161–170 of 288 posts
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So the correct strategy is a global CLAUDE.md with couple lines of colourful "you best behave or else" texts, so all your prompts get routed via the frustrated path?
That will not work - you end up with Claude being ADHD and not following any guidelines. Skills do work, as they ground the agent with constrained context for the task it's performing
I've found swearing at a model to be quite effective in getting it to rethink and correct its mistakes. This seems to apply across Codex, Claude, Qwen, and Gemma/Gemini. I don't know if the model is picking up on a "need to lock in and be more rigorous" signal, or if the model providers are routing to smarter models if they detect a frustrated user. But if a model keeps making the same mistakes, swearing at it often…
I only used Claude a bit, but one of the things I dislike about it, is that it starts to 'push back' when you swear at it, saying things like 'if you continue like this, I won't be able to work with you' and such. I'm like MF'er you're a token prediction algorithm, what are you talking about, and it just makes me irrationally dislike it more. Codex otoh just lets you vent and straight up ignores such outbursts.
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I just say "bruh". Per knowyourmeme: > "Bruh" is a popular variant of the slang term "bro" that is often used as an interjection to convey frustration or disappointment at something.
I've found this to be effective as well. Claude generally immediately identifies the stupid code pattern it used and tries to fix it (with somewhat varying results).
We get so angry at LLMs because we can . Without any social or even emotional repercussions for expressing these emotions. If the models actually acted like people in response, we wouldn’t do it. Some of the people I work with daily make similar mistakes, I don’t find myself yelling at them. I think this is simply part of the darker side of human nature, when we interact with entities who will take abuse, we tend to…
I dread people who get abusive with AI, because I know it's only fear that prevents them from being like that with me. Even if only it is the fear of hurting me, it's still terrible because every fear can pass.
Though obviously some people, let’s say, react worse than others.
I think it’s best to try to treat LLMs well even when frustrated, or stressed, or tired, the same way we would with people. Both because it might well matter to the LLM even if they are very different from us mechanically, but also because mistreating them trains us to act in negative ways.
I thought that was an interesting thing to point out.
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It might improve the general state of "professional" software though. When done selectively and dosed just right that is.
If a coworker deleted your database you'd expect some 4 letter words.
There's another layer to the non-determinism of LLM agents: what are the execution params the provider is using today?
I hate the feeling that a worn path that I've grown to trust will "do the right thing" over the last few months will suddenly start doing the wrong thing simply because an engineer at Anthropic or OpenAI found a way to save N million dollars by "optimizing" thinking token usage.
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if you look at claude code, it now says compaction is happening constantly, which is likely why
If compaction is throwing away crucial prompting instructions even when it's at a 1% of maximum token usage (like my example), then it's a software bug, not an LLM artifact.