An LLM context is a pretty well extended short term memory, and the trained network is a very nice comprehensive long term memory, but due to the way we currently train these networks, an LLM is just fundamentally not able to "move" these experiences to long term, like a human brain does (through sleep, among others).

Once we can teach a machine to experience something once, and remember it (preferably on a local model, because you wouldn't want a global memory to remember your information), we just cannot solve this problem.

I think this is probably the most interesting field of research right now. Actually understanding in depth how the brain learns, and figuring out a way to build a model that implements this. Because right now, with backtracking and weight adjustments, I just can't see us getting there.

I like the idea of using small local model (or several) for tackling this problem, like low rank adaptation, but with current tech, I still have to piece this together or the small local models will forget old memories.

It's not how an llm can work right now, it needs too much iterations & a much bigger dataset than what we can work with. A single time experiencing something and we can remember it. That's orders of magnitude more efficient than an LLM right now can achieve.

This field of research has been around for decades, so who's to say when there'll be a breakthrough.

In fact, LLMs are great despite our very limited understanding, and not because we had some breakthrough about the human brain.

The way an llm learns is a very interesting way of doing it, but it sure isn't what the brain is doing.

But it's indisputable.. We can get enormous results with this technique. It's just probably not the way forward for faster learning to remediate the issue of context loss.

It's like spontaneous implemention of thought experiments from yesteryear. I wonder if all this product-focused experimentation will accidentally impact philosophy of mind after all...

I haven't seen any issues with memory so far. Using one long rolling context window, a diary and a markdown wiki folder seems sufficient to have it do stuff well. It's early days still and I might still encounter issues as I demand more, but I might just create a second or third bot and treat them as 'specialists' as I would with employees.

Useful work includes: bug triage, matching up external user bug reports on GitHub to the internal YouTrack, fixing easy looking bugs, working on a redesign of the website. I also want to extend it to handling the quarterly accounting, which is already largely automated with AI but I still need to run the scripts myself, preparing answers to support queries, and more work on bug fixing+features. It has access to the bug tracker, internal git and CI system as if it were an employee and uses all of those quite successfully.

Meta-work has so far included: making a console so I can watch what it's doing when it wakes up, regularly organizing its own notes and home directory, improving the wakeup rhythm, and packaging up its infrastructure to a repeatable install script so I can create more of them. I work with a charity in the UK whose owner has expressed interest in an OpenClaw but I warned him off because of all the horror stories. If this experiment continues to work out I might create some more agents for people like him.

I'm not sure it's super useful for individuals. I haven't felt any great need to treat it as a personal assistant yet. ChatGPT web UI works fine for most day to day stuff in my personal life. It's very much acting like an extra employee would at a software company, not a personal secretary or anything like that.

It sounds like our experience differs because you wanted something more controlled with access to your own personal information like email, etc, whereas I gave "Axiom" (it chose its own name) its own accounts and keep it strictly separated from mine. Also, so far I haven't given it many regular repeating tasks beyond a nightly wakeup to maintain its own home directory. I can imagine that for e.g. the accounting work we'd need to do some meta-work first on a calendar integration so it doesn't forget.

- I suspect that in this moment, cobbling together your own simple version of a “claw-alike” is far more likely to be productive than a “real” claw. These are still pretty complex systems! And if you don’t have good mental models of what they’re doing under the hood and why, they’re very likely to fail in surprising, infuriating, or downright dangerous ways.

For example, I have implemented my own “sleep” context compaction process and while I’m certain there are objectively better implementations of it than mine… My one is legible to me and therefore I can predict with some accuracy how my productivity tamagotchi will behave day-to-day in a way that I could not if I wasn’t involved in creating it.

(Nb I expect this is a temporary state of affairs while the quality gap between homemade and “professional” just isn’t that big)

- I do use mine as a personal assistant, and I think there is a lot of potential value in this category for people like me with ADD-style brains. For whatever reason, explaining in some detail how a task should be done is often much easier for me than just doing the task (even if, objectively, there’s equal or higher effort required for the former). It therefore doesn’t do anything I _couldn’t_ do myself. But it does do stuff I _wouldn’t_ do on my own.

I think we will eventually end up with models which can be individually trained and customised on regular schedules. After that, real-time.