I'm absolutely right

I wonder if this is a tactic that LLM providers use to coerce the model into doing something.

Gemini will often start responses that use the canvas tool with "Of course", which would force the model into going down a line of tokens that end up with attempting to fulfill the user's request. It happens often enough that it seems like it's not being generated by the model, but instead inserted by the backend. Maybe "you're absolutely right" is used the same way?

It is a tactic. OpenAI is changing the tone of ChatGPT if you use casual language, for example. Sometimes even the dialect. They try to be sympathetic and supportive, even when they should not.

They fight for the user attention and keeping them on their platform, just like social media platforms. Correctness is secondary, user satisfaction is primary.

I find the GPT-5 model having turned the friendliness way, way down. Topics that previously would have rendered long and (usefully) engaging conversations are now met with an "ok cool" kind of response.

I get it - we don't want LLMs to be reinforces of bad ideas, but sometimes you need a little positivity to get past a mental barrier and do something that you want to do, even if what you want to do logically doesn't make much sense.

An "ok cool" answer is PERFECT for me to decide not to code something stupid (and learn something useful), and instead go and play video games (and learn nothing).

How would a "conversation" with an LLM influence what you decide to do, what you decide to code?

It's not like the attitude of your potato peeler is influencing how you cook dinner, so why is this tool so different for you?

I once had a refactoring that I wanted to do, but I was pretty sure it'd hit a lot of code and take a while. Some error handling in a web application.

I was able to ask Claude "hey, how many function signatures will this change" and "what would the most complex handler look like after this refactoring?" and "what would the simplest handler look like after this refactoring?"

That information helped contextualize what I was trying to intuit: is this a large job, or a small one? Is this going to make my code nicer, or not so much?

All of that info then went into the decision to do the refactoring.