there is absolutely zero value in spending time to learn about new models as in few months new model will be out and whatever you learned about the current one will be useless.
Also with models getting better and better you have to know less and less to achieve same results.
As the models get better you need to know more about their capabilities, because otherwise you risk prompting Claude Fable 5 like it's GPT-4o and complaining loudly about how it's all hype and nothing about these models is improving at all (yes, I do see people say that.)
Getting the best results out of these models requires skill, experience, intuition, and domain expertise. There's always room for improving every one of those.
Lower bars are better.
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.
edit: that said, I understand this particular post is about model capability
Prompting differently to the new model seems entirely backwards when trying to determine if the model has improved.
Learning to provide unambiguous, clear directions is a skill. A lot of people who report bad experiences with models aren't yet good at that skill.
More importantly though, the key to successful communication is having a good understanding of what the other side of the conversation already knows and understands.
Saying "use uv and inline script dependencies" won't mean anything to a model with a knowledge cutoff date prior to the launch of uv!
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.