But over the last 2-3 days, it's started exhibiting some of the problems previous models presented. It's been making absurd assumptions without asking about my preferences. More definitively, in 3 instances I've had to either correct it on previous decisions that were made but it ignored. 2 times I've had to double check it on some quite obvious high level performance choices I've come to anticipate the model already understanding from prior experience, especially in the same project. At least twice I've had to call it out only get the remedial "you're totally right I should have done X instead".
Also to be clear, I'm not playing around with more advanced expensive models that fall back. This is all experience within a Pro plan using Opus 5.5 medium.
Anyone else been encountering weirdness?
bbor•44m ago
The second biggest difference, though, is that I haven't used Opus 5.5 in a few days cause I ran out of credits on Thursday! So... you might be doing something right, there ;)
One thing you could check is the files that tend to get read into context, especially their built-in "memory" feature, which can get insanely cluttered if not actively maintained. This is why I prefer keeping everything I can to three other abstractions, none of them harness-specific: reports (roughly per-(long-)turn), policies (human readable, very concise, identified with unique slugs, system-wide), and tasks (Plane/Trello/Linear/etc., mentioned in git commits).
For something like "high level performance choices", a policy to always think about performance explicitly before reporting back could honestly do wonders. As you say yourself, they're typically smart enough to know what to do when reminded of your priorities or asked explicitly. The trick is to not prioritize everything, everywhere, all at once!
...so weird talking about how to best use these agents, BTW. Truly shuffling the deck chairs on our Titanic of a career/civilization.