The new benchmark for LLMs is how much of simonw's new know-how is required.

Lower bars are better.

>> Getting the best results out of these models requires skill, experience, intuition, and domain expertise.

domain expertise has nothing to do with llms. On the contrary, to have it you need to avoid llms.

>>you risk prompting Claude Fable 5 like it's GPT-4o

Thats fine because when GPT came out you had to treat it like a baby, GPT2 and around that time "Prompt engineering" was a thing.

Now its all dead.

After opus 4.8 all you have to do is say "fix it" or add /plan. All that time spend on learning previous models is time wasted.

And in a year or two with developed harness you will be out of the loop, errors are incoming - llm fixes them or adds new features based on some transcripts etc.

Even if model development stops now - there is nothing to learn really. Sure you may need to adjust prompt style a bit. You will do it naturally just like when you communicate with a new person. There is no "knowledge" to it, it is very smart.

I agree but this particular example showed nothing about leveraging skill, experience, or intuition. If anything, this is another straightforward example of a one shot ask.

edit: that said, I understand this particular post is about model capability

Eh, I've have the exact opposite experience.

Way back before instruct models it was pretty difficult, but for the last couple of years I haven't needed anything more complex than the type of text that I might send in a detailed email to a colleague.

Isn't the whole point of a better model that it should be better at understanding you than the previous one? So the same prompt should return a better answer.

Prompting differently to the new model seems entirely backwards when trying to determine if the model has improved.

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