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An update on recent Claude Code quality reports

anthropic.com

521–530 of 778 posts

Re: An update on recent Claude Code quality reports

#521

"On March 26, we shipped a change to clear Claude's older thinking from sessions that had been idle for over an hour, to reduce latency when users resumed those sessions. A bug caused this to keep happening every turn for the rest of the session instead of just once, which made Claude seem forgetful and repetitive. We fixed it on April 10. This affected Sonnet 4.6 and Opus 4.6" This makes no sense to me. I often leav…

Wow, I always thought the context is always stored locally and this is something I have control over. Glad I use kiro-cli which doesn't do this.

you might be biased due to your employment :)

Re: An update on recent Claude Code quality reports

#522
post #450

Earlier quoted context omitted.

I appreciate the reply, but I was never under the impression that gaps in conversations would increase costs nor reduce quality. Both are surprising and disappointing. I feel like that is a choice best left up to users. i.e. "Resuming this conversation with full context will consume X% of your 5-hour usage bucket, but that can be reduced by Y% by dropping old thinking logs"

By caching they mean “cached in GPU memory”. That’s a very very scarce resource. Caching to RAM and disk is a thing but it’s hard to keep performance up with that and it’s early days of that tech being deployed anywhere. Disclosure: work on AI at Microsoft. Above is just common industry info (see work happening in vLLM for example)

Nit: It doesn’t have to live in GPU memory. The system will use multiple levels of caching and will evict older cached data to CPU RAM or to disk if a request hasn’t recently come in that used that prefix. The problem is, the KV caches are huge (many GB) and so moving them back onto the GPU is expensive: GPU memory bandwidth is the main resource constraint in inference. It’s also slow.

The larger point stands: the cache is expensive. It still saves you money but Anthropic must charge for it.

Edit: there are a lot of comments here where people don't understand LLM prefix caching, aka the KV cache. That's understandable: it is a complex topic and the usual intuitions about caching you might have from e.g. web development don't apply: a single cache blob for a single request is in the 10s of GB at least for a big model, and a lot of the key details turn on the problems of moving it in and out of GPU memory. The contents of the cache is internal model state; it's not your context or prompt or anything like that. Furthermore, this isn't some Anthropic-specific thing; all LLM inference with a stable context prefix will use it because it makes inference faster and cheaper. If you want to read up on this subject, be careful as a lot of blogs will tell you about the KV cache as it is used within inference for an single request (a critical detail concept in how LLMs work) but they will gloss over how the KV cache is persisted between requests, which is what we're all talking about here. I would recommend Philip Kiely's new book Inference Engineering for a detailed discussion of that stuff, including the multiple caching levels.

Re: An update on recent Claude Code quality reports

#523
post #502
post #456

Earlier quoted context omitted.

To my eye, gaslighting is a serious accusation. Wikipedia's first line matches how I think of it: "Gaslighting is the manipulation of someone into questioning their perception of reality." Did I miss something? I'm only looking at primary sources to start. Not Reddit. Not The Register. Official company communications. Did Anthropic tell users i.e. "you are wrong, your experience is not worse."? If so, that would reac…

They didn’t say “your experience is not worse” but they did frequently say “just turn reasoning effort back up and it will be fine”. And that pretty explicitly invalidates all the (correct) feedback which said it’s not just reasoning effort. They knew they had deliberately made their system worse, despite their lame promise published today that they would never do such a thing. And so they incorrectly assumed that th…

I'm aiming for intellectual honesty here. I'm not taking a side for a person or an org, but I'm taking a stand for a quality bar.

> They knew they had deliberately made their system worse

Define "they". The teams that made particular changes? In real-world organizations, not all relevant information flows to all the right places at the right time. Mistakes happen because these are complex systems.

Define "worse". There are lot of factors involved. With a given amount of capacity at a given time, some aspect of "quality" has to give. So "quality" is a judgment call. It is easy to use a non-charitable definition to "gotcha" someone. (Some concepts are inherently indefensible. Sometimes you just can't win. "Quality" is one of those things. As soon as I define quality one way, you can attack me by defining it another way. A particular version of this principle is explained in The Alignment Problem by Brian Christian, by the way, regarding predictive policing iirc.)

I'm seeing a lot of moral outrage but not enough intellectual curiosity. It embarrassingly easy to say "they should have done better" ... ok. Until someone demonstrates to me they understand the complexity of a nearly-billion dollar company rapidly scaling with new technology, growing faster than most people comprehend, I think ... they are just complaining and cooking up reasons so they are right in feeling that way. This possible truth: complex systems are hard to do well apparently doesn't scratch that itch for many people. So they reach for blame. This is not the way to learn. Blaming tends to cut off curiosity.

I suggest this instead: redirect if you can to "what makes these things so complicated?" and go learn about that. You'll be happier, smarter, and ... most importantly ... be building a habit that will serve you well in life. Take it from an old guy who is late to the game on this. I've bailed on companies because "I thought I knew better". :/

Re: An update on recent Claude Code quality reports

#524

"On March 26, we shipped a change to clear Claude's older thinking from sessions that had been idle for over an hour, to reduce latency when users resumed those sessions. A bug caused this to keep happening every turn for the rest of the session instead of just once, which made Claude seem forgetful and repetitive. We fixed it on April 10. This affected Sonnet 4.6 and Opus 4.6" This makes no sense to me. I often leav…

It astounds me that a company valued in the hundreds-of-billions-of-dollars has written this. One of the following must be true: 1. They actually believed latency reduction was worth compromising output quality for sessions that have already been long idle. Moreover, they thought doing so was better than showing a loading indicator or some other means of communicating to the user that context is being loaded. 2. What…

The same company that claims they have models that are too "dangerous" to release btw.

Re: An update on recent Claude Code quality reports

#525
post #98

>On March 4, we changed Claude Code's default reasoning effort from high to medium to reduce the very long latency—enough to make the UI appear frozen—some users were seeing in high mode Instead of fixing the UI they lowered the default reasoning effort parameter from high to medium? And they "traced this back" because they "take reports about degradation very seriously"? Extremely hard to give them the benefit of do…

> Instead of fixing the UI they lowered the default reasoning effort parameter from high to medium? And they "traced this back" because they "take reports about degradation very seriously"? Extremely hard to give them the benefit of doubt here.

They had droves of Claude devs vehemently defending and gaslighting users when this started happening

Re: An update on recent Claude Code quality reports

#526
post #5

Wow, bad enough for them to actually publish something and not cryptic tweets from employees. Damage is done for me though. Even just one of these things (messing with adaptive thinking) is enough for me to not trust them anymore. And then their A/B testing this week on pricing.

People come at this with all kinds of life experience. The above notion of trust to me is quaint and simplistic. I suggest another way to frame trust as a more open ended question:

    To what degree do I predict another person/org will give me what I need and why?
This shifts "trust" away from all or nothing and it gets me thinking about things like "what are the moving parts?" and "what are the incentives" and "what is my plan B?".

In my life experience, looking back, when I've found myself swinging from "high trust" to "low trust" the change was usually rooted in my expectations; it was usually rooted in me having a naive understanding of the world that was rudely shattered.

Will you force trust to be a bit? Or can you admit a probability distribution? Bits (true/false or yes/no or trust/don't trust) thrash wildly. Bayesians update incrementally: this is (a) more pleasant; (b) more correct; (c) more curious; (d) easier to compare notes with others.

Re: An update on recent Claude Code quality reports

#527

Earlier quoted context omitted.

> I was never under the impression that gaps in conversations would increase costs nor reduce quality. Both are surprising and disappointing. You didn't do your due diligence on an expensive API. A naïve implementation of an LLM chat is going to have O(N^2) costs from prompting with the entire context every time. Caching is needed to bring that down to O(N), but the cache itself takes resources, so evictions have to…

How do you do "due diligence" on an API that frequently makes undocumented changes and only publishes acknowledgement of change after users complain? You're also talking about internal technical implementations of a chat bot. 99.99% of users won't even understand the words that are being used.

> 99.99% of users won't even understand the words that are being used.

That's a bad estimate. Claude Code is explicitly a developer shaped tool, we're not talking generically ChatGPT here, so my guess is probably closer to 75% of those users do understand what caching is, with maybe 30% being able to explain prompt caching actually is. Of course, those users that don't understand have access to Claude and can have it explain what caching is to them if they're interested.

Re: An update on recent Claude Code quality reports

#528

I’ve stuck to the non-1M context Opus 4.6 and it works really well for me, even with on-going context compression. I honestly couldn’t deal with the 1M context change and then the compounding token devouring nonsense of 4.7 I sincerely hope Anthropic is seeing all of this and taking note. They have their work cut out for them.

absolutely agree: non-1M Opus 4.6 on x20 max was peak AGI

now it's back to regular slop and just to check otherwise i have to spend at least $100

Re: An update on recent Claude Code quality reports

#529
post #77

They lost me at Opus 4.7 Anecdotally OpenAI is trying to get into our enterprise tooth and nail, and have offered unlimited tokens until summer. Gave GPT5.4 a try because of this and honestly I don’t know if we are getting some extra treatment, but running it at extra high effort the last 30 days I’ve barely see it make any mistakes. At some points even the reasoning traces brought a smile to my face as it preemptive…

extra high burns tokens i find. ( run 5.4 on medium for 90% of the tasks and high if i see medium struggling and its very focused and make minimum changes.

Note mini-high is similar perf/latency to medium, but much cheaper

Re: An update on recent Claude Code quality reports

#530

"On March 26, we shipped a change to clear Claude's older thinking from sessions that had been idle for over an hour, to reduce latency when users resumed those sessions. A bug caused this to keep happening every turn for the rest of the session instead of just once, which made Claude seem forgetful and repetitive. We fixed it on April 10. This affected Sonnet 4.6 and Opus 4.6" This makes no sense to me. I often leav…

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