It was interesting to hear your perspective!

I’m a newbie programmer (and have been for quite a few years), but I’ve recently started trying to build useful programs. They’re small ones (under 1000 lines of code), but they accomplish the general task well enough. I’m also really busy, so as much as I like learning this stuff, I don’t have a lot of time to dedicate to it. The first program, which was 300 lines of code, took me about a week to build. I did it all myself in Python. It was a really good learning experience. I learned everything from how to read technical specifications to how to package the program for others to easily install.

The second program I built was about 500 lines of code, a little smaller in scope, and prototyped entirely in ChatGPT. I needed to get this done in a weekend, and so I got it done in 6 hours. It used SQLite and a lot of database queries that I didn’t know much about before starting the project, which surely would have taken hours to research. I spent about 4 hours fixing the things ChatGPT screwed up myself. I think I still learned a lot from the project, though I obviously would have learned more if I had to do it myself. One thing I asked it to do was to generate a man page, because I don’t know Groff. I was able to improve it afterward by glancing at the Groff docs, and I’m pretty happy with it. I still have yet to write a man page for the first program, despite wanting to do it over a year ago.

I was not particularly concerned about my programs being used as training data because they used a free license anyway. LLMs seem great for doing the work you don’t want to do, or don’t want to do right now. In a completely unrelated example, I sometimes ask ChatGPT to generate names for countries/continents because I really don’t care that much about that stuff in my story. The ones it comes up with are a lot better than any half-assed stuff I could have thought of, which probably says more about me than anything else.

On the other hand, I really don’t like how LLMs seem to be mainly controlled by large corporations. Most don’t even meet the open source definition, but even if they did, they’re not something a much smaller business can run. I almost want to reject LLMs for that reason on principle. I think we’re also likely to see a dramatic increase in pricing and enshittification in the next few years, once the excitement dies down. I want to avoid becoming dependent on this stuff, so I don’t use it much.

I think LLMs would be great for automating a lot of the junk work away, as you say. The problem I see is they aren’t reliable, and reliability is a crucial aspect of automation. You never really know what you’re going to get out of an LLM. Despite that, they’ll probably save you time anyway.

I’m no expert, but neither is most of the workforce (although kernel work is, again, much more in the expert realm).

I think experts are the ones who would benefit from LLMs the most, despite LLMs consistently producing average work in my experience. They know enough to tell when it’s wrong, and they’re not so close to the code that they miss the obvious. For years, translators have been using machine translation tools to speed up their work, basically relegating them to being translation checkers. Of course, you’d probably see a lot of this with companies that contract translators at pitiful rates per word who need to work really hard to get decent pay. Which means the company now expects everyone to perform at that level, which means everyone needs to use machine translation tools to keep up, which means efficiency is prioritized over quality.

This is a very different scenario to kernel work. Translation has kind of been like that for a while from what I know, so LLMs are just the latest thing to exacerbate the issues.

I’m still pretty undecided on where I fall on the issue of LLMs. Ugh, nothing in life can ever be simple. Sorry for jumping all over the place, lol. That’s why I would have been interested in Linus Torvalds’ opinion :)

As I said, his change in behaviour towards fellow developers is a welcome change. There’s no doubt about that.

I just wish he would continue to rage against companies up to no good, especially for FOSS. I never want him to get mild with Nvidia, and I want him to praise AMD a bit (they deserve it, and his opinion holds value).

Hell will freeze over before he accepts a pull request on GitHub or uses Issues for discussions. I believe his behaviour serves only to scare away contributors and embolden elitists.

It’s not intelligent because it’s not thinking.

At least my definition of intelligence is thinking. Otherwise a simple pattern matching algorithm like a regexp is also intelligent, or a sorting algorithm that puts things in the right order.

But I agree it’s very efficient and has more data than any single person ever could. It’s a computer, they are great at storing and processing information.

You’re conferring a level of agency where none exists.

It appears to “understand.” It appears to be “knowledgeable. “

But LLMs do neither of those things.

Take this note from an OpenAI dev: <img alt="" src="https://lemmy.ca/pictrs/image/f9e0f536-713c-43f9-a97f-ef2b2dabe0c3.png">

It’s that these models have leveraged so much data they’ve been able to map out relationships between words (or images) in way as to be able to generate what seem like new versions of those things.

I grant you that an LLM has more base level knowledge than any one human, but again this is thanks to terrifyingly large dataset and a design that means it can access this data reasonably reliably.

But it is still a prediction model. It just has more context, better design and (most importantly) data to make predictions at a level never before seen.

If you’ve ever had a chance to play with a model at level where you can control some of its basic parameters it offers a glimpse into just how much of a prediction machine it can be.

My favourite game for a while was to give midjourney a wildly vague prompt but crank the chaos up to 100 (literally the chaos flag at the highest level) to see what kind of wild connections exist but are being filtered out during “normal” use.

The same with the GPT-3.5 API in the “early days” - you could return multiple versions of the response and see the sausage being made to a very small degree.

It doesn’t take away from the sense of magic using these tools. It just helps frame what’s going on under the hood.

they = rust or the linux kernel?

The linux kernel doesn’t have enough contributors because it’s really difficult + the entire organisational side of it works on antique tech (IRC and mailinglists). The majority of the project itself is also in C which has a horrible developer experience: linting, documentation, debugging, code completion, and the lack of a proper IDE. The entire development cycle is convoluted. How do you seriously want to attract people to the project if everything looks like it’s still in a development cycle of the 90s?

If I were to discover a one-line bug in the kernel by reading it, actually testing the one-line fix would take me, as a newbie probably a solid week. Getting it into the kernel itself would probably take even longer.

The kernel is also known for Linus’ outbursts and being filled with neckbeard elitists. The project in my eyes has an image problem.

As for rust, if that’s what you meant, I’d be interested in knowing the source for not having enough contributors.

I assumed that he was talking about the fact the the languages he listed have a lot of syntax in common with each other, and with a few other languages. I could be wrong though

I’ll prep my supercomputer.

Agreed re: Google.
I dunno what the solution is. The world without Google is going to be a very different place. Do you think it’s even possible for them to turn things around?

What if AI will start contributing to the kernel end ends including AI there?