2022 explains that perfectly, albeit leaves me less happy.

What I'm struggling to understand is that _someone_ is paying, right? $400M is probably a lot more than a fraction of what people spent on javascript CRUD apps before? It seems more like centralization than anything else to me? What am I missing?

In relation to that, I guess my question becomes: if the same thing will happen in math research, who will write the ten page math proof prompts in the future?

I wonder how this compares to what we see happening with "juniors" in software development? In math research, do you also get the training for the profession from working on the low hanging fruits for a while, to then move to the medium-hanging, and later go on to work on previously unsolved stuff?

> Everything in the set was real. We couldn't fake any of it, because audiences are so sophisticated now in their knowledge of computers. > ... > - Cory Faucher (Special Effects Coordinator)

This sentiment seems to run throughout the movie, and I believe it's why it's held up so well in terms of visuals, I don't think it would have aged nearly as well as it has if more CGI (or other ways of "faking" things) had been been used.

As for the question (in ):

> Some code associated with Nedryland is visible on screen. It looks like actual source code[9] with Classic Mac OS API functions calls.

That looks like old Pascal, and since the window has MPW (Macintosh Programmers Workshop) in the title, that's probably it?

Comments URL: https://news.ycombinator.com/item?id=48888331

Points: 697

# Comments: 449

I would recommend anyone to dive into it!

> But give it 1-3 months and the open models will catch up.

I wish that this would stopped being thrown around, what is this timeline based on? How good is your open model from between March and May?

Also, having read "Gödel, Escher, Bach" I know that the hare never catches up with the turtle.

UX wise it looks kinda neat though!

Not sure if it's important or not, but for the sake of OP's discussion I note that your value is not necessarily tied to "speed of execution".

Low level systems programming mostly. Embedded systems, *nix userland programs, API and library design, sometimes including writing assembly although in very small fractions.

You will need a lot more to make yourself my enemy, but this is the divisor between us... not that you like to use Claude and I don't.

I think it depends a lot on where your interest in (self) development lies.

My main motivator has always been to understand how things work, and myself being able to create as elegant solutions as my technical role models (in the range from colleagues and mentors to the elders of our field), hopefully even pushing it further. Having the LLM just create the product robs me of that, or at least of the most rewarding parts of that. And that's why I don't like to use it.

Different people are driven by different things, I don't think either trumps the other in the objective sense, we're just wired differently.

> More reports means more fixes means more code changes means more bugs.

Sounds like we'll be riding a downward spiral for the foreseeable future? It will be very interesting to see how stats like the ones you shared develop in the coming year(s).

From the article I find this a bit concerning:

> So: the Claude releases changed way more lines of code than historical ones, but didn't have more bugs. More code, same bugs. That's not what you'd expect if Claude were making things worse.

More code, same bugs, is a net negative, no? I mean unless it's strictly needed for the inherent complexity of the program. But I've seen a tokenizer written by Rob Pike and I've seen a tokenizer written by Claude.... they are not the same :D

Since this is happening in open source, what do you think about the state of the quality of closed source software? AI usage (input as a success metric) is part of what you're being evaluated on as an employee, and people are panicking at the threat of mass layoffs due to AI.

Yikes!

Nailed it!

At my last place this was encouraged (by non-technical leadership driving the AI adoption policies, as well as setting salaries) and seen as a huge win.

The "step change in number of created PR's" was celebrated (cult-style), and by one of the (co) CEO's praised as a paradigm shift of the same magnitude as the personal computer. Meanwhile, I was stuck finding insta-reject level bugs in pull requests from people one-shotting 6000 line PR's "finally solving" long-standing issues from the backlog. Needless to say I left.

Shouldn't we at least be a little bit scared already when shoe companies pivot to AI and their stock goes up ~750%?

Once past IPO, I suspect the 70% -> R&D must shrink, right? I mean, to keep the stock afloat long term P/E must come down right?

Public investors strike me as less willing to pour money into R&D, which is why I'm wondering about the timing of IPO in my initial question.

I'd like to get in front of a whiteboard with someone who knows economics and the token providers businesses well enough to answer my "explain to me like I'm five" questions. But I'll start with these in here:

Is my observation correct that for the token providers this is a margins game, while for the consumers this is a quality of service/product game? If the quality:margin lines will cross at some point on the x-axis, is the race is to reach this point before running out of money? If yes: What historical examples are there where the delta between these two is huge?

I'm guessing LLM's are unique in a sense, since there's really no limit to how good a consumer of the product expects it to get? (Compared to for example email which is much easier to scale in regards to compute.)

Also extreme noob at life question: Why would you want to IPO before having a sustainable business model? What's the upside?