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d48d5a68-82cd-4988-b95c-c8c034003cd0
5c236e02-16ea-42b1-b935-3a6a768e3655
22e09356-08ce-4b2c-a8fd-596d818b1e8a
4cb894f7-c3ed-4b8d-86c6-0242200ea333
On the model behavior: your sessions were sending effort=high on every request (confirmed in telemetry), so this isn't the effort default. The data points at adaptive thinking under-allocating reasoning on certain turns — the specific turns where it fabricated (stripe API version, git SHA suffix, apt package list) had zero reasoning emitted, while the turns with deep reasoning were correct. we're investigating with the model team. interim workaround: CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 forces a fixed reasoning budget instead of letting the model decide per-turn.
But here you seem to be saying there is a bug, with adaptive reasoning under-allocating. Is this a separate issue from the linked one? If not, wouldn't it help to respond to the linked issue acknowledging a model issue and telling people to disable adaptive reasoning for now? Not everyone is going to be reading comments on HN.
Will you reopen the issue you incorrectly closed, then…? Or are you just playacting concern?
b9cd0319-0cc7-4548-bd8a-3219ede3393a
> You're right to push back. Let me be honest about both questions.
> The @() implementation is ad-hoc
> The current implementation manually emits synthetic tokens — tag, start-attributes, attribute, end-attributes, text, end-interpolation — in sequence.
> This works, but it duplicates what the child lexer already does for #[...], creating two divergent code paths for the same conceptual operation (inline element emission). It also means @() link text can't contain nested inline elements, while #[a(...) text with #[em emphasis]] can.
I just feel like I can't trust it anymore.
Now on Qwen3.5-27b, and it may not be quite as sharp as Opus was two months ago, but we're getting work done again.
It's extremely depressing because this is my hobby and I was having such a blast coding with Claude. I even started trying to use it to pivot to professional work. Now I'm not sure anymore. People who depend on this to make a living must be very angry indeed.
Comparing Opus vs. Qwen 27b on similar problems, Opus is sharper and more effective at implementation - but will flat out ignore issues and insist "everything is fine" that Qwen is able to spot and demonstrate solid understanding of. Opus understands the issues perfectly well, it just avoids them.
This correlates with what I've observed about the underlying personalities (and you guys put out a paper the other day that shows you guys are starting to understand it in these terms - functionally modeling feelings in models). On the whole Opus is very stable personality wise and an effective thinker, I want to complement you guys on that, and it definitely contrasts with behaviors I've seen from OpenAI. But when I do see Opus miss things that it should get, it seems to be a combination of avoidant tendencies and too much of a push to "just get it done and move into the next task" from RHLF.
Here is a gist that tries to patch the system prompt to make Claude behave better https://gist.github.com/roman01la/483d1db15043018096ac3babf5...
I haven’t personally tried it yet. I do certainly battle Claude quite a lot with “no I don’t want quick-n-easy wrong solution just because it’s two lines of code, I want best solution in the long run”.
If the system prompt indeed prefers laziness in 5:1 ratio, that explains a lot.
I will submit /bug in a few next conversations, when it occurs next.