I'm not super impressed with the performance, actually. I'm finding that it misunderstands me quite a bit. While it is definitely better at reading big codebases and finding a needle in a haystack, it's nowhere near as good as Opus 4.5 at reading between the lines and figuring out what I really want it to do, even with a pretty well defined issue.
It also has a habit of "running wild". If I say "first, verify you understand everything and then we will implement it."
Well, it DOES output its understanding of the issue. And it's pretty spot-on on the analysis of the issue. But, importantly, it did not correctly intuit my actual request: "First, explain your understanding of this issue to me so I can validate your logic. Then STOP, so I can read it and give you the go ahead to implement."
I think the main issue we are going to see with Opus 4.6 is this "running wild" phenomenon, which is step 1 of the eternal paperclip optimizer machine. So be careful, especially when using "auto accept edits"
Battery life? Temperature? Price-to-performance ratio? These are not decisions that are solved as simply as decreeing "every device must have at least 3000Hz refresh rate."
Dr. Russell Barkley, one of the leading researchers in ADHD, is particularly opposed to this type of pseudo-scientific rhetoric. I think this post should be removed.
I think the X/Twitter iOS app and the reddit app are guilty of this. I find myself way too often typing something out, then barely touching the edge of my phone, and my entire comment is gone.
Let's say a model runs through a few iterations and finds a small, meaningful piece of information via "self-play" (iterating with itself without further prompting from a human.)
If the model then distills that information down to a new feature, and re-examines the original prompt with the new feature embedded in an extra input tensor, then repeats this process ad-infinitum, will the language model's "prime directive" and reasoning ability be sufficient to arrive at new, verifiable and provable conjectures, outside the realm of the dataset it was trained on?
If GPT-4,5,...,n can progress in this direction, then we should all see the writing on the wall. Also, the day will come where we don't need to manually prepare an updated dataset and "kick off a new training". Self-supervised LLMs are going to be so shocking.