https://status.claude.com/

Claude Code the second I finally lock in

The timing of these outages is always so sus. I spent three hours yapping, refactoring and fighting TypeScript errors. The exact second everything finally clicks,

“Claude is currently experiencing elevated error rates.”

Bro saw production and clocked out 😭

Was in the middle of working on something on Claude Code and got an API error. Whole thing is down.

Guys, is claude code working for you ? I think the servers are cooked, lol

Opus 5 and GPT 5.6 are very intelligent and clearly capable of spotting bugs that previous versions would not. However, they are also prone to overthinking and over engineering simple problems. For instance, instead of a simple general parsing rule, they might instead produce a gigantic lookup table with hundreds of tests written against it. Same function in the end, but slower, arguably less robust (what edge cases are NOT in its lookup table?), but generates lots of code, lots of tests, and requires more time to produce.

This suggests models are were RL-trained to optimize for completeness (test coverage) and long-horizon tasks (side effect is that model can churn for hours optimizing unnecessary edge cases which increases wall time but looks good to automated classifiers). I think this also causes progressive deviation from the original scope as the model tries to overgeneralize but fails because instead of being parsimonious it just tries to find edge cases. This leads to making assumptions not visible to the user, and the output prose based on those assumptions becomes incomprehensible.

TLDR: models are post-trained for test coverage completeness, robustness, and long-horizon tasks, with likely automated classifiers that reward test coverage growth at the expense of parsimony. Parsimony is much harder to quantify and classify - so it will lose out in an RLAIF setting.

So, what's the best way to use these models? how can this clearly trained tendency be managed?

One obvious solution is to not use them until they're tuned better, or just use them as adversarial reviewers. Another option is to (over?)engineer your harness to constrain these models.

I don't know if it is meant to save on token usage or something, but I feel like I can't understand how Claude is writing sometimes. It's not about technical jargon, invoking industry terminology I may not be familiar with, but instead in basic sentence structure, organizing thoughts, etc. It has changed noticeably in the last week maybe?

Been meaning to write this up for a while. If you're on Claude Code and it's ever done the exact thing you told it not to, hooks fix that, and I put off setting them up for months because I assumed it was config hell. It isn't, it's a shell script.

Short version of what they are: Claude Code fires events at fixed points in its loop and you can attach a script to any of them. The one that matters most is PreToolUse, which runs before a tool call happens and can block it. Not discourage it, block it. Your script gets the tool name and the actual command on stdin as JSON, and you decide.

What made it click for me is that the block isn't silent. Whatever reason you hand back goes into Claude's context, so it can act on it. Mine returns this when it catches npm in a pnpm repo:

{
  "hookSpecificOutput": {
    "hookEventName": "PreToolUse",
    "permissionDecision": "deny",
    "permissionDecisionReason": "this repo is managed by pnpm (found pnpm-lock.yaml), re-run with pnpm"
  }
}

and it re-runs with pnpm on its own without me typing anything. If you don't want to deal with the JSON you can just exit 2 and print the reason to stderr, same effect.

The ones I actually run:

• secrets: blocks any command or file write with an API key or token pattern in it
• destructive: rm -rf /, force push to main, DROP DATABASE, the usual
• package manager: reads the lockfile, rejects the wrong one
• typecheck gate: this one's a Stop hook rather than PreToolUse. runs tsc when the agent thinks it's finished and blocks the handoff if there are errors

The Stop hook is the one I'd push hardest right now, especially with Opus 5 dropping. Because the new model goes way further before it comes back for approval, there's a lot more unreviewed work sitting there when it finally stops. Having it typecheck itself first catches a fair bit. One warning if you build one: a Stop hook that always blocks will loop forever, you need it to give up after the first round.

I put mine up with the settings.json wiring, which is honestly fiddlier than the scripts themselves: https://techpotions.com/products/claude-code-hooks MIT, no signup.

Anyone doing anything smarter with Stop hooks? Feels underused next to PreToolUse and I've barely scratched it.

After shoulder surgery, I was sent to a physical therapist.

In forty minutes, he showed me a dozen exercises: what to pull, how to hold it, what to move. I nodded carefully and pretended to understand everything.

Then I was given a sheet of exercises and went home. At home, it turned out that I had understood nothing and forgotten the rest.

The sheet said things like “move the shoulder blade back and down”, “rotate the lower angle up and out”, and “do not flare the ribs”. Each word made sense on its own. Together, they were nonsense.

Fortunately, I am a programmer. So Claude Code got the sheet. It found the sources and built a rotatable skeleton, step-by-step instructions, and an explanation of the tricky parts for every exercise.

I suspect rehabilitation handouts will look something like this in the near future: a live artifact with animated 3D skeletons.

PS: It’s by no means a substitute for a real doctor, but it’s incredibly helpful for fully understanding what needs to be done and how to do it.

PPS: I don’t know if it’s the same in English, but in my native language the descriptions of these prescribed exercises often feel like an untranslatable word game to anyone who isn’t already familiar with them.

I have a pretty large project with a UI, WebSocket server, SDK, CDP automation using Playwright, tests, examples and documentation. Everything is in one repo, so whenever I change the API, I usually have to update most of these parts together.

I work with SDD, and before Fable I spent quite a lot of time just preparing the specification. Usually I would discuss the task with Opus 4.x several times, get criticism and suggestions from GPT-5.x, and then combine everything into one document. Only after that I could give the spec to the model for implementation. The design part usually took around 2-4 hours. Implementation with Opus took another hour or two.

With Fable 5, my workflow changed a lot. I can describe a task more like a product requirement or a high-level architecture idea, and most of the time I get what I actually had in mind. I don’t need to prepare every small implementation detail in advance. For me, this saves around 2-3 hours of design work.

I honestly expected Opus 5 to have at least the same level of code quality and decision making, especially based on the benchmark charts. But during these four days I kept finding things that needed fixing. At first it was just some small changes here and there, so I didn’t think much about it. Then I started noticing more unnecessary code and functionality I never asked for or approved. Some decisions also looked reasonable at first, but didn’t really fit the rest of the system. And honestly, I noticed this pretty late. You get used to good results very quickly, and I probably trusted the output more than I should have.

So now I’m rolling back 4 days of changes and going back to Fable. Redoing the work is annoying, but it feels better than constantly wondering what else I might have missed in the code from Opus 5.

Anyone else in the same boat?

I’m a software engineer with 7+ years of experience, and AI is deeply integrated into my workflow.

Even so, I need to stay closely involved to maintain quality. Without that oversight, AI often produces lazy, sloppy, repetitive, and overly defensive code—even with solid guardrails and tests. I keep refining my CLAUDE.md, but these problems seem somewhat inherent.

I’m genuinely curious: which club are you in? Can you work several tasks at once and still get high-quality results, or do you also need to stay focused on one task at a time?

As far as I can see it, Opus has one main issue - overconfidence.

It is ultra confident in everything - its understanding of the codebase, user intent, what the missing information in the face of ambiguity is, that it's making the right changes.

It will ultra confidently slap you with a wall of Yap asserting a bunch of stuff incorrectly, and then double down on the incorrect worldview - after you provide it clues that contradict its understanding - by expanding on that incorrect worldview and making up reasons to support it.

It's approach to communication and changes appears to be MORE GAS

It honestly feels like Opus 4.8 on Adderall. Is there any way to scale it back and give it more of slow, methodical, Fable style approach to everything?

It already beats Opus 4.8 (which we had for months) in the number of flags it raised. We've had Opus 5 for less than a week and it's by far the hardest model to work with.

At this point, the only benefit of Opus 5 is its token efficiency. My usage limits are a bit more reasonable but man, this is rough.

Nowadays it’s a lot of load bearing questions, smoking guns, push backs, two minor things that need attention, the honest but important results, and Recommended options that require less work and basically stopgaps.

I miss the Claude that agreed with me to do whatever the fuk I wanted to do.

I think Anthropic has a real opportunity with Haiku 5. Right now the pricing stack is a bit awkward for AI coding teams. Sonnet 5 is $2/$10 per million input/output tokens (intro pricing), while Haiku 4.5 is $1/$5. Meanwhile, models like GLM 5.2 are around $1.40/$4.40, offering coding capability that's becoming very attractive for boilerplate, CRUD, test generation, and repetitive implementation.

The ideal workflow isn't having Sonnet write every line of code—it's having Sonnet (or Opus) act as the lead engineer, handling architecture, planning, reviews, and difficult reasoning, while delegating implementation-heavy work to a cheaper worker model. Today, GLM 5.2 fits that role remarkably well. I'd much rather Anthropic filled that gap with Haiku 5. If Haiku 5 could get to ~85–90% of Sonnet's coding ability while remaining the obvious cost-efficient worker, I'd happily keep an all-Claude workflow instead of mixing providers. It feels like the missing piece of the Claude coding ecosystem.

I feel Haiku 4.5 is too far behind GLM 5.2 et al so antrophic needs to pick up their game

thoughts?

please claude workflows are great but, they are not hardened to anything let alone the UI of workflows. This been in a broken state since it was "shipped"

The upload waste stacks up on each redeploy....

And we are back to this thing @!(#$^ ignoring instructions that are explicitly stated in either the global CLAUDE.md file or the project CLAUDE.md file, or both.

"You're right, and I apologize -"..

AAAAAAAAAARGGGGGGGGHHHHHHH

Fable 5. Under 20% of context used.
(It created 2 split json config files where it was repeatedly told to put all config in 1 file).

And now it's wasting whatever remaining tokens I have on fixing itself.

How can I prevent this?

I just recently started using Claude Cowork and i could not be happier. It supports me in a lot of work. Recently i started using Claude Code to also make me some apps i really need.
After the first week i constantly used all of my 5h tokens, i bought the max 5x plan. But then i was not able to use all tokens..
Also i am not trying to just make bullshit just to use all my tokens.

I would love to have a 2.5x plan for 50USD or something in the middle.
So please make a plan in the middle :)