This is really interesting. I have felt for the past couple years that the moldability of Common Lisp lends agents the appropriate affordances to “do the right thing” via experimentations.
I see there is a section on RLMs; have you ran Autolith via agentic benchmarks? I would love to see comparisons with Prime Agent.
We have not done that yet, it's one of the major priorities. Autolith implements RLM a bit differently than Prime Agent (although it both comes from the same paper):
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.
will do when I share with you the streaming string library I talked about on your discord. I use these arrows in every single namespace I write so this is already part of the plan.
Sometimes, the repetition of encounters is just as meaningful as the people we meet.
Tacit programming + llm control library (like LangChain) has a lot of untapped potential. Tacit programming shines when the control structures are simple (and functions/variables can be anonymized), and these patterns occur frequently in programming on top of llms.
The discussion basically says you don't follow an edit the full program-compile-run cycle like in other languages with batch compilers. Instead, you interactively create a program by writing individual expressions (e.g. function definitions) in the editor, sending them to the REPL for evaluation and testing, further experimenting at the REPL, and repeating the cycle. If you're already familiar with Lisp environments there's nothing new in the book.
In my experience, perfectionism is crafted post-procrastination. It probably makes no sense, but the mind often does this convoluted thing in order to protect procrastination.
Interesting project. I can't help but ask if you've ever considered how Richard Stallman would feel about people configuring Emacs to upload code into OpenAI's cloud. It amazes me even more that you get people to pay to do it. I'd rather see Stanford helping us put developers back in control of their own AI experiences.
I think it's important to notice that free software is free to use and extend by people who don't necessarily share philosophical or political convictions of the software's author.
I see there is a section on RLMs; have you ran Autolith via agentic benchmarks? I would love to see comparisons with Prime Agent.