Headlong: A microharness for persistent agents

I think the main answer to this concern is that humans are absolutely scheduling wake-ups on a cognitive level -- the best example may be, y'know, sleep! But also on a moment-to-moment basis, which is especially noticable during periods of boredom.

Think of the head LLM as you, and the workers as your subconscious faculties (e.g. the part of you that knows how to ride a bike in ways that you have never had to consciously articulate). The looping part is the unconscious substrate that makes all of that possible, arguably with some room for the faculty above you (metacognition) to control what gets presented to your conscious mind and when. The vast, vast majority of input never makes it that far tho, by design.

(ETA: ...so, that means that we don't need to fundamentally change the architecture of LLMs in order to get some really scary stuff going.)

i've been playing with the project and idea for years now. the codebase started as javascript plus supabase before reasoning models were a thing (i incorrectly predicted the world would call reasoning models Large Thinking Models - bahaha). at one point the focus was almost entirely on a really easy-to-use human prosemirror UI for editing the agent's thought stream--because the models were still so bad at reasoning at that time.

then we pivoted to all bash at some point since it simplified things and i have this hypothesis that the LLMs will do better if as much of its world as possible is command line operations (inspired by terminus the agent we built as part of the terminal bench project)

the bash implementation is entirely written by coding agents - the human time goes into iterating with an AI agent on the design docs in the ./design folder

re security model - i think of the project as a research experiment about what it's like to treat an agent more like a person - so for secrets you have to trust its judgement about what it will share or if it will betray your trust. I don't know the OpenClaw architecture in great depth but my sense is that none of the LLM based agents that can interact with multiple people have strong guards in place that prevent the agent from leaking secrets between people. Definitely an interesting area for research though!

so yeah security is not a priority in this research per se - easier to assume you don't share sensitive things with a headlong agent

Along those lines, because we keeping it research and optimizing the velocity with which we can try new things out, the current aim isn't necessarily to get a bunch of community contributions. but if we do start to get any i'm thinking we will actually require people to only create PRs with design docs (no code allowed) and maybe even also require the logs (or at least the human prompts and the agent's responses) from the coding session where they created the design doc.

maybe i should write up the history, philosophy, and big picture aims of the project at some point

most of the training data is the output of inner monologue. ie ppl dont write their inner monologue on the internet( not as often anyways).

inner monologue isnt just thoughts that you keep to yourself. There is a different stucuture and meaning to it. isnt it?

suggestions for what to try next?