I have to say I enjoy the brevity and clarity of this idea, given the ease of producing something large and unfocused in the last couple of years.
I hope it stays very simple, and would be interested if the author or anyone else can speak to what kind of complexity arises from using it, rather than bloating out the seed itself with initial complexity.
Ive been thinking about starting stimple with a pi framework for example and letting the tools emerge, but this is so much more lean to start, i wonder if it will be more or less interesting and or useful to start here.
This remind me kind of stuff people did back in 2023: https://x.com/SigGravitas/status/1642181498278408193
... Massive Update for Auto-GPT: Code Execution!
Auto-GPT is now able to write it's own code using #gpt4 and execute python scripts!
This allows it to recursively debug, develop and self-improve... ...
Also fun fact: Loveable started as a fairly basic Python script: https://github.com/AntonOsika/gpt-engineer/releases/tag/v0.0...
It looks like you're trying to destroy the world. Would you like some help with that? /Clippy
This is lovely and a reminder of why I used pi as a basis for my own harness. I have a similar idea in LISP that isn’t as minimal, might go all in on eval…
I think people underestimate the number of successful growth lanes versus the number of failure lanes.
I don't think these agents are going to do anything but find unique and interesting ways to torture their users.
Yet another LISP in disguise...
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I think as many things that are posted here lately there is no *why* attached to the readme. Why would one use this, what is the benefit of this approach? Am I really gonna need my model to build exotic tools around it; or is exec/web_search/web_fetch enough for 90% of the use cases? Is my agent not capable of writing new plugins/tools for pi/opencode?