In all the hype and debate and excitement and frustration around AI, I think sometimes the fact that we've achieved something really cool gets lost. I want to talk about that a bit, which I'll try to do in a simple way, without too much math or computer science.

There's a surprising amount of intelligence encoded in applied language. To me that's the most remarkable insight in LLM technology. Except it's not really surprising at all. The whole point of language is to encode intelligence. Communication, at its core, is about sharing intelligence. LLMs agents are a way of turning the intelligence encoded in language, particularly written language, into useful work.

You might imagine that language is just the starting point when building LLMs. That it gets converted into computer stuff and then it's not language anymore. But in a way it's language all the way down. Distill language into math and then run gradient descent on it and the intelligence (for lack of a better term) bubbles out.

Forgive that hyperbole, but there is something a little bit magical about it, and the technology is entirely unmagical. The magic is in the language.

At the core the tech is pretty simple. First you go and get all of the language. Just completely idiotic amounts of applied language. Because there's a threshold: if you don't use enough language you don't get a useful result. The more language you use, assuming it's good quality, the more useful the result.

After you have all of the language, you convert it into tokens, which are essentially pieces of words represented as numbers. You also compute a compressed version of the relationships between the tokens. The frontier labs don't say how much this costs publicly anymore but it's safe to say hundreds of millions in compute are required for this process. Each time you do it.

Once you've determined the token relationships, you've pre trained an LLM. You have the initial weights. At this point your LLM can output credible prose pretty reliably when you run inference on the weights. Meaning that you can take a given set of language tokens (a prompt) and determine which token should come next, over and over again, until you have a response. The fluency of the prose can be uncanny, and there are latent tendencies towards useful output, but you can't really do much with it yet.

The next step, fine tuning, modifies the weights in order to nudge the inference towards useful things like instruction following, tool use and reasoning. Or a simulacrum of reasoning. Fine tuning is a massive, multi step, iterative process that turns your LLM into a useful tool that can do more than output believable prose.

Something that's really fascinating to me about fine tuning is that what you're training is still the language relationship weights, and what comes out during inference is still language. But now it's language that is more likely to result in useful output and behaviors. Language is the substrate that LLMs reason through. A model that's more likely to infer the words "but what if I look at it another way" is more likely to consider multiple possibilities. A model that reliably infers language around tool use in appropriate situations is an agent. The behavior is encoded in the language.

After tens of millions in fine tuning costs, assuming you got it right, you have the core of a LLM that can function as an AI agent. You can put it in a harness and ask it to do things in the language of your choice and it will actually be able to do them a shockingly high percentage of the time. It can even do things that weren't explicitly part of the training. That's something which has never happened before outside of science fiction. Until recently it was one of those computer sciencey things that was probably going to happen someday, after we were all dead.

It sucks that this tech revolution is being driven by the capital class, and that it's happening as a mad dash for market domination and golden IPOs. In a more perfect world technology derived from collective human intelligence would be a public good rather than a profit driver.

But nevertheless, the ability to imprint some part of human intelligence into an autonomous agent is a remarkable achievement. One that we can't yet see the full size of. And it's possible not because we typed instructions into an interface, but because we've spent centuries encoding our intelligence into language.

I'm looking for a new laptop as my current one is falling apart, and one option that seems quite tempting at the moment is getting a desktop machine, leaving it running away in the background, and then accessing it using remote desktop software from a laptop (or even tablet) most of the time. My logic is that I want a fairly decent device for programming on and occasional games, but I also want something portable that I can carry into every room of my flat. I've also been experimenting more with Tailscale and using my laptop as a remote development machine and my phone as a dumb terminal while traveling abroad, and that's worked fairly well, so I'm interested in how far I can take this concept. Plus the idea of also being able to use my main device as a small homelab is kind of appealing as well.

The plan would be:

• Framework Desktop sits somewhere at home and is on all the time. My wife has a Framework laptop that she's quite impressed with, I realise the value proposition for desktop computers is less given there's already so much possibility to configure and upgrade, but I've never built a PC myself before and I don't want to complicate this setup too much.
  '* A dumb terminal device that I can carry around with me. For now, that'll probably be my current laptop as long as it lasts, but in the future maybe a cheap Chromebook or even a tablet that I can connect a keyboard and mouse to.
• Moonlight/Sunshine setup for the remote desktop side of things. From what I can tell, this can stream both individual applications but also the whole desktop, and works with various clients.
• Tailscale to access all of this outside the home. This is less important but it would be convenient. And if I go down the homelab direction, also for being able to access my self-hosted services remotely or running my personal website off my own hardware.

Things I'm concerned about:

• German connection speeds are, broadly speaking, poor, especially with upload speeds. I'm looking at maybe 300M down, 50M up. Inside my local network that won't affect me, but if I did want to take my laptop with me, I don't have a good sense of whether the remote desktop aspect is really feasible.
• The Framework desktop - I like the company, and I like the idea of a repairable desktop that's still sold as a single unit and not something where I need to buy everything myself. But I also don't have a good sense of how much of a premium I'd be paying here, vs either building something completely from scratch or buying a more typical desktop
• The laptop would probably live in the living room because that's where all the other technology (in particular the modem) in our house lives. It's fine if it makes a noise while I'm actively using it to watch things, but I'd need it to be very quiet the rest of the time. Framework does sell a fan that is specifically designed to be quiet, but I'm not sure if this is "it's quiet compared to having a normal computer fan on" or "it's quiet compared to having turned all my devices off for the night".

Does anyone have any experience with this kind of setup, and is this something that would reasonably work, or am I completely missing something obvious that makes this a terrible awful idea that nobody should do?

Thanks in advance!