531–540 of 603 posts
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First time I used Claude I asked it to look at the current repo and just tell me where the database connection string was defined. It added 100 lines of code. I asked it to undo that and it deleted 1000 lines and 2 files
Would `git reset --hard` have worked to in your case? I guess you want to have each babystep in a git commit, in the end you could do a `git rebase -i` if needed.
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I find this thread surprising honestly. Claude Code is my daily driver and I consider myself a real power user. If you have your commands/agents/skills set up correctly you should never be running into these issues
Ahh, "you're holding it wrong" The classics never go out of style
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Appending "Good." before clarifying questions actually helps with that suprisingly well.
You're absolutely right! No, really: I've never had this problem of unprompted changes when I'm just asking, but I always (I think even in real-life conversations with real people) start with feedback: "Works great. What happens if..." I think people having different styles of prompting LLMs leads to different model preferences. It's like you can work better with some colleagues while with others it does not really "…
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> What else is an LLM supposed to do with this prompt? Maybe I saw the build plan and realized I missed something and changed my mind. Or literally a million other trivial scenarios. What an odd question.
> What an odd question. I don't see anything odd about this question. What kind of response did the user expect to get from LLM after spending this request and what was the point of sending it in the first place?
To your original comment, it would be like calling your intern to ask them to order lunch, and them letting you know the sandwich place you asked them to order from was closed, and should they just put in an order for next Tuesday at an entirely different restaurant instead? And then that intern hearing, "no, that's not what I want" saying "well, I don't respect your 'no'" and doing it anyways.
"Do X" -> "Here are the anticipated actions (which might deviate from your explicit intent), should I implement?" -> "no, that's not actually what I want"
is a clear instruction set and a completely normal thought pattern.
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Without git I would have been screwed. AI doesn't commit anything, I do when I'm satisfied
Ah, so you have not yet been forced to tell it DO NOT AMEND THE LAST COMMIT
this has often saved me.
One thing I’ve noticed while building internal tooling is that LLM coding assistants are very good at generating infrastructure/config code, but they don’t really help much with operational drift after deployment.
For example, someone changes a config in prod, a later deployment assumes something else, and the difference goes unnoticed until something breaks.
That gap between "generated code" and "actual running environment" is surprisingly large.
I’ve been experimenting with a small tool that treats configuration drift as an operational signal rather than just a diff. Curious if others here have run into similar issues in multi-environment setups.
and people are worried this machine could be conscious
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> What an odd question. I don't see anything odd about this question. What kind of response did the user expect to get from LLM after spending this request and what was the point of sending it in the first place?
Genuine questions: what do you think the request was? To build the plan? To prepare the commit? Do you never have a second thought after looking at your output, or realize you forgot something you wanted to include? Could it be that they saw "one new function", thought "boy, there should really be two... what happened?" and changed their mind? To your original comment, it would be like calling your intern to ask them…
Like, sure, if the model were "smarter" it would probably generate something like "Okay, I won't do it.". What is the value of the response "Okay, I won't do it."? Why did you just waisted time and compute to generate it?
> Do you never have a second thought after looking at your output, or realize you forgot something you wanted to include? Could it be that they saw "one new function", thought "boy, there should really be two... what happened?" and changed their mind?
Sure, all of those are totally valid. And in each of this cases it would be better to just don't make the request at all or make the request with the correction.
> like calling your intern
LLM is not human. It can't act on it's own without you triggering it to act. Unlike the intern in your example who will be wasting time and getting frustrated if they don't receive a response from you. With model you can just abandon this "conversation" (which is really just a growing context that you send again and again with every request) forever or until you are ready to continue it. There is no situation when just adding "no" to the conversation is useful.