lagrangeinterpolator

This was not such an effective venture.

my current favorite trick for reducing "cognitive debt" (h/t @simonw ) is to ask the LLM to write two versions of the plan:

1. The version for it (highly technical and detailed)
2. The version for me (an entertaining essay designed to build my intuition)

I don't know about them, but I would be offended if I was planning something with a collaborator, and they decide to give me a dumbed down, entertaining, children's storybook version of their plan while keeping all the technical details to themselves.

Also, this is absolutely not what "cognitive debt" means. I've heard technical debt refers to bad design decisions in software where one does something cheap and easy now but has to constantly deal with the maintenance headaches afterwards. But the very concept of working through technical details? That's what we call "thinking". These people want to avoid the burden of thinking.

This is why CCC being able to compile real C code at all is noteworthy. But it also explains why the output quality is far from what GCC produces. Building a compiler that parses C correctly is one thing. Building one that produces fast and efficient machine code is a completely different challenge.

Every single one of these failures is waved away because supposedly it's impressive that the AI can do this at all. Do they not realize the obvious problem with this argument? The AI has been trained on all the source code that Anthropic could get their grubby hands on! This includes GCC and clang and everything remotely resembling a C compiler! If I took every C compiler in existence, shoved them in a blender, and spent $20k on electricity blending them until the resulting slurry passed my test cases, should I be surprised or impressed that I got a shitty C compiler? If an actual person wrote this code, they would be justifiably mocked (or they're a student trying to learn by doing, and LLMs do not learn by doing). But AI gets a free pass because it's impressive that the slop can come in larger quantities now, I guess. These Models Will Improve. These Issues Will Get Fixed.

Congratulations to the maker of a tool that charges you $20 to remind you to buy milk the next morning.

I thought I was sticking my neck out when I said that OpenAI was faking their claims in math, such as with the whole International Math Olympiad gold medal incident. Even many of my peers in my field are starting to become receptive to all of these rumors about how AI is supposedly getting good at math. Sometimes I wonder if I'm going crazy and sticking my head in the sand.

All I can really do is to remember that AI developers are bad faith (and scientists are actually bad at dealing with bad faith tactics like flooding the zone with bullshit). If the boy has cried wolf 10 times already, pardon me if I just ignore him entirely when he does it for the 11th time.

I would not underestimate how much OpenAI and friends would go out of their way to cheat on math benchmarks. In the techbro sphere, math is placed on a pedestal to the point where Math = Intelligence.

It took a full eleven paragraphs before the article even mentions AI. Before that, it was a bunch of stuff about how Wikipedia is conservative and Gen Z and Gen Alpha have no attention span. If the author has to bury the real point and attempt to force this particular rhetorical framing, I think the haters are winning. Well done everyone.

A machine learning researcher points out how the field has become enshittified. Everything is about publications, beating benchmarks, and social media. LLM use in papers, LLM use in reviews, LLM use in meta-reviews. Nobody cares about the meaning of the actual research anymore.

https://www.reddit.com/r/MachineLearning/comments/1qo6sai/d_some_thoughts_about_an_elephant_in_the_room_no/

One of the few benefits of AI is that nowadays some PR threads are very entertaining to read.

“California is, I believe, the only state to give health insurance to people who come into the country illegally,” Kauffman said nervously. “I think we probably should not be providing that.”

“So you’d rather everyone just be sick, and get everyone else sick?” another reporter asked.

“That’s not what I’m saying,” said Kauffman.

“Isn’t that effectively what happens?” the reporter countered. “They don’t have access to health care and they just have to get sick, right?”

Kauffman contemplated that one for a moment. “Then they have to just get sick,” he said. “I mean, it’s unfortunate, but I think that it’s sort of impossible to have both liberal immigration laws and generous government benefits.”

Do I need to comment on this one?

I don't even think many AI developers realize that we're in a hype bubble. From what I see, they genuinely believe that the Models Will Improve and that These Issues Will Get Fixed. (I see a lot of faculty in my department who still have these beliefs.)

What these people do see, however, are a lot of haters who just cannot accept this wonderful new technology for some reason. AI is so magical that they don't need to listen to the criticisms; surely they're trivial by comparison to magic, and whatever they are, These Issues Will Get Fixed. But lately they have realized that with the constant embarrassing AI failures (AI surely doesn't have horrible ethics as well), there are a lot of haters who will swarm the announcement of any AI project now. The haters also tend to be people who actually know stuff and check things (tech journalists are incentivized to not do that), but it doesn't matter because they're just random internet commenters, not big news outlets.

My theory is that now they add a ton of caveats and disclaimers to their announcements in a vain attempt to reduce the backlash. Also if you criticize them, it's actually your fault that it doesn't work. It's Still Early Days. These Issues Will Get Fixed.

I knew the Anthropic blog post was bullshit but every single time the reality is 10x worse that I anticipated.

I wonder what actual experts in compilers think of this. There were some similar claims about vibe coding a browser from scratch that turned out to be a little overheated: https://pivot-to-ai.com/2026/01/27/cursor-lies-about-vibe-coding-a-web-browser-with-ai/

I do not believe that this demonstrates anything other than they kept making the AI brute force random shit until it happened to pass all the test cases. The only innovation was that they spent even more money than before. Also, it certainly doesn't help that GCC is open source, and they have almost certainly trained the model on the GCC source code (which the model can regurgitate poorly into Rust). Hell, even their blog post talks about how half their shit doesn't work and just calls GCC instead!

It lacks the 16-bit x86 compiler that is necessary to boot Linux out of real mode. For this, it calls out to GCC (the x86_32 and x86_64 compilers are its own).

It does not have its own assembler and linker; these are the very last bits that Claude started automating and are still somewhat buggy. The demo video was produced with a GCC assembler and linker.

I wonder why this blog post was brazen enough to talk about these problems. Perhaps by throwing in a little humility, they can make the hype pill that much easier to swallow.

Sidenote: Rust seems to be the language of choice for a lot of these vibe coded "projects", perhaps because they don't want people immediately accusing them of plagiarism. But Rust syntax still reasonably follows languages like C. In most cases, blindly translating C code into Rust kinda works. Now, Rust does have the borrow checker which requires a lot of thinking to deal with, but I think this is not actually a disadvantage for the AI. Borrow checking is enforced by the compiler, so if you screw up in that department, your code won't even compile. This is great for an AI that is just brute forcing random shit until it "works".