Asked in ~comp thread, figured it should be its own post.

Thinking about whether I want to do some upgrades to my 10 year old PC or buy new. I could bump RAM to 16 without breaking the bank since its DDR4, and maybe splurge on a new processor, but I'm not sure if I'd just want to go with a new machine at that point, thoughts?

AMD Ryzen 3 1300
8.00GB Dual-Channel DDR4
MSI B350M PRO-VD PLUS
4GB ATI Radeon RX 480

Edit: To follow up some questions about what I'm using this computer for, not a whole lot at the moment. I was thinking of making it into a media server or a Steam OS console now that it's available to download, but it's more of a whim and a project itch to keep my mind off of personal and global events.

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.

Not sure where this goes but I am looking for an app to help fill in between other reading practice we do with our 5 year old.

This won't be the main tool to help them learn, just trying to find something fun to help learn a bit. Does anyone have recommendations for fun and educational reading apps?

Hopefully the Tilderen aren't completely sick of the topic of AI writing.

To summarize my take: I think LLM agents are profoundly useful. And problematic. And I really really dislike AI writing when I encounter it outside of documentation. It's not only that it usually has a poor signal to noise ratio and a lot of annoying tropes, it also breaks a contract that has existed since writing was invented. And I dislike that it displaces human writing. Human writing is beautiful, we need more of it, not less.

By breaking a contract I mean that human writing implies an investment. The author invested time in creating it and has (sometimes) invested their reputation by publishing it. Whereas LLM prose requires minimal effort and thought. One great summary is:

"If you didn't take the time to write it, why should I take the time to read it?"

But it's not just about time or preference. We rely on signals of investment and competence to judge the value of information both personally and professionally. LLMs short circuit that completely. They incorporate signals of effort despite there being no effort involved. Signals that we could once rely on that someone has a deep understanding of a subject, or did extensive research, or is reasonably intelligent, or interesting, don't have any meaning when it's LLM prose.

Recently, while investigating Fable failure modes (while working in Claude Code), it outputted a summary that I think captures the situation really well: "My polish costs nothing".

Which is to say that LLM prose folds in signals of authority, competence, rigor and insight without any of those things needing to be present in the content. A fluent human is often worth listening to while a fluent LLM is just the default, the fluency carries no information. I think that's part of the reason for AI psychosis, I think it effectively uses, now outdated, expectations to hijack perception.

The reason that I sometimes take the time to investigate why an agent went wrong is partly to refine the scaffolding and partly to remind myself how dumb they are. Each generation is a little bit better, confidently right a little more than it's confidently wrong, so I make a point not to let myself fall into the trap of trusting their output too much.

I suppose I'm just posting to pass on the reminder.

I prefer physical buttons and something that does not have a large screen and looks simple enough that it could be OEM. Does anything like this exist? All options seem to be cheap and ugly with flashing lights, or a giant touch screen.