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 believe this is a interesting issue to post it here because it's very difficult to get writing tools outside the English language. That's exactly why I ended up starting this project. If it's not allowed, I apologise in advance.

I'm a linguist and technical writer (tech writer, dev writer, documenter, technical editor, etc.) and I've always used Hemingway for my English writing. The problem was that I'd never found a text editor capable of suggesting possible improvements to a text in Brazilian Portuguese.

Years passed, and this week I had time to create a fork of Techscriptor with some interface improvements and adapt it to Brazilian Portuguese. That's how version 0.1 of Verbalize was born.

What does it do?

In a basic and summarised way, you can upload a file from your computer (in md or txt, for now) and the editor, besides allowing you to actually edit, will give you hints on how to improve the text (long sentences, complex words, jargon, adjectives and other things we should avoid in texts, especially technical ones).

Once edited, you can download the file in md format.

Access

The application can be installed (Electron), accessed through the web, or you can download the code from GitHub and run it locally in your browser.

• Live version
• Source code

Improvements

I have a few 'next steps' in mind:

• Google Drive/Onedrive integration.
• Possibility to upload a custom rules file.
• Allow it to be used offline as well.
• Improve the GUI.

I've recently come back to studying German, after having taken a small break for a few months for a new job.

My main form of study is immersion (I recently stumbled across the books of Walter Moers and haven't looked back since) and conversation practice on iTalki.

Nowadays, I try my hardest to only buy tech second-hand and preferably as future proof and modular as possible. My go-to machines are a fully modded Lenovo Thinkpad T430, and a more humble Thinkpad X230, both running Linux (Ubuntu and PopOS respectively). They work just fine for my basic needs (mostly surfing, some occasional streaming and word processing). But they struggle during my conversation lessons on iTalki or Zoom, most of the time either overheating or freezing/stumbling. I realize this might be a Linux problem, but I have also found the web camera and built-in microphone on both machines to be really inadequate for video calls. I gave up using my own laptops for my language lessons over a year ago, and now have resorted to stealing my partners Macbook, which isn't ideal.

Do you have any recommendations for any more recent laptops that would offer a better video conference experience, while offering at least a removable battery? Pricewise it would be great to be find something below €500 used.

As a non-native English speaker, I use Grammarly's free tier daily. It is invaluable to help me catch common mistakes, as well as to get a better understanding of the language through the explanations it provides. I will need to write even more English in the next few months, so it seemed like a good idea to get the Premium subscription. Unfortunately, Grammarly's pricing ($144 for the year) is prohibitive when converted to Brazilian Reais. And even if I am capable of making that payment now, I would rather avoid becoming dependent on something that is so expensive for me. So, what are some affordable alternatives to Grammarly's Premium subscription?

Just to be clear, I am aware that it is not ideal to rely too much on that kind of tool. Rest assured that my domain of English is enough that I am entirely capable of taking the suggestions as extra help and not as a crutch.