Yeah. I'm realizing that the models are strong at drafting the overall shape of the writing but the specific phrases are grating once you've seen it hundreds of time in slop.
Even with the examples, I've found that explicitly pointing out what not to do is moderately helpful if the model is given some time to self-evaluate. I wish this was something that came out of the box though.
Ah this is very helpful. I've been pointing out things that the model does, labeling it, and then adding them into a skill.
The models (Opus, etc) are very good at labeling the pattern when I point it out, but if I don't prompt it beforehand it responds like a host from Westworld.
This is a charitable read, but I think that being able to pick from a panoply of models will actually yield much better results in the long run.
The same model that has been post-trained to operate for hours as a Linux admin will be incapable of writing a heartfelt email, but with something like Fugu, you'd get both the Linux admin for driving the browser harness and the smaller writing specialist model for drafting the email itself.
> GLM-5.2 cost a fraction as much. Opus finished in half the time and shipped a cleaner game.
Off topic, but does anyone else instantly pick up on LLMisms like this? It seems like all the models have converged on this style of writing, and improvements aren't really changing it.
Basically, if you combine a bunch of near-frontier models (like GPT 5.5, etc) you can get performance that sometimes surpasses top line models like Claude's Fable.
Sakana seems to have a separate approach using a domain specific model to perform the model routing step.
Their research around building a domain specific model is pretty cool, it's kind of like Karpathy's autoresearch but pointed at deciding the optimal model to use at each step of the inference.
If cost becomes an even bigger problem being able to choose "best performance possible" or "strong but cost effective" will be useful.
you can probably guess my email