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.
If rust is so inflexible that it requires the use of unsafe to solve problems, that's still rust's fault. You have to consider both safe rust behaviour as well as necessary unsafe code.
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.
You're right. That's why I never took the Covid vaccine and I convinced everyone I know to avoid it as well. You cannot trust big pharma after all the evil things they've done.
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
> 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.
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.
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?