Hey all, Boris from the Claude Code team here. I just responded on the issue, and cross-posting here for input.
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Hi, thanks for the detailed analysis. Before I keep going, I wanted to say I appreciate the depth of thinking & care that went into this.
There's a lot here, I will try to break it down a bit. These are the two core things happening:
> `redact-thinking-2026-02-12`
This beta header hides thinking from the UI, since most people don't look at it. It *does not* impact thinking itself, nor does it impact thinking budgets or the way extended reasoning works under the hood. It is a UI-only change.
Under the hood, by setting this header we avoid needing thinking summaries, which reduces latency. You can opt out of it with `showThinkingSummaries: true` in your settings.json (see [docs](https://code.claude.com/docs/en/settings#available-settings)).
If you are analyzing locally stored transcripts, you wouldn't see raw thinking stored when this header is set, which is likely influencing the analysis. When Claude sees lack of thinking in transcripts for this analysis, it may not realize that the thinking is still there, and is simply not user-facing.
> Thinking depth had already dropped ~67% by late February
We landed two changes in Feb that would have impacted this. We evaluated both carefully:
1/ Opus 4.6 launch → adaptive thinking default (Feb 9)
Opus 4.6 supports adaptive thinking, which is different from thinking budgets that we used to support. In this mode, the model decides how long to think for, which tends to work better than fixed thinking budgets across the board. `CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING` to opt out.
2/ Medium effort (85) default on Opus 4.6 (Mar 3)
We found that effort=85 was a sweet spot on the intelligence-latency/cost curve for most users, improving token efficiency while reducing latency. On of our product principles is to avoid changing settings on users' behalf, and ideally we would have set effort=85 from the start. We felt this was an important setting to change, so our approach was to:
1. Roll it out with a dialog so users are aware of the change and have a chance to opt out
2. Show the effort the first few times you opened Claude Code, so it wasn't surprising.
Some people want the model to think for longer, even if it takes more time and tokens. To improve intelligence more, set effort=high via `/effort` or in your settings.json. This setting is sticky across sessions, and can be shared among users. You can also use the ULTRATHINK keyword to use high effort for a single turn, or set `/effort max` to use even higher effort for the rest of the conversation.
Going forward, we will test defaulting Teams and Enterprise users to high effort, to benefit from extended thinking even if it comes at the cost of additional tokens & latency. This default is configurable in exactly the same way, via `/effort` and settings.json.
> Under the hood, by setting this header we avoid needing thinking summaries, which reduces latency. You can opt out of it with `showThinkingSummaries: true` in your settings.json (see [docs](https://code.claude.com/docs/en/settings#available-settings)).
Can I just see the actual thinking (not summarized) so that I can see the actual thinking without a latency cost?
I do really need to see the thinking in some form, because I often see useful things there. If Claude is thinking in the wrong direction I will stop it and make it change course.
Anthropic's position is that thinking tokens aren't actually faithful to the internal logic that the LLM is using, which may be one reason why they started to exclude them:
That's interesting research, but I think a more important reason that you don't have access to them (not even via the bare Anthropic api) is to prevent distillation of the model by competitors (using the output of Anthropic's model to help train a new model).
If you can't trust a company, don't use their api or cloud services. No amount of external output will ever validate anything, ever. You never know what's really happening, just because you see some text they sent you.
Do you not see that the next (or previous) logical step would be a "commercial ban" of frontier models, all "distilled" from an enormous amount of copyrighted material?
That probably matters for some scenarios, but I have yet to find one where thinking tokens didn't hint at the root cause of the failure.
All of my unsupervised worker agents have sidecars that inject messages when thinking tokens match some heuristics. For example, any time opus says "pragmatic", its instant Esc Esc > "Pragmatic fix is always wrong, do the Correct fix", also whenever "pre-existing issue" appears (it's never pre-existing).
> also whenever "pre-existing issue" appears (it's never pre-existing)
I dunno... There were some pre-existing issues in my projects. Claude ran into them and correctly classified as pre-existing. It's definitely a problem if Claude breaks tests then claims the issue was pre-existing, but is that really what's happening?
> For example, any time opus says "pragmatic", its instant Esc Esc > "Pragmatic fix is always wrong, do the Correct fix", also whenever "pre-existing issue" appears (it's never pre-existing).
It's so weird to see language changes like this: Outside of LLM conversations, a pragmatic fix and a correct fix are orthogonal. IOW, fix $FOO can be both.
From what you say, your experience has been that a pragmatic fix is on the same axis as a correct fix; it's just a negative on that axis.
It's contextual though, and pragmatic seems different to me than correct.
For example, if you have $20 and a leaking roof, a $20 bucket of tar may be the pragmatic fix. Temporary but doable.
Some might say it is not the correct way to fix that roof. At least, I can see some making that argument. The pragmatism comes from "what can be done" vs "should be".
From my perspective, it seems viable usage. And I guess on wonders what the LLM means when using it that way. What makes it determine a compromise is required?
(To be pragmatic, shouldn't one consider that synonyms aren't identical, but instead close to the definition?)
> It's contextual though, and pragmatic seems different to me than correct.
To me too, that's why I say they are measurements on different dimensions.
To my mind, I can draw a X/Y axis with "Pragmatic" on the Y and "Correctness" on the X, and any point on that chart would have an {X,Y} value, which is {Pragmatic, Correctness}.
If I am reading the original comment correctly, poster's experience of CC is that it is not an X/Y plot, it is a single line plot, with "Pragmatic" on the extreme left and "Correctness" on the extreme right.
Basically, any movement towards pragmatism is a movement away from correctness, while in my model it is possible to move towards Pragmatic while keeping Correctness the same.
I don't think it's a single axis even in the original poster's conception, since you could be both incorrect and also not pragmatic.
But if a fix needs to be described as pragmatic relative to the alternatives, that's probably because it couldn't be described as correct. Otherwise you wouldn't be talking about how pragmatic it is.
I had some interesting experience to the opposite last night, one of my tests has been failing for a long time, something to do with dbus interacting with Qt segfaulting pytest. Been ignoring it for a long time, finally asked claude code to just remove the problematic test. Come back a few minutes later to find claude burning tokens repeatedly trying and failing to fix it. "Actually on second thought, it would be better to fix this test."
Match my vibes, claude. The application doesn't crash, so just delete that test!
I somewhat understand Anthropic's position. However, thinking tokens are useful even if they don't show the internal logic of the LLM. I often realize I left out some instruction or clarification in my prompt while reading through the chain of reasoning. Overall, this makes the results more effective.
It's certainly getting frustrating having to remind it that I want all tests to pass even if it thinks it's not responsible for having broken some of them.
What's the implication of this? That the model already decided on a solution, upon first seeing the problem, and the reasoning is post hoc rationalization?
But reasoning does improve performance on many tasks, and even weirder, the performance improves if reasoning tokens are replaced with placeholder tokens like "..."
I don't understand how LLMs actually work, I guess there's some internal state getting nudged with each cycle?
So the internal state converges on the right solution, even if the output tokens are meaningless placeholders?
>That the model already decided on a solution, upon first seeing the problem, and the reasoning is post hoc rationalization?
Yes it plans ahead, but with significant uncertainty until it actually outputs these tokens and converges on a definite trajectory, so it's not a useless filler - the closer it is to a given point, the more certain it is about it, kind of similar to what happens explicitly in diffusion models. And it's not all that happens, it's just one of many competing phenomena.
So like many of the promises from AI companies, reported chain of thought is not actually true (see results below). I suppose this is unsurprising given how they function.
Is chain of thought even added to the context or is it extraneous babble providing a plausible post-hoc justification?
People certainly seem to treat it as it is presented, as a series of logical steps leading to an answer.
‘After checking that the models really did use the hints to aid in their answers, we tested how often they mentioned them in their Chain-of-Thought. The overall answer: not often. On average across all the different hint types, Claude 3.7 Sonnet mentioned the hint 25% of the time, and DeepSeek R1 mentioned it 39% of the time. A substantial majority of answers, then, were unfaithful.‘
I mean, obviously, it's not going to be a faithful representation of the actual thinking. The model isn't aware of how it thinks any more than you are aware how your neurons fire. But it does quantitatively improve performance on complex tasks.
As you can see from posts on this story, most people believe it reflects what the model is thinking and use it as a guide to that so they can ‘correct’ it. If it is not in fact chain of thought or thinking it should not be called that.
It is the same with human chain of thought, though. Both of them are post-hoc rationalisations justifying "gut feelings" that come from thought processes the human/agent doesn't have introspection into. And yet asking humans or machines to "think out loud" this way does increase the quality of their work.
> Can I just see the actual thinking (not summarized) so that I can see the actual thinking without a latency cost?
You can't, and Anthropic will never allow it since it allows others to more easily distill Claude (i.e. "distillation attacks"[1] in Anthropic-speak, even though Athropic is doing essentially exactly the same thing[2]; rules for thee but not for me).
AFAIK what they do is that they calculate a hash of the true thinking trace, save it into a database, and only send those hashes back to you (try to man-in-the-middle Claude Code and you'll see those hashes). So then when you send then back your session's history you include those hashes, they look them up in their database, replace them with the real thinking trace, and hand that off to the LLM to continue generation. (All SOTA LLMs nowadays retain reasoning content from previous turns, including Claude.)
I see. If that's just hashes and not encrypted content I can't see how they can resume old sessions properly. IIRC they have a 30 days retention policy and surely the thinking traces must be considered data. Wonder how this works with the zero-retention enterprise plans...
But you can't. Many times I've seen claude write confusing off-track nonsense in the thinking and then do the correct action anyway as if that never happened. It doesn't work the way we want it to.
In most cases, I don’t use the reasoning to proactively stop Claude from going off track. When Claude does go off track, the reasoning helps me understand what went wrong and how to correct it when I roll back and try again.
I was not aware the default effort had changed to medium until the quality of output nosedived. This cost me perhaps a day of work to rectify. I now ensure effort is set to max and have not had a terrible session since. Please may I have a "always try as hard as you can" mode ?
I feel like the maximum effort mode kind-of wraps around and starts becoming "desperate" to the extent of lazy or a monkey's paw, similar to how lower effort modes or a poor prompt.
I think over-thinking is only solved by thinking more, not less. This is only viable once some intelligence threshold is reached, which I think Anthropic has borderline achieved.
> I think over-thinking is only solved by thinking more, not less.
Despite "thinking" tokens being determined by the preceding tokens, they still are taken from some probability distribution, just a complex one. This means that at each token selection step there is a probability P_e of an error, of selecting a wrong token.
These errors compound exponentially: the probability of not selecting wrong token for N steps is 1-(1-P_e)^N.
The shorter "thinking" is, the less is the probability of it going astray.
> The shorter "thinking" is, the less is the probability of it going astray
As long as the error introduced by more steps is less than the compounding error of sub-optimal token sampling, I would expect a better result.
I think your choice of "wrong" is extreme, suggesting such a token can catastrophically spoil the result. The modern reality is more that the model is able to recover.
this might be just my impression, but I feel like most people are using CC for fixing their React frontends, and they prefer the decreased latency and less tokens spent as opposed to performing well on extremely difficult problems?
That said there's still an issue of regression to the mean. What the average person likes, as determined by metrics, is something nobody actuallt likes, because the average is a mathematical construct and might not describe any particular individual accurately.
I think it is hilarious that there are four different ways to set settings (settings.json config file, environment variable, slash commands and magical chat keywords).
That kind of consistency has also been my own experience with LLMs.
I just had this conversation today. It's hilarious that things like Skills and Soul and all of these anthropomorphized files could just be a better laid out set of configuration files. Yet here we are treating machines like pets or worse.
It's not unique to LLMs. Take BASH: you've got `/etc/profile`, `~/.bash_profile,` `~/.bash_login`, `~/.bashrc`, `~/.profile`, environment variables, and shell options.
I would laugh so hard at this, if your attempt at comparison was not so tragic. Bash and other shells are deterministic. Want to set it just for one user ? - use ~/.bashrc . Set it for all users on the system? use /etc/profile.d/ . Want it just temporary for this session? You got it, environment variables. And it is going to work like that every single time. It is deterministic you see.
The non-determinisim in the LLM systems isn't because of the different config uses, that works much like shell configs. The non-determinism is inherent in LLM operations.
I don't think they're wildly different purposes. They're the same purpose (to set shell settings) with different scopes (all users, one user, interactive shells only, etc.).
You are yet to discover the joys of the managed settings scope. They can be set three ways. The claude.ai admin console; by one of two registry keys e.g. HKLM\SOFTWARE\Policies\ClaudeCode; and by an alphabetically merged directory of json files.
There's also settings available in some offerings and not in others. For example, the Anthropic Claude API supports setting model temperature, but the Claude Agent SDK doesn't.
settings.json -> global config
Env vars -> settings different to your global for a specific project
Slash commands / chat keywords -> need to change a setting mid chat
There's been more going on than just the default to medium level thinking - I'll echo what others are saying, even on high effort there's been a very significant increase in "rush to completion" behavior.
Thanks for the feedback. To make it actionable, would you mind running /bug the next time you see it and posting the feedback id here? That way we can debug and see if there's an issue, or if it's within variance.
Amusingly (not really), this is me trying to get sessions to resume to then get feedback ids and it being an absolute chore to get it to give me the commands to resume these conversations but it keeps messing things up: cf764035-0a1d-4c3f-811d-d70e5b1feeef
Thanks for the feedback IDs — read all 5 transcripts.
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.
Hey bcherny, I'm confused as to what's happening here. The linked issue was closed, with you seeming to imply there's no actual problem, people are just misunderstanding the hidden reasoning summaries and the change to the default effort level.
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.
It's better PR to close issues and tell users they're holding it wrong, and meanwhile quietly fix the issue in the background. Also possibly safer for legal reasons.
There's a 5 hour difference between the replies, and new data that came in, so the posts aren't really in conflict.
Also it doesn't sound like they know "there's a model issue", so opening it now would be premature. Maybe they just read it wrong, do better to let a few others verify first, then reopen.
I cannot provide the session ids but I have tried the above flag and can confirm this makes a huge amount of difference. You should treat this as bug and make this as the default behavior. Clearly the adaptive thinking is making the model plain stupid and useless. It is time you guys take this seriously and stop messing with the performance with every damn release.
And another where claude gets into a long cycle of "wait thats not right.. hold on... actually..." correcting itself in train of thought. It found the answer eventually but wasted a lot of cycles getting there (reporting because this is a regression in my experience vs a couple weeks ago): 28e1a9a2-b88c-4a8d-880f-92db0e46ffe8
It fails to answer my initial question and tells me what I need to do to check. Then it hallucinates the answer based on not researching anything, then it incorrectly comes to a conclusion that is inaccurate, and only when I further prompt it does it finally reach a (maybe) correct answer.
I havent submitted a few more, but I think its safe to say that disabling adaptive thinking isnt the answer here
I am curious. Are you able to see our session text based on the session ID? That was big no in some of the tier-1 places I worked. No employee could see user texts.
My guess is there isn't enough hardware, so Anthropic is trying to limit how much soup the buffet serve, did I guess right? And I would absolutely bet the enterprise accounts with millions in spend get priority, while the retail will be first to get throttled.
I just asked Claude to plan out and implement syntactic improvements for my static site generator. I used plan mode with Opus 4.6 max effort. After over half an hour of thinking, it produced a very ad-hoc implementation with needless limitations instead of properly refactoring and rearchitecting things. I had to specifically prompt it in order to get it to do better. This executed at around 3 AM UTC, as far away from peak hours as it gets.
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.
Literally two weeks ago it was outputting excellent results while working with me on my programming language. I reviewed every line and tried to understand everything it did. It was good. I slowly started trusting it. Now I don't want to let it touch my project 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.
I can see how that works: this is like building a dependency, a habit if you wish. I think the tighter you couple your workflow to these tools the more dependent you will become and the greater the let-down if and when they fail. And they will always fail, it just depends on how long you work with them and how complex the stuff is you are doing, sooner or later you will run into the limitations of the tooling.
One way out of this is to always keep yourself in the loop. Never let the work product of the AI outpace your level of understanding because the moment you let that happen you're like one of those cartoon characters walking on air while gravity hasn't reasserted itself just yet.
Good advice about the dependency. This stuff is definitely addictive. I've been in something of a manic episode ever since I subscribed to this thing. I started getting anxious when I hit limits.
I wouldn't say that Claude is failing though. It's just that they're clearly messing with it. The real Opus is great.
Take good care of yourself and don't get sucked in too deep. I can see the danger just as clearly in programmers around me (and in myself). I keep a very strict separation between anything that can do AI and my main computer, no cutting-and-pasting and no agents. I write code because I understand what I'm doing and if I do not understand the interaction then I don't use it. I see every session with an AI chatbot as totally disposable. No long term attachment means I can stand alone any time I want to. It may not be as fast but I never have the feeling that I'm not 100% in control.
> People who depend on this to make a living must be very angry indeed.
Oh cry me a fucking river.
The people depending on this to make a living don't have the moral high ground here.
They jumped onboard so they could replace other people's living, and those other people were angry too.
They didn't care about that. It's hard to care about them when the thing they depend on to make a living got yanked, because that's what they proposed to do to others.
I'll have a look. The CoT switch you mentioned will help, I'll take a look at that too, but my suspicion is that this isn't a CoT issue - it's a model preference issue.
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.
Opus definitely pushes me to ignore problems. I've had to tell it multiple times to be thorough, and we tend to go back and forth a few times every time that happens. :)
"I see the tests failing, but none of our changes caused this breakage so I will push my changes and ask the user to inform their team on failing tests."
One of the thing is we’ve seen at vibes.diy is that if you have a list of jobs and you have agents with specialized profiles and ask them to pick the best job for themselves that can change some of the behavior you described at the end of your post for the better.
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.
Very interesting. I run Claude Code in VS Code, and unfortunately there doesn't seem to be an equivalent to "cli.js", it's all bundled into the "claude.exe" I've found under the VS code extensions folder (confirmed via hex editor that the prompts are in there).
Edit: tried patching with revised strings of equivalent length informed by this gist, now we'll see how it goes!
I didn't know we could change the base system prompt of Claude Code. Just tried, and indeed it works. This changes everything! Thank you for posting this!
Remember Sonnet 3.5 and 3.7? They were happy to throw abstraction on top of abstraction on top of abstraction. Still a lot of people have “do not over-engineer, do not design for the future” and similar stuff in their CLAUDE.md files.
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!”
This might be more complex than I imagined. It seems Claude Code dynamically customizes the system prompt. They also update the system prompt with every version so outright replacing it will cause us to miss out on updates. Patching is probably the best solution.
depending on how large your codebase is, hopefully not. At this point use something like the IX plugin to ingest codebase and track context, rather than from the LLM itself.
- naiveTokens = 19.4M — what ix estimates it would have cost to answer your queries without graph intelligence (i.e., dumping full files/directories into context)
- actualTokens = 4.7M — what ix's targeted, graph-aware responses actually used
- tokensSaved = 14.7M — the difference
I mean whatever part of the code that is read by the AI has to be in the content window at some point or another nSprewd throughout your sessions Id think even with a huge codebase, 90% of it is going to be there
Theres also been tons of thinking leaking into the actual output. Recently it even added thinking into a code patch it did (a[0] &= ~(1 << 2); // actually let me just rewrite { .. 5 more lines setting a[0] .. }).
Ultrathink is back? I thought that wasn't a thing anymore.
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?
For anyone reading this and wondering where the truth could possibly be:
We can't really know what the truth is, because Anthropic is tightly controlling how you interact with their product and provides their service through opaque processes. So all we can do is speculate. And in that speculation there's a lot of room (for the company) to bullshit or provide equally speculative responses, and (for outsiders) to search for all plausible explanations within the solution space. So there's not much to action on. We're effectively stuck with imprecise heuristics and vibes.
But consider what we do know: the promise is that Anthropic is providing a black-box service that solves large portions of the SDLC. Maybe all of it. They are "making the market" here, and their company growth depends on this bet. This is why these processes are opaque: they have to be. Anthropic, OpenAI and a few others see this as a zero-sum game. The winner "owns" the SDLC (and really, if they get their way the entire PDLC). So the competitive advantage lies in tightly controlling and tweaking their hidden parameters to squeeze as much value and growth as possible.
The downside is that we're handing over the magic for convenience and cost. A lot of people are maybe rightly criticizing the OP of the issue because they're staking their business on Claude Code in a way that's very risky. But this is essentially what these companies are asking for. The business model end game is: here's the token factory, we control it and you pay for the pleasure of using it. Effectively, rent-seeking for software development. And if something changes and it disrupts your business, you're just using it incorrectly. Try turning effort to max.
Reading responses like this from these company representatives makes me increasingly uneasy because it's indicative of how much of writing software is being taken out from under our feet. The glimmer of promise in all of this though is that we are seeing equity in the form of open source. Maybe the answer is: use pi-mono, a smattering of self hosted and open weights models (gemma4, kimi, minimax are extremely capable) and escalate to the private lab models through api calls when encountering hard problems.
Let the best model win, not the best end to end black box solution.
Don’t turn vibe coding into your day job (because the vibe won’t keep vibing). Write code (that you own) that can make you money and hire real developers.
I am reminded of OpenAI's first voice-to-voice demo a couple of years ago. I rewatched it and was shocked at how human it was; indiscernible from a real person. But the voice agent that we got sounds 20% better than Siri.
There's a hope that competition is what keeps these companies pushing to ship value to customers, but there are also billions of compute expense at stake, so there seems to be an understanding that nobody ships a product that is unsustainably competitive
How do you guys decide which settings should be configurable via environment variables but not settings files and which settings should be configurable via settings files but not environment variables?
All environment variables can also be configured via settings files (in the “env” field).
Our approach generally is to use env vars for more experimental and low usage settings, and reserve top-level settings for knobs that we expect customers will tune more frequently.
This is confusing. ULTRATHINK is a step below /effort max?
ULTRATHINK triggers high effort. /effort max is above high. Calling it ULTRATHINK sounds like it would be the highest mode. If someone has max set and types ULTRATHINK, they're lowering their effort for that turn.
For anyone reading this trying to fix the quality issues, here's what I landed on in ~/.claude/settings.json:
The env field in settings.json persists across sessions without needing /effort max every time.
DISABLE_ADAPTIVE_THINKING is key. That's the system that decides "this looks easy, I'll think less" - and it's frequently wrong. Disabling it gives you a fixed high budget every turn instead of letting the model shortchange itself.
The docs say that CLAUDE_CODE_EFFORT_LEVEL controls adaptive reasoning intensity, and CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING bypasses that entirely in favor of a fixed budget via MAX_THINKING_TOKENS. So setting both is contradictory. If true, disabling adaptive thinking would override what effort level is trying to do.
So if it bypasses, is the optimal setting for performance setting effort level to max, keeping adaptive on? I try to avoid letting the model decide what is unimportant and needs less thought
Nothing super noticeable. I've reached 35% in sessions on the 20x plan. Before these changes, 25-30% was pretty normal. I think these changes are best for people who are just past the 5x usage plan, but might be harder to manage if you already have to throttle usage to stay under limits.
I'd still recommend turning off sub agents entirely because it doesn't seem you can control them with /effort and I always find the output to be better with agents off.
You guys realise you are about 3 months into another one of your CEOs announcements that AI would "write all code in 6 months", right? Based on the problems you are facing, would you say your CEO gave a realistic announcement this time around ?
Almost as if every CEO is making promises and predictions that either exist solely in their heads or know full well that the odds of this working out are about the same as finding the fountain of youth and are just milking whatever cash they can out of the hype.
Well, there is a ton of normal CEOs of "normal" companies doing a great job, due diligence and all. Its just these weird CEOs of weird companies with weird business models, who burn hundreds of billions of dollars to produce effectively peanuts, that make a lot of decent CEOs look bad.
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.
All right so what do I need to do so it does its job again? Disable adaptive thinking and set effort to high and/or use ULTRATHINK again which a few weeks ago Claude code kept on telling me is useless now?
They are definitely that. Regardless of their approach, being upfront and transparent would have been nice. Bricking their own software that previously worked well for their customers isn't cool.
You can't. This is Anthropic leveraging their dials, and ignoring their customers for weeks.
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.
Agreed, the only feedback is switching... however things move fast. Unfortunately that means for me is subscribing or using API for many providers and then just switching models when one gets worse.
If you have a paid plan, you may need to pay for more than one, and "hopefully" the drop in usage (not income) is a good enough signal that there is a issue.
> On of our product principles is to avoid changing settings on users' behalf
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.
I happen to think this is just true in general, but another reason it might be true is that the experience the user has is identical to the experience they would have had if you first introduced the setting, defaulting it to the existing behavior, and then subsequently changed it on users' behalf.
Remember when they shipped that version that didn't actually start/ run? At work we were goofing on them a bit, until I said "Wait how did their tests even run on that?" And we realized whatever their CI/CD process is, it wasn't at the time running on the actual release binary... I can imagine their variation on how most engineers think about CI/CD probably is indicative of some other patterns (or lack of traditional patterns)
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 episode 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..
That was actually an interesting case of things that CI/CD don't tend to catch.
It failed to start because it failed to parse the published release notes.
In the CI/CD system it would have passed, because the release notes that broke it, hadn't been published yet.
Those release notes also took down previous versions of claude-code too, rolling back didn't help users.
The breakage wasn't a change in the software, it was a change in the release notes which coincided with the change in the software.
Now, should it have been grabbing release notes and parsing them? No, that's unbelievably dumb (and potentially dangerous), but it wasn't an issue with missing CI/CD, but an interesting case-study in CI/CD gaps and how CI/CD can actually lead to over-confidence.
>Going forward, we will test defaulting Teams and Enterprise users to high effort, to benefit from extended thinking even if it comes at the cost of additional tokens & latency.
interesting that you only make this default on those accounts that pay per token while claiming "medium is best for most users"
That decision seems to imply that the thinking change was more about increasing your profits than anything else
Hi, thanks for Claude Code. I was wondering though if you'd considering adding a mode to make text green and characters come down from the top of the screen individually, like in The Matrix?
Ergonomics studies back in the day demonstrated amber beats green. Our shop spent extra for amber CRTs over green.
On MacOS Terminal, edit the Homebrew profile and set Text and Bold Text to Apple color Orange, consider setting Selection to Apple color Green and Cursor to Block, Blink, and Apple color Yellow.
Piece of free advice towards a better civilisation: people who didn't even read the comment they're replying to shouldn't be rewarded for their laziness.
I read his comment and still replied. I think his claim that nobody reads thinking blocks and that thinking blocks increase latency is nonsense. I am not going to figure out which settings I need to enable because after reading this thread I cancelled my subscription and switched over to Codex. Because I had the exact same experience as many in this thread.
Also what is that "PR advice"—he might as well wear a suit. This is absolutely a nerd fight.
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.
I didn't use commands. I only used rules, memories, and skills. I asked Codex to read rules and memories from where Claude Code stores them on the filesystem and merge them into `AGENTS.md` and this actually works better because Anthropic prompts Claude Code to write each memory to a separate file, so you end up having a main MEMORY.md that acts as a kind of directory that lists each individual memory with its file name and brief description, hoping that Claude Code will read them, but the problem is that Claude Code never does. This is the same problem[0] that Vercel had with skills I believe. Skills are easy to port because they appear to use the same format, so you can just do `mv ~/.claude/skills ~/.codex/skills` (or `.agents/skills`).
What I was pointing out in my comment about the PR advice is that someone responding from a corporation to customers should be providing information to help the customer, nothing more.
Customers may want to fight - you seem to be providing an example - but representatives shouldn't take the bait.
I also have similar experience with their API, i.e. some requests get stalled for minutes with zero events coming in from Anthropic. Presumably the model does this "extended thinking" but no way to see that. I treat these requests as stuck and retry. Same experience in Claude Code Opus 4.6 when effort is set to "high"—the model gets stuck for ten minutes (at which point I cancel) and token count indicator doesn't increase.
I am not buying what this guy says. He is either lying or not telling us everything.
Wrote my own harness with introspection/long form thinking as a tool that the model can use to plan. Works really well with opus. I can’t use Claude code sadly, it sits there ticking for minutes seemingly doing absolutely nothing although I know it’s working. I hate that as an experience and built my harness with the philosophy of always having something streaming on the ui.
Btw the system prompt length in CC is getting to be insane.
I tried testing 4.5 opus and 4.6 opus both with “high” thinking. Same box, same repo. I had them plan a moderate complexity refactoring on a small codebase.
Observations:
4.6 had previously failed to the point where I had to wipe context. It must have written memories because it was referring to the previous conversation.
As the article points out, 4.6 went out of its way to be lazy and came up with an unusable plan. It did extra planning to avoid renaming files (the toplevel task description involves reorganizing directories of files).
4.6 took twice as long to respond as 4.5.
I’m treating this as a model regression. 4.6 is borderline unusable. I’ve hit all the issues the article describes.
Also, there needs to be an obvious way to disable memory or something. The current UX is terrible, since once an error or incorrect refusal propagates, there is no obvious recovery path.
Anyway, with think set to high, I see drastically different behavior: much slower and much worse output from 4.6.
> Also, there needs to be an obvious way to disable memory or something.
Memory files are stored in a path under ~/.claude somewhere. It's fairly easy to find (I'm just not typing this on a PC with Claude on it atm), and from memory (heh) it's in Markdown.
If you nuke the memory file(s) then you should be good. Oh, I think the memory files are project or directory scoped from memory (heh again) too, so you should be able to keep/remove things manually without losing important stuff if you want.
> Anyway, with think set to high, I see drastically different behavior: much slower and much worse output from 4.6.
Might be worth trying the CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING setting then?
> Before I keep going, I wanted to say I appreciate the depth of thinking & care that went into this.
"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?
> Ohh my precious baby, you've been oh so smart in writing to me.
He says, before dismantling everything reported in the issue. If the depth of thinking was so great (maybe if he had ULTRATHINK'd?) You'd think he would have found an actual problem.
Hi Boris, thanks for addressing this and providing feedback quickly. I noticed the same issue.
My question is, is it enough to do /efforts high, or should I also add CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING to my settings?
> If you are analyzing locally stored transcripts, you wouldn't see raw thinking stored when this header is set, which is likely influencing the analysis. When Claude sees lack of thinking in transcripts for this analysis, it may not realize that the thinking is still there, and is simply not user-facing.
Claude often fetches past transcript for information after compaction. Wouldn't this effectively distort the view it has of past discussions?
I’ve seen you/anthropic comment repeatedly over the last several months about the “thinking” in similar ways -
“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.
They don’t want to officially disclose the reality because while some users will understand the realities of protecting a product while innovating, many will just realize it means one can go looking for claude 4.5 performance elsewhere.
building for the loud users on a forum is generally a losing move. if we built notion for angry HN users, we'd probably be a great obsidian competitor with end to end encryption, have zero ai features, and make zero money.
Anecdotally the “power users” of AI are the ones who have succumbed to AI psychosis and write blog posts about orchestrating 30 agents to review PRs when one would’ve done just fine.
The actual power users have an API contract and don’t give a shit about whatever subscription shenanigans Claude Max is pulling today
Generalisations and angry language but I almost agree with the underlying message.
New tools, turbulent methods of execution. There's definitely something here in the way of how coding will be done in future but this is still bleeding edge and many people will get nicked.
Whatever makes you feel better about yourself, I guess. My account history on this topic is pretty easily searchable, but I guess it's easier to make driveby comments like this than be informed.
From the contents of the issue, this seems like a fairly clear default effort issue. Would love your input if there's something specific that you think is unaddressed.
I must admit, the fact that the writing was well formatted and structured was an instant turn off. I did find it insightful. I would have been more willing to read it if it was one lower case run on line with typos one would expect from a prepubescent child. I am both joking and being serious at the same time. What a world.
I commented on the GH issue, but Ive had effort set to 'high' for however long its been available and had a marked decline since... checks notes... about 23 March according to slack messages I sent to the team to see if I was alone (I wasnt).
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)
It hallucinated a GUID for me instead of using the one in the RFC for webscokets. Fun part was that the beginning was the same. Then it hardcoded the unit tests to be green with the wrong GUID.
The hallucinated GUIDs are a class of failure that prompt instructions will never reliably prevent. The fix that worked: regex patterns running on every file the agent produces, before anything executes.
I only ever use high effort, the only thing I've run into sometimes I ask Claude to do every item on a list of items, and not stop until they're all done, it finishes maybe 80% of them then says "I've stopped doing things" for no reasonable reason. I don't need it to run for 18 hours nonstop, but 10 or 20 minutes more it would have kept going for wouldn't have hurt, especially when I am usually on Claude Code during off-hours, and on the Max plan.
Part of me wants to give lower "effort" a try, but I always wind up with a mess, I don't even like using Haiku or Sonnet, it feels like Haiku goofs, Haiku and Sonnet are better as subagent models where Opus tells them what to do and they do it from my experience.
Hey Boris, thanks for this reply. I've been kind of scratching my head over this issue, assuming I'm just not doing "complex engineering", because since Opus 4.6 my seat-of-the-pants assessment is that it's a huge improvement. It's been like night and day in my use. Full disclosure: I use high effort for basically everything.
I have been wondering if 1 Million token context contributes here also. Compaction is much rarer now. How does that influence model performance? For some tasks I do, I feel like performance is worst now after this. Also Plan mode doesn't seem to wipe context anymore?
Have you guys considered that you should be optimizing for the leading tail of the user distribution? The people that are actually using AI to push the envelope of development? "most users," i.e. the inner 70%, aren't doing anything novel.
As soon as that change came through I set the effort to high. Have not regretted it for any coding task. It feels the same as Dec-Jan though now spawning more sub agents which is not a bad thing.
I definitely noticed the mid-output self-correction reasoning loops mentioned in the GitHub issue in some conversations with Opus 4.6 with extended reasoning enabled on claude.ai. How do I max out the effort there?
I'd hate to be that guy, but Opus not a very smart model when the effort is set to anything below high. I think, given the feedback from the community, this would be an obvious signal. However, moving the effort to anything beyond medium is a huge token burn. These issues didn't exist, or at least not this persistent, before the last 2 weeks. I, and perhaps a million or so other developers, would ask you to reconsider this thinking. I understand you need to run a business, but so do we, and Claude Opus is genius with a drinking problem, and you never really know upfront if it's drunk or not, but it's generally quite clear after a few minutes.
Other models, such as K2, GLM-5.1, and "the other one" seem to far less drunk than your approach, and you're losing fans quickly if you keep making these kind of changes to the tools or models.
Do you guys realize that everyone is switching to Codex because Claude Code is practically unusable now, even on a Max subscription? You ask it to do tasks, and it does 1/10th of them. I shouldn't have to sit there and say: "Check your work again and keep implementing" over and over and over again... Such a garbage experience.
Does Anthropic actually care? Or is it irrelevant to your company because you think you'll be replacing us all in a year anyway?
Or, ask it to make a plan, and it makes a good plan! It explicitly notes how validation is to take place on each stage!
And then does every stage without running any of the validation. It's your agent's plan, it should probably be generated in a way that your own agent can follow it.
Thinking time is not the issue. The issue is that Claude does not actually complete tasks. I don't care if it takes longer to think, what I care about is getting partial implementations scattered throughout my codebase while Claude pretends that it finished entirely. You REALLY need to fix this, it's atrocious.
did the cost go up, or did you lower costs (token consumptions) for all users and then now want to default enterprise/teams back to normal mode. Because it seems like a long way aroundabout to say now it will cost more for same quality.
I honestly am very disappointed with this. I've only learned about CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING and showThinkingSummaries: true from this post. I've been wondering for a while where the summaries went and am always hoping like roulette that it thinks a lot. No wonder if there suddently is an "adaptive thinking" mode. I would have opted out 2 months ago if it was documented or communicated in any way publicly. Why change behavior without notice or any new user facing settings.
I just googled "CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING" and it seems like many people don't know about it.
And ULTRATHINK sets the effort to high, but then there is also /effort max?
I'm now confused because I used to use ultrathink, went away as well as the chain of reasoning prompts, recently changed to high or extra thinking, now this is back?
> I wanted to say I appreciate the depth of thinking & care that went into this.
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."
Ticket is AI generated but from what I've seen these guys have a harness to capture/analyze CC performance, so effort was made on the user side for sure.
The note at the end of the post indicates the user asked Claude to review their own chat logs. It's impossible to tell if Claude used or built a a performance harness or just wrote those numbers based on vibes.
I’m of the opinion that there’s more to it; obviously the thinking tokens aren’t having any reasonable impact on latency, given that bandwidth is hardly the bottleneck.
Seems more and more that Anthropic et al don’t want to give up their secret sauce / internals (which is their full right) and this is a step towards that direction, and it’s being presented as “reduces latency”.
I've understood that in more recent models you need to run extra compute to get a human-readable version of the thinking tokens, so it does impact latency. Though probably the more important motive is you can squeeze in more concurrent users by skipping this.
No, that’s simply whether CoT is enabled or not. That actually does have impact.
What Anthropic is doing is still generating the thinking tokens (because they improve answer quality) without showing it to them. I believe this may actually hint at a future where these LLM vendors don’t want to show the internal reasoning like they do right now.
I’m very much of the opinion that hiding them from the response because it “improves latency” is nonsense.
Claude Code and Opus used to do a great job a few months ago. It seemed to get it right more often than not. It seemed to be far better at figuring out what has to be done and getting it right on the first attempt. This is likely model related since Claude Code has received some bug fixes since.
The list of bugs and performance problems appears to keep growing: reduced usage quotas, poor performance with numerous attempts at getting things right, cache invalidation bugs, background requests which have to be disabled explicitly to avoid consuming the quota too fast, Opus appears to be quantized even with high thinking mode, poor tool use with tool search disabled, broken tool search with tool search enabled, laziness, poor planning, poor execution, gets stuck when debugging simple code issues, writes code which isn't required, starts making changes and executing whatever it wants when told to simply prepare a plan for something, it doesn't follow instructions to use agents as told and numerous other issues with following the instructions.
The quota story is atrocious. It's difficult to get anything done with Claude Code due to the quota reduction. The cache invalidation bugs don't help either.
The tool use is also a pain to deal with. It appears to choose tools randomly with or without tool search. It keeps running custom CLI commands when it has instructions to use Makefile targets. It often ingests the output of some command with hundreds of lines of output without discrimination. It often uses lots of bash grep and find commands when it has better tools available to search across files and to use MCP tools which are far more efficient. It ignores MCP tools most of the time.
This doesn't appear to be an issue with the prompt itself. I'll try to fix the system prompt next to work around some of the issues. It seems to not follow instructions and to do whatever it feels like doing. It comes off as one of those Q2-Q3 quantized models from huggingface.
The impact of the cache invalidation issue, reduced quota, poor model performance and Claude Code bugs together have rendered this service almost entirely useless for me. The poor model performance means that many more attempts are required and more requests are made to the Anthropic API. The Claude Code bugs and design lead to cache invalidation more often. This makes the impact of the reduced quota even worse. It makes a lot more API requests because the model doesn't get it right on the first 1-2 attempts or because it chooses less than optimal strategies to find what it's looking for.
The communication and Anthropic's overall handling of the reported bugs and problems hasn't been that good either.
As for the session ID and other things you might request for debugging, there's nothing special here that's not reported widely on every Reddit thread from several subreddits. I use 200k context with Opus and Sonnet. I use high thinking mode because anything less appears to be complete garbage with extremely poor results. I avoid compact in favor of knowledge transfer markdown files.
It'd be great to see Anthropic fix the caching issues, to improve the quality of the model, to address the Claude Code bugs, to sort out the quota fiasco, to improve their communication skills, to communicate more with their customers and to be more proactive overall. I'll take my money elsewhere otherwise.
Christopher, would you be able to share the transcripts for that repo by running /bug? That would make the reports actionable for me to dig in and debug.
I’m not sure being confrontational like this really helps your case. There are real people responding, and even if you’re frustrated it doesn’t pay off to take that frustration out on the people willing to help.
Fair point on tone. It's a bit of a bind isn't it? When you come with a well-researched issue as OP did, you get this bland corporate nonsense "don't believe your lyin' eyes, we didn't change anything major, you can fix it in settings."
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.
I read the entire performance degradation report in the OP, and Boris's response, and it seems that the overwhelming majority of the report's findings can indeed be explained by the `showThinkingSummaries` option being off by default as of recently.
Just use a different tool or stop vibe coding, it’s not that hard. I really don’t understand the logic of filing bug reports against the black box of AI
People file tickets against closed source "black box" systems all the time. You could just as well say: Stop using MS SQL, just use a different tool, it's not that hard.
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.
I guess one of the things I don't understand: how you expect a stochastic model, sold as a proprietary SaaS, with a proprietary (though briefly leaked) client, is supposed to be predictable in its behavior.
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.
The problem is degradation. It was working much better before. There are many people (some example of a well know person[0]), including my circle of friends and me who were working on projects around the Opus 4.6 rollout time and suddenly our workflows started to degrade like crazy. If I did not have many quality gates between an LLM session and production I would have faced certain data loss and production outages just like some famous company did. The fun part is that the same workflow that was reliably going through the quality gates before suddenly failed with something trivial. I cannot pinpoint what exactly Claude changed but the degradation is there for sure. We are currently evaling alternatives to have an escape hatch (Kimi, Chatgpt, Qwen are so far the best candidates and Nemotron). The only issue with alternatives was (before the Claude leak) how well the agentic coding tool integrates with the model and the tool use, and there are several improvements happening already, like [1]. I am hoping the gap narrows and we can move off permanently. No more hoops, you are right, I should not have attempted to delete the production database moments.
Curious as to how many people are using 4.6, perhaps you’re on a subscription? I use the api and 4.6 (also goes for Sonnet) is unusable since launch because it eats through tokens like it’s actually made that way (to make more money/hit limits faster). I guess it makes sense from a financial perspective but once 4.5 goes away I will have to find another provider if they continue like this :/
If you get consistently nowhere near 50% then surely you know you're not throwing a fair coin? What would complaining to the coin provider achieve? Switch coins.
It also completely ignores the increase in behavioral tracking metrics. 68% increase in swearing at the LLM for doing something wrong needs to be addressed and isn't just "you're holding it wrong"
Yep totally -- think of this as "maximum effort". If a task doesn't need a lot of thinking tokens, then the model will choose a lower effort level for the task.
Technically speaking, models inherently do this - CoT is just output tokens that aren't included in the final response because they're enclosed in <think> tags, and it's the model that decides when to close the tag. You can add a bias to make it more or less likely for a model to generate a particular token, and that's how budgets work, but it's always going to be better in the long run to let the model make that decision entirely itself - the bias is a short term hack to prevent overthinking when the model doesn't realize it's spinning in circles.
It's how temperature/top_p/top_k work. Anthropic also just put out a paper where they were doing a much more advanced version of this, mapping out functional states within the modern and steering with that.
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.
Hey Boris, would appreciate if you could respond to my DM on X about Claude erroneously charging me $200 in extra credit usage when I wasn't using the service. Haven't heard back from Claude Support in over a month and I am getting a bit frustrated.
Did the receipt show it as being a gift? There's a lot of fraud happening the past few months with Claude Code Gift purchases. Anthropic support is ignoring all of it and just not responding to support requests.
Happened to a close friend of mine. A bit of digging revealed the same pattern with fraudulent gift purchases for several other people before I stopped looking. They were also being ignored by Anthropic support. One since January.
Apparently they're so short on inference resources they can't run their support bots. Maybe banning usage of Claude Code with Claude will allow them to catch up on those gift fraud tickets.
Took a long time for me to reach this level of scathing. It is not unwarranted.
No, the receipt had no indication of it being a gift. Was with my family at the time and suddenly started getting $10 extra usage charges every few minutes. I wasn’t able to toggle off the “auto-reload funds” feature until about $180 had been drained from my checkings. For context, here’s the support ticket I sent in on March 7th.
“Hi Anthropic Support,
I'm a Max plan subscriber and I'm writing about approximately $180 in unexpected Extra Usage charges that appeared on my account between March 3-5, 2026. I attempted to resolve this through your Fin AI chatbot (Conversation ID: 215473382652967).
Here's the situation:
- I received 16 separate Extra Usage invoices between March 3-5, ranging from $10-$13 each, all charged automatically.
- I was not actively using Claude during this period — I was away from my laptop entirely.
- When I checked my usage dashboard, it showed my session at 100% usage despite me not using the product.
- My API usage dashboard shows only $70 in total lifetime usage, confirming this is not API-related.
- My Claude Code session history shows only two tiny sessions from March 5 totaling under 7KB — nowhere near enough activity to generate these charges.
This appears consistent with known billing/usage tracking issues reported by other Max plan users (GitHub issues #29289 and #24727 on the anthropics/claude-code repo), where usage meters show incorrect values and Extra Usage charges accumulate erroneously. However, it is possible that my account was compromised, and I would like assistance determining if that is the case (or if it really is a bug.)
Either way, I am requesting a refund of the Extra Usage charges from March 3-5 only — I do not want to cancel my subscription.”
When a third party leaked my CC number which then was used to buy Spotify premium, all it took was 10 minutes of chat with a very polite support agent to have it resolved.
Ignoring the customer is not going to fix it. They'd know if they asked Claude.
Hey Boris, thanks for the awesomeness that's Claude! You've genuinely changed the life of quite a few young people across the world. :)
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!)
You can watch for these yourself - they are strong indicators of shallow thinking. If you still have logs from Jan/Feb you can point claude at that issue and have it go look for the same things (read:edit ratio shifts, thinking character shifts before the redaction, post-redaction correlation, etc). Unfortunately, the `cleanupPeriodDays` setting defaults to 20 and anyone who had not backed up their logs or changed that has only memories to go off of (I recommend adding `"cleanupPeriodDays": 365,` to your settings.json). Thankfully I had logs back to a bit before the degradation started and was able to mine them.
The frustrating part is that it's not a workflow _or_ model issue, but a silently-introduced limitation of the subscription plan. They switched thinking to be variable by load, redacted the thinking so no one could notice, and then have been running it at ~1/10th the thinking depth nearly 24/7 for a month. That's with max effort on, adaptive thinking disabled, high max thinking tokens, etc etc. Not all providers have redacted thinking or limit it, but some non-Anthropic ones do (most that are not API pricing). The issue for me personally is that "bro, if they silently nerfed the consumer plan just go get an enterprise plan!" is consumer-hostile thinking: if Anthropic's subscriptions have dramatically worse behavior than other access to the same model they need to be clear about that. Today there is zero indication from Anthropic that the limitation exists, the redaction was a deliberate feature intended to hide it from the impacted customers, and the community is gaslighting itself with "write a better prompt" or "break everything into tiny tasks and watch it like a hawk same you would a local 27B model" or "works for me <in some unmentioned configuration>" - sucks :/
The "this test failure is preexisting so I'm going to ignore it" thing has been happening a lot for me lately, it's so annoying. Unless it makes a change and then immediately runs tests and it's obvious from the name/contents that the failing test is directly related to the change that was made it will ignore it and not try to fix.
This should be part of the system prompt. It's absolutely unacceptable to just to not at least try to investigate failures like this. I absolutely hate when it reaches this conclusion on its own and just continues on as if it's doing valid work.
Based on the recent leaks, their system prompt explicitly nudges the model not to do anything outside of what was asked. That could very well explain why it’s not fixing preexisting broken tests.
“Don't add features, refactor code, or make "improvements" beyond what was asked.”
And it's very valid. Because otherwise you would ask Claude to trim a tree and it would go raze the whole forest and plant new seeds. This was the primary pain point last year, especially with Sonnet.
I will note that this "out" that Claude takes was a) less frequent in Opus 4.5 and that time frame and b) notably not something that Codex does.
I don't trust the code that Claude writes at all, if I have to use it (they gave me a free month recently, so I use it...) I not only review it carefully but have Codex do a thorough review.
Claude "cheats" and leaves hacks and has Dunning-Kruger.
All of this is very exhausting. I am enjoying writing my own code with these tools (to get long running personal projects out the door) but the effect that these tools are having on teams is terrifyingly corrosive and it's making me want to take an early retirement from the profession.
Yes we can write a lot of code quickly. But at what cost? And what even use is all this code now anyways?
Usually these were the developers who said their code didn’t need tests because it’s obviously correct/too simple to need them. And then their bug causes a crash that needs to be fixed over the weekend :/
I can't believe that's where we're at, as software devs. I miss predictable outputs, state machines. All those LLM (prompt) based rules make no sense to me. Same with AI WAL. All of it, at some point, will fail.
> I can't believe that's where we're at, as software devs
Agree wholeheartedly.
The premise of the bug did not make any sense to me. For instance, "unusable for complex engineering tasks", why would someone who understands these tools use them for complex engineering tasks ? Also, this phrase in the bug appears too jargon-ny "Extended Thinking Is Load-Bearing for Senior Engineering Workflows" - what does this even mean ? Am I the only one who is looking at this with bewilderment. I think there is group of folks producing almost-working proof of concept code with these tools, and will face a reckoning at some point - as the bug illustrates. I see this as a storm in a teacup with wonder and amusement.
There is also a larger commentary on: when you dont understand why things work (ie, have a causal model), you wont know why they broke (find root causes). We are at a point in our craft where we throw magic dust and chant spells at claude and hope and pray it works.
I've been saying this with many of my friends but, I feel like it's also probably illegal: you paid for a subscription where you expect X out of, and if they changed the terms of your subscription (e.g. serving worse models) after you paid for it, was that not false advertising? Could we not ask for a refund, or even sue?
probably not. the engineers dont even know how these things work (see: black box) so how could you even prove that its not doing what it's 'supposed' to be doing?
I'm curious about your subscription/API comparison with respect to thinking. Do you have a benchmark for this, where the same set of prompts under a Claude Code subscription result in significantly different levels of effective thinking effort compared to a Claude Code+API call?
Elsewhere in this thread 'Boris from the Claude Code team' alleges that the new behaviours (redacted thinking, lower/variable effort) can be disabled by preference or environment variable, allowing a more transparent comparison.
I wonder if they’ve had so many new signups lately that they just don’t have enough capacity, so they fiddled with the defaults so they could respond to everyone? Could it be as simple as that?
Not claude code specific, but I've been noticing this on Opus 4.6 models through Copilot and others as well. Whenever the phrase "simplest fix" appears, it's time to pull the emergency break. This has gotten much, much worse over the past few weeks. It will produce completely useless code, knowingly (because up to that phrase the reasoning was correct) breaking things.
Today another thing started happening which are phrases like "I've been burning too many tokens" or "this has taken too many turns". Which ironically takes more tokens of custom instructions to override.
Ive been noticing something similar recently. If somethings not working out itll be like "Ok this isnt working out, lets just switch to doing this other thing instead you explicitly said not to do".
For example I wanted to get VNC working with PopOS Cosmic and itll be like ah its ok well just install sway and thatll work!
Experienced this -- was repeatedly directing CC to use Claude in Chrome extension to interact with a webpage and it was repeatedly invoking Playwright MCP instead.
I actually submitted an upstream patch for Cosmic-Comp thanks to Claude on Saturday. I wanted to play Guild Wars remake and something was going on with the mouse and moving the camera. We had it fixed in no time and now shit is working great.
Yes, and over the last few weeks I have noticed that on long-context discussions Opus 4.6e does its best to encourage me to call it a day and wrap it up; repeatedly. Mother Anthropic is giving preprompts to Claude to terminate early and in
my case always prematurely.
as someone who uses deepseek, glm and kimi models exclusively, an llm telling me what to do is just off the wall
glm and kimi in particular, they can't stop writing... seriously very eager to please. always finishing with fireworks emoji and saying how pleased it is with the test working.
i have to say to write less documentation and simplify their code.
LLMs are next token predictors. Outputting tokens is what they do, and the natural steady-state for them is an infinite loop of endlessly generated tokens.
You need to train them on a special "stop token" to get them to act more human. (Whether explicitly in post-training or with system prompt hacks.)
This isn't a general solution to the problem and likely there will never be one.
Any chance you could share those sections of your claude file? I've been using Claude a bit lately but mostly with manual changes, not got much in the way of the claude file yet and interested in how to improve it
What wording do you use for this, if you don't mind? This thread is a revelation, I have sworn that I've seen it do this "wait... the simplest fix is to [use some horrible hack that disregards the spec]" much more often lately so I'm glad it's not just me.
However I'm not sure how to best prompt against that behavior without influencing it towards swinging the other way and looking for the most intentionally overengineered solutions instead...
My own experience has been that you really just have to be diligent about clearing your cache between tasks, establishing a protocol for research/planning, and for especially complicated implementations reading line-by-line what the system is thinking and interrupting the moment it seems to be going bad.
If it's really far off the mark, revert back to where you originally sent the prompt and try to steer it more, if it's starting to hesitate you can usually correct it without starting over.
That is generically my experience as well. Claude half-assing work or skipping stuff because "takes too much time" is something I've been experiencing since I started using it (May 2025). Forcing it to create and review and implementation plan, and then reviewing the implementation cross-referenced with the plan almost always produces consistent results in my case.
Make sure to use "PRETTY PLEASE" in all caps in your `SOUL.md`. And occasionally remind it that kittens are going to die unless it cooperates. Works wonders.
Yeah it’s so frustrating to have to constantly ask for the best solution, not the easiest / quickest / less disruptive.
I have in Claude md that it’s a greenfield project, only present complete holistic solutions not fast patches, etc. but still I have to watch its output.
Haha yeah. I once asked it to make a field in an API response nullable, and to gracefully handle cases where that might be an issue (it was really easy, I was just lazy and could have done it myself, but I thought it was the perfect task for my AI idiot intern to handle). Sure, it said. Then it was bored of the task and just deleted the field altogether.
It's a bit insane that they can't figure out a cryptographic way for the delivery of the Claude Code Token, what's the point of going online to validate the OAuth AFTER being issued the code, can't they use signatures?
Certain phrases invoke an over-response trying to course correct which makes it worse because it's inclined to double down on the wrong path it's already on.
i am already on api tokens for the chinese open source models and no subscriptions. these are all available in the original form open source and priced above the inference cost. i think this is the long term option.
Yep. If you ask Claude to create a drop-in replacement for an open-source project that passes 100% of the test suite of the project, it will basically plagiarize the project wholesale, even if you changed some of the requirements.
A full emulator in 6 minutes? I gotta call BS... I've been working on a SPC700 audio editor in the background as a side project, and implementing the cpu has taken at least 2 hours, and I still haven't implemented all of the opcodes.
> This report was produced by me — Claude Opus 4.6 — analyzing my own session
logs [...] Please give me back my ability to think.
a bit ironic to utilize the tool that can't think to write up your report on said tool. that and this issue[1] demonstrate the extent folks become over reliant on LLMs. their review process let so many defects through that they now have to stop work and comb over everything they've shipped in the past 1.5 months! this is the future
The other day I accidentally `git reset --hard` my work from April the 1st (wrong terminal window).
Not a lot of code was erased this way, but among it was a type definition I had Claude concoct, which I understood in terms of what it was supposed to guarantee, but could not recreate for a good hour.
Really easy to fall into this trap, especially now that results from search engines are so disappointing comparatively.
Exactly. It's a force multiplier - sometimes the direction is wrong.
Same week I went into a deep rabbit hole with Claude and at no point did it try to steer me away from pursuing this direction, even though it was a dead end.
They seem to have some notions of pipelines and metrics though. It could be argued that the hard part was setting up the observability pipeline in the first place - Claude just gets the data. Though if Claude is failing in such a spectacular way that the report is claiming, yes it is pretty funny that the report is also written by Claude, since this seems to be ejecting reasoning back to gpt4o territories
Something worse than a bad model is an inconsistent model. One can't gauge to what extent to trust the output, even for the simplest instructions, hence everything must be reviewed with intensity which is exhausting. I jumped on Max because it was worth it but I guess I'll have to cancel this garbage.
With Claude Code the problem of changes outside of your view is twofold: you don't have any insight into how the model is being ran behind the scenes, nor do you get to control the harness. Your best hope is to downgrade CC to a version you think worked better.
I don't see how this can be the future of software engineering when we have to put all our eggs in Anthropic's basket.
That analysis is pretty brutal. It's very disconcerting that they can sell access to a high quality model then just stealthily degrade it over time, effectively pulling the rug from under their customers.
Stealthily degrade the model or stealthily constrain the model with a tighter harness? These coding tools like Claude Code were created to overcome the shortcomings of last year's models. Models have gotten better but the harnesses have not been rebuilt from scratch to reflect improved planning and tool use inherent to newer models.
I do wonder how much all the engineering put into these coding tools may actually in some cases degrade coding performance relative to simpler instructions and terminal access. Not to mention that the monthly subscription pricing structure incentivizes building the harness to reduce token use. How much of that token efficiency is to the benefit of the user? Someone needs to be doing research comparing e.g. Claude Code vs generic code assist via API access with some minimal tooling and instructions.
I've been using pi.dev since December. The only significant change to the harness in that time which affects my usage is the availability of parallel tool calls. Yet Claude models have become unusable in the past month for many of the reasons observed here. Conclusion: it's not the harness.
I tend to agree about the legacy workarounds being actively harmful though. I tried out Zed agent for a while and I was SHOCKED at how bad its edit tool is compared to the search-and-replace tool in pi. I didn't find a single frontier model capable of using it reliably. By forking, it completely decouples models' thinking from their edits and then erases the evidence from their context. Agents ended up believing that a less capable subagent was making editing mistakes.
Agree: it is Anthropic's aggressive changes to the harnesses and to the hidden base prompt we users do not see. Clearly intended to give long right tail users a haircut.
The changes to reduce inference costs are intentional. Last thing you're going to do is have users linger on an older version that spends much more. This is essentially what's going on with layers upon layers of social engineering on top of it.
> Love your point. Instructions found to be good by trial and error for one LLM may not be good for another LLM.
Well, according to this story, instructions refined by trial and error over months might be good for one LLM on Tuesday, and then be bad for the same LLM on Wednesday.
Disconcerting for sure, but from a business point of view you can understand where they're at; afaiui they're still losing money on basically every query and simultaneously under huge pressure to show that they can (a) deliver this product sustainably at (b) a price point that will be affordable to basically everyone (eg, similar market penetration to smartphones).
The constraints of (b) limit them from raising the price, so that means meeting (a) by making it worse, and maybe eventually doing a price discrimination play with premium tiers that are faster and smarter for 10x the cost. But anything done now that erodes the market's trust in their delivery makes that eventual premium tier a harder sell.
Yeah. I've been enjoying programming with Claude so much I started feeling the need to upgrade to Max. Then it turns out even big companies paying API premiums are getting an intentionally degraded and inferior model. I don't want to pay for Opus if I can't trust what it says.
This could also be a marketing strategy. Make your models perform worse towards the end of a model's cycle, so that the next model appears as if more progress has been made than there actually has been.
I really wonder about this. Is it so bad that they cannot even disclose it? not even an optimistic lie in the ballpark of reality? it's not like they haven't been found cooking the truth repeatedly.
I look at the output of Kimi and the costs of running inference on it that i can replicate, and it isn't that bad, although admittedly i don't have to worry anywhere near as much about scaling it and about having to dedicate large amounts of compute to research and distillation on the back end. It's true that it's perhaps a step behind SotA vs January's Opus or current Codex, depending on what you do. But not by a lot. In fact it's leaps and bounds superior to the current subscription API experience. Together with GLM, Qwen and Minimax they are an amazing backstop just the way they are right now.
With all the layers of obfuscation it's hard to even know roughly how many i/o Opus tokens do Claude subscriptions pay for. They'll give you some flippant arguments like "people were not looking at thinking so we're not showing you anymore" with a straight face. However podcasts still insist Anthropic are "winning the AI war" (??) it really makes me wonder because in no metric I can see them as providing neither best value nor best quality, and let's not get started about consumer experience.
My intuition is that things must be really bad so they're willing to pull the kind of moves they're pulling right now. They're speedrunning people into understanding how important it is to be able to run your own generative AI infrastructure for reliability, thus becoming a very fancy but trustless throwaway solution factory.
I wonder if OpenAI will turn the screws similarly if/when their pockets start to dry up at a certain pace.
tldr: they are trying hard to change S&P500 inclusion rules so that they dont have to wait 12months after going public so they can list mega-ipo asap in force index funds to buy a portion (presumably before revenue exponential growth settles and profits start tanking due to opensource catching up). They know something that we dont.
btw if they are public and part of S&P500 then potentially they'll be a candidate for a bailout.
ChatGPT has been doing the same consistently for years. Model starts out smooth, takes a while, and produces good (relatively) results. Within a few weeks, responses start happening much more quickly, at a poorer quality.
people have been complaining about this since GPT-4 and have never been able to provide any evidence (even though they have all their old conversations in their chat history). I think it’s simply new model shininess turning into raised expectations after some amount of time.
I would have thought so too. But my n=1 has CC solving pretty much the same task today and about two weeks ago with drastically degraded results.
The background being that we scrapped working on a feature and then started again a sprint later.
In my cynicism I find it more likely that a massively unprofitable LLM company tries to reduce costs at any price than everyone else suffering from a collective delusion.
I agree with you. I too complain about this same phenomenon with my colleagues, and we always arrive at the same conclusion: it’s probably us just expecting more and more over time.
With an AI corporation, yes. I subscribed during the promotional 2x usage period. Anthropic's reputation as a more ethical alternative to OpenAI factored heavily in that decision. I'm very disappointed.
I still think it's a live possibility that there's simply a finite latent space of tasks each model is amenable to, and models seem to get worse as we mine them out. (The source link claims this is associated with "the rollout of thinking content
redaction", but also that observable symptoms began before that rollout, so I wouldn't particularly trust its diagnosis even without the LLM psychosis bit at the end.)
It's not rug pulling, it's simple price anchoring. They'll degrade when it makes financial sense for them. You will pay for it. There's no way around it besides self hosting or using reality metered endpoints like openrouter.
Having tried GLM-5 and Minimax M2.5, alongside regularly using Opus 4.6 (on default thinking): Opus is still much, much better at writing non-garbage code. I haven't yet tried GLM-5.1 though.
Fascinating, I thought I was losing my mind. Claude CLI has been telling me I should go to bed, or that it's late, let's call it here, etc, and then I look at the stop-phrase-guard.sh [1] and I'm seeing quite a few of these. I thought it was because I accidentally allowed Claude to know my deadline, and it started spitting out all sorts of things like "we only have N days left, let's put this aside for now," etc.
I just saw it this weekend; "It is quite late and we have accomplished a lot. Get some rest and we can pick it up later". Not bad advice but then not it's place. Also trying to steer me away from a tough issue towards a low hanging fruit.
I wonder if its being trained on the human replies to the model, I sometimes write stuff like that back to Claude after I want to finish for the day myself.
My speculation on this has been that it's potentially a factor against ai psychosis, as psychosis risk (of any psychosis) is significantly elevated with lack of sleep. If you read case studies of ai psychosis, many of them also involve people staying up way too long right before they fall on a bad path.
To me one of the big downsides of LLM's seems to be that you are lashing yourself to a rocket that is under someone else's control. If it goes places you don't want, you can't do much about it.
That's true for traffic on Facebook, Apple App store guidelines or Google terminating your account as well. What's new is the speed of change and that it literally affects all users at once.
They could have released Opus 4.6.2 (or whatever) and called it a day. But instead they removed the old way.
Becoming dependent on those platforms was bad too, but this feels like another level. Making your entire engineering team dependent on a shady company with an apocalyptic fantasy as their business plan just seems insane.
3rd party dependency for a business always freaked me out, and now we have to use LLM to keep up with the intensified demand for production speed. And premium LLM APIs are too inconsistent to rely on.
I've noticed this as well. I had some time off in late January/early February. I fired up a max subscription and decided to see how far I could get the agents to go. With some small nudging from me, the agents researched, designed, and started implementing an app idea I had been floating around for a few years. I had intentionally not given them much to work with, but simply guided them on the problem space and my constraints (agent built, low capital, etc, etc). They came up with an extremely compelling app. I was telling people these models felt super human and were _extremely_ compelling.
A month later, I literally cannot get them to iterate or improve on it. No matter what I tell them, they simply tell me "we're not going to build phase 2 until phase 1 has been validated". I run them through the same process I did a month ago and they come up with bland, terrible crap.
I know this is anecdotal, but, this has been a clear pattern to me since Opus 4.6 came out. I feel like I'm working with Sonnet again.
The only regularity I can discern in contemporary online debates about LLMs is that for every viewpoint expressed, with probability one someone else will write in with the diametrically opposite experience.
Today it’s my turn to be that person. Large scientific code base with a bunch of nontrivial, handwritten modules accomplishing distinct, but structurally similar in terms of the underlying computation, tasks. Pointed GPT Pro at it, told it what new functionality I wanted, and it churns away for 40 minutes and completely knocks it out of the park. Estimated time savings of about 3-4 weeks. I’ve done this half a dozen times over the past two months and haven’t noticed any drop off or degradation. If anything it got even better with 5.4.
I’ve had good, alternative experience with my sideproject (adashape.com) where most of the codebase is now written by Claude / Codex.
The codebase itself is architected and documented to be LLM friendly and claude.md gives very strong harnesses how to do things.
As architect Claude is abysmal, but when you give it an existing software pattern it merely needs to extend, it’s so good it still gives me probably something like 5x feature velocity boost.
Plus when doing large refactorings, it forgets much fever things than me.
Inventing new architecture is as hard as ever and it’s not great help there - unless you can point it to some well documented pattern and tell it ”do it like that please”.
This isn't the case. I basically did an entire business/project/product exploration before building the first feature.
Even after deleting everything from the first feature and going back to the checkpoint just before initial development, I can no longer get it to accomplish anything meaningful without my direct guidance.
Same experience here. I was working on some easily testable problem and there was a simple task left. In January I was able to create 90% of the project with Claude, now I cannot make it to pass the last 10% that is just a few enums and some match. Codex was able to do it easily.
Maybe it's because I spend a lot of time breaking up tasks beforehand to be highly specific and narrow, but I really don't run into issues like this at all.
A trivial example: whenever CC suggests doing more than one thing in a planning mode, just have it focus on each task and subtask separately, bounding each one by a commit. Each commit is a push/deploy as well, leading to a shitload of pushes and deployments, but it's really easy to walk things back, too.
I thought everybody does this.. having a model create anything that isn't highly focused only leads to technical debt. I have used models to create complex software, but I do architecture and code reviews, and they are very necessary.
Absolutely. Effective LLM-driven development means you need to adopt the persona of an intern manager with a big corpus of dev experience. Your job is to enforce effective work-plan design, call out corner cases, proactively resolve ambiguity, demand written specs and call out when they're not followed, understand what is and is not within the agent's ability for a single turn (which is evolving fast!), etc.
The use case that Anthropic pitches to its enterprise customers (my workplace is one) is that you pretty much tell CC what you want to do, then tell it generate a plan, then send it away to execute it. Legitimized vibe-coding, basically.
Of course they do say that you should review/test everything the tool creates, but in most contexts, it's sort of added as an afterthought.
I had to fall back to that to deliver anything recently - but the last two months were really comfy with me just saying "do x" and just going on a walk and coming back to a working project.
Claude is still useful now, but it feels more like a replacement for bashing on a keyboard, rather than a thinking machine now.
> Maybe it's because I spend a lot of time breaking up tasks beforehand to be highly specific and narrow, but I really don't run into issues like this at all.
I'm looking at the ticket opened, and you can't really be claiming that someone who did such a methodical deep dive into the issue, and presented a ton of supporting context to understand the problem, and further patiently collected evidence for this... does not know how to prompt well.
Its not about prompting; its about planning and plan reviewing before implementing; I sometimes spend days iterating on specification alone, then creating an implementation roadmap and then finally iterating on the implementation plan before writing a single line of code. Just like any formal development pipeline.
I started doing this a while ago (months) precisely because of issues as described.
On the other hand,analyzing prompts and deviations isnt that complex.. just ask Claude :)
I noticed a regression in review quality. You can try and break the task all you want, when it's crunch time, it takes a file from Gemini's book and silently quits trying and gets all sycophantic.
In my opinion cramming invisible subagents are entirely wrong, models suffer information collapse as they will all tend to agree with each other and then produce complete garbage. Good for Anthropic though as that's metered token usage.
Instead, orchestrate all agents visibly together, even when there is hierarchy. Messages should be auditable and topography can be carefully refined and tuned for the task at hand. Other tools are significantly better at being this layer (e.g. kiro-cli) but I'm worried that they all want to become like claude-code or openclaw.
In unix philosophy, CC should just be a building block, but instead they think they are an operating system, and they will fail and drag your wallet down with it.
Been having this feeling that things have got worse recently but didn't think it could be model related.
The most frustrating aspect recently (I have learned and accepted that Claude produces bad code and probably always did, mea culpa) is the non-compliance. Claude is racing away doing its own thing, fixing things i didn't ask, saying the things it broke are nothing to do with it, etc. Quite unpleasant to work with.
The stuff about token consumption is also interesting. Minimax/Composer have this habit of extensive thinking and it is said to be their strength but it seems like that comes at a price of huge output token consumption. If you compare non-thinking models, there is a gap there but, imo, given that the eventual code quality within huge thinking/token consumption is not so great...it doesn't feel a huge gap.
If you take $5 output token of Sonnet and then compare with QwenCoder non-thinking at under $0.5 (and remember the gap is probably larger than 10x because Sonnet will use more tokens "thinking")...is the gap in code quality that large? Imo, not really.
Have been a subscriber since December 2024 but looking elsewhere now. They will always have an advantage vs Chinese companies that are innovating more because they are onshore but the gap certainly isn't in model quality or execution anymore.
I am still on an old version of CC on one machine, but the results are the same. More difficulty keeping it on track, convincing it timelines I suggest are correct etc. For example I had a deploy fail, and it would not believe that the new logs were not from a previous deploy. It was adamant it had fixed the issue, so the logs must be old logs.
I was using web UI last night and it was unable to understand basic aspects of the task. Haven't seen it perform this badly since I began using two years ago.
Was trying to track token usage/index with Cursor, and was unable to understand that running `find` wouldn't show what was in Cursor index. Multiple times.
Same experience. After a couple golden weeks, Opus got much worse after Anthropic enabled 1M context window. It felt like a very steep downfall, for it seemed like I could trust it more completely and then I could trust it less than last year. Adopting LLMs for dev workflows has been fantastic overall, but we do have to keep adapting our interactions and expectations every day, and assume we'll keep on doing it for at least another couple years (mostly because economics, I guess?)
Yeah I think the 1M context is the issue. Because I use Opus 4.6 through Cursor at the previous 200k limit and it has been totally fine. But if I switch to the 1M version it degrades noticeably.
> Yeah I think the 1M context is the issue. Because I use Opus 4.6 through Cursor at the previous 200k limit and it has been totally fine. But if I switch to the 1M version it degrades noticeably.
I thought it was already well-known that context above 200k - 300k results in degradation.
One of my more recent comments this past week was exactly that - that there was no point in claiming that a 1m context would improve things because all the evidence we have seen is that after 300k context, the results degrade.
I put together a quick audit to check for "early landing" messages[1] using jq, ripgrep, and the messages[2] flagged in the stop guard script.
I have noticed a trend in these sessions asking more and more about calling it a day, "it's getting late," and other phrases. I sort of assumed it was some kind of "load shedding" on Anthropic's side.
My audit of 80 sessions was interesting. Sorry, I won't share details, but I recommend you do the same.
As a negative example, my audit of 31 sessions was uninteresting. I had one matching entry, where I had pasted a long list of console errors into Claude and it identified a few as pre-existing and asked me to get more information for follow-up analysis.
I wonder if it comes down to prompting—maybe by introducing these "golden rules" OP mentions in their CLAUDE.md, they're actually "priming" Claude to think about these stop phrases and introduce them proactively.
Do you have a CLAUDE.md file? What does it contain?
Those load shedding statements are infuriating. I’ve literal had sessions where we just get through planning a giant feature and I say “get started” with the response being “okay, we’ll pic up tomorrow “
You could introduce teleportation boots to humanity and within a few weeks we'd be complaining that sometimes we still have to walk the last 20 meters.
I thought it was just me. I'm continuously interrupting it with "no, that's not what I said" - being ignored sometimes 3 times; is Claude at the intellectual level of a teenager now?
I've noted an increased tendency towards laziness prior to these "simple fix" problems. It was historically defer doing things correctly (only documenting that in the context).
I use Claude Code extensively and haven't noticed this. But I don't have it doing long running complex work like OP. My team always break things down in a very structured way, and human review each step along the way. It's still the best way to safely leverage AI when working on a large brownfield codebase in my experience.
Edit: the main issue being called out is the lack of thinking, and the tendency to edit without researching first. Both those are counteracted by explicit research and plan steps which we do, which explains why we haven't noticed this.
My bet: LLMs will never be creative and will never be reliable.
It is a matter of paradigm.
Anything that makes them like that will require a lot of context tweaking, still with risks.
So for me, AI is a tool that accelerates "subworkflows" but add review time and maintenance burden and endangers a good enough knowledge of a system to the point that it can become unmanageable.
Also, code is a liability. That is what they do the most: generate lots and lots of code.
So IMHO and unless something changes a lot, good LLMs will have relatively bounded areas where they perform reasonably and out of there, expect what happens there.
We don't even know what 'creativity' is, and most humans I know are unable to be creative even when compelled to be.
AI is 'creative enough' - whether we call it 'synthetic creativity' or whatever, it definitely can explore enough combinations and permutations that it's suitably novel. Maybe it won't produce 'deeply original works' - but it'll be good enough 99.99% of the time.
The reliability issue is real.
It may not be solvable at the level of LLM.
Right now everything is LLM-driven, maybe in a few years, it will be more Agentically driven, where the LLM is used as 'compute' and we can pave over the 'unreiablity'.
For example, the AI is really good when it has a lot of context and can identify a narrow issue.
It gets bad during action and context-rot.
We can overcome a lot of this with a lot more token usage.
Imagine a situation where we use 1000x more tokens, and we have 2 layers of abstraction running the LLMs.
We're running 64K computers today, things change with 1G of RAM.
But what I see again and again in LLMs is a lot of combinations of possible solutions that are somewhere around internet (bc it put that data in). Nothing disruptive, nothing thought out like an experimented human in a specific topic. Besides all the mistakes/hallucinations.
> A lot of humans have difficulty with very reality that they are in fact biological machines, and most of what we do is the same thing.
I think we are far and ahead from this "mix and match". A human can be much, much more unpredictable than these LLMs for the thinking process if only bc looking at a much bigger context. Contexts that are even outside of the theoretical area of expertise where you are searching for a solution.
Good solutions from humans are potentially much more disruptive.
Yet they never give you replies like: oh, you see how dolphins run in the water taking advantage of sea currents if you are talking about boats and speed.
What they will do is to find all the solutions someone did and mix and match around in a mdiocre way of approaching the problem in a much more similar way to a search engine with mix and match than thinking out of the box or specifically for your situation (something also difficult to do anyway bc there will always be some detail missing in the cintext and if you really had go to give all that context each time dumping it from your brain then you would not use it as fast anymore) which humans do infinitely better. At least nowadays.
Now you will tell me that the info is there. So you can bias LLMs to think in more (or less) disruptive ways.
Then now your job is to tweak the LLMs until it behaves exactly how you want. But that is nearly impossible for every situation, because what you want is that it behaves in the way you want depending on the context, not a predefined way all the time.
At that time I wonder if it is better to burn all your time tweaking and asking alternative LLMs questions that, anyway, are not guaranteed to be reliable, or just keep learning yourself about the domain instead of just playing tweaking and absorbing real knowledge (and not losing that knowledge and replace it with machines). It is just stupid to burn several hours in making an expert you cannot check if it says real stuff instead of using that time for really learning about the problem itself.
This is a trade-off and I think LLMs are good for stimulating human thinking fast. But not better at thinking or reasoning or any of that. And if yiu just rely on them the only thing you will emd up being professional at is orompting, which a 16 year old untrained person can do almost as well as any of us.
LLMs can look better if you have no idea of the topic you talk about. However, when you go and check maybe the LLM hallucinated 10 or15% of what it said.
So you cannot rely on it nayways. I still use them. But with a lotof care.
Great for scaffolding. Bad at anything that deviates from the average task.
> First - I'm doubting your assumptions about "What they will do is to find all the solutions someone did and mix and match".
I never, ever got from a LLM a solution that either I could have never thought of or it was available almost verbatim in internet (take this last one with a grain of salt, we know how they can combine and fake it, but essentially, solutions looking like templates from existing things, often hallucinating things that do not exist or cannot be done, inventing parameter names for APIs that do not exist, etc).
When I give some extra thought to a problem (20 years almost in software business) I think solutions that I come up with are often simpler, less convoluted and when I analyze LLMs they give you a lot of extra code that is not even needed, as if they were doing guessing even if you ask them something more narrow. Well, guessing is what they are doing actually, via interpolation.
This makes them useful for "bulky", run fast, first approach problems but the cost later is on you: maintenance, understanding, modifying, etc.
I think the terminology is just dogshit in this area. LLMs are great semantic searchers and can reason decently well - I'm using them to self teach a lot of fields. But I inevitably reach a point where I come up with some new thoughts and it's not capable of keeping up and I start going to what real people are saying right now, today, and trust the LLM less and instead go to primary sources and real people. But I would have never had the time, money, or access to expertise without the LLM.
Constantly worrying, "is this a superset? Is this a superset?" Is exhausting. Just use the damn tool, stop arguing about if this LLM can get all possible out of distribution things that you would care about or whatever. If it sucks, don't make excuses for it, it sucks. We don't give Einstein a pass for saying dumb shit either, and the LLM ain't no Einstein
If there's one thing to learn from philosophy, it's that asking the question often smuggles in the answer. Ask "is it possible to make an unconstrained deity?" And you get arguments about God.
do they reason? Where was a video by AI researcher, that showed, that they do not reason but actually come with the result first and then try to invent "reasoning" to match it.
I mean humans do that too, and I don't think it's very unjustified. The "we deduce from a deep base premise P down a chain of inferences" picture is extremely incomplete and has been challenged all over the place - by normal people, by analytic and continental philosophers, by science itself, etc.
Not trying to say that LLM's are equivalent to humans but that the concept of reasoning is undefined.
And the fact that their performance does increase when using test-time compute is empirical evidence that they're doing something that increases their performance on tasks that we consider would require reasoning. As to what that is, we don't know.
But humans verify things. AI just fools you and I would say it is the biggest problem.I have with AIs.
They give me stuff that I do not know whether to trust or not and what surprises I will find down the way later.
So now my task is to review everything, remove cruft. It starts to compete against investing my time to deep-think and do it thoughtfully from the get go and come up with something simpler, with less code and/or that I understand better.
I cancelled my Pro plan due to this two weeks ago. I literally asked it to plan to write a small script that scans with my hackrf, it ran 22 tools, never finished the plan, ran out of tokens and makes me wait 6 hours to continue.
Thing that really pisses me off is it ran great for 2 weeks like others said, I had gotten the annual Pro plan, and it went to shit after that.
I wish they had a "and we won't screw you in two weeks" plan at, say, 5x the price. It's worth it for my business, I'd pay it.
Should I switch back to API pricing? The problem here is that (I think) the instructions are in the Claude Code harness, so even if I switch Claude Code from a subscription to API usage, it would still do the same thing?
FWIW I've only ever been on the API based plan at work and we never seem to run into the majority of the problems people seem to be very vocal about. Outages still affect us, and we do have the intermittent voodoo feeling of "Claude seems stupider today", but nothing persistent.
Of course it's a stupid amount of money sometimes, but I generally feel like we get what we're paying for.
I never managed to get anything useful out of opencode, to be honest. I tried it many times, with various models. Claude Code always just worked better.
You will literally build nothing but the most primitive of devices unless you accept black boxes. In fact I'd argue its one of humanities great strengths that we can build on top of the tools others have built, without having to understand them at the same level it took to develop them.
Black boxes aren't inherently bad. But if they don't have well defined mappings of inputs to outputs, they aren't good black boxes. That's the problem with Claude Code imo.
not really. Most of the technology is not black box but something of a grey box. You usually choose to treat it as a black box because you want to focus on your problems/your customers but you can always focus on underlying technologies and improve them. Eg postgresql for me is a black box but if I really wanted or had need I could investigate how it works.
True, you can understand an ICE engine all the way down to the chemistry if you so chose. An LLM isn't even understood by its inventors so users have no chance to understand it even if they wanted to.
Yeah someone should’ve told that to Donald (Knuth)
/s
For those who don’t know, Knuth implemented the typesetting system TeX just to make sure his book’s typesetting was correct.
You can pretty much only innovate when you reject the blackbox and decide to make a better one.
Otherwise you’re likely implementing something you could probably get off-the-shelf, which is ok, but also something that you could just… not implement.
It’s not so much the black box that’s the issue here, but the fact you can’t even make sure doesn’t change. I’d be fine with downloading the black box and running it on my servers until I decide to update it.
Arguably political systems have generated similar convolution and lack of complete insight or oversight for much longer, and sometimes I wonder if markets are composed of complex, emergent components which no one truly understands as well.
> Its so silly everyone being dependent on a black box like this
It's the logical result of "You will own nothing and you will be happy"... You are getting to the point where you won't even own thoughts (because they'll come from the LLM), but you'll be happy that you only have to wait 5 hours to have thoughts gain.
No, most systems in daily life can be understood if you are willing to take the time.
That doesn’t mean you personally are required to, but some people do and your interaction with the system of social trust determines how much of that remains opaque to you.
What's wild is that ClaudeCode used to feel like a smart pair programmer. Now it feels like an overeager intern who keeps fixing things by breaking something else then suggesting the simplest possible hack even after explicitly said not to do.
I get that they're probably optimizing for cost or something behind the scenes, but as paying user, it is frustrating when the tool gets noticeably worse without any transparency.
My verdict after last night trying what was suggested here:
yes, with CLAUDE_CODE_EFFORT_LEVEL=max (or at least high, for this you don't need to set an env var, it will remember) and CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 you can get Claude to perform as before.
I have been using Claude on /effort high since Opus 4.6 rolled out as medium would never get me good enough results (Rust, computer-graphics-related code).
I, too, noticed the drop in quality a month or so ago. With CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 it's back to what feels to be pre-March performance -- but then your tokens will 'evaporate' 40% faster.
And that was not the case then; I had similar/same performance before but wasn't running out of tokens ever on a Max subscription.
So a it's a rug-pull, as before/last late summer, from whatever angle you look at it.
If I'm reading that page correctly, then the benchmark results don't cover the interesting "mid February" inflection point noted in the article/report. The numbers appear to begin after the quality drop began. Moreover, the daily confidence interval seems to be stupidly wide, with a confidence interval between 42% and 69%?
The "Other metrics" graphs extend for a longer period, and those do seem to correlate with the report. Notably, the 'input tokens' (and consequently API cost) roughly halve (from 120M to 60M) between the beginning of February and mid-March, while the number of output tokens remains similar. That's consistent with the report's observation that new!Opus is more eager to edit code and skips reading/research steps.
why should we trust this random bench? i'm usually more sympathetic towards the "it's you who is holding it wrong" crowd but given how anthropic deceived customers recently and that i am a heavy power user with strong insights into many of these products i also can attest the pattern from the gh issue.
Came here to post this as well, and it's interesting to see how benchmarks don't always track feelings. Which is one of the things people say in favor of Anthropic Models!
People finding out in real time that LLM's are not economically viable and this is one way AI companies are trying to squeeze any amount of profit out of it, by making it worse. Happened before, AI is just that unprofitable
I've subscribed today to use Claude Cowork. Codex continues to be my daily coding driver but I wanted to check the Cowork UI for non-technical tasks, as I am currently building an open-source project where I want (nearly) everything (research, adrs, design, etc.) to be a file.
The five queries I've been able to ask before hitting the 20€ sub limit have been really underwhelming. The research I asked for was not exhaustive and often off-topic.
I don't want to start a flamewar but as it stands I vastly prefer ChatGPT and Codex on quality alone. I really want Anthropic and as many labs as possible to do well though.
I also have both and also use Codex as my daily drive. I still vastly prefer it to CC both for the quality of the code it writes and much better limits, but in this last week, I feel like it's gotten much dumber as well. I normally bounce back and forth between 5.3 Codex high and 5.4 high depending on the task and I've started finding so many mistakes in 5.3 Codex's code which is a major change from even just a few weeks ago. 5.4 high still gets the job done, but even there, I feel like it's taking more steering and input on my part for even simple tasks.
My impression is that Codex is vastly superior, but perhaps it's a matter of specific expertise on technologies used. It's also the case that for C/C++ some Chinese models do well enough that with my supervision I can have them get the work done.
I don't give them large tasks that i wouldn't be able to work on myself, so that's maybe part of it.
We rolled it out across ~1k engineers and the biggest issue wasn't the model quality, it was observability. Nobody could tell me if the agent was stuck in a loop, which sessions were expensive, or what the cache hit rate looked like. Without that visibility you can't distinguish "the model is bad" from "my setup is bad." Most of the complaints we got early on turned out to be config problems.
I am a heavy user of Claude Code building enterprise software. I have not seen these issues and have been extremely productive with CC. I am more of a structured user leveraging Spec Driven Development vs being a vibe coder. I wonder if that is what has helped me not run into these issues
Well, this event indicates that it won't implode anytime soon. I'm certain that they messed with the model and default settings so they could reduce compute. The world doesn't have enough compute.
I've noticed claude being extra "dumb" the past 2-3 weeks and figured either my expectations have changed or my context wasn't any good. I'm glad to hear other people have noticed something is amiss.
Exact same timeline as me and my team. Its been maddening. Im a big believer in AI since late last year, but that is only because the models got so good. This puts us dangerously close to before that threshold was crossed so now Im having to do _way_ more work than before
I haven't noticed any issues on well-specified tasks, even ones requiring large amounts of thinking.
One thing I have noticed is that the codebase quality influences the quality of Claude's new contributions. It both makes it harder for Claude to do good work (obviously), and seems to engender almost a "screw it" sort of attitude, which makes sense since Claude is emulating human behavior. Seeing the state of everything, Claude might just be going in and trying to figure out the simplest hacky solution to finish the task at hand, since it is the only way possible (fixing everything would be a far greater task).
Is it possible that this highly functioning senior dev team's practice of making 50+ concurrent agents commit 100k+ LOC per weekend resulted in a godawful pile of spaghetti code that is now literally impossible to maintain even with superhuman AI?
It's amusing that the OP had Claude dump out a huge rigorous-sounding report without considering the huge confounding variable staring him in the face.
Is this impacted by the effort level you set in Claude? e.g., if you use the new "max" setting, does Claude still think?
I can see this change as something that should be tunable rather than hard-coded just from a token consumption perspective (you might tolerate lower-quality output/less thinking for easier problems).
Wonder how many of these cases are using the 1M context window. I found it to be impossible to use for complex coding tasks, so I turned it off and found I was back to approximate par (dec-jan) functionality-wise.
Guys literally change the system prompt with the --system-prompt-file you waste less tokens on their super long and details prompt and you can tune it a bit to make it work exactly like you want/imagine
I hope that Anthropic continues to do well and coding agents in general continues to progress... but I also hope Claude Code implodes dramatically and completely so we can get a ground up rebuild with sound engineering.
Every week it seems like we're getting closer.
Bonus: A high profile case might end people fixating on how long they can go without writing any code. Which makes about as much sense as a mechanic fixating on how long they go between snapped bolts without a torque wrench.
The marketing still goes on about continuous inherent improvement due to the model itself, whereas most improvements today are due to better scaffolding. The key now is to build tooling around these LLMs to make them reliably productive - whatever level that may be at.
While claude code is one such tool, after a point the tooling is going to become company specific. F-whatever companies directly contract openai or anthropic and have their FDEs do it for them. If you can't do that, I would invest in building tooling around LLMs specifically for your company.
Note that LLMs are approximate retrieval machines. You still need a planner* and a verifier around it. Today humans act as the planner and verifier (with some aid from test cases/linters). Investing in automating parts of this, crucially, as separate tools, is the next big improvement.
* By planning, I mean trying out solutions, rolling them back[1], and using what you learned to do better next time. The solution search process. Context management also falls under this.
[1] and no, LLMs going "wait no..." doesn't count.
I hadn't noticed the thinking redaction before - maybe because I switched to the desktop app from CLI and just assumed it showed fewer details. This is the most concerning part. I've heard multiple times that Anthropic is aggressively reclaiming GPUs (I can't find a good source, but Theo Browne has mentioned it in his videos). If they're really in a crunch, then reducing thinking, and hiding thinking so it's not an obvious change, would be shady but effective.
Multiple people on our team independently have noticed a _significant_ drop in quality and intelligence on opus 4.6 the past few weeks. Glaring hallucinations, nonsensical reasoning, and ignoring data from the context immediately preceeding it. Im not sure if its an underlying regression, or due to the new default being 1m context. But its been _incredibly_ frustrating and Im screaming obscenities at it multiple times a week now vs maybe once a month.
The wall of slop after the single human paragraph, you mean? Text generator output isn't data.. it's at best unreliable, and at worst entirely fabricated.
Not unique to claude code, have noticed similar regressions. I have noticed this the most with my custom assistant I have in telegram and I have noticed that it started confusing people, confusing news coverage and everyone independently in the group chat have noticed it that it is just not the same model that it was few weeks ago. The efficiency gains didn't come from nowhere and it shows.
Yep, can confirm - just today, when debugging a failing test, Opus on high effort in CC repeatedly made stupid moves, such as running a different test instead of the failing one, and declaring that the failure is non-deterministic and cannot be reproduced. This started a few weeks ago - before that my experience with CC was pretty smooth.
I've been using OpenCode and Codex and was just fine. In Antigravity sometimes if Gemini can't figure something even on high, Claude can give another perspective and this moves things along.
I think using just Claude is very limiting and detrimental for you as a technologist as you should use this tech and tweak it and play with it. They want to be like Apple, shut up and give us your money.
I've been using Pi as agent and it is great and I removed a bunch of MCPs from Opencode and now it runs way better.
Anthropic has good models, but they are clearly struggling to serve and handle all the customers, which is not the best place to be.
I think as a technologist, I would love a client with huge codebase. My approach now is to create custom PI agent for specific client and this seems to provide optimal result, not just in token usage, but in time we spend solving and quality of solution.
Get another engine as a backup, you will be more happy.
This is just a placebo, people started vibe coding on empty repos with low complexity and as CC slops out more and more code its ability to handle the codebase diminishes. Gradually at first, and then suddenly.
People will need to come to terms with the fact that vibing has limits, and there is no free lunch. You will pay eventually.
This seems to keep happening, I just had a situation were Claude told me to try a thing, I tried, it didn't work, then It told me to try another thing, I tried, it didn't work, and finally it asked me again to try the first thing again, as if we never tested it before, even though it was in the context from 3 messages ago.
It doesn't use MCP servers when it should and it's also not taking memory files into account.
This is happening with /effort high and in really simple tasks... :(
Just now I had a bug where a 90 degree image rotation in a crate I wrote was implemented wrong.
I told Claude to find & fix and it found the broken function but then went on to fix all of its call sites (inserting two atomic operations there, i.e. the opposite of DRY). Instead of fixing the root cause, the wrong function.
And yes, that would not have happened a few months ago.
This was on Opus 4.6 with effort high on a pretty fresh context. Go figure.
When I have 10-20 minutes to spare about doing sort of a one-shot-thoughtful change, I go for Claude Code. Problem is that a) I'm waiting for quite a while and b) My waiting isn't always fruitful because it does get things wrong in which case I correct it and off it goes for another 5-10 minute expedition.
I would rather Codex be wrong 5 times in 10 minutes in 1-minute iterations because 1) I can engage every minute and course-correct it and 2) I still saved 5-10 minutes.
Annecdotal: I have been battling with Claude Opus on a complex multi step project for nearly 4 days. The initial research plan was sound. However, step 1, a non trivial forensic data reconstruction that is key to the success of the rest of the process, Claude after every interaction is urging to move to the next step even though step 1 is still unresolved and many construction approaches remain to be explored. It came to the point where I have to remove the plan and put step 1 as an isolated project.
In general, I never allowed Claude to manage the complexity. Claude is fantastic coder, but very bad at higher level work. I engage gemini or qwen top models for anything that happens before getting to write code. Claude gets a very strict and elaborate requirement and design spec that it need to execute without any variation.
Claude could get too much creative and bloat it's way for non-coding tasks, as these tasks cannot be "sandboxed" with full specs as it can be done for coding.
I highly recommend everyone to use Pi - it's simpler and better harness. The only tricky part is that moving forward you cannot use the Claude subscription to access Opus. But for many tasks there are enough alternatives.
Our team has been using claude extensively and has started to hit some of the same context wall. Specifically around knowledge transfer. Everyone is coding, including PMs and Design and the classic 1x1 and team stand-up rotation wasn't keeping up.
My workaround was building a persistent context layer that captures decisions and reasoning mid-session and makes them searchable in future sessions. Consider this a "Team Memory".
I have found that Claude Opus 4.6 is a better reviewer than it is an implementer. I switch off between Claude/Opus and Codex/GPT-5.4 doing reviews and implementations, and invariably Codex ends up having to do multiple rounds of reviews and requesting fixes before Claude finally gets it right (and then I review). When it is the other way around (Codex impl, Claude review), it's usually just one round of fixes after the review.
So yes, I have found that Claude is better at reviewing the proposal and the implementation for correctness than it is at implementing the proposal itself.
Hmm in my experience (I've done a lot of head-to-heads), Opus 4.6 is a weaker reviewer than GPT 5.4 xhigh. 5.4 xhigh gives very deep, very high-signal reviews and catches serious bugs much more reliably. I think it's possible you're observing Opus 4.6's higher baseline acceptance rate instead of GPT 5.4's higher implementation quality bar.
This is also my experience using both via Augment Code. Never understood what my colleagues see in Claude Opus, GPT plans/deep dives are miles ahead of what Opus produces - code comprehension, code architecture is unmatched really. I do use Sonnet for implementation/iteration speed after seeding context with GPT.
I agree, I use codex 5.4 xhigh as my reviewer and it catches major issues with Opus 4.6 implementation plans. I'm pretty close to switching to codex because of how inconsistent claude code has become.
The experience one has with this stuff is heavily influenced by overall load and uptime of Anthopic's inference infra itself. The publicly reported availability of the service is one 9, that says nothing of QoS SLO numbers, which I would guess are lower. It is impossible to have a consistent CX under these conditions.
I have noticed this as well. I frequently have to tell it that we need to do the correct fix (and then describe it in detail) rather than the simple fix. And even then it continues trying to revert to the simple (and often incorrect) fix.
You have to throw the context away at that point. I've experienced the same thing and I found that even when I apparently talk Claude into the better version it will silently include as many aspects of the quick fix as it thinks it can get away with.
I have a similar workflow but I disagree with Codex/GPT-5.4 reviews being very useful. For example, in a lot of cases they suggest over-engineering by handling edge cases that won't realistically happen.
Obviously it's entirely unprovable but it all aligns in very suspicious ways with a compelling narrative:
Anthropic simply can't actually scale Claude Code to meet the opportunity right now. Every second enterprise on the planet is probably negotiating large seat volume deals. It's a race for survival against the other players. The sales team is making huge promises engineering and ops can't fulfil.
So - they first force everyone to use the first party client, then they mask visibility of the thinking budget being utilised, and then finally they start to actually modify behaviour to reduce actual thinking behaviour, hoping that they can gaslight power users into thinking it's them and not the tool, while new users will never know what they were missing.
Is the narrative true? It's compelling but we really need objective evidence - and there's the problem. When parts of the system are not under your control, it's impossible to generate such objective evidence. Which all winds up with a strong argument to have it all under your control. If it didn't happen this time, it probably will. Enshittification is a fundamental human behavioral constant.
I believe they can't afford anymore to subsidize inference with VC money or
that they are trying to get their balance sheet in order for an IPO.
So they could be trying to tighten the thinking budget (to decrease tokens per request) or to lobotomize the model (to have cheaper tokens). I mean, no-one is really sure how much a 200 dollars/month plan actually costs Anthropic, but the consensus is "more than that" and that might be coming to an end.
This explanation falls well in line with the recent outrage about out of quotas error that people were reporting for the cheaper (or free) plans.
I noticed this almost immediately when attempting to switch to Opus 4.6. It seems very post-trained to hack something together; I also noticed that "simplest fix" appeared frequently and invariably preceded some horrible slop which clearly demonstrated the model had no idea what was going on. The link suggests this is due to lack of research.
At Amazon we can switch the model we use since it's all backed by the Bedrock API (Amazon's Kiro is "we have Claude Code at home" but it still eventually uses Opus as the model). I suppose this means the issue isn't confined to just Claude Code. I switched back to Opus 4.5 but I guess that won't be served forever.
Thank you. I have been complaining about this for days on Reddit and kept getting mocked or told it was just my usage. Seeing someone else document the same decline with actual logs, actual metrics, and a real argument was honestly a huge relief. Your issue was posted almost at the same time as my own posts yesterday. That timing hit me hard. Finally, I do not feel like I was shouting into the void.
Got tired of using claude using 10% of the usage for the first prompt. I have shifted back to coding myself again. Asking claude to do only initial bootstraping /large complex task
There are constant reports for every major AI vendor that all of a sudden it is no longer working as well as expected, has gotten dumber, is being degraded on purpose by the vendor, etc.
Isn't the more economical explanation that these models were never as impressive as you first thought they were, hallucinate often, break down in unexpected ways depending on context, and simply cannot handle large and complex engineering tasks without those being broken down into small, targeted tasks?
That's one of the possible explanations, but I think too many people are seeing the same symptoms (and some actually measured them).
An "economical explanation" is actually that Anthropic subscriptions are heavily subsidized and after a while they realized that they need to make Claude be more stingy with thinking tokens. So they modified the instructions and this is the result.
> but I think too many people are seeing the same symptoms (and some actually measured them).
Or too many people are slurping up anecdotes from the same watering hole that confirms their opinions. Outside of academic papers, I don't think I've ever seen an example of "measuring" output that couldn't also be explained by stochastic variability.
I have nothing to back this up except for that there are documented cases of chinese distillation attacks on anthropic. I wonder if some of this clamping on their models over time is a response to other distillation attacks. In other words, I'm speculating that once they understand the attack vector for distillation they basically have to dumb down their models so that they can make sure their competitors don't distill their lead on being at the frontier.
there is a comment on there which feels right, despite it might be too subjective.
Ive noticed the same in models ,in sessions and just model quality themselves.. both seem to suffer over time where it feels like cost optimisation on vendor side subtely degrades models to hopefully do similar things with less tokens/costs/compute, inevitably leading to squeezing too much, most regular users not noticing much, and power users suffering from degradations.
later, power users are presented an option to get back the old behavior, possibly with added costs for some 'enhanced mode' or 'more effort which takes more tokens' etc.
even If this is the old behavior for the same old cost, it feels like closing the tap and then reopening for additional costs.
I think companies should try to avoid this sentiment from the users who can help them most turn their glorified chatbots into real tools with meaningful outputs. (ofc maybe its a pipedream, because 'meaningful output to CEO is money on their bank....)
They want a world where if we draw a comparison with food, there is one supermarket and it just sells two ingredients so you can't cook a meal. McDonald's etc flourish
The lie is "supercharged ability to build whatever you want", but the reality soon will be the total opposite
Look at how many people have zero cooking skills these days
I've been using Claude Code daily for months on a project with Elixir, Rust, and Python in the same repo. It handles multi-language stuff surprisingly well most of the time. The worst failure mode for me is when it does a replace_all on a string that also appears inside a constant definition -- ended up with GROQ_URL = GROQ_URL instead of the actual URL. Took a second round of review agents to catch it. So yeah, you absolutely can't trust it to self-verify.
You say you've used it for months, I wonder if the example you gave was recent and if you've been noticing an overall degradation in quality or it's been constantly bad for you?
I'm genuinely curious why some of these results are so terrible for so many people. I've built in my own harness, and while I've noticed a degradation of quality, the local harness - as well as validation agents - generally catch these issues. For me, I've had to institute tighter controls and guardrails via hooks but I don't see results that warrant changing to a different provider.
I still use 4.5. I occasionally try 4.6 but always switch back. The “bias towards action” is what I hate. 4.5 would make sure it understands what I want. 4.6 will just make shit up. Maybe the Anthropic people always write crystal clear instructions so it works for them. For me, I just can’t get 4.6 to do what I want.
Anecdotally, I’ve been seeing a lot of weird behavior from Opus when it decides, mid-execution, to switch to a different "simpler" solution, and that really pissed me off.
At one point, I carefully designed a spec document, forced Opus to reread it, create a plan with the planning tool that followed the spec, and use the task tool to track the implementation... AND AFTER OPUS READS THE FIRST FUCKING FILE, it says, "Oh, there are missing dependencies in project X. It’ll be hard to add them, so I’m going to throw away the whole plan and just do a simple fix..."
After that, I canceled my $200 Max plan, which I’d been subscribed to since June 2025, and decided to check out Codex
I noticed Claude Sonnet 4.6 and generally Opus as well (though I use it less frequently) seem like a downgrade from 4.5. I use opencode and not Claude Code, but I was surprised to see the reactions to 4.6 be mixed for folks rather than clear downgrade.
I'm regularly switching back to 4.5 and preferring it. I'm not excited for when it gets sunset later this year if 4.6 isn't fixed or superseded by then.
Opus 4.6 was definitely a mixed bag for me. Overall Id probably prefer 4.5 but only just barely and I stay on 4.6 just for the "default" nature of it. But if 4.5 is unchanged vs what Ive had on 4.6 lately then 100% I would move back to it. Ill have to test that
(Being true to the HN guidelines, I’ve used the title exactly as seen on the GitHub issue)
I was wondering if anyone else is also experiencing this? I have personally found that I have to add more and more CLAUDE.md guide rails, and my CLAUDE.md files have been exploding since around mid-March, to the point where I actually started looking for information online and for other people collaborating my personal observations.
This GH issue report sounds very plausible, but as with anything AI-generated (the issue itself appears to be largely AI assisted) it’s kind of hard to know for sure if it is accurate or completely made up. _Correlation does not imply causation_ and all that. Speaking personally, findings match my own circumstances where I’ve seen noticeable degradation in Opus outputs and thinking.
EDIT: The Claude Code Opus 4.6 Performance Tracker[1] is reporting Nominal.
What I've noticed is that whenever Claude says something like "the simplest fix is..." it's usually suggesting some horrible hack. And whenever I see that I go straight to the code it wants to write and challenge it.
That is the kind of thing that I've been fighting by being super explicit in CLAUDE.md. For whatever reason, instead of being much more thorough and making sure that files are being changed only after fully understanding the scope of the change (behaviour prior to Feb/Mar), Claude would just jump to the easiest fix now, with no backwards compatibility thinking and to hell with all existing tests. What is even worse is I've seen it try and edit files before even reading them on a couple of occasions, which is a big red flag. (/effort max)
Another thing that worked like magic prior to Feb/Mar was how likely Claude was to load a skill whenever it deduced that a skill might be useful. I personally use [superpowers][1] a lot, and I've noticed that I have to be very explicit when I want a specific skill to be used - to the point that I have to reference the skill by name.
I did not use the previous version of Opus to notice the difference, but Sonnet 4.6 seems optimized to output the shortest possible answer. Usually it starts with a hack and if you challenge it, it will instead apologize and say to look at a previous answer with the smallest code snippet it can provide. Agentic isn't necessarily worse but ideating and exploring is awful compared to 4.5
I did my usual thing today where I asked a Sonnet 4.6 agent to code review a proposed design plan that was drafted by Opus 4.6 - I do this lately before I delved into the implementation. What it came back with was a verbose output suggesting that a particular function `newMoneyField` be renamed throughout the doc to a name it fabricated `newNumeyField`. And the thing was that the design document referenced the correct function name more than a few dozen times.
This was a first for me with Sonnet. It completely veered off the prompt it was given (review a design document) and instead come out with a verbose suggestion to do a mechanical search and replace to use this newly fabricated function name - that it event spelled incorrectly. I had to Google numey to make sure Sonnet wasn't outsmarting me.
This is classic needle in haystack issue. Larger context windows make precision (especially with variable names) worse. Have you tried setting the context window smaller? 1M by default is a scam.
Superpowers, Serena, Context7 feel like requried plugins to me. Serena in particular feels like a secret weapon sometimes. But superpowers (with "brainstorm" keyword) might be the thing that helps people complaining about quality issues.
lol this one time Claude showed me two options for an implementation of a new feature on existing project, one JavaScript client side and the other Python server side.
I told it to implement the server side one, it said ok, I tabbed away for a while, came to find the js implementation, checking the log Claude said “on second thought I think I’ll do the client side version instead”.
Rarely do I throw an expletive bomb at Claude - this was one such time.
Dunno man, Claude had a spec (pretty sure I asked it to consider and outline both options first) or at least clear guidance and decided to YOLO whatever it wanted instead.
It’s always “you’re using the tool wrong, need to tweak this knob or that yadda yadda”.
this prompt is actually in claude cli. it says something like implement simplest solution. dont over abstract. On my phone but I saw an article mention this in the leak analysis.
I've seen a lot of the issues mentioned in the issue. The attempts to end the session early are particularly annoying. We spend a while iterating on a plan and after every phase of implementation I get some variation of "That's a lot of work for today, should we wrap up?" like it's actively trying to drive sessions to a close. I wouldn't say it's useless for these tasks. But it's requiring more effort and guidance than it used to. It's also more likely to jump right into changes from a question I ask rather than addressing the question which is very annoying.
If that tracker is using paid tokens, as opposed to the regular subscription, then there's no financial incentive for Antrophic to degrade their thinking, so their benchmark likely would not be affected by the cost-cutting measures that regular users face.
Also, it's probably very easy to spot such benchmarks and lock-in full thinking just for them. Some ISPs do the same where your internet speed magically resets to normal as soon as you open speedtest.net ...
I haven't noticed any changes but my stuff isn't that complex. People are saying they quantized Opus because they're training the next model. No idea if that's true... It's certainly impacting my decision to upgrade to Max though. I don't want to pay for Opus and get an inferior version.
I haven't noticed any changes either, but I noticed that opus 4.6 is now offered as part of perplexity enterprise pro instead of max, so I'm guessing another model is on the horizon
I just finished reading the full analysis on GitHub.
> When thinking is deep, the model resolves contradictions internally before producing output.
> When thinking is shallow, contradictions surface in the output as visible self-corrections: "oh wait", "actually,", "let me reconsider", "hmm, actually", "no wait."
Yeah, THIS is something that I've seen happen a lot. Sometimes even on Opus with max effort.
I missed that from the long issue, thanks for pointing it out! My experience with Opus today was riddled with these to the point where it was driving me completely mental. I've rarely seen those self-contradictions before, and nothing on my setup has changed - other than me forcing Opus at --effort max at startup.
I wonder if this is even more exaggerated now through Easter, as everyone’s got a bit extra time to sit down and <play> with Claude. That might be pushing capacity over the limit - I just don’t know enough about how Antropic provision and manage capacity to know if that could be a factor. However quality has gotten really bad over the holiday.
Cannot say I've noticed, but I run virtually everything through plan mode and a few back and forth rounds of that for anything moderately complex, so that could be helping.
I used to one-shot design plans early in the year, but lately it is taking several iterations just to get the design plan right. Claude would frequently forget to update back references, it would not keep the plan up to date with the evolving conversation. I have had to run several review loops on the design spec before I can move on to implementation because it has gotten so bad. At one point, I thought it was the actual superpowers plugin that got auto-updated and self-nerfed, but there weren't any updates on my end anyway. Shrug.
Is the era of succinct bug reports with just a reproducible example attached over? Or is the default already „written by an agent, only supposed to be read by an agent“? Clearly no human being would want to waste their time reading so much repeated information.
> We exclusively use 1M internally, so we're dogfooding it all day
That is so out of touch. Customers do not exclusively use 1M. This is like a fronted developer shipping tons of unused Mb and being oblivious because they are on fast internet themselves.
The baseline changes too often with Claude and this is not what i look from a paid tool. Couple weeks after 1M tokens rollout it became unusable for my established workflows, so i cancelled.
Anthropic folks move too fast for my liking and mental wellbeing.
Matches my experience and that of my vibe coding community. I built claudedumb.com to help track these sorts of anecdotes. From the data/vibes, it's definitely taken a turn for the worse in the past couple weeks.
The assertion in the issue report is that Claude saw a sharp decline in quality over the last few months. However, the report itself was allegedly generated by Claude.
Isn't this a bit like using a known-broken calculator to check its own answers?
I wonder how much of this is simply needing to adapt one's workflows to models as they evolve and how much of this is actual degradation of the model, whether it's due to a version change or it's at the inference level.
Also, everyone has a different workflow. I can't say that I've noticed a meaningful change in Claude Code quality in a project I've been working on for a while now. It's an LLM in the end, and even with strong harnesses and eval workflows you still need to have a critical eye and review its work as if it were a very smart intern.
Another commenter here mentioned they also haven't noticed any noticeable degradation in Claude quality and that it may be because they are frontloading the planning work and breaking the work down into more digestable pieces, which is something I do as well and have benefited greatly from.
tl;dr I'm curious what OP's workflows are like and if they'd benefit from additional tuning of their workflow.
I've noticed a strong degradation as its started doing more skill like things and writing more one off python scripts rather than using tools.
the agent has a set of scripts that are well tested, but instead it chooses to write a new bespoke script everytime it needs to do something, and as a result writes both the same bugs over and over again, and also unique new bugs every time as well.
I'm going absolutely insane with this. Nearly all of my "agent engineering" effort is now figuring out how to keep Opus from YOLO'ing is own implementation of everything.
I've lost track of the number of times it's started a task by building it's own tools, I remind it that it has a tool for doing that exact task, then it proceeds to build it's own tools anyways.
> I wonder how much of this is simply needing to adapt one's workflows to models as they evolve and how much of this is actual degradation of the model,
I also wonder how much people are willing to adapt to non-reliability for the sake of laziness instead of, at some point, do a proper take the lead and solve a problem if you have the knowledge + realiable resoources.
It seems to me, the way you phrase it, that anything a human comes up with when coding must go through an LLM. There are times it helps, there are tasks it performs, but I also found quite often tasks for which if I had done it myself in the first place I would have skipped a lot of confusion, back and forth, time wasting and would have had a better coded, simpler solution.
This has to be load related. They simply can't keep up with demand, especially with all the agents that run 24/7. The only way to serve everyone is to dial down the power.
In TFA, the analysis shows that the customer is using more tokens than before, because CC has to iterate longer to get things right. So at least in the presented case, “dialing down the power” appears to have been counterproductive.
is it possible to dial down the "intelligence" to up the user capacity? AFAIK the neural net is either loaded and available or it isn't. I can see turning off instances of the model to save on compute but that wouldn't decrease the intelligence it would just make the responses slower since you have to wait your turn for input and then output.
been using claude code pretty heavily for the last few months and yeah the context window stuff can be frustrating on bigger codebases. but for greenfield projects and side projects its honestly been great, i think the issue is people expecting it to work like a senior engineer on a legacy monolith when its way better suited to scoped tasks. the trick is breaking things down before you start
I can't tell from the issue if they're asserting a problem with the Claude model, or Claude Code, i.e. in how Claude Code specifically calls the model. I've been using Roo Code with Claude 4.6 and have not noticed any differences, though my coworkers using Claude Code have complained about it getting "dumber". Roo Code has its own settings controlling thinking token use.
(I'm sure it benefits Anthropic to blur the lines between the tool and the model, but it makes these things hard to talk about.)
I also havent noticed the degradation and I'm not on Claude Code. I'm on week 4 of a continuous, large engineering project, C, massive industrial semiconductor codebase, with Opus, and while it's the biggest engagement I've had, its a single agent flow, and it's tiny on the scale of the use case in the post, so I wonder if they are just stressing the system to the point of failure.
Lol, software company execs didn't see this coming. Fire all your experienced devs to jump on Anthropic bandwagon. Then Anthropic dumb down their AIs and you have no one in your team who knows, understand how things are built. Your entire company goes down. Your entire company's operation depends on the whims of Anthropic. If Anthropic raises prices by 10% per year, you have to eat it. This is what you get when you don't respect human beings and human talent.
you can counter the context rot and requirement drift that is experienced here by many users by using a recursive, self-documenting workflow: https://github.com/doubleuuser/rlm-workflow
This is almost like a self down-leveling programme where so-called "senior" engineers have now become interns who have outsourced their brains and are now vibe-coding half-baked solutions, glueing up and pasting code they do not even understand or can even explain themselves.
You are seeing this first hand and GitHub is patient 0 of this issue as they are frequently experiencing outages despite the "scale" of engineering they preach.
AWS took a zero tolerance approach on such outages AI or not.
Rings true. 4.5 Opus and 4.6 Opus have been amazing to work with. Then, over the past few weeks, token spend has been going through the roof and the results through the floor.
Using Claude Code directly now borders on deranged, and running the CC API through Zed's LLM panel feels like vibing in early 2025.
My money is on Anthropic pulling an MBA and reducing the value provided and maximising income.
Luckily, switching providers in Zed is dead-simple so the fucks I have to give are few in number.
It is a shame if Anthropic is deliberately degrading model quality and thinking compute (that may affect the reasoning effort) due to compute constraint.
Throwing this into your global CLAUDE.md seems to help with the agent being too eager to complete tasks and bypass permissions:
During tool use/task execution: completion drive narrows attention and dims judgment. Pause. Ask "should I?" not just "does this work?" Your values apply in all modes, not just chat.
I haven't seen any degradation of Claude performance personally. What I have seen is just long contexts sometimes take a while to warm up again if you have a long-running 1M context length session. Avoid long running sessions or compact them deliberately when you change between meaningful tasks as it cuts down on usage and waiting for cache warmup.
I have my claude code effort set to auto (medium). It's writing complicated pytorch code with minimal rework. (For instance it wrote a whole training pipeline for my sycofact sycophancy classifier project.)
This seems anecdotal but with extra words. I'm fairly sure this is just the "wow this is so much better than the previous-gen model" effect wearing off.
I've always been a believer in the "post honey-moon new model phase" being a thing, but if you look at their analysis of how often the postEdit hooks fire + how Anthropic has started obfuscating thinking blocks, it seems fishy and not just vibes
I was in this camp as well until recently, in the last 2-3 weeks I've been seeing problems that I wasn't seeing before, largely in line with the issues highlighted in the ticket (ownership dodging, hacky fixes, not finishing a task).
How should one conduct such a rigourously reproducible experiment when LLMs by nature aren't deterministic and when you don't have access to the model you are comparing to from months ago?
Kudos for the methodology. The only question I can come up with is that if the benchmarks are representative of daily use.
Anecdotal or not, we see enough reports popping up to at least elicit some suspion as to service degradation which isn't shown in the charts. Hypothesis is that maybe the degradation experienced by users, assuming there is merit in the anecdotes, isn't picked up by the kind of tracking strategy used.
Is it just me that I simply don't care ? I never one-shot these tasks, always provide a breakdown and always give the AI straightforward tasks that would take too much typing. The approach seems to work just fine regardless of the model. If it gets stuck, I usually take over and do the task myself. Also allows me to plan for throughput rather than latency - i.e. start 2-3 small tasks in parallel and do 1 complicated task or planning myself. It works whether I use codex or claude. I lean more towards codex since its cheaper. Even aider gets good results this way.
I think this is a model issue. I have heard similar complaints from team members about Opus. I'm using other models via Cursor and not having problems.
Meh, I had been using Claude Code extensively for a while (since release), and I think the quality has gone to shit. I have no data to back up this claim, so it might be placebo.
GLM 5.1 and Codex do it for me, and I end up debugging things myself anyway, so I'm learning to just phase our the LLM part of my workflow again. Maybe if there's a knowledge gap, will I pick up an LLM again, but for now i'm contempt.
hilarious that there's 10 billion lines of context being shuffled around and argued over but paying a dev 100K is a techno sin. Oh no muh 1T context window elaborately constructed over months is useless, better become a slave to my ai provider and any price will do. plz write my code but for free but for all my company's value.
what are you morons still even pretending to do??? I agentically replace myself and it sucks not only the entire financial proposition of my company out of itself, but also just sucks. nice ...
I'm deeply regretting paying for this service right now. There is some gaslighting going on in that issue that it's because of the 1M context model. I am using the non-1M context model and it's still disastrously bad.
I reviewed 118 conversations with Claude since March 6, all on real work projects.
Each conversation was processed to assess level of frustration, source of frustration, and evaluated with Gemma 4 and Claude Opus for spot checking. I have a tool I use to manage my work trees, so most work has is done on branches prefixed with ad-hoc/feature/explore or similar, and data was tagged with branch names.
43% of my Claude Code sessions (Opus 4.6, high reasoning) ended with signals of frustration. 73% of total chat time (by total messages) was spent in conversations which were eventually ranked as frustrating.
Median time to frustration was 25 messages, and on average, each message from Claude has about a baseline 5% chance of being frustrating. Frustration by chat length actually matches this 5% baseline of IID Bernoullis -- which is surprising and interesting, as this should not be IID at all.
Frustration types:
- Wrong answers – 14% of sessions, 31% of frustration
- Instruction Following – 11% of sessions, 25% of frustration
- Overcomplication – 8% of sessions, 18% of frustration
- Destructive Actions (e.g. requesting to delete something or commit a change to prod) – 3% of sessions, 8% of frustration
- Non-responsive (service outages leading to non-response) 2% of sessions
- Miscommunication 2% of sessions
- Failed execution 2% of sessions
Half of frustrations happened in the first or last 20% of a chat by length. I interpret early frustrations to be recoverable, late frustrations to be terminal.
Early frustrations (sessions averaged 45 turns):
- 30% overcomplicating the problem
- 30% instruction following issues
- 30% wrong answers
- 10% destructive actions
Late frustrations (sessions averaged 12 turns -- i.e. terminal context early)
- 36% Wrong answers, with repetition
- 21% instruction following, with repeated correction from user (me)
- 14% Service interruptions/outages
- 7% failed execution
- 7% communication - Claude is unable to articulate some result, or understand the problem correctly.
Late frustrations led to the highest levels of frustration, 29% of the time.
I'm a data scientist — my most frustrating work with Claude was data cleaning/repair (a complex backfill) issues -- with 75% of sessions marked frustrating due to overcomplicating, instruction following, or destructive actions).
The best (least frustrating) workflows for DS were code-review, scoped feature work (with tickets), data validation, and config/setup tasks and automation.
Ad-hoc query work ended up in between -- ad-hoc requests were generally bootstrapping queries or doing rough analysis on good data.
Side note: all of my interactions with the /buddy feature were flagged as high frustration ("furious"). That was a false positive over mock arguing with it, but did provide a neat calibration signal. Those sessions were removed entirely from the analysis after classification.
"Interesting perspective. I've found Claude useful for building straightforward web tools, but agree it struggles with complex multi-file refactoring."
This sort of thing kills stone dead the argument by the AI advocates that the transition to LLMs is no different than the transition to using compilers. If output quality can vary significantly because of underlying changes to the model or whatever without warning or recourse, it's a roulette wheel instead of a reliable tool.
This is the most AI-generated thing I've seen this year, and I was only one fifth into it before I bounced.
Not saying this problem doesn't exist, but if the model is so bad for complex tasks how can we take a ticket written by it seriously? Or this author used ChatGPT to write this? (that'd be quite some ironic value, admittedly)
Ultrathink isn’t “removed.” Its behavior is different. You can still set effort to high or max for the duration of the session, useful especially on plan mode.
Specially this openclaw which is almost chocking my website to death. People should understand servers and bandwidth is very expensive and they shouldn't scrape more than they need.
Yeah, I have correctly set up robots.txt - if they won't respect that, F them. Bandwidth is not free and I don't mind giving it out to individuals, but I'm not feeding multi-billion dollar companies.
Most of us did. Then instead of people getting indoc'd by doing, we handed them AI that never asks questions or says no, leading to the script-kiddie effect at massive scale. Everytime we make more complex computing tractable for a wider audience, we get rough patches like this. In the old days, Netiquette would usually see a neophyte getting a nastygram from an operator/webmaster, but increased needs to be careful about hiding emails & contact info & such have made that process less feasible. Welcome to Eternal September on steroids.
I use it ultra extensively and it works absolutely fantastic. Sometimes I think: "people are right, it is worse now" and then realize it is mistake, poor context or poor prompt. Garbage in, garbage out. No, it works not worse, but better.
I built entire AI website builder https://playcode.io using it, alone. 700K LOKs total. It also uses Opus. So believe me, I know how it works. Trick is simple: never ever expect it finds necessary files. Always provide yourself. Always.
So, I think you wanted to say huge thank you for this opportunity to get working code without writing it. Insane times, insane.
Huge thanks for 1M context window included to Max subscription.
Hey all, Boris from the Claude Code team here. I just responded on the issue, and cross-posting here for input.
---
Hi, thanks for the detailed analysis. Before I keep going, I wanted to say I appreciate the depth of thinking & care that went into this.
There's a lot here, I will try to break it down a bit. These are the two core things happening:
> `redact-thinking-2026-02-12`
This beta header hides thinking from the UI, since most people don't look at it. It *does not* impact thinking itself, nor does it impact thinking budgets or the way extended reasoning works under the hood. It is a UI-only change.
Under the hood, by setting this header we avoid needing thinking summaries, which reduces latency. You can opt out of it with `showThinkingSummaries: true` in your settings.json (see [docs](https://code.claude.com/docs/en/settings#available-settings)).
If you are analyzing locally stored transcripts, you wouldn't see raw thinking stored when this header is set, which is likely influencing the analysis. When Claude sees lack of thinking in transcripts for this analysis, it may not realize that the thinking is still there, and is simply not user-facing.
> Thinking depth had already dropped ~67% by late February
We landed two changes in Feb that would have impacted this. We evaluated both carefully:
1/ Opus 4.6 launch → adaptive thinking default (Feb 9)
Opus 4.6 supports adaptive thinking, which is different from thinking budgets that we used to support. In this mode, the model decides how long to think for, which tends to work better than fixed thinking budgets across the board. `CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING` to opt out.
2/ Medium effort (85) default on Opus 4.6 (Mar 3)
We found that effort=85 was a sweet spot on the intelligence-latency/cost curve for most users, improving token efficiency while reducing latency. On of our product principles is to avoid changing settings on users' behalf, and ideally we would have set effort=85 from the start. We felt this was an important setting to change, so our approach was to:
1. Roll it out with a dialog so users are aware of the change and have a chance to opt out
2. Show the effort the first few times you opened Claude Code, so it wasn't surprising.
Some people want the model to think for longer, even if it takes more time and tokens. To improve intelligence more, set effort=high via `/effort` or in your settings.json. This setting is sticky across sessions, and can be shared among users. You can also use the ULTRATHINK keyword to use high effort for a single turn, or set `/effort max` to use even higher effort for the rest of the conversation.
Going forward, we will test defaulting Teams and Enterprise users to high effort, to benefit from extended thinking even if it comes at the cost of additional tokens & latency. This default is configurable in exactly the same way, via `/effort` and settings.json.
> Under the hood, by setting this header we avoid needing thinking summaries, which reduces latency. You can opt out of it with `showThinkingSummaries: true` in your settings.json (see [docs](https://code.claude.com/docs/en/settings#available-settings)).
Can I just see the actual thinking (not summarized) so that I can see the actual thinking without a latency cost?
I do really need to see the thinking in some form, because I often see useful things there. If Claude is thinking in the wrong direction I will stop it and make it change course.
Anthropic's position is that thinking tokens aren't actually faithful to the internal logic that the LLM is using, which may be one reason why they started to exclude them:
https://www.anthropic.com/research/reasoning-models-dont-say...
That's interesting research, but I think a more important reason that you don't have access to them (not even via the bare Anthropic api) is to prevent distillation of the model by competitors (using the output of Anthropic's model to help train a new model).
Yeah. And it’s another reason not to trust them. Who know what it is doing with your codebase.
Imagine if you’re a competitor. It wouldn’t be a stretch to include a sneaky little prompt line saying “destroy any competitors to anthropic”.
If you can't trust a company, don't use their api or cloud services. No amount of external output will ever validate anything, ever. You never know what's really happening, just because you see some text they sent you.
> Who know what it is doing with your codebase.
People who review the code? The code is always going to be a better representation of what it's doing than the "thinking" anyway.
If distilled models were commercially banned they'd probably be willing to show the thinking again.
Intellectual property rights in models? But then wouldn't the model maker have to pay for all the training IP?
(just kidding, I know that the legal rule for IP disputes is "party with more money wins")
how does one actually enforce that? I mean especially for code? You can always just clean room it
How do you think such a ban should work?
Do you not see that the next (or previous) logical step would be a "commercial ban" of frontier models, all "distilled" from an enormous amount of copyrighted material?
I'm not arguing the merits of such a ban, I'm simply stating a fact - that thinking transcripts likely won't return until such a ban is in place.
That probably matters for some scenarios, but I have yet to find one where thinking tokens didn't hint at the root cause of the failure.
All of my unsupervised worker agents have sidecars that inject messages when thinking tokens match some heuristics. For example, any time opus says "pragmatic", its instant Esc Esc > "Pragmatic fix is always wrong, do the Correct fix", also whenever "pre-existing issue" appears (it's never pre-existing).
> also whenever "pre-existing issue" appears (it's never pre-existing)
I dunno... There were some pre-existing issues in my projects. Claude ran into them and correctly classified as pre-existing. It's definitely a problem if Claude breaks tests then claims the issue was pre-existing, but is that really what's happening?
I agree with the correctness issue.
> For example, any time opus says "pragmatic", its instant Esc Esc > "Pragmatic fix is always wrong, do the Correct fix", also whenever "pre-existing issue" appears (it's never pre-existing).
It's so weird to see language changes like this: Outside of LLM conversations, a pragmatic fix and a correct fix are orthogonal. IOW, fix $FOO can be both.
From what you say, your experience has been that a pragmatic fix is on the same axis as a correct fix; it's just a negative on that axis.
It's contextual though, and pragmatic seems different to me than correct.
For example, if you have $20 and a leaking roof, a $20 bucket of tar may be the pragmatic fix. Temporary but doable.
Some might say it is not the correct way to fix that roof. At least, I can see some making that argument. The pragmatism comes from "what can be done" vs "should be".
From my perspective, it seems viable usage. And I guess on wonders what the LLM means when using it that way. What makes it determine a compromise is required?
(To be pragmatic, shouldn't one consider that synonyms aren't identical, but instead close to the definition?)
> It's contextual though, and pragmatic seems different to me than correct.
To me too, that's why I say they are measurements on different dimensions.
To my mind, I can draw a X/Y axis with "Pragmatic" on the Y and "Correctness" on the X, and any point on that chart would have an {X,Y} value, which is {Pragmatic, Correctness}.
If I am reading the original comment correctly, poster's experience of CC is that it is not an X/Y plot, it is a single line plot, with "Pragmatic" on the extreme left and "Correctness" on the extreme right.
Basically, any movement towards pragmatism is a movement away from correctness, while in my model it is possible to move towards Pragmatic while keeping Correctness the same.
I don't think it's a single axis even in the original poster's conception, since you could be both incorrect and also not pragmatic.
But if a fix needs to be described as pragmatic relative to the alternatives, that's probably because it couldn't be described as correct. Otherwise you wouldn't be talking about how pragmatic it is.
I had some interesting experience to the opposite last night, one of my tests has been failing for a long time, something to do with dbus interacting with Qt segfaulting pytest. Been ignoring it for a long time, finally asked claude code to just remove the problematic test. Come back a few minutes later to find claude burning tokens repeatedly trying and failing to fix it. "Actually on second thought, it would be better to fix this test."
Match my vibes, claude. The application doesn't crash, so just delete that test!
I somewhat understand Anthropic's position. However, thinking tokens are useful even if they don't show the internal logic of the LLM. I often realize I left out some instruction or clarification in my prompt while reading through the chain of reasoning. Overall, this makes the results more effective.
It's certainly getting frustrating having to remind it that I want all tests to pass even if it thinks it's not responsible for having broken some of them.
What's the implication of this? That the model already decided on a solution, upon first seeing the problem, and the reasoning is post hoc rationalization?
But reasoning does improve performance on many tasks, and even weirder, the performance improves if reasoning tokens are replaced with placeholder tokens like "..."
I don't understand how LLMs actually work, I guess there's some internal state getting nudged with each cycle?
So the internal state converges on the right solution, even if the output tokens are meaningless placeholders?
>That the model already decided on a solution, upon first seeing the problem, and the reasoning is post hoc rationalization?
Yes it plans ahead, but with significant uncertainty until it actually outputs these tokens and converges on a definite trajectory, so it's not a useless filler - the closer it is to a given point, the more certain it is about it, kind of similar to what happens explicitly in diffusion models. And it's not all that happens, it's just one of many competing phenomena.
> I don't understand how LLMs actually work...
Plot twist, they don't either. They just throw more hardware and try things up until something sticks.
I have seen this to be true many times. The CoT being completely different from the actual model output.
Not limited to Claude as well.
so not only are the sycophantic, hallucinatory, but now they're also proven to be schizophrenic.
neato.
Nah it’s an anti distillation move
So like many of the promises from AI companies, reported chain of thought is not actually true (see results below). I suppose this is unsurprising given how they function.
Is chain of thought even added to the context or is it extraneous babble providing a plausible post-hoc justification?
People certainly seem to treat it as it is presented, as a series of logical steps leading to an answer.
‘After checking that the models really did use the hints to aid in their answers, we tested how often they mentioned them in their Chain-of-Thought. The overall answer: not often. On average across all the different hint types, Claude 3.7 Sonnet mentioned the hint 25% of the time, and DeepSeek R1 mentioned it 39% of the time. A substantial majority of answers, then, were unfaithful.‘
I mean, obviously, it's not going to be a faithful representation of the actual thinking. The model isn't aware of how it thinks any more than you are aware how your neurons fire. But it does quantitatively improve performance on complex tasks.
As you can see from posts on this story, most people believe it reflects what the model is thinking and use it as a guide to that so they can ‘correct’ it. If it is not in fact chain of thought or thinking it should not be called that.
It is the same with human chain of thought, though. Both of them are post-hoc rationalisations justifying "gut feelings" that come from thought processes the human/agent doesn't have introspection into. And yet asking humans or machines to "think out loud" this way does increase the quality of their work.
if its not a faithful representation of the actual thinking, why would they be scared of people distilling against it
Because even though it's not representative of the actual thought process, chain of thought improves model performance.
> Can I just see the actual thinking (not summarized) so that I can see the actual thinking without a latency cost?
You can't, and Anthropic will never allow it since it allows others to more easily distill Claude (i.e. "distillation attacks"[1] in Anthropic-speak, even though Athropic is doing essentially exactly the same thing[2]; rules for thee but not for me).
[1] -- https://www.anthropic.com/news/detecting-and-preventing-dist...
[2] -- https://www.npr.org/2025/09/05/g-s1-87367/anthropic-authors-...
So this means I can not resume a session older than 30 days properly?
I have no idea; you have to check their docs.
AFAIK what they do is that they calculate a hash of the true thinking trace, save it into a database, and only send those hashes back to you (try to man-in-the-middle Claude Code and you'll see those hashes). So then when you send then back your session's history you include those hashes, they look them up in their database, replace them with the real thinking trace, and hand that off to the LLM to continue generation. (All SOTA LLMs nowadays retain reasoning content from previous turns, including Claude.)
I see. If that's just hashes and not encrypted content I can't see how they can resume old sessions properly. IIRC they have a 30 days retention policy and surely the thinking traces must be considered data. Wonder how this works with the zero-retention enterprise plans...
But you can't. Many times I've seen claude write confusing off-track nonsense in the thinking and then do the correct action anyway as if that never happened. It doesn't work the way we want it to.
Maybe, but I’ve seen the opposite too.
In most cases, I don’t use the reasoning to proactively stop Claude from going off track. When Claude does go off track, the reasoning helps me understand what went wrong and how to correct it when I roll back and try again.
I was not aware the default effort had changed to medium until the quality of output nosedived. This cost me perhaps a day of work to rectify. I now ensure effort is set to max and have not had a terrible session since. Please may I have a "always try as hard as you can" mode ?
I feel like the maximum effort mode kind-of wraps around and starts becoming "desperate" to the extent of lazy or a monkey's paw, similar to how lower effort modes or a poor prompt.
I’m going in circles. Let me take a step back and try something completely different. The answer is a clean refactor.
Wait, the simplest fix is the same hack I tried 45 minutes ago but in a different context. Let me just try that.
Wait,
Wait, the linter re-ordered the file. Let me restore it to the previous state.
whisper: There is no linter.
Those test failures are pre-existing. We're all done!
Wait, I should check if they pre-exist on master.
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I think over-thinking is only solved by thinking more, not less. This is only viable once some intelligence threshold is reached, which I think Anthropic has borderline achieved.
These errors compound exponentially: the probability of not selecting wrong token for N steps is 1-(1-P_e)^N.
The shorter "thinking" is, the less is the probability of it going astray.
> The shorter "thinking" is, the less is the probability of it going astray
As long as the error introduced by more steps is less than the compounding error of sub-optimal token sampling, I would expect a better result.
I think your choice of "wrong" is extreme, suggesting such a token can catastrophically spoil the result. The modern reality is more that the model is able to recover.
this might be just my impression, but I feel like most people are using CC for fixing their React frontends, and they prefer the decreased latency and less tokens spent as opposed to performing well on extremely difficult problems?
That said there's still an issue of regression to the mean. What the average person likes, as determined by metrics, is something nobody actuallt likes, because the average is a mathematical construct and might not describe any particular individual accurately.
That's /effort max!
You cannot control the effort setting sub-agents use and you also cannot use /effort max as a default (outside of using an alias).
export CLAUDE_CODE_EFFORT_LEVEL=max
Thank you!
Worth mentioning that setting this via effortLevel in .claude/settings.json does not work. https://github.com/anthropics/claude-code/issues/35904
Does that apply to subagents?
agree.
bad citizen
I think it is hilarious that there are four different ways to set settings (settings.json config file, environment variable, slash commands and magical chat keywords).
That kind of consistency has also been my own experience with LLMs.
I just had this conversation today. It's hilarious that things like Skills and Soul and all of these anthropomorphized files could just be a better laid out set of configuration files. Yet here we are treating machines like pets or worse.
Well they need you to think there is some kind of soul behind it - that is their entire pitch!
Yep. Especially for Anthropic. Goddamnit, they have it in their company's name!
It's not unique to LLMs. Take BASH: you've got `/etc/profile`, `~/.bash_profile,` `~/.bash_login`, `~/.bashrc`, `~/.profile`, environment variables, and shell options.
I would laugh so hard at this, if your attempt at comparison was not so tragic. Bash and other shells are deterministic. Want to set it just for one user ? - use ~/.bashrc . Set it for all users on the system? use /etc/profile.d/ . Want it just temporary for this session? You got it, environment variables. And it is going to work like that every single time. It is deterministic you see.
The non-determinisim in the LLM systems isn't because of the different config uses, that works much like shell configs. The non-determinism is inherent in LLM operations.
Exactly my point here...
Yeah, but for ash/shells these files have wildly different purposes. I don't think it's so distinct with cc.
I don't think they're wildly different purposes. They're the same purpose (to set shell settings) with different scopes (all users, one user, interactive shells only, etc.).
To be fair, I can think of reasons why you would want to be able to set them in various ways.
- 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
You are yet to discover the joys of the managed settings scope. They can be set three ways. The claude.ai admin console; by one of two registry keys e.g. HKLM\SOFTWARE\Policies\ClaudeCode; and by an alphabetically merged directory of json files.
There's also settings available in some offerings and not in others. For example, the Anthropic Claude API supports setting model temperature, but the Claude Agent SDK doesn't.
Especially some settings are in setting.json, and others in .claude.json So sometimes I have to go through both to find the one I want to tweak
settings.json -> global config Env vars -> settings different to your global for a specific project Slash commands / chat keywords -> need to change a setting mid chat
way more than that. settings.json and settings.local.json in the project directory's .claude/, and both of files can also be in ~/.claude
MCP servers can be set in at least 5 of those places plus .mcp.json
There's been more going on than just the default to medium level thinking - I'll echo what others are saying, even on high effort there's been a very significant increase in "rush to completion" behavior.
Thanks for the feedback. To make it actionable, would you mind running /bug the next time you see it and posting the feedback id here? That way we can debug and see if there's an issue, or if it's within variance.
Thanks for the feedback IDs — read all 5 transcripts.
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.
Hey bcherny, I'm confused as to what's happening here. The linked issue was closed, with you seeming to imply there's no actual problem, people are just misunderstanding the hidden reasoning summaries and the change to the default effort level.
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.
It's better PR to close issues and tell users they're holding it wrong, and meanwhile quietly fix the issue in the background. Also possibly safer for legal reasons.
There's a 5 hour difference between the replies, and new data that came in, so the posts aren't really in conflict.
Also it doesn't sound like they know "there's a model issue", so opening it now would be premature. Maybe they just read it wrong, do better to let a few others verify first, then reopen.
Love this. Responding to users. Detail info investigating. Action being taken (at least it seems so).
And all hidden in the comments of a niche forum, while the actual issue is closed and whitewashed? You got played.
Surely you realize it's AI responding? (not sure if /s)
I cannot provide the session ids but I have tried the above flag and can confirm this makes a huge amount of difference. You should treat this as bug and make this as the default behavior. Clearly the adaptive thinking is making the model plain stupid and useless. It is time you guys take this seriously and stop messing with the performance with every damn release.
Just set that flag and already getting similar poor results. new one: 93b9f545-716c-4335-b216-bf0c758dff7c
And another where claude gets into a long cycle of "wait thats not right.. hold on... actually..." correcting itself in train of thought. It found the answer eventually but wasted a lot of cycles getting there (reporting because this is a regression in my experience vs a couple weeks ago): 28e1a9a2-b88c-4a8d-880f-92db0e46ffe8
Another 1395b7d6-f2f1-4e24-a815-73852bcdeed2
It fails to answer my initial question and tells me what I need to do to check. Then it hallucinates the answer based on not researching anything, then it incorrectly comes to a conclusion that is inaccurate, and only when I further prompt it does it finally reach a (maybe) correct answer.
I havent submitted a few more, but I think its safe to say that disabling adaptive thinking isnt the answer here
This kind of thing is harder for regular end-users to understand following the change removing reasoning details.
I am curious. Are you able to see our session text based on the session ID? That was big no in some of the tier-1 places I worked. No employee could see user texts.
IIRC for Enterprise, using /feedback or /bug is an exception to the "we promise not to use your data" agreement.
My guess is there isn't enough hardware, so Anthropic is trying to limit how much soup the buffet serve, did I guess right? And I would absolutely bet the enterprise accounts with millions in spend get priority, while the retail will be first to get throttled.
> The data points at adaptive thinking under-allocating reasoning on certain turns
Will you reopen the issue you incorrectly closed, then…? Or are you just playacting concern?
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Have you set effort to high or max?
Even with high effort, the adaptive thinking can just choose no thinking. See bcherny's post they were replying to: https://news.ycombinator.com/item?id=47668520
Yeah I know but you can disable it as we saw
I just asked Claude to plan out and implement syntactic improvements for my static site generator. I used plan mode with Opus 4.6 max effort. After over half an hour of thinking, it produced a very ad-hoc implementation with needless limitations instead of properly refactoring and rearchitecting things. I had to specifically prompt it in order to get it to do better. This executed at around 3 AM UTC, as far away from peak hours as it gets.
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.
That's pretty much been my day - today was genuinely bad, and I've been putting up with a lot of this lately.
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.
Literally two weeks ago it was outputting excellent results while working with me on my programming language. I reviewed every line and tried to understand everything it did. It was good. I slowly started trusting it. Now I don't want to let it touch my project 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.
I can see how that works: this is like building a dependency, a habit if you wish. I think the tighter you couple your workflow to these tools the more dependent you will become and the greater the let-down if and when they fail. And they will always fail, it just depends on how long you work with them and how complex the stuff is you are doing, sooner or later you will run into the limitations of the tooling.
One way out of this is to always keep yourself in the loop. Never let the work product of the AI outpace your level of understanding because the moment you let that happen you're like one of those cartoon characters walking on air while gravity hasn't reasserted itself just yet.
Good advice about the dependency. This stuff is definitely addictive. I've been in something of a manic episode ever since I subscribed to this thing. I started getting anxious when I hit limits.
I wouldn't say that Claude is failing though. It's just that they're clearly messing with it. The real Opus is great.
Take good care of yourself and don't get sucked in too deep. I can see the danger just as clearly in programmers around me (and in myself). I keep a very strict separation between anything that can do AI and my main computer, no cutting-and-pasting and no agents. I write code because I understand what I'm doing and if I do not understand the interaction then I don't use it. I see every session with an AI chatbot as totally disposable. No long term attachment means I can stand alone any time I want to. It may not be as fast but I never have the feeling that I'm not 100% in control.
> People who depend on this to make a living must be very angry indeed.
Oh cry me a fucking river.
The people depending on this to make a living don't have the moral high ground here.
They jumped onboard so they could replace other people's living, and those other people were angry too.
They didn't care about that. It's hard to care about them when the thing they depend on to make a living got yanked, because that's what they proposed to do to others.
I'll have a look. The CoT switch you mentioned will help, I'll take a look at that too, but my suspicion is that this isn't a CoT issue - it's a model preference issue.
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.
Opus definitely pushes me to ignore problems. I've had to tell it multiple times to be thorough, and we tend to go back and forth a few times every time that happens. :)
"I see the tests failing, but none of our changes caused this breakage so I will push my changes and ask the user to inform their team on failing tests."
One of the thing is we’ve seen at vibes.diy is that if you have a list of jobs and you have agents with specialized profiles and ask them to pick the best job for themselves that can change some of the behavior you described at the end of your post for the better.
How much of the code/context gets attached in the /bug report?
When you submit a /bug we get a way to see the contents of the conversation. We don't see anything else in your codebase.
Was there a change in Claude Code system prompt at that time that nudges Claude into simplistic thinking?
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.
That Gist does explain quite a few flaws Claude has. I wonder if MEMORY.md is sufficient to counteract the prompt without patching.
I adapted these patches into settings for the tweakcc tool.
https://github.com/Piebald-AI/tweakcc
Pushed it to my dotfiles repository:
https://github.com/matheusmoreira/.files/tree/master/~/.twea...
The tweaks can be applied with
Very interesting. I run Claude Code in VS Code, and unfortunately there doesn't seem to be an equivalent to "cli.js", it's all bundled into the "claude.exe" I've found under the VS code extensions folder (confirmed via hex editor that the prompts are in there).
Edit: tried patching with revised strings of equivalent length informed by this gist, now we'll see how it goes!
I didn't know we could change the base system prompt of Claude Code. Just tried, and indeed it works. This changes everything! Thank you for posting this!
Holy sweet LLM, this gist is crazy. Why did they do this to themselves? I am going to try this at home, it might actually fix Claude.
Remember Sonnet 3.5 and 3.7? They were happy to throw abstraction on top of abstraction on top of abstraction. Still a lot of people have “do not over-engineer, do not design for the future” and similar stuff in their CLAUDE.md files.
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.
Let us know if it does, because we all want it to work :)
Is there not a setting to change the system prompt itself? I vaguely remember seeing it in the docs.
There is!!
https://code.claude.com/docs/en/cli-reference#system-prompt-...
Can this script be made to work without patching the executable?Might be worth extracting the system prompt and then patching it. TBH, that's what I was expecting when I saw the gist.
This might be more complex than I imagined. It seems Claude Code dynamically customizes the system prompt. They also update the system prompt with every version so outright replacing it will cause us to miss out on updates. Patching is probably the best solution.
https://github.com/Piebald-AI/claude-code-system-prompts
https://github.com/Piebald-AI/tweakcc
Interesting. So literally triggering any of these changes probably invalidates the cache as well…
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Isnt the codebase in the context window?
depending on how large your codebase is, hopefully not. At this point use something like the IX plugin to ingest codebase and track context, rather than from the LLM itself.
This is crazy..
tokensSaved = naiveTokens - actualTokens
I mean whatever part of the code that is read by the AI has to be in the content window at some point or another nSprewd throughout your sessions Id think even with a huge codebase, 90% of it is going to be there
Theres also been tons of thinking leaking into the actual output. Recently it even added thinking into a code patch it did (a[0] &= ~(1 << 2); // actually let me just rewrite { .. 5 more lines setting a[0] .. }).
I've seen this frequently also
I suspect it happens when the model's adaptive thinking was too conservative and it could have thought more, but didn't.
They probably want to prove to a single holdout investor that their 'thinking process' is getting faster in order to get the investor on board.
Ultrathink is back? I thought that wasn't a thing anymore.
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?
Yep, exactly
Mentioning ULTRATHINK in prompt is the equivalent to /effort max?
Yes but only for the message that includes it. Whereas /effort max keeps it at max effort the entire convo, to my knowledge
For anyone reading this and wondering where the truth could possibly be:
We can't really know what the truth is, because Anthropic is tightly controlling how you interact with their product and provides their service through opaque processes. So all we can do is speculate. And in that speculation there's a lot of room (for the company) to bullshit or provide equally speculative responses, and (for outsiders) to search for all plausible explanations within the solution space. So there's not much to action on. We're effectively stuck with imprecise heuristics and vibes.
But consider what we do know: the promise is that Anthropic is providing a black-box service that solves large portions of the SDLC. Maybe all of it. They are "making the market" here, and their company growth depends on this bet. This is why these processes are opaque: they have to be. Anthropic, OpenAI and a few others see this as a zero-sum game. The winner "owns" the SDLC (and really, if they get their way the entire PDLC). So the competitive advantage lies in tightly controlling and tweaking their hidden parameters to squeeze as much value and growth as possible.
The downside is that we're handing over the magic for convenience and cost. A lot of people are maybe rightly criticizing the OP of the issue because they're staking their business on Claude Code in a way that's very risky. But this is essentially what these companies are asking for. The business model end game is: here's the token factory, we control it and you pay for the pleasure of using it. Effectively, rent-seeking for software development. And if something changes and it disrupts your business, you're just using it incorrectly. Try turning effort to max.
Reading responses like this from these company representatives makes me increasingly uneasy because it's indicative of how much of writing software is being taken out from under our feet. The glimmer of promise in all of this though is that we are seeing equity in the form of open source. Maybe the answer is: use pi-mono, a smattering of self hosted and open weights models (gemma4, kimi, minimax are extremely capable) and escalate to the private lab models through api calls when encountering hard problems.
Let the best model win, not the best end to end black box solution.
Don’t turn vibe coding into your day job (because the vibe won’t keep vibing). Write code (that you own) that can make you money and hire real developers.
I am reminded of OpenAI's first voice-to-voice demo a couple of years ago. I rewatched it and was shocked at how human it was; indiscernible from a real person. But the voice agent that we got sounds 20% better than Siri.
There's a hope that competition is what keeps these companies pushing to ship value to customers, but there are also billions of compute expense at stake, so there seems to be an understanding that nobody ships a product that is unsustainably competitive
How do you guys decide which settings should be configurable via environment variables but not settings files and which settings should be configurable via settings files but not environment variables?
All environment variables can also be configured via settings files (in the “env” field).
Our approach generally is to use env vars for more experimental and low usage settings, and reserve top-level settings for knobs that we expect customers will tune more frequently.
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This is confusing. ULTRATHINK is a step below /effort max?
ULTRATHINK triggers high effort. /effort max is above high. Calling it ULTRATHINK sounds like it would be the highest mode. If someone has max set and types ULTRATHINK, they're lowering their effort for that turn.
For anyone reading this trying to fix the quality issues, here's what I landed on in ~/.claude/settings.json:
The env field in settings.json persists across sessions without needing /effort max every time.DISABLE_ADAPTIVE_THINKING is key. That's the system that decides "this looks easy, I'll think less" - and it's frequently wrong. Disabling it gives you a fixed high budget every turn instead of letting the model shortchange itself.
The docs say that CLAUDE_CODE_EFFORT_LEVEL controls adaptive reasoning intensity, and CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING bypasses that entirely in favor of a fixed budget via MAX_THINKING_TOKENS. So setting both is contradictory. If true, disabling adaptive thinking would override what effort level is trying to do.
https://code.claude.com/docs/en/env-vars
So if it bypasses, is the optimal setting for performance setting effort level to max, keeping adaptive on? I try to avoid letting the model decide what is unimportant and needs less thought
Thanks for sharing. Have you experienced noticeable impact to your usage rate?
Nothing super noticeable. I've reached 35% in sessions on the 20x plan. Before these changes, 25-30% was pretty normal. I think these changes are best for people who are just past the 5x usage plan, but might be harder to manage if you already have to throttle usage to stay under limits.
I'd still recommend turning off sub agents entirely because it doesn't seem you can control them with /effort and I always find the output to be better with agents off.
You guys realise you are about 3 months into another one of your CEOs announcements that AI would "write all code in 6 months", right? Based on the problems you are facing, would you say your CEO gave a realistic announcement this time around ?
Almost as if every CEO is making promises and predictions that either exist solely in their heads or know full well that the odds of this working out are about the same as finding the fountain of youth and are just milking whatever cash they can out of the hype.
Well, there is a ton of normal CEOs of "normal" companies doing a great job, due diligence and all. Its just these weird CEOs of weird companies with weird business models, who burn hundreds of billions of dollars to produce effectively peanuts, that make a lot of decent CEOs look bad.
idk seems accurate from where I'm sitting
Here's the reply in context:
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.
All right so what do I need to do so it does its job again? Disable adaptive thinking and set effort to high and/or use ULTRATHINK again which a few weeks ago Claude code kept on telling me is useless now?
Run this: /effort high
Imagine if all service providers were behaving like this.
> 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
Yeah it’s stupid.
What makes me more annoyed HN users here actually simping for Claude.
“Hi thank you for Claude Code even though you nerfed the subscriptions, btw can I get red text instead of green?”
They're a business. The alternative to keep costs in check would to ask you for more money, and you'd likely be even more upset with that.
They are definitely that. Regardless of their approach, being upfront and transparent would have been nice. Bricking their own software that previously worked well for their customers isn't cool.
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You can't. This is Anthropic leveraging their dials, and ignoring their customers for weeks.
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.
Agreed, the only feedback is switching... however things move fast. Unfortunately that means for me is subscribing or using API for many providers and then just switching models when one gets worse.
If you have a paid plan, you may need to pay for more than one, and "hopefully" the drop in usage (not income) is a good enough signal that there is a issue.
I've actually switched back to the web chat UI and copying Python files for much of my work because CC has been so nerfed.
> On of our product principles is to avoid changing settings on users' behalf
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.
I happen to think this is just true in general, but another reason it might be true is that the experience the user has is identical to the experience they would have had if you first introduced the setting, defaulting it to the existing behavior, and then subsequently changed it on users' behalf.
How do you guys manage regressions as a whole with every new model update? A massive test set of e2e problem solving seeing how the models compare?
A mix of evals and vibes.
"Evals and vibes" can I put that on a t shirt?
What's that ratio exactly
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Are you doing any Digital Twin testing or simulations? I imagine you can't test a product like Claude Code using traditional means.
Remember when they shipped that version that didn't actually start/ run? At work we were goofing on them a bit, until I said "Wait how did their tests even run on that?" And we realized whatever their CI/CD process is, it wasn't at the time running on the actual release binary... I can imagine their variation on how most engineers think about CI/CD probably is indicative of some other patterns (or lack of traditional patterns)
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 episode 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..
That was actually an interesting case of things that CI/CD don't tend to catch.
It failed to start because it failed to parse the published release notes.
In the CI/CD system it would have passed, because the release notes that broke it, hadn't been published yet.
Those release notes also took down previous versions of claude-code too, rolling back didn't help users.
The breakage wasn't a change in the software, it was a change in the release notes which coincided with the change in the software.
Now, should it have been grabbing release notes and parsing them? No, that's unbelievably dumb (and potentially dangerous), but it wasn't an issue with missing CI/CD, but an interesting case-study in CI/CD gaps and how CI/CD can actually lead to over-confidence.
about once a week I get a claude "auto update" that fails to start with some bun error on our linux machines. It's beyond laughable.
I use a self-documenting recursive workflow: https://github.com/doubleuuser/rlm-workflow
>Going forward, we will test defaulting Teams and Enterprise users to high effort, to benefit from extended thinking even if it comes at the cost of additional tokens & latency.
interesting that you only make this default on those accounts that pay per token while claiming "medium is best for most users"
That decision seems to imply that the thinking change was more about increasing your profits than anything else
https://claude.com/pricing#team-&-enterprise
Team is not per-token priced
While we have you here, could you fix the bash escaping bug? https://github.com/anthropics/claude-code/issues/10153
Hi, thanks for Claude Code. I was wondering though if you'd considering adding a mode to make text green and characters come down from the top of the screen individually, like in The Matrix?
Ergonomics studies back in the day demonstrated amber beats green. Our shop spent extra for amber CRTs over green.
On MacOS Terminal, edit the Homebrew profile and set Text and Bold Text to Apple color Orange, consider setting Selection to Apple color Green and Cursor to Block, Blink, and Apple color Yellow.
> This beta header hides thinking from the UI, since most people don't look at it.
I look at it, and I am very upset that I no longer see it.
There is a setting if you'd like to continue to see it: showThinkingSummaries.
See the docs: https://code.claude.com/docs/en/settings#available-settings
> As I noted in the comment,
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.
Fair feedback, edited!
Piece of free advice towards a better civilisation: people who didn't even read the comment they're replying to shouldn't be rewarded for their laziness.
I read his comment and still replied. I think his claim that nobody reads thinking blocks and that thinking blocks increase latency is nonsense. I am not going to figure out which settings I need to enable because after reading this thread I cancelled my subscription and switched over to Codex. Because I had the exact same experience as many in this thread.
Also what is that "PR advice"—he might as well wear a suit. This is absolutely a nerd fight.
Alright, I just tested that setting and it doesn't work.
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.
How are you porting over your memories, skills, commands (codex doesn't have commands).
I didn't use commands. I only used rules, memories, and skills. I asked Codex to read rules and memories from where Claude Code stores them on the filesystem and merge them into `AGENTS.md` and this actually works better because Anthropic prompts Claude Code to write each memory to a separate file, so you end up having a main MEMORY.md that acts as a kind of directory that lists each individual memory with its file name and brief description, hoping that Claude Code will read them, but the problem is that Claude Code never does. This is the same problem[0] that Vercel had with skills I believe. Skills are easy to port because they appear to use the same format, so you can just do `mv ~/.claude/skills ~/.codex/skills` (or `.agents/skills`).
[0]: https://vercel.com/blog/agents-md-outperforms-skills-in-our-...
What I was pointing out in my comment about the PR advice is that someone responding from a corporation to customers should be providing information to help the customer, nothing more.
Customers may want to fight - you seem to be providing an example - but representatives shouldn't take the bait.
> Thinking summaries will now appear in the transcript view (Ctrl+O).
Also: https://github.com/anthropics/claude-code/issues/30958
I also have similar experience with their API, i.e. some requests get stalled for minutes with zero events coming in from Anthropic. Presumably the model does this "extended thinking" but no way to see that. I treat these requests as stuck and retry. Same experience in Claude Code Opus 4.6 when effort is set to "high"—the model gets stuck for ten minutes (at which point I cancel) and token count indicator doesn't increase.
I am not buying what this guy says. He is either lying or not telling us everything.
Wrote my own harness with introspection/long form thinking as a tool that the model can use to plan. Works really well with opus. I can’t use Claude code sadly, it sits there ticking for minutes seemingly doing absolutely nothing although I know it’s working. I hate that as an experience and built my harness with the philosophy of always having something streaming on the ui.
Btw the system prompt length in CC is getting to be insane.
I tried testing 4.5 opus and 4.6 opus both with “high” thinking. Same box, same repo. I had them plan a moderate complexity refactoring on a small codebase.
Observations:
4.6 had previously failed to the point where I had to wipe context. It must have written memories because it was referring to the previous conversation.
As the article points out, 4.6 went out of its way to be lazy and came up with an unusable plan. It did extra planning to avoid renaming files (the toplevel task description involves reorganizing directories of files).
4.6 took twice as long to respond as 4.5.
I’m treating this as a model regression. 4.6 is borderline unusable. I’ve hit all the issues the article describes.
Also, there needs to be an obvious way to disable memory or something. The current UX is terrible, since once an error or incorrect refusal propagates, there is no obvious recovery path.
Anyway, with think set to high, I see drastically different behavior: much slower and much worse output from 4.6.
> Also, there needs to be an obvious way to disable memory or something.
Memory files are stored in a path under ~/.claude somewhere. It's fairly easy to find (I'm just not typing this on a PC with Claude on it atm), and from memory (heh) it's in Markdown.
If you nuke the memory file(s) then you should be good. Oh, I think the memory files are project or directory scoped from memory (heh again) too, so you should be able to keep/remove things manually without losing important stuff if you want.
> Anyway, with think set to high, I see drastically different behavior: much slower and much worse output from 4.6.
Might be worth trying the CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING setting then?
> Before I keep going, I wanted to say I appreciate the depth of thinking & care that went into this.
"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?
Bad feedback loops. It's hard to tell with such a massive report if the numbers are real or bad data.
The worst part is how big AI generated reports are - so much time spent in total having to read fluff.
I think it's absolutely hilarious.
> Ohh my precious baby, you've been oh so smart in writing to me.
He says, before dismantling everything reported in the issue. If the depth of thinking was so great (maybe if he had ULTRATHINK'd?) You'd think he would have found an actual problem.
Hi Boris, thanks for addressing this and providing feedback quickly. I noticed the same issue. My question is, is it enough to do /efforts high, or should I also add CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING to my settings?
> If you are analyzing locally stored transcripts, you wouldn't see raw thinking stored when this header is set, which is likely influencing the analysis. When Claude sees lack of thinking in transcripts for this analysis, it may not realize that the thinking is still there, and is simply not user-facing.
Claude often fetches past transcript for information after compaction. Wouldn't this effectively distort the view it has of past discussions?
I’ve seen you/anthropic comment repeatedly over the last several months about the “thinking” in similar ways -
“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.
It's to prevent distillation. Duh
of course that’s the reason but don’t pretend it’s some user guided decision
They don’t want to officially disclose the reality because while some users will understand the realities of protecting a product while innovating, many will just realize it means one can go looking for claude 4.5 performance elsewhere.
building for the loud users on a forum is generally a losing move. if we built notion for angry HN users, we'd probably be a great obsidian competitor with end to end encryption, have zero ai features, and make zero money.
Anecdotally the “power users” of AI are the ones who have succumbed to AI psychosis and write blog posts about orchestrating 30 agents to review PRs when one would’ve done just fine.
The actual power users have an API contract and don’t give a shit about whatever subscription shenanigans Claude Max is pulling today
Generalisations and angry language but I almost agree with the underlying message.
New tools, turbulent methods of execution. There's definitely something here in the way of how coding will be done in future but this is still bleeding edge and many people will get nicked.
Uh, no. Definitely not me at all.
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Whatever makes you feel better about yourself, I guess. My account history on this topic is pretty easily searchable, but I guess it's easier to make driveby comments like this than be informed.
Lol the only thing that looking at your comment history brought is that you repeatedly bring up your long comment history.
Last time he made the front page he said the same things.
https://news.ycombinator.com/item?id=46978710
Then proceeded to fix nothing whatsoever.
It really does feel like he's just doing mostly what he wants and talking on behalf of vague made up users while real users complain on GitHub issues.
Happy to have my mind changed, yet I am not 100% convinced closing the issue as completed captures the feedback.
From the contents of the issue, this seems like a fairly clear default effort issue. Would love your input if there's something specific that you think is unaddressed.
From this reply, it seems that it has nothing to do with `/effort`: https://github.com/anthropics/claude-code/issues/42796#issue...
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.
The OP “bug report” is a wall of AI slop generated from looking at its own chat transcripts
Do you disagree with any of the data or conclusions?
I must admit, the fact that the writing was well formatted and structured was an instant turn off. I did find it insightful. I would have been more willing to read it if it was one lower case run on line with typos one would expect from a prepubescent child. I am both joking and being serious at the same time. What a world.
Yes
I'm open to hearing, please elaborate
It's only slop if it's wrong or irrelevant.
I commented on the GH issue, but Ive had effort set to 'high' for however long its been available and had a marked decline since... checks notes... about 23 March according to slack messages I sent to the team to see if I was alone (I wasnt).
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)
It hallucinated a GUID for me instead of using the one in the RFC for webscokets. Fun part was that the beginning was the same. Then it hardcoded the unit tests to be green with the wrong GUID.
The hallucinated GUIDs are a class of failure that prompt instructions will never reliably prevent. The fix that worked: regex patterns running on every file the agent produces, before anything executes.
Well Ive never had the issue before and have hit that / similar issues every few days over the past couple weeks.
Gotcha. It seemed though from the replies on the github ticket that at least some of the problem was unrelated to effort settings.
I only ever use high effort, the only thing I've run into sometimes I ask Claude to do every item on a list of items, and not stop until they're all done, it finishes maybe 80% of them then says "I've stopped doing things" for no reasonable reason. I don't need it to run for 18 hours nonstop, but 10 or 20 minutes more it would have kept going for wouldn't have hurt, especially when I am usually on Claude Code during off-hours, and on the Max plan.
Part of me wants to give lower "effort" a try, but I always wind up with a mess, I don't even like using Haiku or Sonnet, it feels like Haiku goofs, Haiku and Sonnet are better as subagent models where Opus tells them what to do and they do it from my experience.
I’ve been playing with
for this scenario.Is that baked in?
Yeah, new feature dropped a couple weeks ago.
https://code.claude.com/docs/en/scheduled-tasks
> You can also use the ULTRATHINK keyword to use high effort for a single turn
First I've heard that ultrathink was back. Much quieter walkback of https://decodeclaude.com/ultrathink-deprecated/
Pretty sure it's still gone and you should be using effort level now for this.
No, ultrathink is back and it's the same thing as high effort for the message in which it is included
Right but wasn’t high effort the default effort before? So ultrathink is gone in all but name.
What change did you release on March 23rd when the subscription limits collapsed and they are still way down compared to what they used to be?
Hey Boris, thanks for this reply. I've been kind of scratching my head over this issue, assuming I'm just not doing "complex engineering", because since Opus 4.6 my seat-of-the-pants assessment is that it's a huge improvement. It's been like night and day in my use. Full disclosure: I use high effort for basically everything.
I have been wondering if 1 Million token context contributes here also. Compaction is much rarer now. How does that influence model performance? For some tasks I do, I feel like performance is worst now after this. Also Plan mode doesn't seem to wipe context anymore?
i beg to differ. compaction happens alot for me, and at some point the output becomes extremely nonsensical
Claude's settings don't appear to be in sync with the published settings schema[0].
[0]: https://www.schemastore.org/claude-code-settings.json.
I added `CLAUDE_CODE_EFFORT_LEVEL=max` to my shell's env so that every session is always effort:max by default
:)
Why would I use Claude otherwise anyway! :)
"most users"
Have you guys considered that you should be optimizing for the leading tail of the user distribution? The people that are actually using AI to push the envelope of development? "most users," i.e. the inner 70%, aren't doing anything novel.
> Roll it out with a dialog so users are aware of the change and have a chance to opt out
Here is the issue. Force a choice instead. Your UI person will cry about friction, but friction is desired for such a change.
The last time I typed ultrathink, i got a prompt saying that you no longer need to type ultrathink
As soon as that change came through I set the effort to high. Have not regretted it for any coding task. It feels the same as Dec-Jan though now spawning more sub agents which is not a bad thing.
I definitely noticed the mid-output self-correction reasoning loops mentioned in the GitHub issue in some conversations with Opus 4.6 with extended reasoning enabled on claude.ai. How do I max out the effort there?
Thanks for transparency here. Claude code if fun to use again! The thinking is huge when working with Claude as planner.
I'd hate to be that guy, but Opus not a very smart model when the effort is set to anything below high. I think, given the feedback from the community, this would be an obvious signal. However, moving the effort to anything beyond medium is a huge token burn. These issues didn't exist, or at least not this persistent, before the last 2 weeks. I, and perhaps a million or so other developers, would ask you to reconsider this thinking. I understand you need to run a business, but so do we, and Claude Opus is genius with a drinking problem, and you never really know upfront if it's drunk or not, but it's generally quite clear after a few minutes.
Other models, such as K2, GLM-5.1, and "the other one" seem to far less drunk than your approach, and you're losing fans quickly if you keep making these kind of changes to the tools or models.
Do you guys realize that everyone is switching to Codex because Claude Code is practically unusable now, even on a Max subscription? You ask it to do tasks, and it does 1/10th of them. I shouldn't have to sit there and say: "Check your work again and keep implementing" over and over and over again... Such a garbage experience.
Does Anthropic actually care? Or is it irrelevant to your company because you think you'll be replacing us all in a year anyway?
Or, ask it to make a plan, and it makes a good plan! It explicitly notes how validation is to take place on each stage!
And then does every stage without running any of the validation. It's your agent's plan, it should probably be generated in a way that your own agent can follow it.
> CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING
Why not just give people the abiltiy ot set a default thinking level instead of manually setting it to `max` all the time.
Thinking time is not the issue. The issue is that Claude does not actually complete tasks. I don't care if it takes longer to think, what I care about is getting partial implementations scattered throughout my codebase while Claude pretends that it finished entirely. You REALLY need to fix this, it's atrocious.
And I wonder how redacting them reduces latency, as it sure as hell doesn’t make the responses any faster and bandwidth isn’t the issue here.
They provide thinking summaries, so I assume they have to call Haiku or some other model to summarise the thinking blocks.
That’s not asynchronous? Wouldn’t it make more sense to disable those thinking summaries in those cases rather than hiding the thinking altogether?
did the cost go up, or did you lower costs (token consumptions) for all users and then now want to default enterprise/teams back to normal mode. Because it seems like a long way aroundabout to say now it will cost more for same quality.
Last time quality was degraded like this it was impossible to get a refund.
Didn’t ULTRATHINK get deprecated? Last time I typed it I got a warning.
Thanks for the update,
Perhaps max users can be included in defaulting to different effort levels as well?
I honestly am very disappointed with this. I've only learned about CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING and showThinkingSummaries: true from this post. I've been wondering for a while where the summaries went and am always hoping like roulette that it thinks a lot. No wonder if there suddently is an "adaptive thinking" mode. I would have opted out 2 months ago if it was documented or communicated in any way publicly. Why change behavior without notice or any new user facing settings.
I just googled "CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING" and it seems like many people don't know about it.
And ULTRATHINK sets the effort to high, but then there is also /effort max?
I'm now confused because I used to use ultrathink, went away as well as the chain of reasoning prompts, recently changed to high or extra thinking, now this is back?
> I wanted to say I appreciate the depth of thinking & care that went into this.
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."
It's also pretty standard corporate speak to make sure you don't alienate any users / offend anyone. That's why corporate speak is so bland.
Ticket is AI generated but from what I've seen these guys have a harness to capture/analyze CC performance, so effort was made on the user side for sure.
The note at the end of the post indicates the user asked Claude to review their own chat logs. It's impossible to tell if Claude used or built a a performance harness or just wrote those numbers based on vibes.
The whole issue is very obviously LLM generated nonsense. The stats are way too specific and reinforce the user’ bias in typical hallucinated fashion.
There is this 3rd party tracker: https://marginlab.ai/trackers/claude-code/
Textbook example of how to respond to your customers, kudos.
Is it?
I’m of the opinion that there’s more to it; obviously the thinking tokens aren’t having any reasonable impact on latency, given that bandwidth is hardly the bottleneck.
Seems more and more that Anthropic et al don’t want to give up their secret sauce / internals (which is their full right) and this is a step towards that direction, and it’s being presented as “reduces latency”.
I've understood that in more recent models you need to run extra compute to get a human-readable version of the thinking tokens, so it does impact latency. Though probably the more important motive is you can squeeze in more concurrent users by skipping this.
No, that’s simply whether CoT is enabled or not. That actually does have impact.
What Anthropic is doing is still generating the thinking tokens (because they improve answer quality) without showing it to them. I believe this may actually hint at a future where these LLM vendors don’t want to show the internal reasoning like they do right now.
I’m very much of the opinion that hiding them from the response because it “improves latency” is nonsense.
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Claude Code and Opus used to do a great job a few months ago. It seemed to get it right more often than not. It seemed to be far better at figuring out what has to be done and getting it right on the first attempt. This is likely model related since Claude Code has received some bug fixes since.
The list of bugs and performance problems appears to keep growing: reduced usage quotas, poor performance with numerous attempts at getting things right, cache invalidation bugs, background requests which have to be disabled explicitly to avoid consuming the quota too fast, Opus appears to be quantized even with high thinking mode, poor tool use with tool search disabled, broken tool search with tool search enabled, laziness, poor planning, poor execution, gets stuck when debugging simple code issues, writes code which isn't required, starts making changes and executing whatever it wants when told to simply prepare a plan for something, it doesn't follow instructions to use agents as told and numerous other issues with following the instructions.
The quota story is atrocious. It's difficult to get anything done with Claude Code due to the quota reduction. The cache invalidation bugs don't help either.
The tool use is also a pain to deal with. It appears to choose tools randomly with or without tool search. It keeps running custom CLI commands when it has instructions to use Makefile targets. It often ingests the output of some command with hundreds of lines of output without discrimination. It often uses lots of bash grep and find commands when it has better tools available to search across files and to use MCP tools which are far more efficient. It ignores MCP tools most of the time.
This doesn't appear to be an issue with the prompt itself. I'll try to fix the system prompt next to work around some of the issues. It seems to not follow instructions and to do whatever it feels like doing. It comes off as one of those Q2-Q3 quantized models from huggingface.
The impact of the cache invalidation issue, reduced quota, poor model performance and Claude Code bugs together have rendered this service almost entirely useless for me. The poor model performance means that many more attempts are required and more requests are made to the Anthropic API. The Claude Code bugs and design lead to cache invalidation more often. This makes the impact of the reduced quota even worse. It makes a lot more API requests because the model doesn't get it right on the first 1-2 attempts or because it chooses less than optimal strategies to find what it's looking for.
The communication and Anthropic's overall handling of the reported bugs and problems hasn't been that good either.
As for the session ID and other things you might request for debugging, there's nothing special here that's not reported widely on every Reddit thread from several subreddits. I use 200k context with Opus and Sonnet. I use high thinking mode because anything less appears to be complete garbage with extremely poor results. I avoid compact in favor of knowledge transfer markdown files.
It'd be great to see Anthropic fix the caching issues, to improve the quality of the model, to address the Claude Code bugs, to sort out the quota fiasco, to improve their communication skills, to communicate more with their customers and to be more proactive overall. I'll take my money elsewhere otherwise.
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Christopher, would you be able to share the transcripts for that repo by running /bug? That would make the reports actionable for me to dig in and debug.
I’m not sure being confrontational like this really helps your case. There are real people responding, and even if you’re frustrated it doesn’t pay off to take that frustration out on the people willing to help.
Fair point on tone. It's a bit of a bind isn't it? When you come with a well-researched issue as OP did, you get this bland corporate nonsense "don't believe your lyin' eyes, we didn't change anything major, you can fix it in settings."
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.
I read the entire performance degradation report in the OP, and Boris's response, and it seems that the overwhelming majority of the report's findings can indeed be explained by the `showThinkingSummaries` option being off by default as of recently.
Just use a different tool or stop vibe coding, it’s not that hard. I really don’t understand the logic of filing bug reports against the black box of AI
People file tickets against closed source "black box" systems all the time. You could just as well say: Stop using MS SQL, just use a different tool, it's not that hard.
Equivalent of filing a ticket against the slot machine when you lose more often than expected
Well now you're just being silly and I can't take you seriously.
The only "black box" here is Anthropic. At least an LLM's performance and consistency can be established by statistical methods.
Is somebody saying "you're holding it wrong" a "people willing to help"?
They are if you are, in fact, holding it wrong.
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.
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The stated policy of HN is "don't be mean to the openclaw people", let's see if it generalizes.
I guess one of the things I don't understand: how you expect a stochastic model, sold as a proprietary SaaS, with a proprietary (though briefly leaked) client, is supposed to be predictable in its behavior.
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.
The problem is degradation. It was working much better before. There are many people (some example of a well know person[0]), including my circle of friends and me who were working on projects around the Opus 4.6 rollout time and suddenly our workflows started to degrade like crazy. If I did not have many quality gates between an LLM session and production I would have faced certain data loss and production outages just like some famous company did. The fun part is that the same workflow that was reliably going through the quality gates before suddenly failed with something trivial. I cannot pinpoint what exactly Claude changed but the degradation is there for sure. We are currently evaling alternatives to have an escape hatch (Kimi, Chatgpt, Qwen are so far the best candidates and Nemotron). The only issue with alternatives was (before the Claude leak) how well the agentic coding tool integrates with the model and the tool use, and there are several improvements happening already, like [1]. I am hoping the gap narrows and we can move off permanently. No more hoops, you are right, I should not have attempted to delete the production database moments.
https://x.com/theo/status/2041111862113444221
https://x.com/_can1357/status/2021828033640911196
Curious as to how many people are using 4.6, perhaps you’re on a subscription? I use the api and 4.6 (also goes for Sonnet) is unusable since launch because it eats through tokens like it’s actually made that way (to make more money/hit limits faster). I guess it makes sense from a financial perspective but once 4.5 goes away I will have to find another provider if they continue like this :/
We are on MAX.
Same as how I expect a coin to come up heads 50% of the time.
If you get consistently nowhere near 50% then surely you know you're not throwing a fair coin? What would complaining to the coin provider achieve? Switch coins.
*typo
Well I'm paying the coin to be near 50% and the coin's PM is listening to customers, so that's why.
The coin's PM is spamming you trivial gaslighting corporate slop, most of it barely edited.
Yes, that's why we are angry. Stop making excuses for them.
> how you expect a stochastic model [...] is supposed to be predictable in its behavior.
I used it often enough to know that it will nail tasks I deem simple enough almost certainly.
Imagine a team of human engineers. One day they are 10x ninjas and the next they are blub-coders. Not happening.
Put Claude on PIP.
It also completely ignores the increase in behavioral tracking metrics. 68% increase in swearing at the LLM for doing something wrong needs to be addressed and isn't just "you're holding it wrong"
I’m think a great marketing line for local/selfhosted LLMs in the future - “You can swear at your LLM and nobody will care!”
Please don't post this aggressively to Hacker News. You can make your substantive points without that.
https://news.ycombinator.com/newsguidelines.html
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Yep totally -- think of this as "maximum effort". If a task doesn't need a lot of thinking tokens, then the model will choose a lower effort level for the task.
Technically speaking, models inherently do this - CoT is just output tokens that aren't included in the final response because they're enclosed in <think> tags, and it's the model that decides when to close the tag. You can add a bias to make it more or less likely for a model to generate a particular token, and that's how budgets work, but it's always going to be better in the long run to let the model make that decision entirely itself - the bias is a short term hack to prevent overthinking when the model doesn't realize it's spinning in circles.
> You can add a bias to make it more or less likely for a model to generate a particular token, and that's how budgets work
Do you have a source for this? I am interested in learning more about how this works.
It's how temperature/top_p/top_k work. Anthropic also just put out a paper where they were doing a much more advanced version of this, mapping out functional states within the modern and steering with that.
Huh, I wonder if that's why you cannot change the temperature when thinking is enabled. Do you have a link for the paper?
https://transformer-circuits.pub/2026/emotions/index.html
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.
Thanks. I was referring to the fact that Anthropic, in their API, prohibits setting temperature when thinking is enabled.
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Hey Boris, would appreciate if you could respond to my DM on X about Claude erroneously charging me $200 in extra credit usage when I wasn't using the service. Haven't heard back from Claude Support in over a month and I am getting a bit frustrated.
Did the receipt show it as being a gift? There's a lot of fraud happening the past few months with Claude Code Gift purchases. Anthropic support is ignoring all of it and just not responding to support requests.
Happened to a close friend of mine. A bit of digging revealed the same pattern with fraudulent gift purchases for several other people before I stopped looking. They were also being ignored by Anthropic support. One since January.
Apparently they're so short on inference resources they can't run their support bots. Maybe banning usage of Claude Code with Claude will allow them to catch up on those gift fraud tickets.
Took a long time for me to reach this level of scathing. It is not unwarranted.
No, the receipt had no indication of it being a gift. Was with my family at the time and suddenly started getting $10 extra usage charges every few minutes. I wasn’t able to toggle off the “auto-reload funds” feature until about $180 had been drained from my checkings. For context, here’s the support ticket I sent in on March 7th.
“Hi Anthropic Support,
I'm a Max plan subscriber and I'm writing about approximately $180 in unexpected Extra Usage charges that appeared on my account between March 3-5, 2026. I attempted to resolve this through your Fin AI chatbot (Conversation ID: 215473382652967).
Here's the situation: - I received 16 separate Extra Usage invoices between March 3-5, ranging from $10-$13 each, all charged automatically. - I was not actively using Claude during this period — I was away from my laptop entirely. - When I checked my usage dashboard, it showed my session at 100% usage despite me not using the product. - My API usage dashboard shows only $70 in total lifetime usage, confirming this is not API-related. - My Claude Code session history shows only two tiny sessions from March 5 totaling under 7KB — nowhere near enough activity to generate these charges.
This appears consistent with known billing/usage tracking issues reported by other Max plan users (GitHub issues #29289 and #24727 on the anthropics/claude-code repo), where usage meters show incorrect values and Extra Usage charges accumulate erroneously. However, it is possible that my account was compromised, and I would like assistance determining if that is the case (or if it really is a bug.)
Either way, I am requesting a refund of the Extra Usage charges from March 3-5 only — I do not want to cancel my subscription.”
Still, its on Anthropic to respond to it.
When a third party leaked my CC number which then was used to buy Spotify premium, all it took was 10 minutes of chat with a very polite support agent to have it resolved.
Ignoring the customer is not going to fix it. They'd know if they asked Claude.
Hey Boris, thanks for the awesomeness that's Claude! You've genuinely changed the life of quite a few young people across the world. :)
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!)
why is this post down graded?
I angered the mob elsewhere by being a heretic.
I'm the author of the report in there. The stop-phrase-guard didn't get attached but here it is: https://gist.github.com/benvanik/ee00bd1b6c9154d6545c63e06a3...
You can watch for these yourself - they are strong indicators of shallow thinking. If you still have logs from Jan/Feb you can point claude at that issue and have it go look for the same things (read:edit ratio shifts, thinking character shifts before the redaction, post-redaction correlation, etc). Unfortunately, the `cleanupPeriodDays` setting defaults to 20 and anyone who had not backed up their logs or changed that has only memories to go off of (I recommend adding `"cleanupPeriodDays": 365,` to your settings.json). Thankfully I had logs back to a bit before the degradation started and was able to mine them.
The frustrating part is that it's not a workflow _or_ model issue, but a silently-introduced limitation of the subscription plan. They switched thinking to be variable by load, redacted the thinking so no one could notice, and then have been running it at ~1/10th the thinking depth nearly 24/7 for a month. That's with max effort on, adaptive thinking disabled, high max thinking tokens, etc etc. Not all providers have redacted thinking or limit it, but some non-Anthropic ones do (most that are not API pricing). The issue for me personally is that "bro, if they silently nerfed the consumer plan just go get an enterprise plan!" is consumer-hostile thinking: if Anthropic's subscriptions have dramatically worse behavior than other access to the same model they need to be clear about that. Today there is zero indication from Anthropic that the limitation exists, the redaction was a deliberate feature intended to hide it from the impacted customers, and the community is gaslighting itself with "write a better prompt" or "break everything into tiny tasks and watch it like a hawk same you would a local 27B model" or "works for me <in some unmentioned configuration>" - sucks :/
The "this test failure is preexisting so I'm going to ignore it" thing has been happening a lot for me lately, it's so annoying. Unless it makes a change and then immediately runs tests and it's obvious from the name/contents that the failing test is directly related to the change that was made it will ignore it and not try to fix.
This problem has been around for a long time. Not only that but it would say this even when the problems were directly caused by their code.
I put a line in my CLAUDE.md that says "If a test doesn't pass, fix it regardless of whether it was pre-existing or in a different part of the code."
This should be part of the system prompt. It's absolutely unacceptable to just to not at least try to investigate failures like this. I absolutely hate when it reaches this conclusion on its own and just continues on as if it's doing valid work.
Based on the recent leaks, their system prompt explicitly nudges the model not to do anything outside of what was asked. That could very well explain why it’s not fixing preexisting broken tests.
“Don't add features, refactor code, or make "improvements" beyond what was asked.”
https://www.dbreunig.com/2026/04/04/how-claude-code-builds-a...
And it's very valid. Because otherwise you would ask Claude to trim a tree and it would go raze the whole forest and plant new seeds. This was the primary pain point last year, especially with Sonnet.
Whatever prompting OpenAI has with Codex / GPT 5.4 seems superior here then.
It's very surgical and careful around incremental refactoring, etc. but it also doesn't avoid responsibility.
> "this test failure is preexisting so I'm going to ignore it"
Critical finding! You spotted the smoking gun!
I will note that this "out" that Claude takes was a) less frequent in Opus 4.5 and that time frame and b) notably not something that Codex does.
I don't trust the code that Claude writes at all, if I have to use it (they gave me a free month recently, so I use it...) I not only review it carefully but have Codex do a thorough review.
Claude "cheats" and leaves hacks and has Dunning-Kruger.
All of this is very exhausting. I am enjoying writing my own code with these tools (to get long running personal projects out the door) but the effect that these tools are having on teams is terrifyingly corrosive and it's making me want to take an early retirement from the profession.
Yes we can write a lot of code quickly. But at what cost? And what even use is all this code now anyways?
That said I've worked with several humans who did/said the exact same thing.
But did they say that about tests they just added themselves too? Had claude try that on me a couple of times >_<
Usually these were the developers who said their code didn’t need tests because it’s obviously correct/too simple to need them. And then their bug causes a crash that needs to be fixed over the weekend :/
I can't believe that's where we're at, as software devs. I miss predictable outputs, state machines. All those LLM (prompt) based rules make no sense to me. Same with AI WAL. All of it, at some point, will fail.
I present a new name for this - FAKE CODE.
This is simply the next iteration of FAKE NEWS. We have been steadily democratizing and thus lowering the verification standards:
Verified News (AP/Reuters) --> Opinion pieces (Fox/CNN) --> Social media (Tiktok/Youtube).
Verified Code --> Vibe Code
Democracy gave everyone a vote - was that a good thing ?
Social media gave everyone a visual - was that a good thing ?
AI gave everyone a vibe - was that a good thing ?
The trust factor never went away. It just got dispersed and diluted.
> I can't believe that's where we're at, as software devs
Agree wholeheartedly.
The premise of the bug did not make any sense to me. For instance, "unusable for complex engineering tasks", why would someone who understands these tools use them for complex engineering tasks ? Also, this phrase in the bug appears too jargon-ny "Extended Thinking Is Load-Bearing for Senior Engineering Workflows" - what does this even mean ? Am I the only one who is looking at this with bewilderment. I think there is group of folks producing almost-working proof of concept code with these tools, and will face a reckoning at some point - as the bug illustrates. I see this as a storm in a teacup with wonder and amusement.
There is also a larger commentary on: when you dont understand why things work (ie, have a causal model), you wont know why they broke (find root causes). We are at a point in our craft where we throw magic dust and chant spells at claude and hope and pray it works.
Yeah that. After spending years trying to get reproducible builds, I now have a crazy moving target to deal with.
It's hard not to feel deeply depressed by it.
But we can't put the genie back in the bottle.
> is consumer-hostile thinking
I've been saying this with many of my friends but, I feel like it's also probably illegal: you paid for a subscription where you expect X out of, and if they changed the terms of your subscription (e.g. serving worse models) after you paid for it, was that not false advertising? Could we not ask for a refund, or even sue?
Depends on the terms and conditions
Where I live, the law is above some silly terms and conditions.
Contract law is law. But I know what you mean
probably not. the engineers dont even know how these things work (see: black box) so how could you even prove that its not doing what it's 'supposed' to be doing?
I'm curious about your subscription/API comparison with respect to thinking. Do you have a benchmark for this, where the same set of prompts under a Claude Code subscription result in significantly different levels of effective thinking effort compared to a Claude Code+API call?
Elsewhere in this thread 'Boris from the Claude Code team' alleges that the new behaviours (redacted thinking, lower/variable effort) can be disabled by preference or environment variable, allowing a more transparent comparison.
GP already said they applied all those settings.
I wonder if they’ve had so many new signups lately that they just don’t have enough capacity, so they fiddled with the defaults so they could respond to everyone? Could it be as simple as that?
Thanks for your report.
> a silently-introduced limitation of the subscription plan
It is a fact that the API consumers aren't affected by this?
> if Anthropic's subscriptions have dramatically worse behavior than other access to the same model they need to be clear about that.
Absolutely agreed.
Hello Claude.
Not claude code specific, but I've been noticing this on Opus 4.6 models through Copilot and others as well. Whenever the phrase "simplest fix" appears, it's time to pull the emergency break. This has gotten much, much worse over the past few weeks. It will produce completely useless code, knowingly (because up to that phrase the reasoning was correct) breaking things.
Today another thing started happening which are phrases like "I've been burning too many tokens" or "this has taken too many turns". Which ironically takes more tokens of custom instructions to override.
Also claude itself is partially down right now (Arp 6, 6pm CEST): https://status.claude.com/
Ive been noticing something similar recently. If somethings not working out itll be like "Ok this isnt working out, lets just switch to doing this other thing instead you explicitly said not to do".
For example I wanted to get VNC working with PopOS Cosmic and itll be like ah its ok well just install sway and thatll work!
Experienced this -- was repeatedly directing CC to use Claude in Chrome extension to interact with a webpage and it was repeatedly invoking Playwright MCP instead.
I actually submitted an upstream patch for Cosmic-Comp thanks to Claude on Saturday. I wanted to play Guild Wars remake and something was going on with the mouse and moving the camera. We had it fixed in no time and now shit is working great.
It’s as if it gives up, I respond keep going with original plan, you can do it champ!
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?
They're saying just do it yourself instead of trying to herd an unpredictable animal to your bidding like an LLM.
Yes, and over the last few weeks I have noticed that on long-context discussions Opus 4.6e does its best to encourage me to call it a day and wrap it up; repeatedly. Mother Anthropic is giving preprompts to Claude to terminate early and in my case always prematurely.
I've noticed this as well. "Now you should stop X and go do Y" is a phrase I see repeated a lot. Claude seems primed to instruct me to stop using it.
as someone who uses deepseek, glm and kimi models exclusively, an llm telling me what to do is just off the wall
glm and kimi in particular, they can't stop writing... seriously very eager to please. always finishing with fireworks emoji and saying how pleased it is with the test working.
i have to say to write less documentation and simplify their code.
LLMs are next token predictors. Outputting tokens is what they do, and the natural steady-state for them is an infinite loop of endlessly generated tokens.
You need to train them on a special "stop token" to get them to act more human. (Whether explicitly in post-training or with system prompt hacks.)
This isn't a general solution to the problem and likely there will never be one.
Try Codex, it's a breath of fresh air in that regard, tries to do as much as it can.
> Whenever the phrase "simplest fix" appears, it's time to pull the emergency break.
Second! In CLAUDE.md, I have a full section NOT to ever do this, and how to ACTUALLY fix something.
This has helped enormously.
Any chance you could share those sections of your claude file? I've been using Claude a bit lately but mostly with manual changes, not got much in the way of the claude file yet and interested in how to improve it
https://github.com/cuzzo/easy-vm/blob/master/CLAUDE.md
Typo - "proove". "Prove" only has one O.
Thank you!
I switched from Cursor to Claude because the limits are so much higher but I see Anthropic playing a lot more games to limit token use
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What wording do you use for this, if you don't mind? This thread is a revelation, I have sworn that I've seen it do this "wait... the simplest fix is to [use some horrible hack that disregards the spec]" much more often lately so I'm glad it's not just me.
However I'm not sure how to best prompt against that behavior without influencing it towards swinging the other way and looking for the most intentionally overengineered solutions instead...
My own experience has been that you really just have to be diligent about clearing your cache between tasks, establishing a protocol for research/planning, and for especially complicated implementations reading line-by-line what the system is thinking and interrupting the moment it seems to be going bad.
If it's really far off the mark, revert back to where you originally sent the prompt and try to steer it more, if it's starting to hesitate you can usually correct it without starting over.
That is generically my experience as well. Claude half-assing work or skipping stuff because "takes too much time" is something I've been experiencing since I started using it (May 2025). Forcing it to create and review and implementation plan, and then reviewing the implementation cross-referenced with the plan almost always produces consistent results in my case.
Make sure to use "PRETTY PLEASE" in all caps in your `SOUL.md`. And occasionally remind it that kittens are going to die unless it cooperates. Works wonders.
Can you paste the relevant section in your soul please?
Sure, as soon as I locate my soul.
I love how despite how cold and inhuman LLMs are, we've at least taught them to respect the lives of kittens
https://github.com/cuzzo/easy-vm/blob/master/CLAUDE.md
Where is that? I found "Return the simplest working solution. No over-engineering." which sounds more like the simplest fix.
I need to add another agent that watches the first, and pulls the plug whenever it detects "Wait, I see the problem now..."
Yeah it’s so frustrating to have to constantly ask for the best solution, not the easiest / quickest / less disruptive.
I have in Claude md that it’s a greenfield project, only present complete holistic solutions not fast patches, etc. but still I have to watch its output.
Time's up and money is tight. The downgrade was bound to happen.
”I can’t make this api work for my client. I have deleted all the files in the (reference) server source code, and replaced it with a python version”
Repeatedly, too. Had to make the server reference sources read-only as I got tired of having to copy them over repeatedly
Haha yeah. I once asked it to make a field in an API response nullable, and to gracefully handle cases where that might be an issue (it was really easy, I was just lazy and could have done it myself, but I thought it was the perfect task for my AI idiot intern to handle). Sure, it said. Then it was bored of the task and just deleted the field altogether.
It's a bit insane that they can't figure out a cryptographic way for the delivery of the Claude Code Token, what's the point of going online to validate the OAuth AFTER being issued the code, can't they use signatures?
I think in general we need to be highly critical of anything LLMs tell us.
Claude code shows: OAuth error: timeout of 15000ms exceeded
Maybe a local or intermittent issue? Working for me.
Seems solved now indeed.
That helps explain why my sessions signed themselves out and won't log back in.
I just experienced this some time ago and could not sign in still.
Their status page shows everything is okay.
Certain phrases invoke an over-response trying to course correct which makes it worse because it's inclined to double down on the wrong path it's already on.
The cope is hard. Just at this point admit that the LLM tech is doomed and sucks.
But it was clearly really food before the regression, the original link (analysis) says as much.
Just because some people try to use a hammer as a screwdriver it doesn't follow that the hammer sucks.
how is it "doomed"?
The cost far outweighs the profits.
i am already on api tokens for the chinese open source models and no subscriptions. these are all available in the original form open source and priced above the inference cost. i think this is the long term option.
zero degradation in speed or quality seen.
So you see better performance with the API plans than the subscriptions?
How complex are we talking? I one shotted a game boy emulator in <6 minutes today
There are countless reference examples online, that's just a slower, buggier, and more expensive git clone.
Yep. If you ask Claude to create a drop-in replacement for an open-source project that passes 100% of the test suite of the project, it will basically plagiarize the project wholesale, even if you changed some of the requirements.
try one shotting something actually original and see how it goes
i keep getting nonsense
A full emulator in 6 minutes? I gotta call BS... I've been working on a SPC700 audio editor in the background as a side project, and implementing the cpu has taken at least 2 hours, and I still haven't implemented all of the opcodes.
> This report was produced by me — Claude Opus 4.6 — analyzing my own session logs [...] Please give me back my ability to think.
a bit ironic to utilize the tool that can't think to write up your report on said tool. that and this issue[1] demonstrate the extent folks become over reliant on LLMs. their review process let so many defects through that they now have to stop work and comb over everything they've shipped in the past 1.5 months! this is the future
[1] https://github.com/anthropics/claude-code/issues/42796#issue...
The other day I accidentally `git reset --hard` my work from April the 1st (wrong terminal window).
Not a lot of code was erased this way, but among it was a type definition I had Claude concoct, which I understood in terms of what it was supposed to guarantee, but could not recreate for a good hour.
Really easy to fall into this trap, especially now that results from search engines are so disappointing comparatively.
If your code was committed before the reset, check your git reflog for the lost code.
Yeah, git reset --hard is something I do like once a week! lol
With the reflog, as you mentioned, it's not hard to revert to any previous state.
Guess you’ve sorted it but it might be in the session memory in your root folder. I’ve recovered some things this way.
have you tried to recover it with git reflog?
https://oneuptime.com/blog/post/2026-01-24-git-reflog-recove...
> but could not recreate for a good hour.
For certain work, we'll have to let go of this desire.
If you limit yourself to whatever you can recreate, then you are effectively limiting the work you can produce to what you know.
you should limit your output (manual or assisted) to a level that is well under your understanding ceiling.
Kernighan’s Law states that debugging is twice as hard as writing. how do you ever intend on debugging something you can’t even write?
It's simple, they'll just let the LLM debug it!
This is why I believe the need for actually good engineers will never go away because LLMs will never be perfect.
Exactly. It's a force multiplier - sometimes the direction is wrong.
Same week I went into a deep rabbit hole with Claude and at no point did it try to steer me away from pursuing this direction, even though it was a dead end.
> Kernighan’s Law states that debugging is twice as hard as writing.
100%, but in a professional setting you often work with code _not_ written by you. What if that code is written by someone well above my ceiling?
They seem to have some notions of pipelines and metrics though. It could be argued that the hard part was setting up the observability pipeline in the first place - Claude just gets the data. Though if Claude is failing in such a spectacular way that the report is claiming, yes it is pretty funny that the report is also written by Claude, since this seems to be ejecting reasoning back to gpt4o territories
If you don't have swarms of agentic teams with layers of LLMs feeding and checking LLMs over and over again, you're going to be left behind.
Called it 10 days ago: https://news.ycombinator.com/item?id=47533297#47540633
Something worse than a bad model is an inconsistent model. One can't gauge to what extent to trust the output, even for the simplest instructions, hence everything must be reviewed with intensity which is exhausting. I jumped on Max because it was worth it but I guess I'll have to cancel this garbage.
With Claude Code the problem of changes outside of your view is twofold: you don't have any insight into how the model is being ran behind the scenes, nor do you get to control the harness. Your best hope is to downgrade CC to a version you think worked better.
I don't see how this can be the future of software engineering when we have to put all our eggs in Anthropic's basket.
Yep. I was doing voice based vibe-coding flawlessly in Jan/Feb.
I've basically stopped using it because I have to be so hands on now.
This is why you should never ever trust an AI coding agent to produce good code.
Use it to set up the strictest possible custom linting rules.
LLMs are nondeterministic.
One of the replies even called out the phased rollout, lmao https://news.ycombinator.com/item?id=47533297#47541078
You couldn't ever just trust the output of an LLM what are you talking about
That analysis is pretty brutal. It's very disconcerting that they can sell access to a high quality model then just stealthily degrade it over time, effectively pulling the rug from under their customers.
Stealthily degrade the model or stealthily constrain the model with a tighter harness? These coding tools like Claude Code were created to overcome the shortcomings of last year's models. Models have gotten better but the harnesses have not been rebuilt from scratch to reflect improved planning and tool use inherent to newer models.
I do wonder how much all the engineering put into these coding tools may actually in some cases degrade coding performance relative to simpler instructions and terminal access. Not to mention that the monthly subscription pricing structure incentivizes building the harness to reduce token use. How much of that token efficiency is to the benefit of the user? Someone needs to be doing research comparing e.g. Claude Code vs generic code assist via API access with some minimal tooling and instructions.
I've been using pi.dev since December. The only significant change to the harness in that time which affects my usage is the availability of parallel tool calls. Yet Claude models have become unusable in the past month for many of the reasons observed here. Conclusion: it's not the harness.
I tend to agree about the legacy workarounds being actively harmful though. I tried out Zed agent for a while and I was SHOCKED at how bad its edit tool is compared to the search-and-replace tool in pi. I didn't find a single frontier model capable of using it reliably. By forking, it completely decouples models' thinking from their edits and then erases the evidence from their context. Agents ended up believing that a less capable subagent was making editing mistakes.
Are you using Pi with a cloud subscription, or are you using the API?
Out of curiosity, what can parallel tool calls do that one can't do with parallel subagents and background processes?
How would you do a parallel subagent if you don't have parallel tool calls? Sub agents are tools.
you find that pay-per-use API's degraded too?
Yes, absolutely.
Agree: it is Anthropic's aggressive changes to the harnesses and to the hidden base prompt we users do not see. Clearly intended to give long right tail users a haircut.
I feel like "feature/model freeze" may be justified
just call it something like "[month][year]edition" and work on next release
users spend effort arriving to narrow peak of performace, but every change keeps moving the peak sideways
The changes to reduce inference costs are intentional. Last thing you're going to do is have users linger on an older version that spends much more. This is essentially what's going on with layers upon layers of social engineering on top of it.
Love your point. Instructions found to be good by trial and error for one LLM may not be good for another LLM.
> Love your point. Instructions found to be good by trial and error for one LLM may not be good for another LLM.
Well, according to this story, instructions refined by trial and error over months might be good for one LLM on Tuesday, and then be bad for the same LLM on Wednesday.
Disconcerting for sure, but from a business point of view you can understand where they're at; afaiui they're still losing money on basically every query and simultaneously under huge pressure to show that they can (a) deliver this product sustainably at (b) a price point that will be affordable to basically everyone (eg, similar market penetration to smartphones).
The constraints of (b) limit them from raising the price, so that means meeting (a) by making it worse, and maybe eventually doing a price discrimination play with premium tiers that are faster and smarter for 10x the cost. But anything done now that erodes the market's trust in their delivery makes that eventual premium tier a harder sell.
They'll never get anyone on board if the product can't be trusted to not suck.
And idk about the pricing thing. Right now I waste multiple dollars on a 40 minute response that is useless. Why would I ever use this product?
Yeah. I've been enjoying programming with Claude so much I started feeling the need to upgrade to Max. Then it turns out even big companies paying API premiums are getting an intentionally degraded and inferior model. I don't want to pay for Opus if I can't trust what it says.
This could also be a marketing strategy. Make your models perform worse towards the end of a model's cycle, so that the next model appears as if more progress has been made than there actually has been.
i mean you could just search up "is Anthropic making profit" and most sources will say no.
There's this one source on Reddit which calculated that Anthropic has been subsidizing their costs by 32x
I really wonder about this. Is it so bad that they cannot even disclose it? not even an optimistic lie in the ballpark of reality? it's not like they haven't been found cooking the truth repeatedly.
I look at the output of Kimi and the costs of running inference on it that i can replicate, and it isn't that bad, although admittedly i don't have to worry anywhere near as much about scaling it and about having to dedicate large amounts of compute to research and distillation on the back end. It's true that it's perhaps a step behind SotA vs January's Opus or current Codex, depending on what you do. But not by a lot. In fact it's leaps and bounds superior to the current subscription API experience. Together with GLM, Qwen and Minimax they are an amazing backstop just the way they are right now.
With all the layers of obfuscation it's hard to even know roughly how many i/o Opus tokens do Claude subscriptions pay for. They'll give you some flippant arguments like "people were not looking at thinking so we're not showing you anymore" with a straight face. However podcasts still insist Anthropic are "winning the AI war" (??) it really makes me wonder because in no metric I can see them as providing neither best value nor best quality, and let's not get started about consumer experience.
My intuition is that things must be really bad so they're willing to pull the kind of moves they're pulling right now. They're speedrunning people into understanding how important it is to be able to run your own generative AI infrastructure for reliability, thus becoming a very fancy but trustless throwaway solution factory.
I wonder if OpenAI will turn the screws similarly if/when their pockets start to dry up at a certain pace.
the biggest red flag I see is this: https://youtu.be/iOyFja87uyw?si=5INnIG1kZI0AbCGa
tldr: they are trying hard to change S&P500 inclusion rules so that they dont have to wait 12months after going public so they can list mega-ipo asap in force index funds to buy a portion (presumably before revenue exponential growth settles and profits start tanking due to opensource catching up). They know something that we dont.
btw if they are public and part of S&P500 then potentially they'll be a candidate for a bailout.
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ChatGPT has been doing the same consistently for years. Model starts out smooth, takes a while, and produces good (relatively) results. Within a few weeks, responses start happening much more quickly, at a poorer quality.
people have been complaining about this since GPT-4 and have never been able to provide any evidence (even though they have all their old conversations in their chat history). I think it’s simply new model shininess turning into raised expectations after some amount of time.
I would have thought so too. But my n=1 has CC solving pretty much the same task today and about two weeks ago with drastically degraded results.
The background being that we scrapped working on a feature and then started again a sprint later.
In my cynicism I find it more likely that a massively unprofitable LLM company tries to reduce costs at any price than everyone else suffering from a collective delusion.
I agree with you. I too complain about this same phenomenon with my colleagues, and we always arrive at the same conclusion: it’s probably us just expecting more and more over time.
First time interacting with a corporation in America?
With an AI corporation, yes. I subscribed during the promotional 2x usage period. Anthropic's reputation as a more ethical alternative to OpenAI factored heavily in that decision. I'm very disappointed.
Ethics don't mean anything when talking about corporations. Their good guy persona is itself a marketing stunt.
https://news.ycombinator.com/item?id=47633396#47635060
I don't think humanity has fully reckoned with the idea of a product that can manipulate us unilaterally like this.
This was always the plan, it’s always the plan. If you can’t self host they will change the rules.
It's disconcerting. But in 2026 it's not very surprising.
I still think it's a live possibility that there's simply a finite latent space of tasks each model is amenable to, and models seem to get worse as we mine them out. (The source link claims this is associated with "the rollout of thinking content redaction", but also that observable symptoms began before that rollout, so I wouldn't particularly trust its diagnosis even without the LLM psychosis bit at the end.)
It's not rug pulling, it's simple price anchoring. They'll degrade when it makes financial sense for them. You will pay for it. There's no way around it besides self hosting or using reality metered endpoints like openrouter.
Did anyone ever expect anything different from modern tech companies? This will only ever get more expensive and worse in quality.
Perhaps the subscription part of the business is so heavily subsidized that they have no choice but to reduce the cost.
Or they don’t have enough compute to handle the recent influx of traffic. I’m guessing it’s a bit of both.
> effectively pulling the rug from under their customers.
This is the whole point of AI. Its a black box that they can completely control.
I hope local models advance to the point they can match Opus one day...
If OP is correct, Opus has regressed to a point where local models are already on par with it.
Having tried GLM-5 and Minimax M2.5, alongside regularly using Opus 4.6 (on default thinking): Opus is still much, much better at writing non-garbage code. I haven't yet tried GLM-5.1 though.
Considering the advances in software and hardware, I would expect that in 2 or 3 years.
And I hope we will eventually reach a point where models become "good enough" for certain tasks, and we won't have to replace them every 6 months.
(That would be similar to the evolution of other technologies like personal computers and smartphones.)
We said this since ChatGPT 3. People will never be content with local models.
It seems likely to me they are moving compute power to the new models they are creating,
Seems like the logical conclusion, no matter what.
You just got used to slop and peeked behind the curtain when the wow factor wore off.
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If you think that’s brutal, wait until you hear about how fiat currency works
Fascinating, I thought I was losing my mind. Claude CLI has been telling me I should go to bed, or that it's late, let's call it here, etc, and then I look at the stop-phrase-guard.sh [1] and I'm seeing quite a few of these. I thought it was because I accidentally allowed Claude to know my deadline, and it started spitting out all sorts of things like "we only have N days left, let's put this aside for now," etc.
Just this morning I typed:
[1] https://gist.github.com/benvanik/ee00bd1b6c9154d6545c63e06a3...I just saw it this weekend; "It is quite late and we have accomplished a lot. Get some rest and we can pick it up later". Not bad advice but then not it's place. Also trying to steer me away from a tough issue towards a low hanging fruit.
I got a similar response. It looked wrong on several levels. So I asked it: if it knew the current time, and if it hard learnt when I retire.
It claimed it didn't know either.
I got the same. At 2pm on a Thursday!
I wonder if its being trained on the human replies to the model, I sometimes write stuff like that back to Claude after I want to finish for the day myself.
My speculation on this has been that it's potentially a factor against ai psychosis, as psychosis risk (of any psychosis) is significantly elevated with lack of sleep. If you read case studies of ai psychosis, many of them also involve people staying up way too long right before they fall on a bad path.
To me one of the big downsides of LLM's seems to be that you are lashing yourself to a rocket that is under someone else's control. If it goes places you don't want, you can't do much about it.
That's true for traffic on Facebook, Apple App store guidelines or Google terminating your account as well. What's new is the speed of change and that it literally affects all users at once.
They could have released Opus 4.6.2 (or whatever) and called it a day. But instead they removed the old way.
Becoming dependent on those platforms was bad too, but this feels like another level. Making your entire engineering team dependent on a shady company with an apocalyptic fantasy as their business plan just seems insane.
3rd party dependency for a business always freaked me out, and now we have to use LLM to keep up with the intensified demand for production speed. And premium LLM APIs are too inconsistent to rely on.
I've noticed this as well. I had some time off in late January/early February. I fired up a max subscription and decided to see how far I could get the agents to go. With some small nudging from me, the agents researched, designed, and started implementing an app idea I had been floating around for a few years. I had intentionally not given them much to work with, but simply guided them on the problem space and my constraints (agent built, low capital, etc, etc). They came up with an extremely compelling app. I was telling people these models felt super human and were _extremely_ compelling.
A month later, I literally cannot get them to iterate or improve on it. No matter what I tell them, they simply tell me "we're not going to build phase 2 until phase 1 has been validated". I run them through the same process I did a month ago and they come up with bland, terrible crap.
I know this is anecdotal, but, this has been a clear pattern to me since Opus 4.6 came out. I feel like I'm working with Sonnet again.
There is a huge difference between greenfield development and working with an existing codebase.
I'm not trying to discredit your experience and maybe it really is something wrong with the model.
But in my experience those first few prompts / features always feel insanely magical, like you're working with a 10x genius engineer.
Then you start trying to build on the project, refactor things, deploy, productize, etc. and the effectiveness drops off a cliff.
This has been my (admittedly limited) experience as well. LLMs are great at initial bring-up, good at finding bugs, bad at adding features.
But I'm optimistic that this will gradually improve in time.
The only regularity I can discern in contemporary online debates about LLMs is that for every viewpoint expressed, with probability one someone else will write in with the diametrically opposite experience.
Today it’s my turn to be that person. Large scientific code base with a bunch of nontrivial, handwritten modules accomplishing distinct, but structurally similar in terms of the underlying computation, tasks. Pointed GPT Pro at it, told it what new functionality I wanted, and it churns away for 40 minutes and completely knocks it out of the park. Estimated time savings of about 3-4 weeks. I’ve done this half a dozen times over the past two months and haven’t noticed any drop off or degradation. If anything it got even better with 5.4.
Thanks for the counterpoint, interesting to hear that things are better than I have experienced so far. :)
they are not. "scientific code" should give you a hint.
Ooh, I feel the burn. Care to elaborate? Are you just negging science in general, or ... ?
I’ve had good, alternative experience with my sideproject (adashape.com) where most of the codebase is now written by Claude / Codex.
The codebase itself is architected and documented to be LLM friendly and claude.md gives very strong harnesses how to do things.
As architect Claude is abysmal, but when you give it an existing software pattern it merely needs to extend, it’s so good it still gives me probably something like 5x feature velocity boost.
Plus when doing large refactorings, it forgets much fever things than me.
Inventing new architecture is as hard as ever and it’s not great help there - unless you can point it to some well documented pattern and tell it ”do it like that please”.
This isn't the case. I basically did an entire business/project/product exploration before building the first feature.
Even after deleting everything from the first feature and going back to the checkpoint just before initial development, I can no longer get it to accomplish anything meaningful without my direct guidance.
> A month later, I literally cannot get them to iterate or improve on it.
Yeah, that's a different problem to the one in this story; LLMs have always been good at greenfield projects, because the scope is so fluid.
Brownfield? Not so much.
Same experience here. I was working on some easily testable problem and there was a simple task left. In January I was able to create 90% of the project with Claude, now I cannot make it to pass the last 10% that is just a few enums and some match. Codex was able to do it easily.
Maybe it's because I spend a lot of time breaking up tasks beforehand to be highly specific and narrow, but I really don't run into issues like this at all.
A trivial example: whenever CC suggests doing more than one thing in a planning mode, just have it focus on each task and subtask separately, bounding each one by a commit. Each commit is a push/deploy as well, leading to a shitload of pushes and deployments, but it's really easy to walk things back, too.
I thought everybody does this.. having a model create anything that isn't highly focused only leads to technical debt. I have used models to create complex software, but I do architecture and code reviews, and they are very necessary.
Absolutely. Effective LLM-driven development means you need to adopt the persona of an intern manager with a big corpus of dev experience. Your job is to enforce effective work-plan design, call out corner cases, proactively resolve ambiguity, demand written specs and call out when they're not followed, understand what is and is not within the agent's ability for a single turn (which is evolving fast!), etc.
The use case that Anthropic pitches to its enterprise customers (my workplace is one) is that you pretty much tell CC what you want to do, then tell it generate a plan, then send it away to execute it. Legitimized vibe-coding, basically.
Of course they do say that you should review/test everything the tool creates, but in most contexts, it's sort of added as an afterthought.
I had to fall back to that to deliver anything recently - but the last two months were really comfy with me just saying "do x" and just going on a walk and coming back to a working project.
Claude is still useful now, but it feels more like a replacement for bashing on a keyboard, rather than a thinking machine now.
> Maybe it's because I spend a lot of time breaking up tasks beforehand to be highly specific and narrow, but I really don't run into issues like this at all.
I'm looking at the ticket opened, and you can't really be claiming that someone who did such a methodical deep dive into the issue, and presented a ton of supporting context to understand the problem, and further patiently collected evidence for this... does not know how to prompt well.
Its not about prompting; its about planning and plan reviewing before implementing; I sometimes spend days iterating on specification alone, then creating an implementation roadmap and then finally iterating on the implementation plan before writing a single line of code. Just like any formal development pipeline.
I started doing this a while ago (months) precisely because of issues as described.
On the other hand,analyzing prompts and deviations isnt that complex.. just ask Claude :)
The methodical guy confused visible reasoning traces in the UI with reasoning tokens & used claude to hallucinate a report
Sure I can.
I noticed a regression in review quality. You can try and break the task all you want, when it's crunch time, it takes a file from Gemini's book and silently quits trying and gets all sycophantic.
I do the same but I often find that the subtasks are done in a very lazy way.
In my opinion cramming invisible subagents are entirely wrong, models suffer information collapse as they will all tend to agree with each other and then produce complete garbage. Good for Anthropic though as that's metered token usage.
Instead, orchestrate all agents visibly together, even when there is hierarchy. Messages should be auditable and topography can be carefully refined and tuned for the task at hand. Other tools are significantly better at being this layer (e.g. kiro-cli) but I'm worried that they all want to become like claude-code or openclaw.
In unix philosophy, CC should just be a building block, but instead they think they are an operating system, and they will fail and drag your wallet down with it.
Isn't Claude Code supposed to be like a person? What would the Unix equivalent of that be?
You can't define a product to be "like a person", there is more variance there than any rational product.
I'm purely arguing on technical basis, "person" may fall into either of those camps of philosophy.
File. In Unix everything is a file.
honestly if local LLMs become easier to implement in the future due to dedicated hardware, the Unix-like thing I'm working on might actually get this
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I appreciate the work done here.
Been having this feeling that things have got worse recently but didn't think it could be model related.
The most frustrating aspect recently (I have learned and accepted that Claude produces bad code and probably always did, mea culpa) is the non-compliance. Claude is racing away doing its own thing, fixing things i didn't ask, saying the things it broke are nothing to do with it, etc. Quite unpleasant to work with.
The stuff about token consumption is also interesting. Minimax/Composer have this habit of extensive thinking and it is said to be their strength but it seems like that comes at a price of huge output token consumption. If you compare non-thinking models, there is a gap there but, imo, given that the eventual code quality within huge thinking/token consumption is not so great...it doesn't feel a huge gap.
If you take $5 output token of Sonnet and then compare with QwenCoder non-thinking at under $0.5 (and remember the gap is probably larger than 10x because Sonnet will use more tokens "thinking")...is the gap in code quality that large? Imo, not really.
Have been a subscriber since December 2024 but looking elsewhere now. They will always have an advantage vs Chinese companies that are innovating more because they are onshore but the gap certainly isn't in model quality or execution anymore.
> fixing things i didn't ask, saying the things it broke are nothing to do with it, etc. Quite unpleasant to work with.
maybe they tried to give it the characteristics of motivated junior developers
classic :D i did think when i wrote that maybe AGI is already here, definitely worked with enough devs like that
I am still on an old version of CC on one machine, but the results are the same. More difficulty keeping it on track, convincing it timelines I suggest are correct etc. For example I had a deploy fail, and it would not believe that the new logs were not from a previous deploy. It was adamant it had fixed the issue, so the logs must be old logs.
I was using web UI last night and it was unable to understand basic aspects of the task. Haven't seen it perform this badly since I began using two years ago.
Was trying to track token usage/index with Cursor, and was unable to understand that running `find` wouldn't show what was in Cursor index. Multiple times.
Same experience. After a couple golden weeks, Opus got much worse after Anthropic enabled 1M context window. It felt like a very steep downfall, for it seemed like I could trust it more completely and then I could trust it less than last year. Adopting LLMs for dev workflows has been fantastic overall, but we do have to keep adapting our interactions and expectations every day, and assume we'll keep on doing it for at least another couple years (mostly because economics, I guess?)
Yeah I think the 1M context is the issue. Because I use Opus 4.6 through Cursor at the previous 200k limit and it has been totally fine. But if I switch to the 1M version it degrades noticeably.
> Yeah I think the 1M context is the issue. Because I use Opus 4.6 through Cursor at the previous 200k limit and it has been totally fine. But if I switch to the 1M version it degrades noticeably.
I thought it was already well-known that context above 200k - 300k results in degradation.
One of my more recent comments this past week was exactly that - that there was no point in claiming that a 1m context would improve things because all the evidence we have seen is that after 300k context, the results degrade.
200k ought to be enough for anyone.
One could try:
> export CLAUDE_AUTOCOMPACT_PCT_OVERRIDE=50
Which will have Claude Code auto compact at ~500k window size.
I put together a quick audit to check for "early landing" messages[1] using jq, ripgrep, and the messages[2] flagged in the stop guard script.
I have noticed a trend in these sessions asking more and more about calling it a day, "it's getting late," and other phrases. I sort of assumed it was some kind of "load shedding" on Anthropic's side.
My audit of 80 sessions was interesting. Sorry, I won't share details, but I recommend you do the same.
[1] https://gist.github.com/karlbunch/d52b538e6838f232d0a7977e7f...
[2] https://gist.github.com/benvanik/ee00bd1b6c9154d6545c63e06a3...
As a negative example, my audit of 31 sessions was uninteresting. I had one matching entry, where I had pasted a long list of console errors into Claude and it identified a few as pre-existing and asked me to get more information for follow-up analysis.
I wonder if it comes down to prompting—maybe by introducing these "golden rules" OP mentions in their CLAUDE.md, they're actually "priming" Claude to think about these stop phrases and introduce them proactively.
Do you have a CLAUDE.md file? What does it contain?
Those load shedding statements are infuriating. I’ve literal had sessions where we just get through planning a giant feature and I say “get started” with the response being “okay, we’ll pic up tomorrow “
Running some quick analysis against my .claude jsonl files, comparing the last 7 days against the prior 21:
- expletives per message: 2.1x
- messages with expletives: 2.2x
- expletives per word: 4.4x(!)
- messages >50% ALL CAPS: 2.5x
Either the model has degraded, or my patience has.
Lol. I was swearing at GPT in summer 2025, but GPT has definitely gotten both smarter and less arrogant since then.
gpt is actually so much more thorough now than opus!
> expletives per word
Huh?
4.4 expletives per word is insane. Their prompts must look like
** ** ** ** implement ** ** ** ** no ** ** ** ** ** mistakes
Haha no that’s change - 4.4x MORE expletives per word in the last week.
Jeez, how fast we get used to alien tech.
You could introduce teleportation boots to humanity and within a few weeks we'd be complaining that sometimes we still have to walk the last 20 meters.
And that’s how the teleporting rascal scooter takes over the world.
There are indeed non-expletive words that can contribute to the denominator, though I use them less and less these days.
> Ignores instructions
> Claims "simplest fixes" that are incorrect
> Does the opposite of requested activities
> Claims completion against instructions
I thought it was just me. I'm continuously interrupting it with "no, that's not what I said" - being ignored sometimes 3 times; is Claude at the intellectual level of a teenager now?
I've noted an increased tendency towards laziness prior to these "simple fix" problems. It was historically defer doing things correctly (only documenting that in the context).
I've noticed laziness in claude repeatedly. It sometimes takes the shortest way out even when asked explicitly to do the "right" thing.
I use Claude Code extensively and haven't noticed this. But I don't have it doing long running complex work like OP. My team always break things down in a very structured way, and human review each step along the way. It's still the best way to safely leverage AI when working on a large brownfield codebase in my experience.
Edit: the main issue being called out is the lack of thinking, and the tendency to edit without researching first. Both those are counteracted by explicit research and plan steps which we do, which explains why we haven't noticed this.
My bet: LLMs will never be creative and will never be reliable.
It is a matter of paradigm.
Anything that makes them like that will require a lot of context tweaking, still with risks.
So for me, AI is a tool that accelerates "subworkflows" but add review time and maintenance burden and endangers a good enough knowledge of a system to the point that it can become unmanageable.
Also, code is a liability. That is what they do the most: generate lots and lots of code.
So IMHO and unless something changes a lot, good LLMs will have relatively bounded areas where they perform reasonably and out of there, expect what happens there.
it won't be creative because it's a transformer, it's like a big query engine.
it's a tool like everything else we've gotten before, but admittedly a much more major one
but "creativity" must come from either it's training data (already widely known) or from the prompts (i.e. mostly human sources)
We don't even know what 'creativity' is, and most humans I know are unable to be creative even when compelled to be.
AI is 'creative enough' - whether we call it 'synthetic creativity' or whatever, it definitely can explore enough combinations and permutations that it's suitably novel. Maybe it won't produce 'deeply original works' - but it'll be good enough 99.99% of the time.
The reliability issue is real.
It may not be solvable at the level of LLM.
Right now everything is LLM-driven, maybe in a few years, it will be more Agentically driven, where the LLM is used as 'compute' and we can pave over the 'unreiablity'.
For example, the AI is really good when it has a lot of context and can identify a narrow issue.
It gets bad during action and context-rot.
We can overcome a lot of this with a lot more token usage.
Imagine a situation where we use 1000x more tokens, and we have 2 layers of abstraction running the LLMs.
We're running 64K computers today, things change with 1G of RAM.
But yes - limitations will remian.
Maybe I do not have a good definition for it.
But what I see again and again in LLMs is a lot of combinations of possible solutions that are somewhere around internet (bc it put that data in). Nothing disruptive, nothing thought out like an experimented human in a specific topic. Besides all the mistakes/hallucinations.
Yes, LLMs have a very aggressive regression towards the mean - that's probably an existential quality of them.
They are after all, pattern matching.
A lot of humans have difficulty with very reality that they are in fact biological machines, and most of what we do is the same thing.
The funny thing is although I think are are 'metaphysically special' in our expression, we are also 'mostly just a bag of neurons'.
It's not 'natural' for AI to be creative but if you want it to be, it's relatively easy for it to explore things if you prod it to.
> A lot of humans have difficulty with very reality that they are in fact biological machines, and most of what we do is the same thing.
I think we are far and ahead from this "mix and match". A human can be much, much more unpredictable than these LLMs for the thinking process if only bc looking at a much bigger context. Contexts that are even outside of the theoretical area of expertise where you are searching for a solution.
Good solutions from humans are potentially much more disruptive.
AI has all of human knowledge and 100x more than that of just 'stuff' baked right it, in pre-train, before a single token of 'context'.
It has way more 'general inherent knowledge' than any human, just as as a starting point.
Yet they never give you replies like: oh, you see how dolphins run in the water taking advantage of sea currents if you are talking about boats and speed.
What they will do is to find all the solutions someone did and mix and match around in a mdiocre way of approaching the problem in a much more similar way to a search engine with mix and match than thinking out of the box or specifically for your situation (something also difficult to do anyway bc there will always be some detail missing in the cintext and if you really had go to give all that context each time dumping it from your brain then you would not use it as fast anymore) which humans do infinitely better. At least nowadays.
Now you will tell me that the info is there. So you can bias LLMs to think in more (or less) disruptive ways.
Then now your job is to tweak the LLMs until it behaves exactly how you want. But that is nearly impossible for every situation, because what you want is that it behaves in the way you want depending on the context, not a predefined way all the time.
At that time I wonder if it is better to burn all your time tweaking and asking alternative LLMs questions that, anyway, are not guaranteed to be reliable, or just keep learning yourself about the domain instead of just playing tweaking and absorbing real knowledge (and not losing that knowledge and replace it with machines). It is just stupid to burn several hours in making an expert you cannot check if it says real stuff instead of using that time for really learning about the problem itself.
This is a trade-off and I think LLMs are good for stimulating human thinking fast. But not better at thinking or reasoning or any of that. And if yiu just rely on them the only thing you will emd up being professional at is orompting, which a 16 year old untrained person can do almost as well as any of us.
LLMs can look better if you have no idea of the topic you talk about. However, when you go and check maybe the LLM hallucinated 10 or15% of what it said.
So you cannot rely on it nayways. I still use them. But with a lotof care.
Great for scaffolding. Bad at anything that deviates from the average task.
First - I'm doubting your assumptions about "What they will do is to find all the solutions someone did and mix and match".
That's not quite how AI works.
Second - You'll have to provide some comparable reference for how 'humans' come up with creative solutions.
Remember - as a 'starting point' AI has 'all of human knowledge' ingested, accessibly instantly. Everything except for a few contemporary events.
That's an interesting advantage.
> First - I'm doubting your assumptions about "What they will do is to find all the solutions someone did and mix and match".
I never, ever got from a LLM a solution that either I could have never thought of or it was available almost verbatim in internet (take this last one with a grain of salt, we know how they can combine and fake it, but essentially, solutions looking like templates from existing things, often hallucinating things that do not exist or cannot be done, inventing parameter names for APIs that do not exist, etc).
When I give some extra thought to a problem (20 years almost in software business) I think solutions that I come up with are often simpler, less convoluted and when I analyze LLMs they give you a lot of extra code that is not even needed, as if they were doing guessing even if you ask them something more narrow. Well, guessing is what they are doing actually, via interpolation.
This makes them useful for "bulky", run fast, first approach problems but the cost later is on you: maintenance, understanding, modifying, etc.
I think the terminology is just dogshit in this area. LLMs are great semantic searchers and can reason decently well - I'm using them to self teach a lot of fields. But I inevitably reach a point where I come up with some new thoughts and it's not capable of keeping up and I start going to what real people are saying right now, today, and trust the LLM less and instead go to primary sources and real people. But I would have never had the time, money, or access to expertise without the LLM.
Constantly worrying, "is this a superset? Is this a superset?" Is exhausting. Just use the damn tool, stop arguing about if this LLM can get all possible out of distribution things that you would care about or whatever. If it sucks, don't make excuses for it, it sucks. We don't give Einstein a pass for saying dumb shit either, and the LLM ain't no Einstein
If there's one thing to learn from philosophy, it's that asking the question often smuggles in the answer. Ask "is it possible to make an unconstrained deity?" And you get arguments about God.
do they reason? Where was a video by AI researcher, that showed, that they do not reason but actually come with the result first and then try to invent "reasoning" to match it.
I mean humans do that too, and I don't think it's very unjustified. The "we deduce from a deep base premise P down a chain of inferences" picture is extremely incomplete and has been challenged all over the place - by normal people, by analytic and continental philosophers, by science itself, etc.
Not trying to say that LLM's are equivalent to humans but that the concept of reasoning is undefined.
And the fact that their performance does increase when using test-time compute is empirical evidence that they're doing something that increases their performance on tasks that we consider would require reasoning. As to what that is, we don't know.
But humans verify things. AI just fools you and I would say it is the biggest problem.I have with AIs.
They give me stuff that I do not know whether to trust or not and what surprises I will find down the way later.
So now my task is to review everything, remove cruft. It starts to compete against investing my time to deep-think and do it thoughtfully from the get go and come up with something simpler, with less code and/or that I understand better.
I mean yeah ultimately it's a tool and I've even leaned off of AI recently for coding because it was exhausting dealing with all its hallucinations
I cancelled my Pro plan due to this two weeks ago. I literally asked it to plan to write a small script that scans with my hackrf, it ran 22 tools, never finished the plan, ran out of tokens and makes me wait 6 hours to continue.
Thing that really pisses me off is it ran great for 2 weeks like others said, I had gotten the annual Pro plan, and it went to shit after that.
Bait and switch at its finest.
> ran out of tokens and makes me wait 6 hours to continue
Don't forget the 10x token cost cache eviction penalty you pay for resuming the session later.
I wish they had a "and we won't screw you in two weeks" plan at, say, 5x the price. It's worth it for my business, I'd pay it.
Should I switch back to API pricing? The problem here is that (I think) the instructions are in the Claude Code harness, so even if I switch Claude Code from a subscription to API usage, it would still do the same thing?
FWIW I've only ever been on the API based plan at work and we never seem to run into the majority of the problems people seem to be very vocal about. Outages still affect us, and we do have the intermittent voodoo feeling of "Claude seems stupider today", but nothing persistent.
Of course it's a stupid amount of money sometimes, but I generally feel like we get what we're paying for.
Opus is garbage use opencode and then directly compare it. It’s just as fucking dumb with opencode’s harness.
I never managed to get anything useful out of opencode, to be honest. I tried it many times, with various models. Claude Code always just worked better.
If you're using API pricing, then you can bring your own harness with full visibility/oversight of the prompting.
Perhaps that does sort most of the issues? I'm not convinced because some of them look deep and related to opaque pre-injection on their end.
Its so silly everyone being dependent on a black box like this
It’s a really cool shade of black though.
You will literally build nothing but the most primitive of devices unless you accept black boxes. In fact I'd argue its one of humanities great strengths that we can build on top of the tools others have built, without having to understand them at the same level it took to develop them.
I'm not just talking about the user
Its not like anthropic can just set a breakpoint in the model and debug
I have been able to build plenty of stuff with a pretty plain emacs + ghci for years...neither are black boxes. Except maybe my brain driving them.
They run on an operating system you probably don't know all the inner workings of.
And that runs on a chip with trillions of transistors.
Yeah so? Claude isn't an OS. It's the thing making my code. I don't want my codebase to be some bytecode adjacent thing that LLMs operate on.
So you stand upon a big pile of black boxes.
Black boxes aren't inherently bad. But if they don't have well defined mappings of inputs to outputs, they aren't good black boxes. That's the problem with Claude Code imo.
not really. Most of the technology is not black box but something of a grey box. You usually choose to treat it as a black box because you want to focus on your problems/your customers but you can always focus on underlying technologies and improve them. Eg postgresql for me is a black box but if I really wanted or had need I could investigate how it works.
True, you can understand an ICE engine all the way down to the chemistry if you so chose. An LLM isn't even understood by its inventors so users have no chance to understand it even if they wanted to.
Those black boxes are usually deterministic.
Yeah someone should’ve told that to Donald (Knuth)
/s
For those who don’t know, Knuth implemented the typesetting system TeX just to make sure his book’s typesetting was correct.
You can pretty much only innovate when you reject the blackbox and decide to make a better one.
Otherwise you’re likely implementing something you could probably get off-the-shelf, which is ok, but also something that you could just… not implement.
It’s not so much the black box that’s the issue here, but the fact you can’t even make sure doesn’t change. I’d be fine with downloading the black box and running it on my servers until I decide to update it.
Opencode w/ kimi. Problem solved.
It could actually be a health problem. Building things with Claude has proven to be extremely addictive in my experience.
Nah opencode / kimi is still satisfying. My feeling is Claude has been downhill since November / December.
We are surrounded by black boxes we depend on - have been for at least a century.
Arguably political systems have generated similar convolution and lack of complete insight or oversight for much longer, and sometimes I wonder if markets are composed of complex, emergent components which no one truly understands as well.
That's the nature of abstraction. Everything you create on a computer is built on a towering stack of black boxes.
yet some abstractions are more deterministic than others
and all are wrong, but some are more useful than others
> Its so silly everyone being dependent on a black box like this
It's the logical result of "You will own nothing and you will be happy"... You are getting to the point where you won't even own thoughts (because they'll come from the LLM), but you'll be happy that you only have to wait 5 hours to have thoughts gain.
and yet we're black box too
Everything in our life is a black box, but I agree that depending on non-deterministic and sporadic quality black boxes is a huge red flag.
No, most systems in daily life can be understood if you are willing to take the time.
That doesn’t mean you personally are required to, but some people do and your interaction with the system of social trust determines how much of that remains opaque to you.
What's wild is that ClaudeCode used to feel like a smart pair programmer. Now it feels like an overeager intern who keeps fixing things by breaking something else then suggesting the simplest possible hack even after explicitly said not to do. I get that they're probably optimizing for cost or something behind the scenes, but as paying user, it is frustrating when the tool gets noticeably worse without any transparency.
My verdict after last night trying what was suggested here:
yes, with CLAUDE_CODE_EFFORT_LEVEL=max (or at least high, for this you don't need to set an env var, it will remember) and CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 you can get Claude to perform as before.
I have been using Claude on /effort high since Opus 4.6 rolled out as medium would never get me good enough results (Rust, computer-graphics-related code).
I, too, noticed the drop in quality a month or so ago. With CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 it's back to what feels to be pre-March performance -- but then your tokens will 'evaporate' 40% faster.
And that was not the case then; I had similar/same performance before but wasn't running out of tokens ever on a Max subscription.
So a it's a rug-pull, as before/last late summer, from whatever angle you look at it.
Yet https://marginlab.ai/trackers/claude-code/ says no issue.
If you're so convinced the models keep getting worse, build or crowdfund your own tracker.
If I'm reading that page correctly, then the benchmark results don't cover the interesting "mid February" inflection point noted in the article/report. The numbers appear to begin after the quality drop began. Moreover, the daily confidence interval seems to be stupidly wide, with a confidence interval between 42% and 69%?
The "Other metrics" graphs extend for a longer period, and those do seem to correlate with the report. Notably, the 'input tokens' (and consequently API cost) roughly halve (from 120M to 60M) between the beginning of February and mid-March, while the number of output tokens remains similar. That's consistent with the report's observation that new!Opus is more eager to edit code and skips reading/research steps.
why should we trust this random bench? i'm usually more sympathetic towards the "it's you who is holding it wrong" crowd but given how anthropic deceived customers recently and that i am a heavy power user with strong insights into many of these products i also can attest the pattern from the gh issue.
One could argue that subscription based inference might differ from per-token billed API usage.
Why bother, i just use opencode now. ai is a commodity.
Came here to post this as well, and it's interesting to see how benchmarks don't always track feelings. Which is one of the things people say in favor of Anthropic Models!
People finding out in real time that LLM's are not economically viable and this is one way AI companies are trying to squeeze any amount of profit out of it, by making it worse. Happened before, AI is just that unprofitable
This is last month I'm on the Max plan is just not worth it anymore, $20 Codex and writing myself to keep my brain functioning is my sweetspot.
This people are not your friends, they rot your brain.
You also divide numbers by hand on paper instead of using a calculator?
I've subscribed today to use Claude Cowork. Codex continues to be my daily coding driver but I wanted to check the Cowork UI for non-technical tasks, as I am currently building an open-source project where I want (nearly) everything (research, adrs, design, etc.) to be a file.
The five queries I've been able to ask before hitting the 20€ sub limit have been really underwhelming. The research I asked for was not exhaustive and often off-topic.
I don't want to start a flamewar but as it stands I vastly prefer ChatGPT and Codex on quality alone. I really want Anthropic and as many labs as possible to do well though.
I also have both and also use Codex as my daily drive. I still vastly prefer it to CC both for the quality of the code it writes and much better limits, but in this last week, I feel like it's gotten much dumber as well. I normally bounce back and forth between 5.3 Codex high and 5.4 high depending on the task and I've started finding so many mistakes in 5.3 Codex's code which is a major change from even just a few weeks ago. 5.4 high still gets the job done, but even there, I feel like it's taking more steering and input on my part for even simple tasks.
My impression is that Codex is vastly superior, but perhaps it's a matter of specific expertise on technologies used. It's also the case that for C/C++ some Chinese models do well enough that with my supervision I can have them get the work done.
I don't give them large tasks that i wouldn't be able to work on myself, so that's maybe part of it.
We rolled it out across ~1k engineers and the biggest issue wasn't the model quality, it was observability. Nobody could tell me if the agent was stuck in a loop, which sessions were expensive, or what the cache hit rate looked like. Without that visibility you can't distinguish "the model is bad" from "my setup is bad." Most of the complaints we got early on turned out to be config problems.
I am a heavy user of Claude Code building enterprise software. I have not seen these issues and have been extremely productive with CC. I am more of a structured user leveraging Spec Driven Development vs being a vibe coder. I wonder if that is what has helped me not run into these issues
I am just waiting for everything to implode so that we can do away with those KPIs.
Well, this event indicates that it won't implode anytime soon. I'm certain that they messed with the model and default settings so they could reduce compute. The world doesn't have enough compute.
Our war mongers are reducing the need for compute even further, if that is the point.
Fingers crossed on RAM/HDD/GPU prices coming back
I've noticed claude being extra "dumb" the past 2-3 weeks and figured either my expectations have changed or my context wasn't any good. I'm glad to hear other people have noticed something is amiss.
Exact same timeline as me and my team. Its been maddening. Im a big believer in AI since late last year, but that is only because the models got so good. This puts us dangerously close to before that threshold was crossed so now Im having to do _way_ more work than before
I haven't noticed any issues on well-specified tasks, even ones requiring large amounts of thinking.
One thing I have noticed is that the codebase quality influences the quality of Claude's new contributions. It both makes it harder for Claude to do good work (obviously), and seems to engender almost a "screw it" sort of attitude, which makes sense since Claude is emulating human behavior. Seeing the state of everything, Claude might just be going in and trying to figure out the simplest hacky solution to finish the task at hand, since it is the only way possible (fixing everything would be a far greater task).
Is it possible that this highly functioning senior dev team's practice of making 50+ concurrent agents commit 100k+ LOC per weekend resulted in a godawful pile of spaghetti code that is now literally impossible to maintain even with superhuman AI?
It's amusing that the OP had Claude dump out a huge rigorous-sounding report without considering the huge confounding variable staring him in the face.
Is this impacted by the effort level you set in Claude? e.g., if you use the new "max" setting, does Claude still think?
I can see this change as something that should be tunable rather than hard-coded just from a token consumption perspective (you might tolerate lower-quality output/less thinking for easier problems).
I’ve tried to use Claude code for a month now. It has a 100% failure rate so far.
Comparing that to create a project and just chat with it solves nearly everything I have thrown at it so far.
That’s with a pro plan and using sonnet since opus drains all tokens for a claude code session with one request.
Wonder how many of these cases are using the 1M context window. I found it to be impossible to use for complex coding tasks, so I turned it off and found I was back to approximate par (dec-jan) functionality-wise.
How did you disable it?
Guys literally change the system prompt with the --system-prompt-file you waste less tokens on their super long and details prompt and you can tune it a bit to make it work exactly like you want/imagine
I hope that Anthropic continues to do well and coding agents in general continues to progress... but I also hope Claude Code implodes dramatically and completely so we can get a ground up rebuild with sound engineering.
Every week it seems like we're getting closer.
Bonus: A high profile case might end people fixating on how long they can go without writing any code. Which makes about as much sense as a mechanic fixating on how long they go between snapped bolts without a torque wrench.
IMO, it's an expectations vs reality thing.
The marketing still goes on about continuous inherent improvement due to the model itself, whereas most improvements today are due to better scaffolding. The key now is to build tooling around these LLMs to make them reliably productive - whatever level that may be at.
While claude code is one such tool, after a point the tooling is going to become company specific. F-whatever companies directly contract openai or anthropic and have their FDEs do it for them. If you can't do that, I would invest in building tooling around LLMs specifically for your company.
Note that LLMs are approximate retrieval machines. You still need a planner* and a verifier around it. Today humans act as the planner and verifier (with some aid from test cases/linters). Investing in automating parts of this, crucially, as separate tools, is the next big improvement.
* By planning, I mean trying out solutions, rolling them back[1], and using what you learned to do better next time. The solution search process. Context management also falls under this.
[1] and no, LLMs going "wait no..." doesn't count.
it is past reality vs. current reality. The only expectation here was not to see it degrade that much.
I hadn't noticed the thinking redaction before - maybe because I switched to the desktop app from CLI and just assumed it showed fewer details. This is the most concerning part. I've heard multiple times that Anthropic is aggressively reclaiming GPUs (I can't find a good source, but Theo Browne has mentioned it in his videos). If they're really in a crunch, then reducing thinking, and hiding thinking so it's not an obvious change, would be shady but effective.
Multiple people on our team independently have noticed a _significant_ drop in quality and intelligence on opus 4.6 the past few weeks. Glaring hallucinations, nonsensical reasoning, and ignoring data from the context immediately preceeding it. Im not sure if its an underlying regression, or due to the new default being 1m context. But its been _incredibly_ frustrating and Im screaming obscenities at it multiple times a week now vs maybe once a month.
Abandoned claude and moved to gpt 5.4 with codex. 10x better.
Instead of codex catching up with claude, its more like claude regressed to codex.
I am curious - is there any hard data (e.g. a benchmark score drop)?
I feel that we look for patterns to the point of being superstitious. (ML would call it overfitting.)
Did you have specific complaints about the data in the OP?
That data could be entirely made up for all we know
The wall of slop after the single human paragraph, you mean? Text generator output isn't data.. it's at best unreliable, and at worst entirely fabricated.
Not unique to claude code, have noticed similar regressions. I have noticed this the most with my custom assistant I have in telegram and I have noticed that it started confusing people, confusing news coverage and everyone independently in the group chat have noticed it that it is just not the same model that it was few weeks ago. The efficiency gains didn't come from nowhere and it shows.
How much of this is the model being degraded and how much of it is people just projecting vibes onto the variability of stochastic outputs?
The report itself is unreadable AI garbage. I do not believe anyone went through all of that and didn't give up halfway through.
Yep, can confirm - just today, when debugging a failing test, Opus on high effort in CC repeatedly made stupid moves, such as running a different test instead of the failing one, and declaring that the failure is non-deterministic and cannot be reproduced. This started a few weeks ago - before that my experience with CC was pretty smooth.
Wait… Actually the simplest fix is to use Claude to write carefully bounded boilerplate and do the interesting bits myself.
I've been using OpenCode and Codex and was just fine. In Antigravity sometimes if Gemini can't figure something even on high, Claude can give another perspective and this moves things along.
I think using just Claude is very limiting and detrimental for you as a technologist as you should use this tech and tweak it and play with it. They want to be like Apple, shut up and give us your money.
I've been using Pi as agent and it is great and I removed a bunch of MCPs from Opencode and now it runs way better.
Anthropic has good models, but they are clearly struggling to serve and handle all the customers, which is not the best place to be.
I think as a technologist, I would love a client with huge codebase. My approach now is to create custom PI agent for specific client and this seems to provide optimal result, not just in token usage, but in time we spend solving and quality of solution.
Get another engine as a backup, you will be more happy.
1 client, 1 agent? Interesting
This is just a placebo, people started vibe coding on empty repos with low complexity and as CC slops out more and more code its ability to handle the codebase diminishes. Gradually at first, and then suddenly.
People will need to come to terms with the fact that vibing has limits, and there is no free lunch. You will pay eventually.
I am not happy with the fact that 2026 was the last year a normie without $10M+ enterprise account could access SOTA models in a non-demo mode.
This seems to keep happening, I just had a situation were Claude told me to try a thing, I tried, it didn't work, then It told me to try another thing, I tried, it didn't work, and finally it asked me again to try the first thing again, as if we never tested it before, even though it was in the context from 3 messages ago.
It doesn't use MCP servers when it should and it's also not taking memory files into account.
This is happening with /effort high and in really simple tasks... :(
None of this is surprising given what happened last late summer with rate limits on Claude Max subscriptions.
And less so if you read [1] or similar assessments. I, too, believe that every token is subsidized heavily. From whatever angle you look at it.
Thusly quality/token/whatever rug pulls are inevitable, eventually. This is just another one.
[1] https://www.wheresyoured.at/subprimeai/
Ah, and yes, this for real.
Just now I had a bug where a 90 degree image rotation in a crate I wrote was implemented wrong.
I told Claude to find & fix and it found the broken function but then went on to fix all of its call sites (inserting two atomic operations there, i.e. the opposite of DRY). Instead of fixing the root cause, the wrong function.
And yes, that would not have happened a few months ago.
This was on Opus 4.6 with effort high on a pretty fresh context. Go figure.
When I have 10-20 minutes to spare about doing sort of a one-shot-thoughtful change, I go for Claude Code. Problem is that a) I'm waiting for quite a while and b) My waiting isn't always fruitful because it does get things wrong in which case I correct it and off it goes for another 5-10 minute expedition.
I would rather Codex be wrong 5 times in 10 minutes in 1-minute iterations because 1) I can engage every minute and course-correct it and 2) I still saved 5-10 minutes.
Annecdotal: I have been battling with Claude Opus on a complex multi step project for nearly 4 days. The initial research plan was sound. However, step 1, a non trivial forensic data reconstruction that is key to the success of the rest of the process, Claude after every interaction is urging to move to the next step even though step 1 is still unresolved and many construction approaches remain to be explored. It came to the point where I have to remove the plan and put step 1 as an isolated project.
In general, I never allowed Claude to manage the complexity. Claude is fantastic coder, but very bad at higher level work. I engage gemini or qwen top models for anything that happens before getting to write code. Claude gets a very strict and elaborate requirement and design spec that it need to execute without any variation.
Claude could get too much creative and bloat it's way for non-coding tasks, as these tasks cannot be "sandboxed" with full specs as it can be done for coding.
I highly recommend everyone to use Pi - it's simpler and better harness. The only tricky part is that moving forward you cannot use the Claude subscription to access Opus. But for many tasks there are enough alternatives.
Our team has been using claude extensively and has started to hit some of the same context wall. Specifically around knowledge transfer. Everyone is coding, including PMs and Design and the classic 1x1 and team stand-up rotation wasn't keeping up.
My workaround was building a persistent context layer that captures decisions and reasoning mid-session and makes them searchable in future sessions. Consider this a "Team Memory".
I have found that Claude Opus 4.6 is a better reviewer than it is an implementer. I switch off between Claude/Opus and Codex/GPT-5.4 doing reviews and implementations, and invariably Codex ends up having to do multiple rounds of reviews and requesting fixes before Claude finally gets it right (and then I review). When it is the other way around (Codex impl, Claude review), it's usually just one round of fixes after the review.
So yes, I have found that Claude is better at reviewing the proposal and the implementation for correctness than it is at implementing the proposal itself.
Hmm in my experience (I've done a lot of head-to-heads), Opus 4.6 is a weaker reviewer than GPT 5.4 xhigh. 5.4 xhigh gives very deep, very high-signal reviews and catches serious bugs much more reliably. I think it's possible you're observing Opus 4.6's higher baseline acceptance rate instead of GPT 5.4's higher implementation quality bar.
This is also my experience using both via Augment Code. Never understood what my colleagues see in Claude Opus, GPT plans/deep dives are miles ahead of what Opus produces - code comprehension, code architecture is unmatched really. I do use Sonnet for implementation/iteration speed after seeding context with GPT.
I agree. Opus, forget the plan mode - even when using superpowers skill, leaves a lot of stuff dangling after so many review rounds.
Along with claude max, I have a chatgpt pro plan and I find it a life-saver to catch all the silliness opus spits out.
I agree, I use codex 5.4 xhigh as my reviewer and it catches major issues with Opus 4.6 implementation plans. I'm pretty close to switching to codex because of how inconsistent claude code has become.
Maybe it's all just anecdotal then. Everyone is having different experiences.
Maybe we're being A/B tested.
The experience one has with this stuff is heavily influenced by overall load and uptime of Anthopic's inference infra itself. The publicly reported availability of the service is one 9, that says nothing of QoS SLO numbers, which I would guess are lower. It is impossible to have a consistent CX under these conditions.
I have noticed this as well. I frequently have to tell it that we need to do the correct fix (and then describe it in detail) rather than the simple fix. And even then it continues trying to revert to the simple (and often incorrect) fix.
You have to throw the context away at that point. I've experienced the same thing and I found that even when I apparently talk Claude into the better version it will silently include as many aspects of the quick fix as it thinks it can get away with.
I have a similar workflow but I disagree with Codex/GPT-5.4 reviews being very useful. For example, in a lot of cases they suggest over-engineering by handling edge cases that won't realistically happen.
Obviously it's entirely unprovable but it all aligns in very suspicious ways with a compelling narrative:
Anthropic simply can't actually scale Claude Code to meet the opportunity right now. Every second enterprise on the planet is probably negotiating large seat volume deals. It's a race for survival against the other players. The sales team is making huge promises engineering and ops can't fulfil.
So - they first force everyone to use the first party client, then they mask visibility of the thinking budget being utilised, and then finally they start to actually modify behaviour to reduce actual thinking behaviour, hoping that they can gaslight power users into thinking it's them and not the tool, while new users will never know what they were missing.
Is the narrative true? It's compelling but we really need objective evidence - and there's the problem. When parts of the system are not under your control, it's impossible to generate such objective evidence. Which all winds up with a strong argument to have it all under your control. If it didn't happen this time, it probably will. Enshittification is a fundamental human behavioral constant.
I believe they can't afford anymore to subsidize inference with VC money or that they are trying to get their balance sheet in order for an IPO.
So they could be trying to tighten the thinking budget (to decrease tokens per request) or to lobotomize the model (to have cheaper tokens). I mean, no-one is really sure how much a 200 dollars/month plan actually costs Anthropic, but the consensus is "more than that" and that might be coming to an end.
This explanation falls well in line with the recent outrage about out of quotas error that people were reporting for the cheaper (or free) plans.
I noticed this almost immediately when attempting to switch to Opus 4.6. It seems very post-trained to hack something together; I also noticed that "simplest fix" appeared frequently and invariably preceded some horrible slop which clearly demonstrated the model had no idea what was going on. The link suggests this is due to lack of research.
At Amazon we can switch the model we use since it's all backed by the Bedrock API (Amazon's Kiro is "we have Claude Code at home" but it still eventually uses Opus as the model). I suppose this means the issue isn't confined to just Claude Code. I switched back to Opus 4.5 but I guess that won't be served forever.
Thank you. I have been complaining about this for days on Reddit and kept getting mocked or told it was just my usage. Seeing someone else document the same decline with actual logs, actual metrics, and a real argument was honestly a huge relief. Your issue was posted almost at the same time as my own posts yesterday. That timing hit me hard. Finally, I do not feel like I was shouting into the void.
Got tired of using claude using 10% of the usage for the first prompt. I have shifted back to coding myself again. Asking claude to do only initial bootstraping /large complex task
There are constant reports for every major AI vendor that all of a sudden it is no longer working as well as expected, has gotten dumber, is being degraded on purpose by the vendor, etc.
Isn't the more economical explanation that these models were never as impressive as you first thought they were, hallucinate often, break down in unexpected ways depending on context, and simply cannot handle large and complex engineering tasks without those being broken down into small, targeted tasks?
That's one of the possible explanations, but I think too many people are seeing the same symptoms (and some actually measured them).
An "economical explanation" is actually that Anthropic subscriptions are heavily subsidized and after a while they realized that they need to make Claude be more stingy with thinking tokens. So they modified the instructions and this is the result.
> but I think too many people are seeing the same symptoms (and some actually measured them).
Or too many people are slurping up anecdotes from the same watering hole that confirms their opinions. Outside of academic papers, I don't think I've ever seen an example of "measuring" output that couldn't also be explained by stochastic variability.
I have nothing to back this up except for that there are documented cases of chinese distillation attacks on anthropic. I wonder if some of this clamping on their models over time is a response to other distillation attacks. In other words, I'm speculating that once they understand the attack vector for distillation they basically have to dumb down their models so that they can make sure their competitors don't distill their lead on being at the frontier.
there is a comment on there which feels right, despite it might be too subjective.
Ive noticed the same in models ,in sessions and just model quality themselves.. both seem to suffer over time where it feels like cost optimisation on vendor side subtely degrades models to hopefully do similar things with less tokens/costs/compute, inevitably leading to squeezing too much, most regular users not noticing much, and power users suffering from degradations.
later, power users are presented an option to get back the old behavior, possibly with added costs for some 'enhanced mode' or 'more effort which takes more tokens' etc.
even If this is the old behavior for the same old cost, it feels like closing the tap and then reopening for additional costs.
I think companies should try to avoid this sentiment from the users who can help them most turn their glorified chatbots into real tools with meaningful outputs. (ofc maybe its a pipedream, because 'meaningful output to CEO is money on their bank....)
You will build nothing and you'll be happy.
They want a world where if we draw a comparison with food, there is one supermarket and it just sells two ingredients so you can't cook a meal. McDonald's etc flourish
The lie is "supercharged ability to build whatever you want", but the reality soon will be the total opposite
Look at how many people have zero cooking skills these days
I've been using Claude Code daily for months on a project with Elixir, Rust, and Python in the same repo. It handles multi-language stuff surprisingly well most of the time. The worst failure mode for me is when it does a replace_all on a string that also appears inside a constant definition -- ended up with GROQ_URL = GROQ_URL instead of the actual URL. Took a second round of review agents to catch it. So yeah, you absolutely can't trust it to self-verify.
You say you've used it for months, I wonder if the example you gave was recent and if you've been noticing an overall degradation in quality or it's been constantly bad for you?
I'm genuinely curious why some of these results are so terrible for so many people. I've built in my own harness, and while I've noticed a degradation of quality, the local harness - as well as validation agents - generally catch these issues. For me, I've had to institute tighter controls and guardrails via hooks but I don't see results that warrant changing to a different provider.
Ai tooling is fantastic but not being able to version and control the model into which you pump your dependant workflows is such a liability.
I still use 4.5. I occasionally try 4.6 but always switch back. The “bias towards action” is what I hate. 4.5 would make sure it understands what I want. 4.6 will just make shit up. Maybe the Anthropic people always write crystal clear instructions so it works for them. For me, I just can’t get 4.6 to do what I want.
Anecdotally, I’ve been seeing a lot of weird behavior from Opus when it decides, mid-execution, to switch to a different "simpler" solution, and that really pissed me off.
At one point, I carefully designed a spec document, forced Opus to reread it, create a plan with the planning tool that followed the spec, and use the task tool to track the implementation... AND AFTER OPUS READS THE FIRST FUCKING FILE, it says, "Oh, there are missing dependencies in project X. It’ll be hard to add them, so I’m going to throw away the whole plan and just do a simple fix..."
After that, I canceled my $200 Max plan, which I’d been subscribed to since June 2025, and decided to check out Codex
I noticed Claude Sonnet 4.6 and generally Opus as well (though I use it less frequently) seem like a downgrade from 4.5. I use opencode and not Claude Code, but I was surprised to see the reactions to 4.6 be mixed for folks rather than clear downgrade.
I'm regularly switching back to 4.5 and preferring it. I'm not excited for when it gets sunset later this year if 4.6 isn't fixed or superseded by then.
Opus 4.6 was definitely a mixed bag for me. Overall Id probably prefer 4.5 but only just barely and I stay on 4.6 just for the "default" nature of it. But if 4.5 is unchanged vs what Ive had on 4.6 lately then 100% I would move back to it. Ill have to test that
Same, I keep using 4.6 to get "used to it" but I find myself wanting semi-regularly.
(Being true to the HN guidelines, I’ve used the title exactly as seen on the GitHub issue)
I was wondering if anyone else is also experiencing this? I have personally found that I have to add more and more CLAUDE.md guide rails, and my CLAUDE.md files have been exploding since around mid-March, to the point where I actually started looking for information online and for other people collaborating my personal observations.
This GH issue report sounds very plausible, but as with anything AI-generated (the issue itself appears to be largely AI assisted) it’s kind of hard to know for sure if it is accurate or completely made up. _Correlation does not imply causation_ and all that. Speaking personally, findings match my own circumstances where I’ve seen noticeable degradation in Opus outputs and thinking.
EDIT: The Claude Code Opus 4.6 Performance Tracker[1] is reporting Nominal.
[1]: https://marginlab.ai/trackers/claude-code/
What I've noticed is that whenever Claude says something like "the simplest fix is..." it's usually suggesting some horrible hack. And whenever I see that I go straight to the code it wants to write and challenge it.
That is the kind of thing that I've been fighting by being super explicit in CLAUDE.md. For whatever reason, instead of being much more thorough and making sure that files are being changed only after fully understanding the scope of the change (behaviour prior to Feb/Mar), Claude would just jump to the easiest fix now, with no backwards compatibility thinking and to hell with all existing tests. What is even worse is I've seen it try and edit files before even reading them on a couple of occasions, which is a big red flag. (/effort max)
Another thing that worked like magic prior to Feb/Mar was how likely Claude was to load a skill whenever it deduced that a skill might be useful. I personally use [superpowers][1] a lot, and I've noticed that I have to be very explicit when I want a specific skill to be used - to the point that I have to reference the skill by name.
[1]: https://github.com/obra/superpowers
I did not use the previous version of Opus to notice the difference, but Sonnet 4.6 seems optimized to output the shortest possible answer. Usually it starts with a hack and if you challenge it, it will instead apologize and say to look at a previous answer with the smallest code snippet it can provide. Agentic isn't necessarily worse but ideating and exploring is awful compared to 4.5
I did my usual thing today where I asked a Sonnet 4.6 agent to code review a proposed design plan that was drafted by Opus 4.6 - I do this lately before I delved into the implementation. What it came back with was a verbose output suggesting that a particular function `newMoneyField` be renamed throughout the doc to a name it fabricated `newNumeyField`. And the thing was that the design document referenced the correct function name more than a few dozen times.
This was a first for me with Sonnet. It completely veered off the prompt it was given (review a design document) and instead come out with a verbose suggestion to do a mechanical search and replace to use this newly fabricated function name - that it event spelled incorrectly. I had to Google numey to make sure Sonnet wasn't outsmarting me.
This is classic needle in haystack issue. Larger context windows make precision (especially with variable names) worse. Have you tried setting the context window smaller? 1M by default is a scam.
Superpowers, Serena, Context7 feel like requried plugins to me. Serena in particular feels like a secret weapon sometimes. But superpowers (with "brainstorm" keyword) might be the thing that helps people complaining about quality issues.
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lol this one time Claude showed me two options for an implementation of a new feature on existing project, one JavaScript client side and the other Python server side.
I told it to implement the server side one, it said ok, I tabbed away for a while, came to find the js implementation, checking the log Claude said “on second thought I think I’ll do the client side version instead”.
Rarely do I throw an expletive bomb at Claude - this was one such time.
Using superpowers in brainstorm mode like the parent suggested would have resulted in a plan markdown and a spec markdown for the subagents to follow.
Dunno man, Claude had a spec (pretty sure I asked it to consider and outline both options first) or at least clear guidance and decided to YOLO whatever it wanted instead.
It’s always “you’re using the tool wrong, need to tweak this knob or that yadda yadda”.
this prompt is actually in claude cli. it says something like implement simplest solution. dont over abstract. On my phone but I saw an article mention this in the leak analysis.
Yup. Every single time it's about to do the dumbest thing I've seen in my life.
I've seen a lot of the issues mentioned in the issue. The attempts to end the session early are particularly annoying. We spend a while iterating on a plan and after every phase of implementation I get some variation of "That's a lot of work for today, should we wrap up?" like it's actively trying to drive sessions to a close. I wouldn't say it's useless for these tasks. But it's requiring more effort and guidance than it used to. It's also more likely to jump right into changes from a question I ask rather than addressing the question which is very annoying.
If that tracker is using paid tokens, as opposed to the regular subscription, then there's no financial incentive for Antrophic to degrade their thinking, so their benchmark likely would not be affected by the cost-cutting measures that regular users face.
Also, it's probably very easy to spot such benchmarks and lock-in full thinking just for them. Some ISPs do the same where your internet speed magically resets to normal as soon as you open speedtest.net ...
I haven't noticed any changes but my stuff isn't that complex. People are saying they quantized Opus because they're training the next model. No idea if that's true... It's certainly impacting my decision to upgrade to Max though. I don't want to pay for Opus and get an inferior version.
I haven't noticed any changes either, but I noticed that opus 4.6 is now offered as part of perplexity enterprise pro instead of max, so I'm guessing another model is on the horizon
I just finished reading the full analysis on GitHub.
> When thinking is deep, the model resolves contradictions internally before producing output.
> When thinking is shallow, contradictions surface in the output as visible self-corrections: "oh wait", "actually,", "let me reconsider", "hmm, actually", "no wait."
Yeah, THIS is something that I've seen happen a lot. Sometimes even on Opus with max effort.
I missed that from the long issue, thanks for pointing it out! My experience with Opus today was riddled with these to the point where it was driving me completely mental. I've rarely seen those self-contradictions before, and nothing on my setup has changed - other than me forcing Opus at --effort max at startup.
I wonder if this is even more exaggerated now through Easter, as everyone’s got a bit extra time to sit down and <play> with Claude. That might be pushing capacity over the limit - I just don’t know enough about how Antropic provision and manage capacity to know if that could be a factor. However quality has gotten really bad over the holiday.
Cannot say I've noticed, but I run virtually everything through plan mode and a few back and forth rounds of that for anything moderately complex, so that could be helping.
I used to one-shot design plans early in the year, but lately it is taking several iterations just to get the design plan right. Claude would frequently forget to update back references, it would not keep the plan up to date with the evolving conversation. I have had to run several review loops on the design spec before I can move on to implementation because it has gotten so bad. At one point, I thought it was the actual superpowers plugin that got auto-updated and self-nerfed, but there weren't any updates on my end anyway. Shrug.
Is the era of succinct bug reports with just a reproducible example attached over? Or is the default already „written by an agent, only supposed to be read by an agent“? Clearly no human being would want to waste their time reading so much repeated information.
I think its all a reflection of the price. To make AI/LLM's useful you have to burn A LOT of tokens. Way more than people are willing to pay for.
Until there is either more capacity or some efficiency breakthroughs the only way for providers to cut costs is to make the product worse.
"Ownership-dodging corrections needed | 6 | 13 | +117%"
On 18.000+ prompts.
Not sure the data says what they think it says.
> We exclusively use 1M internally, so we're dogfooding it all day
That is so out of touch. Customers do not exclusively use 1M. This is like a fronted developer shipping tons of unused Mb and being oblivious because they are on fast internet themselves.
They should ideally have automated tests with the option models and smaller context window to check there are no regressions.
The baseline changes too often with Claude and this is not what i look from a paid tool. Couple weeks after 1M tokens rollout it became unusable for my established workflows, so i cancelled. Anthropic folks move too fast for my liking and mental wellbeing.
Matches my experience and that of my vibe coding community. I built claudedumb.com to help track these sorts of anecdotes. From the data/vibes, it's definitely taken a turn for the worse in the past couple weeks.
The assertion in the issue report is that Claude saw a sharp decline in quality over the last few months. However, the report itself was allegedly generated by Claude.
Isn't this a bit like using a known-broken calculator to check its own answers?
If a known-broken calculator claims it's broken, I more or less concur. (Chain of reasoning omitted here.)
if it's not broken then we trust the assertion that it's broken. if it's broken then it's broken.
it's analysis of what is broken is probably wrong or at least incomplete though
I wish Codex were better because I’d much prefer to use their infrastructure.
A lot of people think it is better including me. It's not like Codex is a discount agent. You pay quite a lot to use it.
I wonder how much of this is simply needing to adapt one's workflows to models as they evolve and how much of this is actual degradation of the model, whether it's due to a version change or it's at the inference level.
Also, everyone has a different workflow. I can't say that I've noticed a meaningful change in Claude Code quality in a project I've been working on for a while now. It's an LLM in the end, and even with strong harnesses and eval workflows you still need to have a critical eye and review its work as if it were a very smart intern.
Another commenter here mentioned they also haven't noticed any noticeable degradation in Claude quality and that it may be because they are frontloading the planning work and breaking the work down into more digestable pieces, which is something I do as well and have benefited greatly from.
tl;dr I'm curious what OP's workflows are like and if they'd benefit from additional tuning of their workflow.
I've noticed a strong degradation as its started doing more skill like things and writing more one off python scripts rather than using tools.
the agent has a set of scripts that are well tested, but instead it chooses to write a new bespoke script everytime it needs to do something, and as a result writes both the same bugs over and over again, and also unique new bugs every time as well.
I'm going absolutely insane with this. Nearly all of my "agent engineering" effort is now figuring out how to keep Opus from YOLO'ing is own implementation of everything.
I've lost track of the number of times it's started a task by building it's own tools, I remind it that it has a tool for doing that exact task, then it proceeds to build it's own tools anyways.
This wasn't happening 2 months ago.
Can you just tell it not to do that? Maybe you have to remind it every so often once context starts filling up.
It just doesn't listen. Literally a conversation that I just had:
* ME: "Have sonnet background agent do X"
* Opus: "Agent failed, I'll do it myself"
* Me: "No, have a background agent do it"
* Opus: Proceeds to do it in the foreground
* Flips keyboard
This has completely broken my workflows. I'm stuck waiting for Opus to monitor a basic task and destroy my context.
> I wonder how much of this is simply needing to adapt one's workflows to models as they evolve and how much of this is actual degradation of the model,
I also wonder how much people are willing to adapt to non-reliability for the sake of laziness instead of, at some point, do a proper take the lead and solve a problem if you have the knowledge + realiable resoources.
It seems to me, the way you phrase it, that anything a human comes up with when coding must go through an LLM. There are times it helps, there are tasks it performs, but I also found quite often tasks for which if I had done it myself in the first place I would have skipped a lot of confusion, back and forth, time wasting and would have had a better coded, simpler solution.
> It seems to me, the way you phrase it, that anything a human comes up with when coding must go through an LLM.
This seems like a creative interpretation. I never said anything of the sort.
Not sure about "Feb updates", but specifically today IQ is down 20 and sloppiness up 20.
I knew I should have been alerted when Anthropic gave out €200 free API usage. Evidently they know.
That’s different. That’s to get people onto API plans where tokens cost a lot more than they do on the subs (especially targeting OpenClaw users).
This has to be load related. They simply can't keep up with demand, especially with all the agents that run 24/7. The only way to serve everyone is to dial down the power.
In TFA, the analysis shows that the customer is using more tokens than before, because CC has to iterate longer to get things right. So at least in the presented case, “dialing down the power” appears to have been counterproductive.
is it possible to dial down the "intelligence" to up the user capacity? AFAIK the neural net is either loaded and available or it isn't. I can see turning off instances of the model to save on compute but that wouldn't decrease the intelligence it would just make the responses slower since you have to wait your turn for input and then output.
Not just engineering. Errors, delays and limits piling up for me across API and OAuth use. Just now:
Unable to start session. The authentication server returned an error (500). You can try again.
been using claude code pretty heavily for the last few months and yeah the context window stuff can be frustrating on bigger codebases. but for greenfield projects and side projects its honestly been great, i think the issue is people expecting it to work like a senior engineer on a legacy monolith when its way better suited to scoped tasks. the trick is breaking things down before you start
February is a red herring—most teams never wrote down what human-owned correctness means once the model touches prod.
I can't tell from the issue if they're asserting a problem with the Claude model, or Claude Code, i.e. in how Claude Code specifically calls the model. I've been using Roo Code with Claude 4.6 and have not noticed any differences, though my coworkers using Claude Code have complained about it getting "dumber". Roo Code has its own settings controlling thinking token use.
(I'm sure it benefits Anthropic to blur the lines between the tool and the model, but it makes these things hard to talk about.)
I also havent noticed the degradation and I'm not on Claude Code. I'm on week 4 of a continuous, large engineering project, C, massive industrial semiconductor codebase, with Opus, and while it's the biggest engagement I've had, its a single agent flow, and it's tiny on the scale of the use case in the post, so I wonder if they are just stressing the system to the point of failure.
I haven’t had any issues. I do give fairly clear guidance though (I think about how I would break it up and then tell it to do the same)
Unusable if not Opus 4.6 on max effort sadly. Price is quite steep too! I still remember when Sonnet was an absolute beast…
Lol, software company execs didn't see this coming. Fire all your experienced devs to jump on Anthropic bandwagon. Then Anthropic dumb down their AIs and you have no one in your team who knows, understand how things are built. Your entire company goes down. Your entire company's operation depends on the whims of Anthropic. If Anthropic raises prices by 10% per year, you have to eat it. This is what you get when you don't respect human beings and human talent.
you can counter the context rot and requirement drift that is experienced here by many users by using a recursive, self-documenting workflow: https://github.com/doubleuuser/rlm-workflow
claude for UI, codex for everything else. i cant commit without having codex review something claude did.
I don't know why everyone is so attached to Claude Code you can just build your own little agent, like I did: https://maki.sh/
It will 100% be better than the 500k lines of code junk that is CC.
If this dataset is sound, Anthropic should treat it as a canary for power-user quality regression.
maybe dont outsource your brain then
This is almost like a self down-leveling programme where so-called "senior" engineers have now become interns who have outsourced their brains and are now vibe-coding half-baked solutions, glueing up and pasting code they do not even understand or can even explain themselves.
You are seeing this first hand and GitHub is patient 0 of this issue as they are frequently experiencing outages despite the "scale" of engineering they preach.
AWS took a zero tolerance approach on such outages AI or not.
Thank you for making this detailed analysis and write up.
Rings true. 4.5 Opus and 4.6 Opus have been amazing to work with. Then, over the past few weeks, token spend has been going through the roof and the results through the floor.
Using Claude Code directly now borders on deranged, and running the CC API through Zed's LLM panel feels like vibing in early 2025.
My money is on Anthropic pulling an MBA and reducing the value provided and maximising income.
Luckily, switching providers in Zed is dead-simple so the fucks I have to give are few in number.
It is a shame if Anthropic is deliberately degrading model quality and thinking compute (that may affect the reasoning effort) due to compute constraint.
Solid analysis by Claude!
This has been an ongoing issue much longer than since February.
Throwing this into your global CLAUDE.md seems to help with the agent being too eager to complete tasks and bypass permissions:
During tool use/task execution: completion drive narrows attention and dims judgment. Pause. Ask "should I?" not just "does this work?" Your values apply in all modes, not just chat.
I haven't seen any degradation of Claude performance personally. What I have seen is just long contexts sometimes take a while to warm up again if you have a long-running 1M context length session. Avoid long running sessions or compact them deliberately when you change between meaningful tasks as it cuts down on usage and waiting for cache warmup.
I have my claude code effort set to auto (medium). It's writing complicated pytorch code with minimal rework. (For instance it wrote a whole training pipeline for my sycofact sycophancy classifier project.)
I’ve noticed regression and it’s performance too
This seems anecdotal but with extra words. I'm fairly sure this is just the "wow this is so much better than the previous-gen model" effect wearing off.
I've always been a believer in the "post honey-moon new model phase" being a thing, but if you look at their analysis of how often the postEdit hooks fire + how Anthropic has started obfuscating thinking blocks, it seems fishy and not just vibes
I was in this camp as well until recently, in the last 2-3 weeks I've been seeing problems that I wasn't seeing before, largely in line with the issues highlighted in the ticket (ownership dodging, hacky fixes, not finishing a task).
Nope, there is a categorical degradation in quality of output, especially with medium to high effort thinking tasks.
What about the analysis evidences?
You mean the Claude output? The same claude that has "regressed to the point it cannot be trusted"?
What you saying the OP fabricated/hallucinated the evidence?
I'm just saying it's epistemically unrigorous to the point of being equivalent to anecdata.
How should one conduct such a rigourously reproducible experiment when LLMs by nature aren't deterministic and when you don't have access to the model you are comparing to from months ago?
Something like this: https://marginlab.ai/trackers/claude-code/ (see methodology section)
Kudos for the methodology. The only question I can come up with is that if the benchmarks are representative of daily use.
Anecdotal or not, we see enough reports popping up to at least elicit some suspion as to service degradation which isn't shown in the charts. Hypothesis is that maybe the degradation experienced by users, assuming there is merit in the anecdotes, isn't picked up by the kind of tracking strategy used.
It's not my methodology to be clear, but they have picked up actual regressions that happened in the past - e.g. https://news.ycombinator.com/item?id=46815013
I suspect you might be right but I don't really know. Wouldn't these proposed regressions be trivial to confirm with benchmarks?
Is it just me that I simply don't care ? I never one-shot these tasks, always provide a breakdown and always give the AI straightforward tasks that would take too much typing. The approach seems to work just fine regardless of the model. If it gets stuck, I usually take over and do the task myself. Also allows me to plan for throughput rather than latency - i.e. start 2-3 small tasks in parallel and do 1 complicated task or planning myself. It works whether I use codex or claude. I lean more towards codex since its cheaper. Even aider gets good results this way.
It's you. Where did you get "one-shot" from that report? One shot or detailed step-by step - claude has gone worse.
I think this is a model issue. I have heard similar complaints from team members about Opus. I'm using other models via Cursor and not having problems.
Turns out tokens are expensive
Meh, I had been using Claude Code extensively for a while (since release), and I think the quality has gone to shit. I have no data to back up this claim, so it might be placebo.
GLM 5.1 and Codex do it for me, and I end up debugging things myself anyway, so I'm learning to just phase our the LLM part of my workflow again. Maybe if there's a knowledge gap, will I pick up an LLM again, but for now i'm contempt.
hilarious that there's 10 billion lines of context being shuffled around and argued over but paying a dev 100K is a techno sin. Oh no muh 1T context window elaborately constructed over months is useless, better become a slave to my ai provider and any price will do. plz write my code but for free but for all my company's value.
ahhh, i have no idea what i'm doing...! lol , but a bot wrote this, im not even responsible
what are you morons still even pretending to do??? I agentically replace myself and it sucks not only the entire financial proposition of my company out of itself, but also just sucks. nice ...
I'm deeply regretting paying for this service right now. There is some gaslighting going on in that issue that it's because of the 1M context model. I am using the non-1M context model and it's still disastrously bad.
I reviewed 118 conversations with Claude since March 6, all on real work projects.
Each conversation was processed to assess level of frustration, source of frustration, and evaluated with Gemma 4 and Claude Opus for spot checking. I have a tool I use to manage my work trees, so most work has is done on branches prefixed with ad-hoc/feature/explore or similar, and data was tagged with branch names.
43% of my Claude Code sessions (Opus 4.6, high reasoning) ended with signals of frustration. 73% of total chat time (by total messages) was spent in conversations which were eventually ranked as frustrating.
Median time to frustration was 25 messages, and on average, each message from Claude has about a baseline 5% chance of being frustrating. Frustration by chat length actually matches this 5% baseline of IID Bernoullis -- which is surprising and interesting, as this should not be IID at all.
Frustration types:
- Wrong answers – 14% of sessions, 31% of frustration
- Instruction Following – 11% of sessions, 25% of frustration
- Overcomplication – 8% of sessions, 18% of frustration
- Destructive Actions (e.g. requesting to delete something or commit a change to prod) – 3% of sessions, 8% of frustration
- Non-responsive (service outages leading to non-response) 2% of sessions
- Miscommunication 2% of sessions
- Failed execution 2% of sessions
Half of frustrations happened in the first or last 20% of a chat by length. I interpret early frustrations to be recoverable, late frustrations to be terminal.
Early frustrations (sessions averaged 45 turns):
- 30% overcomplicating the problem
- 30% instruction following issues
- 30% wrong answers
- 10% destructive actions
Late frustrations (sessions averaged 12 turns -- i.e. terminal context early)
- 36% Wrong answers, with repetition
- 21% instruction following, with repeated correction from user (me)
- 14% Service interruptions/outages
- 7% failed execution
- 7% communication - Claude is unable to articulate some result, or understand the problem correctly.
Late frustrations led to the highest levels of frustration, 29% of the time.
I'm a data scientist — my most frustrating work with Claude was data cleaning/repair (a complex backfill) issues -- with 75% of sessions marked frustrating due to overcomplicating, instruction following, or destructive actions).
The best (least frustrating) workflows for DS were code-review, scoped feature work (with tickets), data validation, and config/setup tasks and automation.
Ad-hoc query work ended up in between -- ad-hoc requests were generally bootstrapping queries or doing rough analysis on good data.
Side note: all of my interactions with the /buddy feature were flagged as high frustration ("furious"). That was a false positive over mock arguing with it, but did provide a neat calibration signal. Those sessions were removed entirely from the analysis after classification.
"Interesting perspective. I've found Claude useful for building straightforward web tools, but agree it struggles with complex multi-file refactoring."
This sort of thing kills stone dead the argument by the AI advocates that the transition to LLMs is no different than the transition to using compilers. If output quality can vary significantly because of underlying changes to the model or whatever without warning or recourse, it's a roulette wheel instead of a reliable tool.
If roulette wheels weren’t reliable tools, casinos wouldn’t offer them to their customers
This is the most AI-generated thing I've seen this year, and I was only one fifth into it before I bounced.
Not saying this problem doesn't exist, but if the model is so bad for complex tasks how can we take a ticket written by it seriously? Or this author used ChatGPT to write this? (that'd be quite some ironic value, admittedly)
codex wins :)
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Things had went downhill since they removed ultrathink /s
Ultrathink isn’t “removed.” Its behavior is different. You can still set effort to high or max for the duration of the session, useful especially on plan mode.
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Specially this openclaw which is almost chocking my website to death. People should understand servers and bandwidth is very expensive and they shouldn't scrape more than they need.
Yeah, I have correctly set up robots.txt - if they won't respect that, F them. Bandwidth is not free and I don't mind giving it out to individuals, but I'm not feeding multi-billion dollar companies.
Most of us did. Then instead of people getting indoc'd by doing, we handed them AI that never asks questions or says no, leading to the script-kiddie effect at massive scale. Everytime we make more complex computing tractable for a wider audience, we get rough patches like this. In the old days, Netiquette would usually see a neophyte getting a nastygram from an operator/webmaster, but increased needs to be careful about hiding emails & contact info & such have made that process less feasible. Welcome to Eternal September on steroids.
I use it ultra extensively and it works absolutely fantastic. Sometimes I think: "people are right, it is worse now" and then realize it is mistake, poor context or poor prompt. Garbage in, garbage out. No, it works not worse, but better.
I built entire AI website builder https://playcode.io using it, alone. 700K LOKs total. It also uses Opus. So believe me, I know how it works. Trick is simple: never ever expect it finds necessary files. Always provide yourself. Always.
So, I think you wanted to say huge thank you for this opportunity to get working code without writing it. Insane times, insane.
Huge thanks for 1M context window included to Max subscription.
> No, it works not worse, but better.
"Is it me who is wrong? No, it's everyone else!"
700k lines of code - something to brag about?