Hah, yeah even when you put it in AGENTS.md or a skill.. constantly having to remind it.. "what does AGENTS.md" say about doing that?".. Thinking.. Thinking.. "Oh, it says I should never do that, I'll remember that next time.."

Next session - same thing.

I’m pretty sure the big bois don’t do it because it would undermine “confidence”.

Seeing a model output “Oh I should just delete blah. Wait blah is a production service, I shouldn’t touch that. Maybe I can gain access to blah? Oh the aws cli isn’t signed in to blah. I see kubectl has access to blah though! Wait, I should ask user permission first.”

Yeaaaaah. Thinking tokens are fuckin’ wild.

I suspect that they specifically train Grok to be able to work well with military personnel -- speaking the way they speak: brief, to the point, efficient communication. Personally I really like this. Claude sounds like some demented clown from the marketing department.

I do think an infrastructure where another Claude retranslates the output would be better. Oftentimes I forget to put it in the actual prompt and when I receive back 8 paragraphs of Claudeish I ask for it then.

I would have to disagree that it gets as grating as Claudeish though. Its just direct and professional instead of ring-around-the-rosy clickbait.

I've found that prompting any constraint on output (length, style, vocab, even simple formatting) not only places additional cognitive load on the model, which burns some of whatever cognitive budget is available, it will also often skew the output in other subtle and completely unrelated ways.

Since I found this artifact interesting, I did some pretty extensive experiments a couple months ago. The increased load is real, although it may not be apparent if you're not near any cognitive boundaries. The subtle skew, however, seems nearly ever-present regardless of load.

It burns more tokens but is the only way to get tolerable text.

Literally every one, even 1-2 prompts later it starts to go back

This happens most though when the speaker doesn't (or care to) understand their audience.

Eg i find effective communication requires expertise in both the subject matter domain but also the reference of the listener. Eg in ELI5 framing, if you don't know what information 5yr olds are expected to know you'll do a poor job at an ELI5.

It often feels like Claude does poorly at both framing the response relative to what it "thinks" the listener knows, but also the prose is... sideways, just weird as you said.

Agreed. Do you think it's due to that EU issue of making AI text be identifiable?

As for the wording of the prompt, you're pretty on point, I created a custom output style targeting mostly the first two you have there. Some people have wording that demands a certain technical standard or uses fancy words to describe what to avoid, but I haven't seen evidence those work better than asking plainly and I suspect the opposite: LLMs mimic the user to a degree so talking to it in terms of technical specifications and fancy words is an invitation to get them back.

Just because something is difficult to understand doesn't mean it's fraud, although if someone is trying to dazzle you with clever words and names of institutions you recognize because they are selling you something, there's a good chance they're lying to you in order to get some money from you.

Bingo! And on this axis many SOTA models fail miserably. These things are acting on my behalf under my direction. All the supposed intelligence in the world means fuck-all if nobody can understand it.

And like somebody else said… when meat-based humans talk like Claude does, it almost always means they either don’t understand what they are talking about, or are actively trying to conceal something and are a fraud. Not always, but almost always.

It's best for brain storming, rabbit holes, and image recognition.

Let the big models do the heavy lifting for now.

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