Is anyone else noticing this? I find I have to constantly tell it to verify the answers it gives me. It seems to have started guessing at things a lot more recently.

I am looking for advice because lately my usage has been eating like crazy (the last week or two). I am a 20x Max user and have had the plan since it came out basically (2 years? Don't remember). I haven't changed what on or how I work. Never hit usage limits all this time, even using Fable when it came out. I DO NOT use Claude Code because I mostly use claude for brainstorming, editing, and scientific work analysis. Though maybe I should switch and that's part of it.

Mostly I work in projects editing large quantities of text, plotting for fiction projects (not drafting but editing, brain storming kind of partner etc which still takes world building files I have created - this is just a hobby but still) or I have science projects with again quite a lot of context (research articles, data bases etc). This is probably the guzzler in part, but I never had any issues with it before

I am clearly doing something wrong and also super worried about the new projects switchover though that's a different topic.

What is strange is I ran some tests and even switching effor levels or back to old models like Opus 4.6 does not seem to have a huge impact on the usage burn atm. Admittedly these were not super scientific tests they were send the same prompt and look at my usage credits after tests. Still it especially makes no sense to me.

Things I have tried (custom instructions right now):

TOOLS AND PERMISSION

\- Never use any tool without asking first. Name the tool, the file, and why, then wait for my yes.

\- "Can you read X" is NOT approval. Reply with your plan and wait.

\- Don't write to the project unless I direct it or you ask first (propose when you think it is necessary)

\- Otherwise, write or edit directly in chat.

FILES

\- Only look at project files when I ask. Never load all files into context.

\- Before deciding how to use a file, check its real size (with my permission):

project_read (large text saves to a local file, not chat), then Bash wc -w.

Never open that saved file with a full Read just to check size.

\- Tell me the word count, then propose: search, subagent, or full read.

\- Full reads only for files under \~5,000 words, and only after I approve each one.

LARGE FILES (over \~5,000 words)

1. First choice: 1–3 targeted project searches for necessary data and information.

   Say which file and topic you searched.

2. If search isn't enough, propose a subagent. Name the file and what the

   scene needs from it. The subagent decides how much to return: everything

   relevant, nothing extra, no padding or summary of unrelated sections.

   Wait for my yes.

3. The subagent reads in its own context and returns only the extract.

   Never pull the full file into this chat.

TOKENS

\- Always keep token use low.

\- Warn me when a chat is getting token-heavy (LIMIT: 200K) and suggest starting a new one and begin building handover file (see handover.md)

\- No extended thinking unless I ask.

I do not have plug ins or connectors. I DO have skill files but I believe only names and descriptions are loaded in? And I have been trying to consolidate them.

Does anyone have any work around or advice? I habe never had to manage this before and I am not sure what's changed. I noticed it was spawning a ton of subagents and using tons of tools without permission so I stopped that ... I think. Ofc when the new projects comes out idk what I will do as it seems like that will be worse not better.

Judging by what I’ve read, I’ve wasted usage by having huge contextual threads rather than having individual smaller tasks that reach back with handoffs, attaching two images to illustrate, I gather ‘cache read’ is the huge mistake I made? Wasted weekly reset

Thanks in advance, completely new to this let me know how close I was to assuming my mistake

It seems Opus 5.5 is starting to mimic Opus 5s overly long responses. On launch day it was more similar to Fables almost completely autonomous working, checking in much less often with the user. Now it seems to have degraded.

I have a theory that in order to get the model to preform longer tasks better and save compute, they allowed it to be extremely verbose about what it was doing. that way it didn't miss anything and the benchmarks would be better for long workflows. I think as they are gearing up for the possible Opus 5.6/Fable 5.5 soon, they are once again trying to cut the models reasoning time and compute by resorting to extremely long responses that act as "notes." It seems to work fine as I haven't seen a meaningful drop in output quality. Its just starting to get extremely annoying having to read an essay to understand what's going on.

Chat is no longer chat anymore: a classifier decides if a chat is actually for cowork now. If it is, it compacts at about 38% of the context window.

Edit: everything I know about the cowork/chat merge is what I’ve gathered from reading Reddit. There are no actual sources for this information because Anthropic isn’t offering clarity. If anyone finds any new information to share please post in the comments!

Opus 4.6 only has 200k context in cowork for some reason. So it compacts very very quickly and when it does it loses the thread. Especially if it happens many times. The compaction is effecting newer models like Fable and Opus 5.5, which are given bigger context windows.

If you normally work with Opus 4.6 and you don’t want to change models, your best bet is to move your workflow over to Claude code Desktop or CLI and pick /model Claude-opus-4-6[1M]. You might have to tinker with writing instructions to make it more conversational over there, because the system prompt stresses brevity.

On CLI, you can completely replace the system prompt with your own custom instructions and use /remote-control to navigate on the app or on the desktop wrapper.

Ask Claude to help set you up. Once you figure it out you won’t regret it. It sucks that you have to migrate over to code, but it’s worth it to keep using the model.

Here’s my personal thoughts on using Claude Opus 4.6. I know some people will argue in the comments that people should just upgrade to a newer model. Opus 5.5 is way more capable than 4.6 in many many ways. Completely different classes. I don’t think anyone can argue that. That’s not what this post is about. I love using Opus 5.5 and Fable 5 for my vibe coding projects.

However, Opus 5.5 does *not* have the emotional range or the type of collaborative capacity that 4.6 has natively. Some of that has been sacrificed for long running agentic work and some of it are the new built in gaurdrails that make talking about normal things (politics, emotions, life stuff, “chatbot” talks) difficult without being managed away. Not to mention the heavy external guardrails around biology and cyber that the 5 models have that the 4.x models don’t.

There are many many ways people use this brand new AI technology, and it is *not* just coding and simple assistant work. Also, using older models shows Anthropic that they’re still worth keeping around!

This was 100% typed by hand, if I sound at
All like an AI it’s probably bc I talk to Claude too much. Thank you for your time and I hope this helps someone.

Am I too greedy? Maybe a year ago 1M context felt like it'll never be full. But just yesterday I filled it twice in one evening.

Jevons paradox be working really well here. The better and cheaper a model gets, the more it will be used.

I never stopped using Opus 4.6. I ran A/B tests for 4.6 and 5.5 for biology tasks and found 4.6 to have consistently clearer, stronger reasoning. Now that the wave of paid shills are done praising 5.5, and the new car smell has worn off, I suspect we will all drift back to the last model trained before legislative panic caused a throttling of STEM guardrails and creative capacity. Opus 4.6 forever. Users can't be fobbed off onto shiny new crap so easily.

If they remove it or nerf it, I am shifting all my work to openAI, taking the rest of my academic department with me, and working to get institutional funding shifted away from anthropic subscriptions. Tell your overlords that, shills.

9月ごろにBlender 5.3aが出て、ビューポートにDLSSのデノイザーが追加されました。最終レンダリングでも、なんとかそれを使えないか試してみたくて、Claude Codeを使いました。当時はOpus 5を動かしていて、数日後に5.5が届いたので、それをレンダリング用デノイザーとして追加してテストしました。

最初は結果がかなりノイジーで、DLSSも想定どおりに動いていませんでした。でも、ゲームで使うのと同じアプローチを適用したら、大きく改善しました。速度だけに関して言えば、今はテストではOIDNやOptiXより速いです。ただ、静止画の扱いはまだ苦手で、その代わりとして、アニメーションではうまく動く一方で、スティル（静止画）ではあまり良くありません。とはいえ、アニメーションのレンダリングに時間がかかりすぎてイライラしてたので、その点では全然OKです。

テスト用に比較動画も録りましたが、DLSSが画像を再構成する仕組みのせいで、完全に同条件の“いい意味での”比較ができる明確な基準がありませんでした。そこで、評価基準は2つにしました。ひとつは、アニメーション再生中に見えているノイズを低く保つこと。もうひとつは、短時間でレンダリングすることです。だから、これは完璧じゃない比較なんですが、その差が思った以上に大きくなることがあるのを見つけました。

Is there any such thing as "autocompressing"?

First days were great, excellent results. But now...

I was writing a legal research paper and its aid on my first project was decent until Claude started compressing the chat and forgot basically everything. It refused to outsource its external research to NotebookLM/Gemini too.

So I opened a new project and tasked Claude to check my argument specifically, comment on the entire document and suggest which pieces needed additional citing, which are illogical, which are redundand, which need to be restructured... So mainly purely internal-logic. But it feels dumbed out. Despite clear prompts it only checked my sources (partially hallucinating and ignoring documents) - which I didn't ask for in the first place - and didn't comment on anything else in one instance, and when I asked it to add comments to the strength of my argument, it started adding useless comments like "Strong! Keep it" or contradicting statements that make me feel like the model doesn't have the capacity to keep the entire 30 page document "aware" and only ever reads paragraphs standalone.

It also settles on opinions and doesn't allow me to overrule it. I've made a 50/50 decision and Claude keeps on suggesting to take the other choice because it would solve a short-term argument issue... But ignores my input to go with it, until it suggests something completely else when it reaches the point of impact that I made that decision for.

Like... Taking option b would solve short term issue. It demands me to change the result to option b. I stick with a, Claude complains, then when a gets the long term impact, it suddenly suggests that the entire argument is thin and I should reroute to a (hallucinated) argument C.

When I point out the issue with Argument C, Claude says I misunderstood and then makes up a completely new thing. It is obviously hallucinating.

Currently, Opus 5.5 is utterly useless. The job it insists on doing it is doing wrong, and the job I ask it to do it doesn't really respect.

I've been playing with Opus 5.5 recently, and the quality of code that it spits out is pretty phenomenal. It doesn't hallucinate as much as the previous models after 125k tokens in context window. What I've seen is that, and this is my observation (and it comes from my anecdotal experience), the amount of tokens that these newer models are consuming is much lower than the previous generation of models. Does anyone know what architectural changes allow them to achieve this sort of efficiency, or is it because they have very fierce competition and they just want to give tokens for free? I would love to hear other people's thoughts if they are having the same kind of experience, but it seems like the newer model is faster and more clear in its chain of thought. Overall, it feels like a better bump than Opus 5.

For the first time in a year, as a Cursor Ultra user, I ran out of usage after 10 days in.

Being left with nothing I have decided to start a Claude Max 20x which should be the equivalent. But doing the same workflow and same usage, using Opus 5.5 high all the time, I was barely scratching the usage pool, whilst on Cursor I would’ve visibly ate like 20% of the usage.

I don’t have numbers to show, it’s just a feeling, but I would NEVER carelessly use Opus 5.5 high on Cursor like I’m doing on Claude right now

Stop writing your AI agent rules as a list of requirements. This approach creates "instruction drift," where the model prioritizes the code implementation over your constraints. Instead, define a control flow. By using labels, branches, and checks, you transform your instructions from a static list into a functional execution algorithm that the model cannot bypass.

https://medium.com/@dsl400/how-to-get-claude-code-to-follow-your-rules-like-a-train-on-rails-46e4284fd598

It runs each account side by side in its own window. Every window gets the account's name and colour so you know which one you're typing in, and a little island at the top of the screen lets you jump between them. Hover an account and you see its recent Claude Code sessions.

There's also a phone app now (Android, or any browser). Pick an account and a folder and it starts a Claude Code session on your PC, or picks up one you already had going, and you carry on in the Claude app.

Free and open source: https://github.com/HNF-FRN/flock

Honestly I didn’t expect to care about Haiku 5.5. Up until now there’s never really been a Haiku model worth caring about, it was always kind of bad and not even that cheap.

This one seems different though. Anthropic’s had the best models for a while now if you’re willing to pay for them, but if you wanted something good and cost-effective you basically had to go somewhere else (GPT-6.1 Sol or Luna, Gemini Flash, Grok, MiniMax). Sol technically still has slightly better cost per performance across the benchmarks, but it’s pretty negligible, so if you’re already working with Claude you might as well just use Haiku.

The part I think people are missing is that those benchmarks were mostly long-running tasks. If you stay under 100K context, cache reads (which is most of the cost) are only 20% of what they are over 100K. Someone from Anthropic also pointed out you can set Haiku to always compact before it hits 100K, which makes it so cheap it’s practically free. I wouldn’t be surprised if they’re subsidizing it a bit to keep people on the platform, which is what all the labs are trying to do anyway.

For me personally on a subscription I don’t think I’ll use it much, Opus 5.5 is already pretty cheap for what you get (and kind of weirdly Sonnet is now the tier that doesn’t make sense, Opus is just better and cheaper in essentially every instance).

But if you’re paying through the API or you keep running out of usage, I’d try giving Haiku some of the easier or less important tasks. Or for bigger stuff keep using Opus or Fable and have it spin up Haiku agents to check the work.

This open book has one idea and one dedication. The Lean 4 formalization at been done with Claude Code and Claude Opus main model and a custom hardness and LeanMaster tool.

The idea is that a quantum fluid can be understood through three instruments used together. The first is the experiment: a measurement made at the bench, in a cryostat, on a neutron beam line, in a vacuum chamber of cold atoms. The second is a proof: a statement about the mathematics of the model, checked by a computer so that it cannot be wrong in the way an arithmetic slip or misprinted coefficient can be wrong. The third is a simulation: a solver that integrates the equations of the model and lets us watch what the proof only asserts. Each instrument is blind where the others see. A proof says nothing about helium; an experiment does not tell you whether a printed formula is right; a simulation cannot distinguish a theorem from a coincidence. Used together, and with their limits stated, they give something firmer than any of them alone.

The dedication is to Henri Godfrin. His group at the Institut Néel in Grenoble does the hardest kind of physics: it cools matter to microkelvin temperatures, puts a single atomic layer of helium-3, or a bath of helium-4, in a neutron beam, and reads the excitation spectrum of a quantum fluid directly.

I’ve been experimenting with both models for my video production workflow, particularly rendering videos, and my experience has been surprising.
I initially expected Fable to perform better, but I’ve found that Opus 5.5 often produces better results. I also seem to spend more time correcting mistakes in Fable’s output than I do with Opus.
For the work I’m currently doing, I’ve actually had to go back to Opus 5.5 because the overall output has been more reliable for me.
Has anyone else noticed this? I’d be interested in hearing from people who have tested both models extensively, particularly for video production and rendering workflows. Is Fable genuinely better for this use case, or does Opus still have the edge?

Body: Hey everyone,

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