It usually depends on how it is used. Plain direct usage of prompting (even through Plan mode) most of the time result if crap output. Could be subjective of course, but it writes duplicated code, do not reuse already written functions and module, ignore codebase standards (even if they are directly written to local CLAUDE.md).

Sometimes psychosis starts out of nowhere, and it’s starting outputting pure crap. Or it states he did something, but never actually wrote that code. It can also easily wipe hours of work but just resetting git state. Things happens, to what Claude usually say - “I as so sorry!“.

Only way. I was able to keep it in line is through having very strict orchestration framework with a lot of hooks. So, in short - your friend is correct.

Besides those “features“, service has terrible uptime - it is down very often. It’s not a feeling - their status page all red.

Thanks for the feedback. But it’s weird I haven’t experienced any this except for the suboptimal code generation (which I’m accustomed to regarding LLMs in general, but I’ve learned how to deal with it and get from it what I need and how I need it).

TBH, my only worry here is this being some kind of actually deliberate policy. That’s the only thing I’d actually hate. If it’s a result of peak demand or whatever technical reason, then it’s subject for improvement and will most likely go away, but if it’s the result of a corporate policy, then it’s too bad.

Opus 4.5 was a bit of a tipping point for me, and I’ve been an heavy user now for months. I haven’t experienced degrading overall, and for the large majority of the time it is fine, but there are times when I get the fruitcake Claude. That will typically happen after getting a new one after compaction, but it has also happened a few times after very long session. There was a period earlier this year where that happened more often, but these days not so much.

I have gotten better at spotting the confused crazy talk early on, and I just compact that Claude away and usually get a good Claude one again afterwards.

I do git commits often as Claude will not always have a way to undo code changes that don’t work out. I also do additional more comprehensive backups for major milestones. That didn’t change because of Claude, but they have been used a few times when Claude have accidentely overwritten or deleted data source files. Frequent backups do so much better than “I did a horrible mistake. I’m so sorry”.

To be fair sometimes I’ve been the idiot and implicitly assumed Claude has a level of common sense. There is none. I once had ssh into a more powerful computer, and once the heavy processing was done, I asked Claude to clean up and remove all files no longer needed for the project. That did not work out well.

Anyway, I can’t say have experienced any systematic degradation. Rather the opposite as Opus 4.6 and 4.7 seem like improvements.

Thanks!

What exactly do you mean by this? (compacting that Claude away)

Frequent backups of what? Your repo is already versioned (and hopefully pushed to remote).

/compact or just /clear

I have two projects which have been going on for months. There are many huge data source files which are git ignored due to size.

Besides, confused Claude version have at times suggested git actions that could mess up that backup so a separate one makes me sleep much better.

It does feel like deliberate policy. I’ve been experimenting heavily on max plan, mostly doing R&D besides real work. As soon as new model appears - it’s fast, smart, and you feel like a real change. After a week feeling is gone. Everything is slow again. Opus 4.7 started to often hang in the middle of the work (friends report similar behavior) - just stops at some point doing nothing. The worst situation when it hangs within subagents - it’s not stopping subagents, and it feels like it’s just working for really long time.

Without harness or orchestration frameworks like superpowers, plain Claude feels really silly. It’s still way better than let’s say Mistral’s devstral-2, but with Kimi 2.5 I get very similar level of quality with 0.25 of the price (if used through Factory Droid subscription for example).

To sum up, I think it’s taking at least few months to start feeling the pain and understand the AI tax. You lose knowledge of codebase, and often you just blindly trust it. Then you check some parts of the code that AI covered by tests and that work in production “correctly“ just to discover total mess, that will get you into cold sweat.

Yes.

On forums such as the OpenAI Community Forum, there are many threads describing what you are calling “nerfed” behavior.

From longer-term observation across multiple platforms and vendors (including OpenAI and Anthropic), this pattern is not isolated to a single model or provider. Users often report cycles where a model initially performs very well, then appears to degrade.

A few factors—both user-side and vendor-side—can explain much of this:

User-side factors

• Long conversations and context compaction
  As sessions grow, systems may summarize or compress earlier context. This can drop details that were implicitly guiding good responses.
  Mitigation: periodically start a new session and carry forward only the essential state (a “continuation prompt”).
• Prompt drift vs. model updates
  When new model versions are released, guidance in model cards or documentation often changes. Prompts that previously worked well may become less effective.
  Mitigation: periodically revise prompts to align with current recommendations.

Vendor-side factors

• Model updates and tuning changes
  Providers do update models over time (e.g., safety tuning, instruction-following behavior, latency/cost optimizations). These changes can alter output style or reliability.
• System prompt and policy adjustments
  Changes to system-level instructions or safety layers can have noticeable downstream effects on responses.

I could likely spend a week covering this in depth, but in practice it comes down to understanding how LLMs operate, reviewing model documentation, and gaining experience through use.

Note: I did use ChatGPT to polish the reply but the starting reply was created by me then polished with the help of ChatGPT.

https://openai.com/index/gpt-5-system-card/

Best practices for using Claude Opus 4.7 with Claude Code

Prompting best practices

Using Claude Code: session management and 1M context

Note: I do not actively use the 1M context and it eats tokens faster.

I doubt there’s any conspiracy here. Probably just operational difficulties.

But FWIW, there are other ways to use these models. For example, we use Opus via Opencode talking to Amazon Bedrock, so it’s running on Amazon’s infrastructure, not Anthropic’s. I haven’t noticed the kinds of issues people talk about with Claude Code. And as a bonus, in theory we could switch to another vendor’s model (although so far Opus has been great, and I like Anthropic more than I like its competitors).

Really, I can not edit my own post after a few hours.

An update on recent Claude Code quality reports

Btw, this morning it got a bit lazy/superficial. Asked it about the slip-ups in execution and it admitted being lazy. Just had it add “Don’t ever EVER be lazy!” to its project memory at the very top. It also added the 3 instances of the morning laziness as reminders/arguments on its own initiative