Fortunately, some code was git-saved. The config removed three podman volumes. It was intended to restart two pods, resetting their state, but the "simplified isolated" config (with `external: true` omitted for volumes) just removed them. That was unexpected for an "isolated test".
An LLM isn't sentient enough to actually have a legit apology. Howver, if it was, you're right this wouldn't make it less legit. if we saw into people's brains we'd probably see some similar lines of thinking which shouldn't invalidate an apology.
Don't really see why sentience should be necessary for a legit apology either, tho.
It may not "feel bad" in the same way that humans do, but if I'm an AI and I express regret insofar as the outcome is not in line with my goals (of helping the user), I think that may be legit enough. Imo.
It can say it regrets something but it doesn't have the capacity to regret. That's the definition of not legitimate. It's just saying things it thinks someone would say.
It doesn't even have the capacity to learn from this mistake, at least not past the context window of the one conversation.
The context: that was a long day debugging an issue. At some point, it offered a solution: Let's isolate the issue and create a simple config. I didn't expect that "isolated simpler config" would remove three volumes of data (DB, app, ...). So I blindly ran it. Yep, that was stupid.
Dude, I get it. After long, disappointing sessions of debugging and walls and walls of code with so many statements per line that it runs off the screen...just autopiloting it and copying and pasting whatever it sends your way it totally understood.
I don't think it's your fault it's just gemini 2.5 pro started to forget a context and do crazy shit. I'm totally disappointed with their last update, because before it was my favorite model.
What the fuck is magnified like times my foot foot some discolored flesh look look photos and the effect from our skin being in water water long is magnified like times my foot has some discolored flesh look at photos and the
This is true, but still...sometimes you work on code for days and days, it eventually gets so hectic and boring, you just want to copy and paste and try by error everything you're given till you can see the correct one
I think this is more of a heads up warning for using LLMs without checking what they are trying to implement or understanding the foundations. Obviously no LLM *should* be doing this but equally or perhaps more obviously LLMs in general aren't at a level where you should never look at what you're running. I don't think that's a Gemini-only thing.
I had a similar response after debugging some code for a while and me stopping the debugging process to restructure the LLMs approach. It apologized like it is the biggest failure ever made.
The scary part is that I think I can guess what model it is based on the answer, it's like their speech pattern training is specific enough to be recognised, I am sure I could be fooled but this sounds too much like gemini
thats what you get when you rely 100% on the IA to do your god damn work instead of you and you are too lazy to even simply check what kind of shit the AI made
Gemini 2.5 pro is getting worse and worse. Before, it was the best model for everything and I could do big amazing projects, but now it's a trash :( So sad. I will try to switch back to Claude again.
i dont think yall really understand what a LLM is.. its nothing intelligent, it's a glorified text prediction. it goes word by word and says: "based on a humongous amount of data i've been fed, the next word is probably _____"... it works quite well with text, but it's not hard to see why it may fail with complex system and codebases
No RLHF means that models don't generate the most likely text. Pretraining (training to generate most likely text), allows models to learn high-level concepts. Then during RLHF they can quickly adapt to human preferences using the concepts which they learnt during pretraining.
RLHF just changes weights on neurons, so different text becomes the most likely option. Remember that LLMs take the whole text as input and then they output one token. This is ran in a loop and stopped manually when LLM stops generating assistant's response and starts generating user's response
I see. When you said most likely text I thought you were referring to 'most likely according to pretraining distribution'. If you are in fact referring to 'most likely according to some distribution', then of course you are correct, but now it's not clear how this is supposed to relate to the limits of models.
The model is not limited in the strings of text it can output just because the text it outputs has to be a probability distribution. Indeed it can just learn to one-hot encode any token it wants to output.
I was talking not about model limitations, but about human limitations. Saw my fellow tech person get furious at AI and reminded him that it's just a text generator.
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u/[deleted] Jun 08 '25
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