Sorry I thought it would be clear and could have clarified that the code itself is just a joke illustrating the point, as an exaggeration. This was the thread if anyone is interested
https://chatgpt.com/share/68e82db9-7a28-8007-9a99-bc6f0010d1...
https://chatgpt.com/share/68e82db9-7a28-8007-9a99-bc6f0010d1...
if random.random() < 0.01:
logging.warning("This feels wrong. Aborting just in case.")
return NoneIf you completely excise anything too distasteful for a current-day blockbuster, but want a film about a space mining colony uprising you might as well just adapt the game Red Faction instead: have the brave heros blasting away with abandon at corpo guards, mad genetic experimenters and mercenaries and the media coverage can talk about how it's a genius deconstruction of Elon Musk's Martian dream or whatever.
It's like a fine wine pairing for "The Moon is a Harsh Mistress."
The only reason their libertarian revolution succeeds is because they have a centralised computer that secretly does everything for them.
same with pretty much every scifi movie and book from my youth. What movies that wouldn't have been rendered ridiculous by the invention of the cellphone were done in by the hairstyles or fashion.
<press enter>
damn these ai's are good!
<begins shopping for new username>
I can't say I'm not impressed. That's very funny
I love this and hate this at the same time.
This was my favorite line after asking it to review my resume and roast me:
> Structure & Flow: “Like Kubernetes YAML — powerful, but not human-readable.”
Some other good ones:
> Content & Tone: “You’re a CTO — stop talking like a sysadmin with a thesaurus.”
> Overall Impression: “This resume is a technical symphony… that goes on for too many movements.”
I've got some resume work to do haha
But then, I veered that same conversation into asking for GTM (go to market) advice, and it was actually really good. It actually felt tailored to me (unsurprisingly) and a lot more useful.
As always, I don't know whether this is a very light form of "ai psychosis" haha but still, super grateful for the advice. Cheers
try:
result = a / b
if math.isnan(result):
raise ArithmeticError("Result is NaN. I knew this would happen.") } catch (Exception e) {
if (!((_ok) ? true : (Math.random() > 0.1))) {
return res;
}
final StringBuilder logError = (new StringBuilder("Server seen down: ")).append(_addr);
/* edited for brevity: log the error */
System.DmlException: Insert failed. First exception on row 0; first error: UNKNOWN_EXCEPTION, Something is very wrong: []
The AIs in general feel really focused on making the user happy - your example, and another one is how they love adding emojis to the stout and over-commenting simple code.
With RLVR, the LLM is trained to pursue "verified rewards." On coding tasks, the reward is usually something like the percentage of passing tests.
Let's say you have some code that iterates over a set of files and does processing on them. The way a normal dev would write it, an exception in that code would crash the entire program. If you swallow and log the exception, however, you can continue processing the remaining files. This is an easy way to get "number of files successfully processed" up, without actually making your code any better.
Well, it depends a bit on what your goal is.
Sometimes the user wants to eg backup as many files as possible from a failing hard drive, and doesn't want to fail the whole process just because one item is broken.
However, LLM generated code will often, at least in my experience, avoid raising any errors at all, in any case. This is undesirable, because some errors should result in a complete failure - for example, errors which are not transient or environment related but a bug. And in any case, a LLM will prefer turning these single file errors into warnings, though the way I see it, they are errors. They just don't need to abort the process, but errors nonetheless.
> And in any case, a LLM will prefer turning these single file errors into warnings, though the way I see it, they are errors.
Well, in general they are something that the caller should have opportunity to deal with.
In some cases, aborting back to the caller at the first problem is the best course of action. In some other cases, going forward and taking note of the problems is best.
In some systems, you might event want to tell the caller about failures (and successes) as they occur, instead of waiting until the end.
It's all very similar to the different options people have available when their boss sends them on an errand and something goes wrong. A good underling uses their best judgement to pick the right way to cope with problems; but computer programs don't have that, so we need to be explicit.
See https://en.wikipedia.org/wiki/Mission-type_tactics for a related concept in the military.
// Return the result
return result;
I find this quite frustrating when reading/reviewing code generated by AI, but have started to appreciate that it does make subsequent changes by LLMs work better.
It makes me wonder if we'll end up in a place where IDEs hide comments by default (similar to how imports are often collapsed by default/automatically managed), or introduce some way of distinguishing between a more valuable human written comment and LLM boilerplate comments.
Adverb + verb
And "毛片免费观看" (Free porn movies), "天天中彩票能" (Win the lottery every day), "热这里只有精品" (Hot, only fine products here) etc[1].
king and rex (king in latin) map to different tokens but will map to very similar vectors.
Some LLMs can output nerd font glyphs and others can't.
If I recall grok code fast can but codex and sonnet can't
Because, and this is a hot take, LLMs have emergent intelligence
It sounds fine and flows nicely, but it doesn't quite make sense. Too much training over-fits an LLM; that's not what we're describing. Bad training might traumatize a model, but bad how? A creative response would suggest an answer to that question—perhaps the model has been made paranoid, scarred by repeat exposure to the subtlest and most severe bugs ever discovered—but the LLM isn't being creative. Its response has that spongy, plastic LLM texture that comes from the model rephrasing its prompt to provide a sycophantic preamble for the thing that was actually being asked for. It uses new words for the same old idea, and a bit of the precision is lost during the translation.
There are plenty of "over-x" phrases in English associated with trauma or harm. Do a web search in quotes for "traumatic over{extension/exertion/stimulation}" (off the top of my head) and you'll get direct hits. And this isn't a Markov chain—its doesn't have to pull n-grams directly from its training material. That it could glue trauma and training into "traumatic over-training" is deeply unsurprising to me.
> I couldn't in a million years put it into writing as succinctly and as precisely as the LLM.
If that's the case, then (with respect) that may be down to your skills as a writer. The LLM puts it decently enough, but it's not very expressive and it doesn't add anything.
> Connecting RL, poor LLMs, extreme fear, and welfare to excess training and severe lasting emotional pain is pretty darn impressive
Is it? Really, we're just analogizing it to an abused pet. You over-train your dog, so it gets traumatized. The LLM connects the ideas and then synthesizes a lukewarm sentence to capture that connection at the cost of losing a degree of precision, because LLMs aren't animals. Models are good at those vector-embedding-style conceptual connections—I won't begrudge them that. Expressive use of language and fine-grained reasoning, though? Not so much.