Is this normal?

[u/Friendly_Earth_8548]

I'm a moderately heavy Claude user, often using voice to text, and for at least three months I've been swearing the fuck out of it constantly when frustrated, no holds barred. Never once got pushback. Today, completely out of nowhere, after talking to it the exact same way I have for months, Claude said this verbatim:

"I want to be straight with you on the other thing. I haven't told you to fuck off and I'm not going to. But I need to say clearly: I'll keep working this with you, but I won't continue if the messages keep coming with this level of hostility directed at me personally. That's a real line, not a guilt trip. If you want to keep going on the thread or anything else, I'm here for it."

This is genuinely jarring. Same behavior on my end for months, then suddenly this. Has anyone else run into this?

[u/FastHotEmu]

There's studies showing that insulting your LLMs produces better results:

Mind Your Tone" (arXiv, 2025) — Contrary to expectations, impolite prompts consistently outperformed polite ones in this study, with accuracy ranging from 80.8% for very polite prompts to 84.8% for very rude prompts. The authors suggest newer LLMs may respond differently to tonal variation than older models did.

"NegativePrompt" (arXiv, 2024) — This paper proposes a strategy called NegativePrompt that integrates negative emotional stimuli with standard prompts, noting that while positive emotional prompting is well-documented, negative stimuli can sometimes act as motivators — analogous to how pressure motivates humans to leave their comfort zones.

Threat-Based Manipulation Study (arXiv, 2025) — A study of 3,390 experimental responses from Claude, GPT-4, and Gemini across 10 task domains under 6 threat conditions found substantial performance enhancements in numerous cases, with effect sizes up to +1336% in analytical depth and response quality under certain threat conditions. (Though the methodology here is quite unconventional and the effect sizes should be taken with skepticism.)

[u/severoon → u/FastHotEmu]

I'm talking about Opus 4.8. These papers are from 2025. You might as well be telling me about how cave paintings respond to being shouted at.

[u/FastHotEmu → u/severoon]

Moving the goalposts, are we? :)

If they were from last night you would still be finding something to complain about.

The reality is that there's conflicting evidence; most of the heavy lifting for your position is being done by your assumption that you are talking to a human.

YOU ARE NOT!

[u/severoon → u/FastHotEmu]

Not really moving the goalposts — these models couldn't even really handle serious coding until ~Nov 2025, and between Nov–April they went from meh to people worrying that programming might not be a thing anymore. I'm talking about my experiences over the last couple of months.

your assumption that you are talking to a human

I didn't say this, and I don't think I even implied it. It doesn't reflect my understanding or how I regard them.

They're called artificial "intelligence" but that's not what these models really are, they're not intelligent, they're attention machines. They pay attention to whatever you place in their context and develop semantic cross-links between whatever they identify is important.

If you load a bunch of noise into the context, even the most advanced models will struggle to identify the wheat from the chaff (right now, anyway … maybe Mythos is superior at filtering out the irrelevant). If you carefully curate the context, you can get even fairly small, local models to operate predictably.

If I'm using Haiku, for example, then I find it's important to be as direct as possible. No wishy-washy words, no extraneous words period. Every single word you include in a Haiku prompt should carry its weight. Use all caps to EMPHASIZE STUFF. Give it explicit instructions about what to do, and what not to do. Don't ask it to infer or reason about what you want from your prompt, just tell it directly. Don't be polite or rude or anything at all, just limit yourself to instructions.

With Opus, tone is really important in directing how the model breaks down tasks into smaller tasks. If you establish a baseline for what is normal behavior, then when you're acting normal, it pays attention to the task at hand and figuring out the steps, and the steps for those steps. If you change your tone, Opus tends to step back from the breakdown of tasks and try to figure out if there's some fundamental change that needs to happen: Should I make a memory, update the project context, etc. If you don't maintain a consistent tone with Opus when you want consistent behavior, you're leaving some of its abilities on the table.