently making things/feedback loops easier to reason about in llms? (only using ts here for all example because it's probably one of the most "trained on" pl)
The niche language thing is really not a problem at all any more. If you're working in some esolang it doesn't take more than a 1-2k token primer in the context to get great results, and lisp is popular enough to not even need that.
The benefit of having the agent directly in the image like with Autolith here is that it can directly inspect all defined symbols and explore and orient itself automatically. Really doesn't need much guidance to get great results.
I have been trying Scheme and CL with LLMs for the last three years or so, and in recent months, I have finally decided that they are good enough.
My idea is that well, it's good enough that I can now produce more training data just by using Autolith with the most basic claude/gpt subs, haha
In fact, I've had much easier time maintaining LLM assisted programs in Scheme and Clojure than other languages I've tried using because functional style naturally leads to low coupling. And that makes controlling context far easier than the rats nest of shared state that you have in imperative languages.