namr2000

I mostly agree with this sentiment, and have a hard time describing anything LLM generated as "mine". However, would you say that the designers at Apple, or Steve Jobs for example didn't "bring the Macintosh to life" because they didn't work on any of the engineering or code? It's a hard line to draw.

This is the first comment in this thread that adequately captures how I feel. If I spend a weekend working on a project, there are two equal parts that make it worth my time: enjoying the process, and enjoying the final result.

It's inevitable (no matter how much you enjoy programming) that you will run into frustrating parts of working on code. If at the end of that process, you end up with a product that you know could have been better if LLMs were used, its a shitty feeling and makes it harder to power through the frustrating parts of the work.

What runtime are you using with the 2x RTX Pro 6000 Blackwell machine? I have the same setup and tried DSv4 Flash on vLLM and ran into a ton of kernel bugs that don't seem to have been fixed yet.

Yeah the Yitang Zhang situation just sounds like a total nightmare all around.

There was definitely at least some progress on the problem. I get the general sense that there were potential counterexamples that were close but not quite enough, and that its possible (or even likely) that Claude built on those in order to construct its solution. I also get the sense that when Zhang was working on the problem it was believed that it would be proved true, but since then there were bounds found on the problem that pointed researchers to believe it was false. I am not a research mathematician in this field though, so I could definitely be wrong.

Also, in fairness to Zhang, I believe the dissertation he ended up writing was focused on the 2D case in particular, which is still unsolved (the counter example is only for 3D and above). I cannot imagine that anyone looking at the 2D problem was not also looking at the general case as well though.

I understand the frustration with the constant PR-hype these AI labs keep spewing out, but on other hand I just can't understand this sentiment at all. These are real problems mathematicians and computer scientists have been working on and were unable to make progress on. Now they have been given a new tool and using that tool have solved those problems. And its not just one or two problems, its many very difficult problems. The mathematicians I know are saying that the latest crop of models is changing the way people do research math, I think that's a pretty big deal.

It depends on the facilities the language offers to you by default right?

C++ offers much higher level primitives out of the box compared to Zig, so I'd say its a higher level language. Of course you can ignore all the features of C++ and just write C, but that's not why people are picking the language.

I agree with you that package management has nothing to do with how low-level a language is.

That being said Rust is definitely a much higher level language than either C or Zig. The availability of `Arc` and `Box`, the existence and reliance on `drop`, and all of `async` are things that just wouldn't exist in Zig and allow Rust programmers to think at higher levels of abstraction when it comes to memory management.

> Having a rich standard library isn't just a pure positive. More code means more maintenance.

I would argue it's much worse to rely on packages that are not in the standard library since its harder to gain trust on maintenance and quality of the code you rely on. I do agree that more code is almost always just more of a burden though.

The languages trade complexity in different areas. Rust tries to prevent a class of problems that appear in almost all languages (i.e two threads mutating the same piece of data at the same time) via a strict type system and borrow checker. Zig won't do any of that but will force you to think about the allocator that you're using, when you need to free memory, the exact composition of your data structures, etc. Depending on the kind of programmer you are you may find one of these more difficult to work with than the other.

Rust is a world away from Zig as far as being low-level. Rust does not have manual memory management and revolves around RAII which hides a great deal of complexity from you. Moreover it is not unusual for a Rust project to have 300+ dependencies that deal with data structures, synchronization, threading etc. Zig has a rich std lib, but is otherwise very bare and expects you to implement the things you actually want.

This has not been my experience with Waymo. I drove a total of about ~3.5 hours in Waymos in LA when I was visiting and their robustness to very unusual situations absolutely floored me.

I am sure you can find truly out-of-distribution cases where the car will make a mistake, but the data shows that this is more rare than a human driver making a mistake.

GPT2 was definitely a risk, just not of the same magnitude. It would have (and did!) make social media bot farms way more convincing and widespread. There was specific worry about that being used to sway elections, which is why they held back the model.

I don't really understand what you mean by this. The claim is that the same prompt with the same question produces worse results when it's queried in a model that has more than 200k tokens in its context. That doesn't have to do much with the "skillfulness" of using a model.