this is the exact kind of clown who’d go “uh actually I have an editor” and fire up ChatGPT again

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Absolutely this, shuf would easily come up in a normal google search (even in googles deteriorated relevancy).

For fun, “two” lines of bash + jq can easily achieve the result even without shuf (yes I know this is pointlessly stupid)

cat /usr/share/dict/words | jq -R > words.json
cat /dev/urandom | od -A n -D | jq -r -n '
  import "words" as $w;
  ($w | length) as $l |
  label $out | foreach ( inputs * $l / 4294967295 | floor ) as $r (
    {i:0,a:[]} ;
    .i = (if .a[$r] then .i  else .i + 1 end) | .a[$r] = true ;
    if .i > 100 then break $out else $w[$r] end
  )
'

Incidentally this is code that ChatGPT would be utterly incapable of producing, even as toy example but niche use of jq.

It’s so incredibly easy to randomly select a few lines from a file that it really doesn’t need to be in the standard library. Something like 4 lines of code could do it. Could probably even do it in a single unreadable line of code.

I’ve been conducting DevOps and SRE interviews for years now. There’s a huge difference between someone that can copypasta SO code and someone that understands the SO code. LLMs are just another extension of that. GitHub Copilot is great for quickly throwing together an entire Terraform file. Understanding how to construct the project, how to tie it all together, how to test it, and the right things to feed into Copilot requires actually having some skill with the work.

I might hire this person at a very junior level if they exhibited a desire to actually understand what’s going on with the code. Here an LLM can serve as a “mentor” by spitting out code very quickly. Assuming you take the time to understand that code, it can help. If you just commit, push, deploy, you can’t figure out the deeper problems that span files and projects.

To me the only jobs that might not be safe are for executives a good programmer probably doesn’t want to work for.

I help maintain an open-source OS for industrial embedded applications.

fuck yes. there’s something weirdly exciting about work like that — not only is it a unique set of constraints, but it’s very likely that an uncountable number of people (myself possibly included) have interacted with your code without ever knowing they did

But the explicit purpose of generative AI is the devaluation of intellectual and creative labour, and right now, a lot of money is being spent on an attempt to make people like me redundant. Perhaps this is just my anxiety speaking, but it makes me terribly uneasy.

absolitely same. I keep seeing other programmers uncritically fall for poorly written puff pieces like this and essentially do everything they can to replace themselves with an LLM, and the pit drops out of my stomach every time. I’ve never before seen someone misunderstand their own career and supposed expertise so thoroughly that they don’t understand that the only future in that direction is one where they’re doing a much more painful version of the same job (programming against cookie cutter LLM code) for much, much less pay. it’s the kind of goal that seems like it could only have been dreamed up by someone who’s never personally survived poverty, not to mention the damage LLM training is doing to the concept of releasing open source code or even just programming for yourself, since there’s nothing you can do to stop some asshole company from pilfering your code.

reading back, I wonder if they were looking for a bash command or something that’d do it? which both isn’t programming, and makes their inability to find an answer in seconds much worse

So I haven’t programmed in a long time but like isn’t a simple approach for this sort of thing (if want low numbers like 100) just something like:

from distribution I like(0, len(file)) get 100 samples read line at sample forall samples

or if file big

sort samples, stream file, if line = current sample add line to array, remove sample from other array.

Like that is literally off the top of my head. I’m sure there are real approachs but if googling is too hard isn’t shit like that obvious?

edit: wait you’d have to dedupe this. also the real approach is called: (unspellable French word for pit of holdy water etc) sampling

the good parts of their evenings aren’t very long

and I mean, same

wat indeed.

Twenty minutes, in a language you hadn’t seen before? Sure.

Bonus: this is how I visualise LLMs generating results: https://youtu.be/Jn4k2TPIJf0

you know, you’re fucking right. I was imagining taking a dictionary and generating every valid crossword for an N x N grid from it, but like you said he claims to already have a puzzle generator. how in fuck is that puzzle generator’s output just a list of words (or a list of whatever the fuck "s""c""a""r""*""k""u""n""i""s""*" "a""r""e""a" is supposed to mean, cause it’s not valid syntax for a list of single characters with words delimited from * in most languages, and also why is that your output format for a crossword?) if it’s making valid crossword puzzles?

fractally wrong is my favorite kind of wrong, and so many of these AI weirdos go fractal

if you never have, find YouTube videos of folks trying to use an LLM to generate code for a mildly obscure language. one I watched that gave the game away was where someone tried to get ChatGPT to write a game in Commodore BASIC, which they then pasted directly into a Commodore 64 emulator to run. not only did the resulting “game” perform like a nonsensical mashup of the simple example code from two old programming books, there was a gigantic edit in the middle of the video where they had to stop and make a significant number of fixes to the LLM’s output, where it either fictionalized something like a line number or constant, or where the mashup of the two examples just didn’t function. after all that programming on their part for an incredibly substandard result, their conclusion was still (of course) that the LLM did an amazing job