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samdoesnothing

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It's just moving the goalposts. "If it compiles it works" to "it eliminates all memory bugs" to "well, it's safer than c...".

If Rust doesn't live up to its lofty promises, then it changes the cost-benefit analysis. You might give up almost anything to eliminate all bugs, a lot to eliminate all memory bugs, but what would you give up to eliminate some bugs?

I think it's pretty telling that there are people in this thread trying to pre-empt the expected criticism in this thread. Might be worth thinking why there might be criticism, and why it wouldn't be the case if it was a different language.

All bugs is typically a strawman typically only used by detractors. The correct claim is: safe Rust eliminates certain classes of bugs. I'd wager the design of std eliminates more (e.g. the different string types), but that doesn't really apply to the kernel.

Which is either 1) not true as evidenced by this bug or 2) a tautology whereby Rust eliminates all bugs that it eliminates.

Anybody who thought the simple action of rewriting things in Rust would eliminate all bugs was hopelessly naive.

Classic Motte and Bailey. Rust is often said "if it compiles it runs". When that is obviously not the case, Rust evangelicals claim nobody actually means that and that Rust just eliminates memory bugs. And when that isn't even true, they try to mischaracterize it as "all bugs" when, no, people are expecting it to eliminate all memory bugs because that's what Rust people claim.

A lot of people are criticizing this for unnecessary complexity, but it's a little more complicated than that. I actually think it makes sense given where they are at right now. The complexity stems from Vercel and Next.js - had they used a different tech, say Cloudflare directly and architected their own systems designed to handle rapidly changing static content none of this would have been necessary. So I guess it depends on your definition of unnecessary complexity. It's definitely unnecessary for the problem space, but probably necessary for their existing stack.

Writing isn't about the produced artifact, it's about the process of taking abstract thought patterns and translating them into written text. In the same way that art isn't about coloured pixels on a screen or paint on a canvas. In our new world of AI slop, human writing is becoming more important, not less important.

“Nobody needs this” / “It’s not original”

We need it more than ever. Who cares if it's not original, AI slop isn't original either.

“AI can explain most topics better than I can”

Don't write tutorials.

A bit of fear: shipping something that feels naive or low-signal

Life is about overcoming your fears.

For example, I've had Gemini 3 produce really high quality UI/UX mockups and wireframes

Is the author a competent UX designer who can actually judge the quality of the UX and mockups?

I write about web development, AI tooling, performance optimization, and building better software. I also teach workshops on AI development for engineering teams. I've worked on dozens of enterprise software projects and enjoy the intersection between commercial success and pragmatic technical excellence.

Nope.

I don't think people should be obligated to spend time and effort justifying their reasoning on this. Firstly it's highly asymmetrical; you can generate AI content with little effort, whereas composing a detailed analysis requires a lot more work. It's also not easily articulatable.

However there is evidence that writers who have experience using LLMs are highly accurate at detecting AI generated text.

Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such “expert” annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts’ free-form explanations shows that while they rely heavily on specific lexical clues, they also pick up on more complex phenomena within the text that are challenging to assess for automatic detectors. [0]

Like the paper says, it's easy to point to specific clues in ai generated text, like the overuse of em dashes, overuse of inline lists, unusual emoji usage, tile case, frequent use of specific vocab, the rule of three, negative parallelisms, elegant variation, false ranges etc. But harder to articulate and perhaps more important to recognition is overall flow, sentence structure and length, and various stylistic choices that scream AI.

Also worth noting that the author never actually stated that they did not use generative AI for this article. Saying that their hands were on the keyboard or that they reworked sentences and got feedback from coworkers doesn't mean AI wasn't used. That they haven't straight up said "No AI was used to write this article" is another indication.

0: https://arxiv.org/html/2501.15654v2

Comparing the two articles, they have a completely different style. I wasn't totally convinced the linked article was AI generated but I am now. Clearly the author can write, so I'm a bit saddened that they used an LLM for this article

It's like a reflection of Nvidia, Oracle and, OpenAI selling each other products and just trading the same money back and forth. Which is of course a reflection of the classic economist joke about eating poo in the forest. "GDP is up though!"

Meanwhile nothing actually changed and the result is pretty much the same anyways.

Would be nice but you could probably edit it enough or splice different chat outputs together to break it.

Honestly with the way the world is going, you might as well just ask AI to generate the chat logs from the article. Who cares if it's remotely accurate, doesn't seem like anyone cares when it comes to anything else anyways.

There are some obvious tells like the headings ("Markdown is nice for LLMs. That’s not the point", "What Lee actually built (spoiler: a CMS)"), the dramatic full stops ("\nThis works until it doesn't.\n"), etc. It's difficult to describe because it's sort of a gut feeling you have pattern matching what you get from your own LLM usage.

It sort of reminds me of those marketing sites I used to see selling a product, where it's a bunch of short paragraphs and one-liners, again difficult to articulate but those were ubiquitous like 5 years ago and I can see where AI would have learned it from.

It's also tough because if you're a good writer you can spot it easier and you can edit LLM output to hide it, but then you probably aren't leaning on LLM's to write for you anyways. But if you aren't a good writer or your English isn't strong you won't pick up on it, and even if you use the AI to just rework your own writing or generate fragments it still leaks through.

Now that I think about it I'm curious if this phenomenon exists in other languages besides English...

I'm really getting tired of gen AI and this article is like a perfect microcosm. Partially or at least fully AI generated, discussing a vibe-coded CMS built by an AI startup. It's several layers of marketing and no serious engineering.

Where are the grownups in the room?

My source shows an even 5/5 split for best performance in 2024. And 7/10 of the worst performers are Republicans (lol they can't even insider trade without messing up).

The previous democratic president and a significant number of Democratic members of congress support banning members of congress from trading stocks

So why didn't they do it when they were in power last term. See this is what I mean, they do a decent job of sounding less corrupt whereas it's like the Republicans aren't even trying. But the outcome is the same, and it just fools people into thinking there is some significant difference.

In my country there are way bigger differences between the parties compared to the states, and even so I and a lot of other people still consider them mostly the same. So when people talk about massive differences between D & R I think they're just zoomed way in.