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GP was arguing against the OP, not a comment, and AI written posts are fair game.

Also, the comment you responded to was criticizing the attack to the substance of the post based on who/what wrote is. The comment neologism actually fits, IMO.


Agree with the sentiment! But "securing ... in all ways possible"? I know many people who would choose "password" as their password in 2026. The better of the bunch will use their date of birth, and maybe add their name for a flourish.

/rant


Is it a good proxy, though? My intuition is that many economic effects play out very differently if they are limited to one country vs the whole world.

To make this more concrete, tax havens only work because most countries keep producing for real. AI will take all jobs, not just Angolan jobs.


Not everyone has or wants yet another package manager in their system.


*Samsung phones. Known for a long time for their crapware infested devices. At the other end of the spectrum, Pixel phones are quite easy and smooth to set up.


This one here is the future I am most scared of.


What makes you think this trend will continue? In a situation with finite resources (eg the number of parameters), the default is to assume things will plateau.


Trying to be language agnostic: it should be as self-explanatory as possible. 2>&1 is all but.

Why is there a 2 on the left, when the numbers are usually on the right. What's the relationship between 2 and 1? Is the 2 for std err? Is that `&` to mean "reference"? The fact you only grok it if you know POSIX sys calls means it's far from self explanatory. And given the proportion of people that know POSIX sys calls among those that use Bash, I think it's a bit of an elitist syntax.


POSIX has a manual for shell. You can read 99% of it without needing to know any syscalls. I'm not as familiar with it but Bash has an extensive manual as well, and I doubt syscall knowledge is particularly required there either.

If your complaint is "I don't know what this syntax means without reading the manual" I'd like to point you to any contemporary language that has things like arrow functions, or operator overloading, or magic methods, or monkey patching.


No, the complaint is that "the syntax is not intuitive even knowing the simpler forms of redirection": this one isn't a competition of them, but rather an ad-hoc one.

I know about manuals, and I have known this specific syntax for half of my life.

Arrow functions etc are mechanisms in the language. A template you can build upon. This one is just one special operator. Learn it and use it, but it will serve no other purpose in your brain. It won't make anything easier to understand. It won't help you decipher other code. It won't help you draw connections.


> the syntax is not intuitive even knowing the simpler forms of redirection

The MDN page for arrow functions in JS has, I shit you not, 7 variations on the syntax. And your complaint is these are not intuitively similar enough?

call > output

call 2>&1

call > output 2> error

call 1> output 2> error

Give me a fucking break.


Do we actually know that goodness/evilness is 0% inheritable?


0%? Extremely unlikely. [0] Is your plan as an evil overlord to implement the stack ranking killing the evilest 10% generation after generation for the good of humanity?:)

Also hope you’re going global since other populations will quickly outcompete the docile sub-population if given chance.

[0] https://en.wikipedia.org/wiki/Genetics_of_aggression


Worth pointing out that calculating p-values on a wide set of metrics and selecting for those under $threshold (called p-hacking) is not statistically sound - who cares, we are not an academic journal, but a pill of knowledge.

The idea is, since data has a ~1/20 chance of having a p < 0.05, you are bound to get false positives. In academia it's definitely not something you'd do, but I think here it's fine.

@OP have you considered calculating Cohen's effect size? p only tells us that, given the magnitude of the differences and the number of samples, we are "pretty sure" the difference is real. Cohen's `d` tells us how big the difference is on a "standard" scale.


> The idea is, since data has a ~1/20 chance of having a p < 0.05

Are you saying p is uniformly distributed over any data set? That doesn't jive with my limited understanding of entropy. What's this based on?


Yes, if OP did a full vocabulary comparison and took just those sub-threshold, it would be hacking. I'm not sure that's the case here, though? Given that (the post) OP started with em-dash, and probably didn't do repeated sampling, then it should be a pretty fair hypothesis that em-dash usage is a marker.

Your comment about p<0.05, feels out of place to me. The p-values here are << 0.05. Like waaaaay lower.

Perhaps Fisher's exact is more appropriate, on the per-word basis?


A Bonferroni correction would be suitable. I usually see it used in genome-wide association studies (GWAS) that check to see if a trait or phenotype is influenced by any single nucleotide polymorphisms (SNPs) in a genome. So it's doing multiple testing on a scale of ~1 million.

> One of the simplest approaches to correct for multiple testing is the Bonferroni correction. The Bonferroni correction adjusts the alpha value from α = 0.05 to α = (0.05/k) where k is the number of statistical tests conducted. For a typical GWAS using 500,000 SNPs, statistical significance of a SNP association would be set at 1e-7. This correction is the most conservative, as it assumes that each association test of the 500,000 is independent of all other tests – an assumption that is generally untrue due to linkage disequilibrium among GWAS markers.

https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

cf: https://en.wikipedia.org/wiki/Bonferroni_correction


I think these term frequency comparisons are probably a pretty blunt tool, as some of the most well known AI indicators aren't words, but turns of phrase and sentence structure.

IMO a more interesting experiment would be to show comments to people (that haven't seen these conclusions), and have them assess whether they suspect them of being bots or AI authored, and then correlate that with account age.


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