FWIW I strongly recommend using some of the recent, good Chinese OSS models. I love GLM-4.5, and Kimi K2 0905 is quite good as well.

ive been thinking its that my company mcp has blown up in context size, but using claude without claude code, i get context window overflows constantly now.

another option could be a system prompt change to make it too long?

I have read so many anecdotes about so many models that "were great" and aren't now.

I actually think this is psychological bias. It got a few things right early on, and that's what you remember. As time passes, the errors add up, until the memory doesn't match reality. The "new shiny" feeling goes away, and you perceive it for what it really is: a kind of shitty slot machine

> personally am frustrated that there’s no refund or anything after a month of degraded performance

lol, LMAO. A company operates a shitty slot machine at a loss and you're surprised they have "issues" that reduce your usage?

I'm not paying for any of this shit until these companies figure out how to align incentives. If they make more by applying limits, or charge me when the machine makes errors, that's good for them and bad for me! Why should I continue to pay to pull on the slot machine lever?

It's a waste of time and money. I'll be richer and more productive if I just write the code myself, and the result will be better too.

I think it's a psychological bias of some sort. When the feeling of newness wears off and you realize the model is still kind of shit, you have an imperfect memory of the first few uses when you were excited and have repressed the failures from that period. As the hype wears off you become more critical and correctly evaluate the model

I think the models deteriorate over time with more inputs. I think the noise increases like photocopies of photocopies

No, that's just the normal slope of the hype curve as you start figuring out how the man behind the curtain operates.

AI is not useful in the long term is is unsustainable. News at 11.

It’s important to jump on new models super early while the rails get out in.

Anyone remember GPT4 the day it launched? :)

> Importantly, we never intentionally degrade model quality as a result of demand or other factors, and the issues mentioned above stem from unrelated bugs.

Things they could do that would not technically contradict that:

- Quantize KV cache

- Data aware model quantization where their own evals will show "equivalent perf" but the overall model quality suffers.

Simple fact is that it takes longer to deploy physical compute but somehow they are able to serve more and more inference from a slowly growing pool of hardware. Something has to give...

The problem is twofold:

- They're reporting that only impacted Haiku 3.5 and Sonnet 4. I used neither model during the time period I'm concerned with.

- It took them a month to publicly acknowledge that issue, so now we lack confidence there isn't another underlying issue going undetected (or undisclosed, less charitably) that affects Opus.

Does anyone know if this also affected Claude Sonnet models running in AWS Bedrock, or if it was just when using the model via Anthropic’s API?

The simplest thing I frequently ask of regular Claude (not Code) in the desktop app:

"Use your web search tool to find me the go-to component for doing xyz in $language $framework. Always link the GitHub repo in your response."

Previously Sonnet 4 would return a good answer to this at least 80% of the time.

Now even Opus 4.1 with extended thinking frequently ignores my ask for it to use the search tool, which allows it to hallucinate a component in a library. Or maybe an entire repo.

It's gone backwards severely.

(If someone from Anthropic sees this, feel free to reach out for chat IDs/share links. I have dozens.)

I’m running into this as well.

Might be Claude optimizing for general use cases compared to code and that affecting the code side?

Feels strange, because Claude api isn’t the same as the web tool so I didn’t expect Claude code to be the same.

It might be a case of having to learn to read Claude best practice docs and keep up with them. Normally I’d have Claude read them itself and update an approach to use. Not sure that works as well anymore.

> But it's getting so unstable. It generates code, I see it doing that, and then it throws the code away and gives me the previous version of something 1:1 as a new version.

I've had the same experience. Totally unreliable.

You're absolutely right! I should have used the correct word when writing the Hacker News comment.

(lol, yes, thank you.)

This one is interesting because I have seen a fair amount of "can't be understated" on reddit also. Interesting case of linguistic drift.

I’m considering trying the api directly for a bit with Claude code to compare but need a test quite first to compare all 3.

Anthropic literally stated yesterday that they suffered degraded model performance over the last month due to bugs:

https://status.anthropic.com/incidents/72f99lh1cj2c

Suggesting people are "out of their mind" is not really appropriate on this forum, especially so in this circumstance.

> Have you considered perhaps that you are, indeed, out of your mind?

Yes, but I'll revisit.

It seems plausible enough that they're trying to squeeze as much out of their hardware as possible and getting the balance wrong. As prices for hardware capable of running local LLMs drop and local models improve, this will become less prevalent and the option of running your own will become more widespread, probably killing this kind of service outside of enterprise. Even if it doesn't kill that service, it'll be _considerably_ better to be operating your own as you have control over what is actually running.

On that note, I strongly recommend qwen3:4b. It is _bonkers_ how good it is, especially considering how relatively tiny it is.

Just because one can’t concieve something being possible doesn’t mean it’s not possible.

"that every model is either about to be great or was great in the past but now is not"

FWIW, Codex-CLI w/ ChatGPT5 medium is great right now. Objectively accelerating me. Not a coding god like some posters would have it, but overall freeing up time for me. Observably.

Assuming I haven't had since-cured delusions, the same was true for Claude Code, but isn't any more.

Concrete supporting evidence: From time to time, I have coding CLIs port older projects of varying (but small-ish) sizes from JS to TS. Claude Code used to do well on that. Repeatedly. I did another test last Sunday, and it dug a momentous hole for itself that even liberal sprinkling of 'as unknown' everywhere couldn't solve. Codex managed both the ab-initio port and was able to undig from CC's massive hole abandoned mid-port.

So I'd say the evidence points somewhat against random process, given repeated testing shows clear signal both of past capability and of recent loss of capability.

The idea that it's a "random" process is misguided.

I was going to tell you a joke about a broken pencil, but there's no point.

>Or more precisely, that you could be rationalizing what is essentially a random process?

You mean like our human brains and our entire bodies? We are the result of random processes.

>Sucks for those of us who are stuck in the now, though

I don't know what you are doing- but GPT5 is incredible. I literally spent 3 hours last night going back and forth on a project where I loaded some files for a somewhat complicated and tedious conversion between two data formats. And I was able to keep going back and forth and making the improvements incrementally and have AI do 90% of the actual tedious work.

To me it's incredible people don't seem to understand the CURRENT value. It has literally replaced a junior developer for me. I am 100% better off working with AI for all these tedious tasks than passing them off to someone off. We can argue all day if that's good for the world (it's not) but in terms of the current state of AI- it's already incredible.