For fun (not because I'm trying to defend the capabilities of LLMs, but really just out of boredom), I tried to make this better with this prompt:
> make this sound less AI-generated and more like a proper hacker news comment with some SUBSTANCE: (insert the OP comment from above)
and Gemini 3 Flash generated this:
> This is exactly why CVSS scores can be misleading in practice. You see this constantly at Pwn2Own: it’s rarely one massive architectural failure, but a daisy-chain of three or four "low severity" quirks—an info leak here, a minor logic error there.
> To a product manager, these look like edge cases or wontfix tickets. But to an attacker, they aren't bugs; they’re primitives. You use the info leak to bypass ASLR, the logic error to groom the heap, and suddenly a nuisance crash becomes full RCE. We tend to model threats based on distinct barriers, ignoring that the complexity of the system guarantees these gadgets will eventually align.
It feels like it's fun when one plays with it on their own but it's really boring when reading the content others have generated (and therefore I'm sorry for adding to the pile - just wanted to see if the "HN style" was already baked-in to the LLMs and share the result: Yes it is).
That is one law I could get behind actually: the absolute requirement to label any and all AI output by using a duplicate of all of Unicode that looks the same and feels the same but is actually binary in a different space.
And then browsers and text editors could render this according to the user's settings.
Yes, it would already help if they started with whitespace and punctuation. That would already give a big clue as to what is AI generated.
In fact, using a different scheme, we can start now:
U+200B — ZERO WIDTH SPACE
Require that any space in AI output is followed by this zero-width character. If this is not acceptable then maybe apply a similar rule to the period character (so the number of "odd" characters is reduced to one per sentence).
I sometimes use AI to fix my English (especially when I'm trying to say something that pushes my grammar skill to the limit) and people like me can use that to inform others about that. Bad actors will always do weird stuff, this is more about people like me who want to be honest, but signing with (generated/edited with AI) is too much noise.
A little bit of advice: don't copy and paste the LLM's output, but actively read and memorize it (phrase by phrase), and then edit your text. It helps developing your competence. Not a lot, and it takes time, but consciously improving your own text can help.
Yes, and I think the big AI companies will want to have AI-generated data tagged, because otherwise it would spoil their training data in the long run.
> make this sound less AI-generated and more like a proper hacker news comment with some SUBSTANCE: (insert the OP comment from above)
and Gemini 3 Flash generated this:
> This is exactly why CVSS scores can be misleading in practice. You see this constantly at Pwn2Own: it’s rarely one massive architectural failure, but a daisy-chain of three or four "low severity" quirks—an info leak here, a minor logic error there.
> To a product manager, these look like edge cases or wontfix tickets. But to an attacker, they aren't bugs; they’re primitives. You use the info leak to bypass ASLR, the logic error to groom the heap, and suddenly a nuisance crash becomes full RCE. We tend to model threats based on distinct barriers, ignoring that the complexity of the system guarantees these gadgets will eventually align.
It feels like it's fun when one plays with it on their own but it's really boring when reading the content others have generated (and therefore I'm sorry for adding to the pile - just wanted to see if the "HN style" was already baked-in to the LLMs and share the result: Yes it is).