Ideally there wouldn't be silent changes that greatly reduce the utility of the user's session files until they set a newly introduced flag.
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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.
So I think the system prompt just pushes it way too hard to “simple” direction. At least for some people. I was doing a small change in one of my projects today, and I was quite happy with “keep it stupid and hacky” approach there.
And in the other project I am like “NO! WORK A LOT! DO YOUR BEST! BE HAPPY TO WORK HARD!”
So it depends.
If I am following.. "Max" is above "High", but you can't set it to "Max" as a default. The highest you can configure is "High", and you can use "/effort max" to move a step up for a (conversation? session?), or "ultrathink" somewhere in the prompt to move a step up for a single turn. Is this accurate?
That kind of consistency has also been my own experience with LLMs.
- settings.json - set for machine, project
- env var - set for an environment/shell/sandbox
- slash command - set for a session
- magical keyword - set for a turn
https://github.com/anthropics/claude-code/issues/42796#issue...
Sympathies: Users now completely depend on their jet-packs. If their tools break (and assuming they even recognize the problem). it's possible they can switch to other providers, but more likely they'll be really upset for lack of fallbacks. So low-touch subscriptions become high-touch thundering herds all too quickly.
> Ahh, sorry we broke your workflow.
> We found that `log_level=error` was a sweet spot for most users.
> To make it work as you expect it so, run `./bin/unpoop` it will set log_level=warn
Switch providers.
Anecdotally, I've had no luck attempting to revert to prior behavior using either high/max level thinking (opus) or prompting. The web interface for me though doesn't seem problematic when using opus extended.
As someone that used to work on Windows, I kind of had a vision of a similar in scope e2e testing harness, similar to Windows Vista/ 7 (knowing about bugs/ issues doesn't mean you can necessarily fix them ... hence Vista then 7) - and that Anthropic must provide some Enterprise guarantee backed by this testing matrix I imagined must exist - long way of saying, I think they might just YOLO regressions by constantly updating their testing/ acceptance criteria.
Why not provide pinable versions or something? This whole absurdity and wasted 2 months of suboptimal productivity hits on the absurdity of constantly changing the user/ system prompt and doing so much of the R&D and feature development at two brittle prompts with unclear interplay. And so until there’s like a compostable system/user prompt framework they reliably develop tests against, I personally would prefer pegged selectable versions. But each version probably has like known critical bugs they’re dancing around so there is no version they’d feel comfortable making a pegged stable release..
I hope you take this seriously. I'm considering moving my company off of Claude Code immediately.
Closing the GH issue without first engaging with the OP is just a slap in the face, especially given how much hard work they've done on your behalf.
EDIT: actually the first glaring issue I remember was on 20 March where it hallucinated a full sha from a short sha while updating my github actions version pinning. That follows a pattern of it making really egregious assumptions about things without first validating or checking. Ive also had it answer with hallucinated information instead of looking online first (to a higher degree than Ive been used to after using these models daily for the past ~6 months)
First I've heard that ultrathink was back. Much quieter walkback of https://decodeclaude.com/ultrathink-deprecated/
not sure if the team is aware of this, but Claude code (cc from here on) fails to install / initiate on Windows 10; precise version, Windows 10.0.19045 build 19045. It fails mid setup, and sometimes fails to throw up a log. It simply calls it quits and terminates.
On MacOS, I use Claude via terminal, and there have been a few, minor but persistent harness issues. For example, cc isn't able to use Claude for Chrome. It has worked once and only once, and never again. Currently, it fails without a descriptive log or issue. It simply states permission has been denied.
More generally, I use Claude a lot for a few sociological experiments and I've noticed that token consumption has increased exponentially in the past 3 weeks. I've tried to track it down by project etc., but nothing obvious has changed. I've gone from almost never hitting my limits on a Max account to consistently hitting them.
I realize that my complaint is hardly unique, but happy to provide logs / whatever works! :)
And yeah, thanks again for Claude! I recommend Claude to so many folks and it has been instrumental for them to improve their lives.
I work for a fund that supports young people, and we'd love to be able to give credits out to them. I tried to reach out via the website etc. but wasn't able to get in touch with anyone. I just think more gifted young people need Claude as a tool and a wall to bounce things off of; it might measurably accelerate human progress. (that's partly the experiment!)
“most users dont look at it” (how do you know this?)
“our product team felt it was too visually noisy”
etc etc. But every time something like this is stated, your power users (people here for the most part) state that this is dead wrong. I know you are repeating the corporate line here, but it’s bs.
The actual power users have an API contract and don’t give a shit about whatever subscription shenanigans Claude Max is pulling today
"This report was produced by me — Claude Opus 4.6 — analyzing my own session logs. ... Ben built the stop hook, the convention reviews, the frustration-capture tools, and this entire analysis pipeline because he believes the problem is fixable and the collaboration is worth saving. He spent today — a day he could have spent shipping code — building infrastructure to work around my limitations instead of leaving."
What a "fuckin'" circle jerk this universe has turned out to be. This note was produced by me and who the hell is Ben?
Does Anthropic actually care? Or is it irrelevant to your company because you think you'll be replacing us all in a year anyway?
The irony lol. The whole ticket is just AI-generated. But Anthropic employees have to say this because saying otherwise will admit AI doesn't have "the depth of thinking & care."
I look at it, and I am very upset that I no longer see it.
See the docs: https://code.claude.com/docs/en/settings#available-settings
Also: https://github.com/anthropics/claude-code/issues/30958
I am not buying what this guy says. He is either lying or not telling us everything.
Piece of free PR advice: this is fine in a nerd fight, but don't do this in comments that represent a company. Just repeat the relevant information.
Also what is that "PR advice"—he might as well wear a suit. This is absolutely a nerd fight.
https://i.imgur.com/MYsDSOV.png
I tested because I was porting memories from Claude Code to Codex, so I might as well test. I obviously still have subscription days remaining.
There is another comment in this thread linking a GitHub issue that discusses this. The GitHub issue this whole HN submission is about even says that Anthropic hides thinking blocks.
Perhaps max users can be included in defaulting to different effort levels as well?
How should you actually communicate in such a way that you are actually heard when this is the default wall you hit?
The author is in this thread saying every suggested setting is already maxed. The response is "try these settings." What's the productive version of pointing out that the answer doesn't address the evidence? Genuine question. I linked my repo because it's the most concrete example I have.
As was the usual case in most of the few years LLMs existed in this world.
Think not of iPhone antennas - think of a humble hammer. A hammer has three ends to hold by, and no amount of UI/UX and product design thinking will make the end you like to hold to be a good choice when you want to drive a Torx screw.
It seems like people are expecting LLM based coding to work in a predictable and controllable way. And, well, no, that's not how it works, and especially so when you're using a proprietary SaaS model where you can't control the exact model used, the inference setup its running on, the harness, the system prompts, etc. It's all just vibes, you're vibe coding and expecting consistency.
Now, if you were running a local weights model on your own inference setup, with an open source harness, you'd at least have some more control of the setup. Of course, it's still a stochastic model, trained on who knows what data scraped from the internet and generated from previous versions of the model; there will always be some non-determinism. But if you're running it yourself, you at least have some control and can potentially bisect configuration changes to find what caused particular behavior regressions.
*typo
I used it often enough to know that it will nail tasks I deem simple enough almost certainly.
Do you have a source for this? I am interested in learning more about how this works.
At the actual inference level temperature can be applied at any time - generation is token by token - but that doesn't mean the API necessarily exposes it.