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?

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

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.

I'm sure they were saying the same thing in the 1800s

That's just blatantly untrue?

> “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.

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.

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