This is really good - miles ahead of almost all other math writing I have read. One particular thing I really liked was the explanation of the determinant. When I was in college I spent a very long time trying to get some intuitive explanation for a determinant of a matrix and the best I could get is that you just multiplied and subtracted some numbers and poof there you go. I KNEW there had to be some more comprehensible explanation and it drove me crazy that people just parroted textbooks without any real understanding.
This reminds me of that silly video everyone was shown in middle school about inverting a sphere :-)
I am very curious how it was written (will there be a write up to the write up?!). It feels likely AI assisted, but this seems to me to be the best possible use of AI -- crunching out a bunch of visualizations of complex ideas so that they are easy to understand just by looking. The writing also feels a little AI written but definitely feels very human-assisted.
But surely at some point you need to stop, right? Most web developers are perfectly capable without understanding the intricacies of the file system or OS, even though the browser sits on top them. Most engineers don’t know assembly, or transistors or electrical engineering. Steve Jobs didn’t know every line of Objective-C that went into OSX. I don’t think Michelangelo collected all his paint materials by hand when painting the Sistine Chapel. In some sense I don’t understand a lot of the panic around AI when the entire career progression of what it means to be a software engineer has simply been to deal with ever-increasing levels of abstraction.
To be blunt the degree to which I take these projects seriously is basically the same as how extensive their eval suite is. A single guided refactor is better than nothing, but one success could just be a fluke. I want to see hundreds, if not thousands, of evals to convince me you’re a project worth looking into.
The AI generated text is also not doing you any favors.
This doesn't seem quite right. For one, I don't need all the intelligence of an expensive model like Opus 5 to do the relatively simple task of choosing a correct model for a task. Additionally, since this isn't something Anthropic would ever put effort into doing well, you could tune a model to do better and faster than Opus 5 does out of the box.
Sure Claude would comply, but Anthropic has no financial (or other) incentive to optimize this, so there’s no reason to expect it to be particularly good.
It would be like asking the clerk at a Whole Foods which grocery store in the city sells the cheapest eggs. He’d probably answer - he might not even say Whole Foods - but WF is hardly teaching all their staff the best methods to answer this question in training. (Heh, training.)
I see a pretty big gap between finding software vulnerabilities and “the world is about to end”. It is literally true that AI models are finding software vulnerabilities. It is also to my mind a reasonable thing that you’d want to be cautious about rolling out a model that can find more vulnerabilities. So what is the objection you have to these sources?
I think the burden of proof is on you to argue that Codeberg will stop at exactly two policies, given they haven’t said they will do that, but they have already banned things they don’t like. A slippery slope argument isn't a fallacy when you've already slipped down the slope twice.
Why would rewriting Claude code, an app which probably has 30-40 (I might be significantly underestimating) extremely active contributors be easier than rewriting Bun, which has fewer contributors and almost certainly also less lines of code?
The article does not engage with its subject matter with good-faith engagement and curiosity. "Always has been. Always will be." is, inherently, the author's lack of engagement with the subject matter. "They cannot see the world outside" is a shut door that does not admit curiosity or an interesting comment. And so you are seeing this reflected back in the response.
Do you really think threads like [1], [2], [3] are pro-AI? I literally just selected three at random from today's feed. I think the only positive threads about AI here are typically new model releases.
Speaking personally I was not particularly moved by the article because I have seen the same thing, in different shapes, thousands of times on HN and elsewhere. Really, AI can't feel and therefore it is inferior? Never heard that one before. Really, an AI can't feel friction and therefore can't adapt to it? Daring today, aren't we? (And a more interesting question: is that even true..?) I realize I am being unnecessarily harsh here, but this article is very much preaching to the choir on HN, which has an anti-AI bent. No one is showing up because there's nothing really to show up to here -- and that is why you are left with "sly jibes" and not much else.
I think of LLM tells like grammatical issues. If you read an essay full of grammatical mistakes you’d immediately start thinking less of the author, even if the essay isn’t about grammar. You wonder if someone who doesn’t pay close enough attention to catch a mistake “their” from “they’re” took attention to the rest of their work. This isn’t necessarily fair because the content of the essay might still be good. But on the internet I don’t have the time to evaluate the quality of every piece of writing I come across. It is very much the burden of the author to, as fast as possible, prove to me that the rest of the article will not waste my time. There is already so much content to read, and in some sense the amount of time to evaluate if an article is well-founded can be unbounded (imagine how long it would take to tell if an article about why a programming language is thoughtful without going out and also learning that language).
I find LLM-isms to be exactly the same as grammatical errors, but worse. At least when writing before you had to take the effort to type every word, so there was a minimum amount of effort you’d need to expend. If you aren’t catching obvious things like “the honest part” then that likely says bad things about your attention to detail elsewhere.
I mean I think I agree with most of what you’re saying, I do agree it’s a bit of a forest grasslands scenario, but the key difference is that he said there’d be a forest at a time when you’d be hard pressed to find someone saying there would be even a microorganism on the ground. It’s pretty darn easy today in 2026 to say wow, his predictions were so off. And it is true: they are very wrong compared to any other prediction made in the last 5 years. But few people were saying this stuff in 2008.
Yudkowsky said AI would be a problem when very few people taken seriously in the mainstream were saying it was going to be a problem. Saying "specific predictions are wrong" is missing the forest for the trees here.
I was responding to someone who said Yudkowsky was "consistently wrong with all their predictions". Yes, fair enough, Yudkowsky wasn't the literal first person ever to say that AI might be bad. The point was that the common academic response at the time if you were to say AI could be bad was to laugh you out of the room. IMO, you get a lot of points for making a prediction when almost everyone else in the world disagrees with you. The reason those movies you cited were blockbusters was because very view people believed that they were realistic. No one made a movie about a pandemic in 2020.
I don't think there's much to be gained out of hashing out whether every prediction Yudkowsky has said was right or wrong: I likely directionally agree with you there, as I also find some of his more extreme predictions to be inaccurate. I mostly take issue with "consistently wrong". The results in longtermwiki are not "consistently wrong". He's definitely wrong sometimes. But consistently?
The discussion isn't about whether it's an "original or interesting thought", it's about whether Yudkowsky is "consistently wrong with all their predictions". You keep shifting the topic.