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Discussion (14 Comments)Read Original on HackerNews
Developers write plenty of bad code and comments too, trying to detect AI just feels more like treating the symptom than the actual problem.
Anyway, I wish the LLMs were trained with a human-text-detector in their training loop. And they can still use a different LLM to produce code and mathematics. However, using the same LLM for both tasks, and we're going to have a hard time understanding what is happening.
The problem is that Anthropic and others use the text generated by LLMs also to power the AI's internal thinking loop. This means that text is used in two different ways: one as input to AI, and the other as input to humans.
They are always trying to make the LLMs more intelligent. Guess what kind of output will suffer?
I know exactly what you mean and it's fatiguing if you used the tool or whatever for more than a few hours
> One thing in this comment caught my mind: the small parenthetical that says βthe usual caseβ. If that were true, it would be important information! However, this comment is generated by the llm model Claude, which has no sense of what counts as the usual case in this domain.
That's from the post referenced early on. I think that's the fundamental misunderstanding of what these tools are supposed to be used for. And I'm just as guilty myself at work. The problem isn't that the LLM generated that text, the problem is that a human didn't read it properly and ask what that meant and fix it before pushing.
The amount of garbage AI generates quickly overwhelms the human. The whole reason for computers to exist is to remove burden from humans. And yet here we are.
I ended up vibe-coding a comment linter for my projects that catches the garbage and forces the models to rewrite. Just look at this: https://xcancel.com/dmitriid/status/2097632371650552084#m
you are not the only one, Pangram and GPTZero also think it is written by a bot.
ai detection may be an unsolvable issue.
https://xkcd.com/810/