I suppose ultimately, the external behaviour of the system is what matters. You can see the LLM as the system, on a low level, or even the entire organisation of e.g. OpenAI at a high level.

If it's the former: Yeah, I'd argue they don't "learn" much (!) past inference. I'd find it hard to argue context isn't learning at all. It's just pretty limited in how much can be learned post inference.

If you look at the entire organisation, there's clearly learning, even if relatively slow with humans in the loop. They test, they analyse usage data, and they retrain based on that. That's not a system that works without humans, but it's a system that I would argue genuinely learns. Can we build a version of that that "learns" faster and without any human input? Not sure, but doesn't seem entirely impossible.

Do either of these systems "learn like a human"? Dunno, probably not really. Artificial neural networks aren't all that much like our brains, they're just inspired by them. Does it really matter beyond philosophical discussions?

I don't find it too valuable to get obsessed with the terms. Borrowed terminology is always a bit off. Doesn't mean it's not meaningful in the right context.

To stretch the human analogy, it's short term memory that's completely disconnected from long term memory.

The models currently have anteretrograde amnesia.

I can't speak for parent, but I use gptel, and it sounds like they do as well. It has a number of features, but primarily it just gives you a chat buffer you can freely edit at any time. That gives you 100% control over the context, you just quickly remove the parts of the conversation where the LLM went off the rails and keep it clean. You can replace or compress the context so far any way you like.

While I also use LLMs in other ways, this is my core workflow. I quickly get frustrated when I can't _quickly_ modify the context.

If you have some mastery over your editor, you can just run commands and post relevant output and make suggested changes to get an agent like experience, at a speed not too different from having the agent call tools. But you retain 100% control over the context, and use a tiny fraction of the tokens OpenCode and other agents systems would use.

It's not the only or best way to use LLMs, but I find it incredibly powerful, and it certainly has it's place.

A very nice positive effect I noticed personally is that as opposed to using agents, I actually retain an understanding of the code automatically, I don't have to go in and review the work, I review and adjust on the fly.

That's a job for a multi agent system.

I can't count the times I've told clients and prospects to _not_ hire us to build something they wanted. Because they could just use off the shelf solutions that were cheaper financially, at least in the short to mid term, and much, much cheaper in terms of opportunity costs. I struggle to put even billed hours into something that doesn't make sense to me.

Of course some overdo it. I've seen companies with more random SaaS tools than staff, connected with shaky Zapier workflows, manual processes, or not at all. No backups, no sense of risks, just YOLOing. That's OK in some cases, in others really not.

I suppose it does need some engineering thinking to find the right things and employ them in a good way. Unfortunately even developers commonly lack that.

It's not tool use with natural language search queries? That's what I'd expect.

It's RAG via tool use, where the storage and retreival method is an implementation detail.

I'm not a huge fan of the term RAG though because if you squint almost all tool use could be considered RAG.

But if you stick with RAG being a form of "knowledge search" then I think Google search easily fits.

It is tool use with natural language search queries but going down a layer they are searched on a vector DB, very similar to RAG. Essentially Google RankBrain is the very far ancestor to RAG before compute and scaling.

I _think_ the idea is that the first one to hit self improving AGI will, in a short period of time, pull _so_ far ahead that competition will quickly die out, no longer having any chance to compete economically.

At the same time, it'd give the country controlling it so much economic, political and military power that it becomes impossible to challenge.

I find that all to be a bit of a stretch, but I think that's roughly what people talking about "the AI race" have in mind.

Well, wouldn't you be on board?

1. Does xAI seem more valuable than X?

2. Does SpaceX seem more valuable than xAI+X?

Not sure a lot of people would say "no" to either of these questions.

The only other question that I think is worth asking for investors, is how much stock in the acquiring company they get for their stocks in the acquired company. If the valuation of the acquired company in the deal is... optimistic enough, that seems like a no brainer.

First thought: In my experience, this is a muscle we build over time. Humans are pretty great at pattern detection, but we need some time to get there with new input. Remember 3D graphics in movies ~15 years ago? Looked mind blowingly realistic. Watching old movies now, I find they look painfully fake. YMMV of course.

Second thought: Does it _really_ matter? You find it interesting, you continue reading. You don't like it, you stop reading. That's how I do it. If I read something from a human, I expect it to be their thoughts. I don't know if I should expect it to be their hand typing. Ghost writers were a thing long before LLMs. That said, it wouldn't even _occur_ to me to generate anything I want to say. I don't even spell check. But that's me. I can understand that others do it differently.

Exactly! It must be exhausting to have this huge preoccupation with determining if something has come from an LLM or not. Just judge the content on it's own merits! Just because an LLM was involved doesn't mean the underlying ideas are devoid of value. Conversely, the fact that an LLM wasn't involved doesn't mean the content is worth your time of day. It's annoying to read AI slop, but if you're spending more effort suspiciously squinting at it for LLM sign versus assessing the content itself, then you're doing yourself a disservice IMO.

Just like how writing helps memorisation. Our brains are efficient, they only do what they have to do. Just like you won't build much muscles from using forklifts.

I've seen multiple cases of... inception. Someone going all in with ChatGPT and what not to create their strategy. When asked _anything_ about it, they defended it as if they came up with it, but could barely reason about it. Almost as if they were convinced it was their idea, but it really wasn't. Weird times.

Perfection.