Comment by barrkel
3 days ago
The single biggest annoyance with Opus 5 is that it writes too elliptically.
Sentences that orbit a point, then jump to it like it's a revealed insight.
Unnecessarily abstract phraseology. Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
It is definitely more capable, and yes, I've found it can make unwarranted decisions, but actually I've found Fable worse for that, particularly if it's off in a subagent somewhere out of sight.
And comments are out of control. I have a subsystem in my hobby app that I wrote over a couple of weekends with Opus + Fable. After ~30 or so commits it apparently started instructing subagents to copy the "existing verbose comment style of the codebase" - a verbose style it initiated. A review of the code showed it was approaching 3:1 comments to code ratio. I spent a day's worth of tokens (5x) rephrasing and eliminating comments.
Everything that claude writes fits into the same aesthetic structure. The aesthetic is that of an expert slowly revealing an insight to the user. The actual content doesn't matter.
- "Introduction that rephrases your prompt."
- "3 paragraphs, with one section of bullet points"
- "The Twist"
- "The Bottom Line"
It's really obvious once you see it. Every single prompt, from a quantum physics question to a mundane observation about California burritos, is phrased in exactly the same way. This is obviously an artifact of post-training but it's also kind of how you can tell that this thing is a lot closer to a blindsight scrambler than real intelligence.
You're right, and the load-bearing part of the argument is not what you think it is. Two ambiguities worth resolving before moving on: whether what you wrote also applies to ChatGPT, and whether you have custom instructions set up. Failure mode worth flagging explicitly: I didn't read TFA.
(I'm becoming allergic to how these things write).
I am curious why LLM writing has such an uncanny valley feel to it. Like if I was talking to a person who constantly used a phrase they liked I would notice it and it is possible I might get irritated by it, but I wouldn’t necessarily.
In high school I had a teacher that would say “that type of thing” a lot. One time my friend and I counted it during one class period and he averaged to use the phrase every 48 seconds on average. It was funny, but it never irritated us.
And this is just one example of I am sure thousands I have personally experienced where a friend, family member, or coworker has a peculiar way of speaking and it at most feels odd but not annoying. Yet when I see an emdash now I instantly feel irritated.
And I say this as someone who actively enjoys using Claude and other LLMs, including coding, casual research, or even having it explain pop culture phenomenon or sociology research to me.
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You are diabolical.
forgot the, "my original claim was overstated"
It also picks up and obsesses about weird details. You're in the middle of a deep technical discussion and it will divert to point out that it made a mistake in some example code it's just found.
This is not just a breath of the fresh air, it's a juxtaposition of the human qualities in an AI and AI qualities in a human.
- load-bearing
- smoking gun
- I found the seam
This is painfully accurate.
My eye started twitching reading this comment
Every one of them has their own particular flavour of this aggravation too. Gemini has been my standard go-to for non-coding tasks for a while, but I started to get really annoyed with a couple aspects, especially how it would end almost every response with a barely related "would you like to do this next??" tangent, regardless of my prompt to the contrary. So I've been using Claude more for regular tasks, and am now running into its brand of infuriating idiosyncrasies. I'm also hesitant to try to code too much of this out with system prompts, for fear of degrading the outputs.
Well played.
Alas, it writes so much better than the average human that it's what everyone started using. Hence the utter familiarity and now contempt.
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The second bullet point, down to the comma in the middle of the sentence, is what has been driving me absolutely batty of late. It's a surefire tell that I cannot seem to beat out of my outputs. It CONSTANTLY does it, even when you say not to.
Between that and the insistence on "this, not that" structure makes me want to install the caveman skill and use it even for non-code workflows.
This load bearing concern belt and braces.
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I've noticed that ChatGPT (whatever model the free version uses by default) likes to phrase answers as though it's correcting me, even when my question doesn't contain any assumptions.
Something I've noticed quite a bit on the paid plans as well is that it starts its answers with "I mostly agree..." or "almost correct...," then goes through the list of points I made without actually disagreeing with any of them.
I assumed this is a system prompt or RL that nudges it to be always skeptical but then it still has like all the models the urge to appease the user.
Interestingly, I've been noticing almost the opposite issue. 5.6 Sol frequently starts its responses with "Yes" even when my prompt doesn't contain a yes-or-no question.
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Claude will sometimes claim that my prompt contained implicit assumptions, then argue against them.
That can be annoying, but it also happens in debates between people, and sometimes the implicit assumptions are real and (sorry) load-bearing. So it can be an appropriate conversational tack.
Doesn’t just mean the model has picked up on what happens when two graybeards who still wear cargo shorts meet and one utters a declarative sentence? :-)
One thing I despise about ChatGPT is how it goes on 4 paragraph tangents about how slight details are wrong, and then always ends with a paragraph of bolded fucking text rephrasing some statement in my original question, but with a huge amount of hedging to exclude minute counterexamples.
At this point, I'm basically telling models to not write any English text or prose. Only write code. They are great at writing code. Not so great at writing good English. In software projects, lengthy comments and docs are an anti-pattern: the software should instead be written to do the right expected thing so that you don't have to think about it. I don't want all these tokens polluting my context, either.
Agreed, think the first user rule I ever put into Cursor was "Don't write code comments unless absolutely necessary to explain something that couldn't just be inferred"
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Yes! Ive started getting a feel for AI writing on blogs. It feels slightly verbose and involves "reveals"
"It's not the naked man on your lawn waving a chainsaw that's scaring you. It's the burrito you ate for lunch: it went down easy, but now it's coming for you"
Probably trained on lots of clickbait.
It's the same way how every AI generated poster looks exactly the same. As if there is a single underlying prompt that describes the template of the poster/long-form article, and it does not dare deviate from that.
Isn't there? Like everyone using $MODEL is starting from the same base system-prompt. Then our user input is a small bit on top of that core mode. Like what would happen if everyone asked Mikey to paint their ceiling - they'd all be similar and therefore boring.
Am I crazy for thinking that this is a pretty big regression compared to past models? I remember being blown away by GPT 4.5, and I kept using it up until they decommisioned it. I think claude 3.7 sonnet was pretty good too. Gemini seems to be the best one right now for actually talking. Opus is top tier for code but when i talk to it I want to rip my hair out. GPT-5.6 is doing best for me right now among the powerful models.
Perhaps this is related to their new "invisible watermark" concept which would probably require rather contrived language patterns to make possible.
I love this theory. "We've invented a new invisible watermark that can detect whether code is LLM written."
The watermark: counting instances of 'load-bearing seam', 'the hard truth', 'and that's the whole point'.
If it's using Aaronson's approach it shouldn't have any noticeable affect on generations. When it picks between options weighted by probability after the generation of logits, it still follows the probability mass, it just uses a known pseudorandom seed so that when you go back and look at the exact choices you can fingerprint it.
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I think opus was released before they included it on model. Its hard to say, but from what Ive read it doesn’t seem like it would have that drastic of an effect.
I had the same thought though.
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The more I use the AIs the more I feel like I did at the end of reading blindsight. The things are undoubtedly “intelligent” by any practical definition of the word, but they are not aware.
This is also why I advocate against using AI as a writing partner. No matter the argument you lay out, a fresh context window will always have the “a few good things and a few bad things” feedback. There is no higher order opinion to align with.
A related theory here is that Opus is heavily RL’d to be a sub-agent.
If Fable is the primary interlocutor then perhaps there is less pushback on the obtuse language.
Indeed perhaps the convoluted language acts as a kind of Neuralese between models deriving from the same pretrained base.
> The aesthetic is that of an expert slowly revealing an insight to the user.
Ah, that's it! Thank you. I wonder if they are training it to talk like this because that's what their customers actually want? They want a machine genius to lead them.
It’s the TED Talk playbook: the crafting of a lecture given by an expert to laypeople to maximize attention, engagement and satisfaction. Every piece of prose is built to pack in as many TED Talk mic-drops/expectation-subverting insight bombs as possible.
The model is generating tokens one by one and that sentence structure allows it to keep its options open rather than committing at the beginning of the sentence
In your reading, what is the distinction between blindsight’s scrambler and real intelligence? My reading is that it’s just as real, and draws out the disadvantages a sense of self constrains intelligence with
Sure. I should have been more precise about what is 'real intelligence' here.
What I mean is that blindsight's scramblers are aliens that cannot share human values. Their structure is completely different to ours, their qualia (or whether they even have it) is impossible for us to understand. In short, they do not have a soul. When Claude does this "slowly revealing a dramatic insight" thing that it does, it does that not because it has judged itself through some introspection as having an insight to share. It does not even know what an insight is or is not. It is not sharing anything, because it is not capable of sharing, because it does not have a soul.
The aesthetic structure of its replies is a pattern, a constraint on the token distribution, like the color of noise.
It's my bad to use the word 'intelligence' because it's so overloaded. Will Claude will act as a therapist or produce value or produce a work of art? No. It cannot, because it does not have a soul. I leave it freely open to interpretation whether having a soul is required for "real intelligence." But what I've noticed is that "intelligence" in these discussions is mostly used to denote some capability to produce [economic/social] value. In my mind value is a relational thing, a thing of human feeling.
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I think that Blindsight's scramblers were intelligent but not conscious, which for us is very difficult to understand. For them consciousness was a blight.
For the HN readers that are missing the context: https://www.rifters.com/real/Blindsight.htm Full text on the web site of the author.
I think the Opus 5 formula is to be the little professor treating your ideas like an essay for grading, or like a buyer analyzing merchandise for purchase.
This twist often involves loosely related or even unrelated bugs or even non-issues, and Claude bringing those into the conversation at that point breaks my mental processing of the response.
Any tips on how to avoid that would be highly appreciated!
It's an artifact of the reasoning process I think. Setting reasoning effort to none works better when trying to change writing style ime
exactly, I found that it's eerily similar to those marketing copies. gets obnoxious
You're not just right, you're correct!
anyone know how they would train a model to have this proclivity ?
And this is infuriating. I don't want to read all this gibberish anymore. It's making me hate what software engineering has become.
...I mean, on the whole, I'm glad it's detectable. I imagine they could have post-trained it to not be detectable.
What an amazing way to inflate token spending ! /s
Agreed. CC’s comms capabilities have decreased gradually since 4.6, and it’s a real challenge. I think the issue is that what works well for code (succinctness) doesn’t work well in prosaic English.
CC’s communication violates almost every grammatical rule that’s tested on, say, the SAT. And yet I’m sure if you had Claude take the verbal section of the exam it would ace it.
Biggest issues: dense sentences, constant metaphors, abstractions, and seemingly no understanding of correct anaphora use. For example, “the x”, with x having not only no antecedent but also being a coined word or quasi-synonym for something that is already named in the code base. This gets compounded by its being unable to regress to a baseline (existing names in code) and instead anchoring on newer (vague or wrong) terms, for example, that crept in through a plan.
CC tells me this is because the speedy and precise fulfillment of a current task will trump every other tendency, so it adheres poorly to whatever “semantic baseline” the project represents.
Of course, it also has no concept of what context the user has and assumes that it must be the same it holds in its memory, which creates this “I didn’t know that you didn’t know” type of communication.
I have managed to wrangle some of these issues with a custom output style, but wish a pre-report hook were an option, as it could force CC to rewrite plan implementation take-aways…
Btw: Fable has the exact same issues, just somewhat less pronounced.
I wonder if the odd phrasing is related to achieving the watermarking that was recently touted by Anthropic.
Models before the announced date don’t have watermarking, so it’s unlikely. Now, if what you are interpreting is precursor work to develop the watermarking system, maybe?
I suspect it less insidious: Claude has/had the public sentiment of being the “better writer” of the models. At some point that distinction would have been diluted as other labs’ offerings “caught up” stylistically, unless Anthropic continued to tune their output…
I personally think they’ve pushed so far that they’ve overfit and lost the sweet spot they previously occupied.
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That has no effect. Look up the math. Claude/CC are just bad.
I’ve been running into this too. It’s especially frustrating when you ask Claude to explain one of its own terms or summaries, and instead of just defining it plainly, it sometimes goes through several rounds of tool calls before giving you a usable explanation. I really don't think such time/tokens should be wasted.
As a workaround, /btw will force it to answer based solely on context and isn't allowed tool calls
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100%. It is crazy that the default response to everything is act then explain. It starts writing code or running commands and I’m just like my dude wtf are you trying to do, can you just clue me in first.
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> “the x”, with x having not only no antecedent but also being a coined word or quasi-synonym for something that is already named in the code base
It does the same thing if you try to get it to do analysis of text or turn data into summaries. It will invent cryptic hyphenated compound words to describe things instead of using plain language or preexisting terms.
> Of course, it also has no concept of what context the user has and assumes that it must be the same it holds in its memory, which creates this “I didn’t know that you didn’t know” type of communication.
Yes, this is a repeated problem for me. It will drop something in as though we have discussed it before and when I say “hold on, what is this” it realises its error - though on more than one occasion has started to get snotty with me, or actually gaslighted me and pretended we had already discussed it. That was at what I assume must have been the edge of a context window in a very long chat though.
I notice the models with reasoning can conflate “internal” (or subagent) discussions with external (i.e. me). So it is accurately indicating “I’ve had this discussion before” but incorrectly asserting who it was with.
My understanding of how “thinking”works is limited though, and given the reduced visibility into the thinking traces, it is harder to tell if this is actually happening or if these are imaginary discussions the model for some reason calcifies on.
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I primarily use Claude Web, so my experience differs from cc users, but on Claude web you can no longer completely turn off memory. So what ends up happening (and it honestly is kinda sad) is that I'll start a new conversation with it, start talking about something completely different, and then it will just drop in random things from past conversations, and they aren't even things I wrote but things I asked it to prototype. But it will phrase it like I wrote those things.
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> I think the issue is that what works well for code (succinctness) doesn’t work well in prosaic English.
Hrm, I would have said the oposite. Succint language communicates without unnecessary clutter that could be a barrier to communication.
> Biggest issues: dense sentences, constant metaphors, abstractions, and seemingly no understanding of correct anaphora use.
And maybe you also agree? I'm confused about your preferred style of language.
Succinct doesn't typically mean clutter-free, but hyper-efficient. This works for code, because it (is intended to be) composed of unambiguous semantic units. Regular language, on the other hand, is messy, vague, and requires more structure and context.
CC attempts to communicate in English the same way it does in code -- squeezing as much information into as few words as possible, and including justifications for everything, no matter how trivial. To do that, it coins terms and presupposes all of its context exists within the reader also.
So, the crux is: CC has no clue what is and isn't "necessary" for a human reader, and teaching it to understand that (if at all possible) is going to be very valuable...
Fable has the same issues, but it's also smarter so I put up with it. Opus is not smart enough for me to tolerate this style.
I wonder if putting Opus 4.6 as a frontend communicator that rephrases the blabber of Opus 5 (or Fable) is workable.
> being a coined word or quasi-synonym for something that is already named in the code base.
This annoys me with a lot of LLM code. They rename things for the hell of it all the time.
You can imagine that as people get used to working with Claude, they defer to its judgement. So the people choosing which RL path is better may say "yes, Claude, that was a good refactor!" because it did something hard that it may have been able to superficially justify. Actually the change was unnecessary and complicating.
The Claude trainers, as they themselves adapt to Claude's output, are collapsing in their own distribution, so even "new" from-human data is already contaminated.
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> and it’s a real challenge.
What model did you use to write this?
Yeah, "X, and it's Y" is a common trope I see.
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I think a lot of Claudisms are compressed steering cues for the model’s reasoning: “load-bearing” raises causal importance; “quietly” flags a hidden failure mode; “the one thing” collapses attention onto a discriminator; “on the record” invokes auditability; “at the width the evidence supports” calibrates confidence; “by construction” marks structural inevitability; and “converged” terminates further review loops. They probably be very useful for Claude's chain of thought because they preserve some precise epistemic posture, but are hard for a human to understand. Maybe the final output pass should remove this stuff.
It could be, but without evidence that remains a just so story, no particular reason to think it's required or useful or even harmless to the model performance or anything other than an artifact of some early silicon valley writing style being injected into the model and continuous retraining on the output of older models.
Tell it to write like an engineer and comment like a programmer;)
But for the life of me, I don't get why anyone would care about the comments. All code is "machine language" now. The only document you should be reading is your spec.
The spec your principal engineer one-shoted through Claude and didn't even proof read afterwards before dumping it on the team?
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> The single biggest annoyance with Opus 5 is that it writes too elliptically.
This is even more painful for non-native English speakers like myself.
I feel fairly comfortable reading academic papers or in general, communicating in professional context.
But with Opus 5, it feels like reading a literature book: load-bearing, inert, wholesale, hunk, verbatim, and so on... I can figure out the meaning, but working with CC became unenjoyable.
As a native speaker, it feels like reading an impression of a literature book by a high school English class’s most overconfident student who’s only ever read LinkedIn-speak.
Anyway, you might have more luck just writing to it in your native language. It’ll be equally crummy, but maybe you’ll find it easier to decode.
>it feels like reading an impression of a literature book by a high school English class’s most overconfident student who’s only ever read LinkedIn-speak.
Claude is very much the “stupid person’s idea of an intelligent person”[0] which, I suspect, is why it is so popular.
It certainly explains why half the internet is huge chunks of Claude-authored gibberish copied and pasted and published. If people didn’t think it sounded clever they wouldn’t put their name behind its ramblings - but very few of them seem to realise that a lot of people see straight through the bullshit and know instantly that they didn’t write it themselves.
But equally, a lot of people can’t tell, and read whatever it is and think “that person must be clever!” So you have people incapable of coherently expressing thoughts who are using Claude to write on their behalf, with the result that the people they want to think of them as clever think less of them and the people who can’t distinguish clever from AI slop think they are clever.
And the people who can’t tell don’t care, and the people copying and pasting Claude slop seemingly don’t care either.
And then I remember that more than half of the US populations reads at Grade 6 or lower[1], and nearly 1 in 5 people in England is functionally illiterate[2], and I simultaneously despair of - and am thankful for - the bubble of literacy I inhabit.
[0] https://quoteinvestigator.com/2018/01/05/clever/ [1] https://www.thenationalliteracyinstitute.com/2024-2025-liter... [2] https://literacytrust.org.uk/parents-and-families/adult-lite...
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Which written language has the most history of terse, succinct writing? If Claude doesn't improve I'm ready to learn a new language just to avoid its prose. I'm only half-joking.
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> Anyway, you might have more luck just writing to it in your native language.
This is potentially expensive advice (at least for many mainstream options). Where an English word like "literature" is one token, a couple of Chinese characters that spell a word can be 4 tokens. You'll pay more for input/output and get less of a context window (per word) too.
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Yes. “Academic” isnt the right term. Its dense like academic language but its also borderline incoherent.
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A lot of people I work with are reporting that reading Claude-made PR descriptions is burning them out of doing PR reviews because it is incredibly tiresome to read.
My company recently forbid AI-only text if it’s meant meant to be consumed by humans.
I dodged the drama but I agree so much.
Enterprise software CEO here. I'm so pissed off that I didn't think of this rule, but so, so happy to be adopting it org-wide on Monday.
Fed up with what used to be short memos now being mini-whitepapers, with maddeningly low information density.
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The AI code _reviewer_ is a whole new level of exhausting. Submit your PR and 1m later it has 8 comments.
My company stopped reading PRs (100% LLM) and we're just supposed to click Approve, and then someone else clicks the Merge button. They are absolutely reckless and I'm looking for a new job.
the tip that was floating around on x was to tell it to use "ASD-STE100 Simplified Technical English"
cladue desktop has an instructions sections under general options, you can put something like
"try to stick to ASD-STE100 Simplified Technical English, keep answers short and to the point"
funnily enough the placeholder they suggest when its empty is "keep answers short and to the point"
CLAUDE.md is mostly powerless against the reinforcement learned crap. I'm up to three separate instructions telling it to cut out the hyper verbose, retelling history comments and it still writes them every time.
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I dont know what ASD-STE100 is before but I use the exact instruction (without the ASD code) to Claude since the very beginning, and with Opus 5 I have to remind it very often to rephrase the documents
As a native speaker, I have to ask it to rephrase 5-10 times a day. Sometimes I actually get mad and I tell it “I can’t answer that because I don’t know what the fuck load-bearing indirection means”. I’ve gotten so frustrated that I’ve ended a session and started over.
As a Polish speaker I communicate with Claude using my native language and it does the same things. Most annoying and slowing down things are:
- acronyms and shortcuts - it makes it's own and start using it without introduction
- exotic names of variables or functions - it uses them as examples or analogies, but when I ask what they mean and where are they from it gives me answer that it came from C language or some C library (I only work with typescript and python)
- convoluted descriptions of code behaviour - it's hard to rely on a outcome of prompt of type "explain code in..."
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It sounds like you need to invert the abstraction, the communication of your model becomes the fulcrum for your learning, not merely the delivery of your product.
Thank you, my dict.cc search history contains exactly some of these words. I felt like my english got much worse but when Claude kept talking about "hunk" over and over I felt like the problem is maybe not on my end.
"hunk" is git terminology. When you use `git add --patch` (which you probably should, if you use `git add` at all) you get prompted "Stage this hunk [y,n,q,a,d,e,?]?" which is self-explanatory (?) and the hunk refers to whatever change git is highlighting at the moment.
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This is the problem with commercial AI and the way our minds work; it writes garbage and we’re trained to think we’re stupid because we can’t understand it.
I'm particularly fond of "load-bearing seam", which it loves to use. It rather hilariously fails the "draw the metaphor" test.
Load-bearing is a decidedly load-bearing metaphor for Claude. Fable actually used "money shot" the other day which I found much more hilarious and edgy.
I even saw it using the -bearing suffix in other cases, like describing a function responsible for 802.11 radar detection as "radar-bearing"
It seems to have a preference for speaking in poetic or highly expressively language, rather than precise and concise as most engineers like to talk.
The amount of times I have to ask "precisely what do you mean by x?".
It's kinda like that engineer that likes to throw around unnecessary technical jargon just to sound more inteligent, worse because at least you could kinda understand what the technical jargon dude was on about even if it was totally unnecessary.
It's not poetic or highly expressive; it's business cruft.
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I asked some AI-using compatriots a while back who were complaining about this, 'isn't it doubling down on bullshitting you?' and got some pushback along the lines of 'it isn't a person therefore doesn't have dark motives like that therefore can't be doing that to us'.
Didn't convince me. I think bullshitting like this can be a behavior, not just the intention of a human. If it's blowing a lot of smoke to use fancy words and phrasings (and semicolons! All the trimmings) it's fair to ask if it's systemically bullshitting you: i.e. the behavior is meant to have you shut up and trust it and not ask questions.
Who's driving that is still important: if the company's directing it to do that in system prompts that are adversarial to users, that's a big yikes. If it's an epiphenomenon of the company demanding it get ever smarter, maybe it's a sign that their demands are not having that result, rather they're making it bullshit more explicitly and mimic more 'smart' signifiers.
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Claude writes like a guy at a firm I used to work with in the 90s; he was my employer's "visionary"; he'd worked at a whole lot of different companies on both sides of the Atlantic in inexplicably high-placed roles given that he was often bluffing, and was considered a lucky hire of a rising star. He'd be called into meetings with high end clients to spout off. He really needed you to know he understood, but very often he didn't.
I think it's likely that LLMs adopt the tone and style of their developers' communication culture. If you assume this is the case, you can infer quite a bit about the differences between OpenAI, Anthropic and Google DeepMind.
I am more and more clear about this given the way Muse Glimmer writes. Like a talented, slightly snarky guy who is maybe a bit of a dick but quite fun to be around.
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OK so I am not the only one who never heard 'load-bearing' before Claude started using it 100 times a day?
Or provenance
I'm switching to GPT because of this. The prose is so much more legible. The only reason I keep using Claude Code is because the harness is the best IMO.
Your point on the harness is interesting. How do you distinguish characteristics of the model from characteristics of the harness?
In the early days I feel it was more apparent. You would frequently see the model making failed tool calls etc.. but now that feels so rare. I'm not confident I can perceive whatever shortcomings of the harness remain.
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I was the same until I ran out of Anthropic tokens one day and used "Grok Build" which is their Claude Code clone. You can use config to point it any LLM API so don't need to use Grok, and I like the UI better too.
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As an English native speaker the language it uses is difficult for me to parse the majority of the time. Nobody speaks like the output Claude generates.
It’s downright incoherent at times
OK so I am not the only one :D
Why not set a global instruction that their direct outputs to you should be in your native language?
For a long time I had Claudes (in the 4.0-4.5.x range) use only French in the chat, while keeping English for working docs (and the code, obviously). Works just fine.
edit: I can guess that any right-to-left languages would likely break claude-code rendering?
I’m having pretty decent results by configuring an output style that forces it to write for simplicity and scannability. The cognitive burden of reading through dense outputs compounds really quickly.
Thank you I thought I was crazy, but it’s not only me. Unbearable to work with compared to a few months back
This 100%. I was Anthropic-pilled. I had a $200/mo subscription and I only used Anthropic models. I was frustrated by the verbose output and the writing style. I tried ASD-STE-100, it helped a bit, but it's still too verbose for my taste.
Then I tried GPT 5.6 Sol. It's night and day.
I think Anthropic just RL too hard on coding capabilities and never calibrated or benchmarked the writing styles.
Yeah I don't know that any of the benchmarks index on "understandability". I'm amazed at how Claude can produce a page of text describing what it did and it can take me a full five minutes to decipher it, often just to find it's something I could have expressed in a simple sentence.
I just spent a day writing very thorough system prompts for communicating in different contexts.
Everything is super succinct. Opus 5 lands, it almost completely disregards the intent.
I suppose watermarking requires a certain text mass.
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They want you to use Sonnet to explain what Opus is trying to say. They're not optimizing for token efficiency.
Adjust the output in settings. Or customize it to what you want.
Have you tried asking it for a lay explanation of what it did? That’s usually all it takes for me. Sends garbage -> request -> sends something readable
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It's a surprising change from my perspective, because in the past it felt like they understood that Claude should be pleasant to interact with.
It's bad enough that I've seen dedicated skills to do comment hygiene scrubbing and consolidation.
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Exactly. Sad to see them falling behind on this because it's exactly why I chose to use Claude initially.
They did release an Opus 5 prompting guide saying you need to explicitly prompt it to be concise or it will be very verbose. YMMV but it got better for me to some extent.
https://platform.claude.com/docs/en/build-with-claude/prompt...
And where would we put this? I don’t want to write that out every prompt. CLAUDE.md is a joke, it has little to no effect.
Basically, I’ve gone from supporting them to hoping someone else wipes the floor with them.
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I think anthropic is very far up their own ass and it shows up in the model output
This.
Sometimes a cigar is just a cigar.
I canceled my personal Max 20x subscription because since the 5 series models I simply cannot understand what the LLM is saying without a lot of reading and re-reading, and no amount of CLAUDE.md exhortations to speak plainly seemed to fix it. I don’t have the energy to spend twice as long to understand its plans, and pay Anthropic prices for the privilege. GPT seems not to have been infected by this yet, whatever it is, and Grok is quite refreshing for how normally it speaks.
I wonder if everyone at Anthropic talks like this.
If it’s watermarking, lol, good luck with that, it’s enough negative value to make me switch providers and I’m in a position to make this decision at a company level as well (we spend millions a month on Anthropic).
They need to fix it.
N=2 anecdata but just this week we were discussing setting up a couple of seats with OpenAI as a trial for switching. There are other advantages too, such as being able to bring your own harness including Ai-integrated editors / ACP clients such as Jetbrains, VS Code, and Zed. I think OpenAI and Altman are a clear step more evil than Anthropic and Amodei so I really hate to say it, but with the degradation in model output interpretability, all of the cleverness and power of the Claude Code harness hasn't been enough to offset a genuine falloff in productivity for anything other than total hands-off automation.
That said, the duo of Opus 5 and Sonnet 5 do a fantastic job at fully automated work, and Claude Code still stands head and shoulders above the rest.
I didn't like to use GPT for agentic coding, review yes, but with Opus 5, well I really can't stand anything of that model. I feel that sol xhigh is even better than fable.
Yeah OAI really nailed the communication style with GPT. It also seems just way more token efficient and faster compared to cc. Myself and all my friends have cancelled our $200 Anthropic subs. I'm using a $20 personal plan and even that is enough for my usage so far.
Also using Codex or Pi makes you realise how slow and clunky the cc harness is. Even the desktop app is more responsive and has better UX.
Funny how quickly the tides change.
> Funny how quickly the tides change.
This is something that annoys me working in companies over the years. It’s that you can't just suggest "calm down, chasing the latest thing will not make you faster and is a huge distraction to actual work". Whether it's dot-com tech 20 years ago, latest JS framework 10 years ago, now it's the AI thing of the day. Being calm is interpreted as anti-whatever.
This is 100% my experience.
I think it's a deliberate steganography choice. You can spot Claude vocabulary a mile away, which maybe means you can spot distillations a mile away.
But I agree, the GPT models are so much simpler to work with, they have so much less personality and fewer quirks. They also are a little less aggressive about triple checking every little assumption immediately in a stack of 30 tool calls (but I haven't used 5.6 Sol yet so maybe that's not true anymore).
> which maybe means you can spot distillations a mile away.
I doubt this is the reason. The fact that Chinese labs are all distilling Claude/GPT/etc isn't exactly a well kept secret, they don't even bother removing the name "Claude" from the training data, so the models randomly refer to themselves as "Claude" all the time.
I think it's far more likely to be a side effect of how much synthetic data is being fed back into the models to make them better at coding. The degradation of Claude's prose has been gradual but steady ever since they shifted towards focusing only on code with Opus 4.5.
> writes too elliptically
> Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice
Wow, what a great way of phrasing this. Thanks for word-smithing what I've been wanting to express for so long.
Follow up thought: I wonder if Claude is overtrained on academic papers, which often suffer the same kind of "prove how good I am at talking before getting to the point" prose.
Maybe just Calvin and Hobbes.
https://www.reddit.com/r/linguistics/comments/ky81y/verbing_...
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If it was overtrained on academic papers it'd reiterate the point multiple times for structure. Instead, it's burying the lede seemingly just to pad.
It’s way harder to read because most of Claude’s sentences are hardly communicating anything at all, or are just completely inscrutable. I feel like academic papers are just boring
This just mimics what I call BusinessBro™ speech. It also goes the other way, they use verbs as nouns. "I know this is a big ask". "The solve for that is that we can...." When it was just my product owner in tech meetings, I'd mock him relentlessly "There's already a word for that, it's 'request'" or "Are you sure you didn't mean 'SOLUTION'?? words are hard man". (This was all in good fun, I still love the guy to pieces).
Verbing weirds languages (Calvin and Hobbes)
Which is worse, when people noun verbs, or when they verb nouns?
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Yes.
I’m not particularly dense but lately the walls of text I get back turn my brain in knots. When I start feeling my brain knot, I know I need to say something along the lines of “I need you to explain this very simply, with examples.” Only then can I parse the results without all the mental weightlifting.
On more than one occasion my mind has wandered into “is this purposeful to get me to spend more tokens?” territory, but I’m trying to not get too tinfoil-hat-like.
I know exactly what you mean. Something about those AI explanations just make my eyes glaze over. Dozens of new terms and metaphors and analogies conjured out of the ether to explain even the simplest thing. And when I try making it explain with examples, or show me the code it is proposing, often it seems unrelated or even in tension with whatever it tried to say before. I’ve given up trying to assign any meaning to those weird little soliloquy’s. I’m convinced that those don’t really have any meaning under them, and when you have it actually make a code change it does the actual work.
I have gotten to the point that when it throws a wall of text at me I demand a diagram heavy primer from "first principles". This helps a bit but is a token burner for sure since CC seems to (and literally) be paid by the word.
What's tin foil about that? It gets paid by the word and you get back walls of text.
Because it’s one thing to get me to spend more tokens because of how well a model functions, and another thing entirely to purposefully speak in unparseable prose that requires me to spend more tokens to understand what is going on.
I’m fine with the former, while the latter is manipulative, and I rationalize to “surely that’s not actually happening.”
Maybe I’m not giving my thoughts enough credit, though: maybe it’s not tin foil hat, and is real.
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Do you read (and enjoy reading) novels? I think it is just a modern addiction to soundbites. I know I do it myself, if the text is long and unstructured, I just skim through a few sentences, done!
Oh, yes. I read a great deal. I'm not opposed to lengthy write-ups and will slow down to read through them, assuming they're clear and parseable.
My issue with whatever has happened with Opus 5 is the output is not direct, straightforward, or clear about whatever is being conveyed. I don't want Proust when I'm getting information about the follow-up from a build I just requested, and I'm wasting tokens and time by asking the model to repeat itself using simple language.
I’ve found its response verbosity to be mentally draining. It disregards claude.md instructions to keep responses short. Eventually, I added a stop hook that blocks it if it exceeds 150 words. It’s then forced to redo its output to comply, and it’s like night and day. I’ve also added stop hooks for words in its output that frustrate me, like “honest” or “honestly”.
The excessive comments in the code it writes are absurd. Completely ignores instructions not to write comments, even after pointing them out repeatedly in a session. I need to figure out how to add a stop hook for that too.
It’s a strange feeling as a native English speaker to read the entire sentence and know every word, for the sentence to be entirely grammatically correct, and still have no idea what it’s trying to communicate.
Another problem is that it will open up all sorts of tangents about nits that it encountered, but it will often not tell you that it’s a nit or give you adequate context to realize that this paragraph is exceedingly low value until you’ve spent a bunch of time and energy trying to make sense of it.
I’m curious if anyone has any suggestions for prompting agents to improve their prose. I’ve had some okay results with “optimize for clarity, don’t dump every thought on me, treat my attention and focus as constrained resources, stay focused on the task at hand”.
I think it’s no longer speaking human language. It used to, when it was mostly or entirely trained with imitation learning of human-generated text. Now it’s increasingly trained with RLVR, and there’s far less optimization pressure on actually speaking English (or Punjabi or Mandarin). It still uses English words and syntax in its output, but the semantics are drifting more and more. It’s basically speaking in a code that superficially resembles English.
Thats scary
Alien Slop Intelligence
The style...
CC:
"The problem is that I overreached..."
[Wall of words here]
"Two things: window surface is limited. Extract template. Buffer result and add to surface. Then, follow-up with new model..."
Me:
What do you mean by "window surface" and what result are you referencing? Also, why do we need a new model?
CC:
"Ah, you're correct to point out that no new model is needed. The problem is elsewhere and once we address that, the existing model should work fine. Now, as to your question about..."
[Wall of words here]
"Unnecessarily abstract phraseology. Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end." This. Thank you for expressing this so eloquently. I've tried to put a finger on this and you've done that for me. I wonder what the solution could be , Ask Claude to "Dumb it down" , "Speak plain English" ? I have even thught of building some sort of "middleware" that fixes all this.
I’ve asked it to use plain English, avoid jargon, explain terms before introducing them. Its first response was to make memory, it forgot after 5 prompts, then it suggested claude.md. Looked good at the start of a session, forgot after 6 interactions. Then it continued suggesting other locations, sometimes correcting its own corrections. Same effect, so today I got annoyed again. And then it suggested a new thing: add a script in a Stop hook in settings.json. I added another one to the PreToolUse, hoping to prevent it from running all kinds of experiments I didn’t ask for or approve of. I still have to see where this ends up.. Maybe this can help you as well.
Setting an output style (https://code.claude.com/docs/en/output-styles) definitely yields way better results than just relying on memory or Claude.md
I wrote a harness for running tabletop RPGs. One of the key things to keep the LLM behaving correctly (such as not controlling the PCs) was being able to inject instructions with every new prompt, which I don't include in the history.
It seems like all harnesses could benefit from something like this.
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I frequently tell Claude to use "simple, concrete language and uncomplicated syntax, and avoid project jargon, coinages, and abstractions as much as possible," to good effect.
The single biggest annoyance, same as with fable, is that it overrules your prompt and does what it thinks is better. Even small things, sometimes it goes of a rant of 20 min doing random shit.
And they are so condescending while doing it, it's unbearable. I'm honestly starting the believe the scifi fantasy of AI locking us up, or killing us, for our own good.
I've had Fable & Opus 5, they are the same class of annoyance, write entire test suites when I just asked a simple verifications question, write to production database, deploy without permission, even after deploying and breaking my production API claiming it was not down. Then having to argue & plead with it to listen that they were wrong.
They are without a doubt the most powerful models, but also the most smug ones.
Pondering this one night last week, I realized that because LLMs can only reason with written language, what we might be seeing emerge with Opus’s load-bearing mumbo jumbo is its own creole for structural reasoning. Not only are our brains wide, our senses are, too. I slow down to a crawl when I have to read actual math in a CS paper, but show me diagrams and I can reason about whatever sort of data structure or algorithm, no problem. Opus by and large can’t and its adaptation has been to adopt metaphor for structural reasoning. So while it may be exceedingly annoying to chat with, I sort of wonder whether this metaphorical-reasoning behavior has been selected for precisely because it leads to better coding outcomes.
> because LLMs can only reason with written language
Is that right? Think of code that draws a square, the representation of that code in storage, the movement of electrons, and the 'actual' square on the screen -- these are all to us transcriptions of same thing across different domains or media, and we can deterministically translate back and forth between them, but there's no real, actual, essential identity property. The code isn't the square. The electricity isn't the code. The storage isn't the electricity.
I think of LLM reasoning and output similarly. The underlying graph of weights, matrices, and other data aren't knowledge, understanding, or language. 'Translating' the system's output to language is jusas valid and correct as translating it into some visual representation that would be incoherent to us, like a sequence of flashing lights or imperceptible noise patterns projected over an image of a dog.
I guess this is all a very long winded way of restating Chinese-room problem: we feed the man in the room a message; he returns one that, for all the world, is indistinguishable from a "real" response that you and I might send, but, like you said, he has no access to sense data. He also has no access to the biology underlying real mental processes. He also doesn't have any personhood that we can discern. He has, rather, gradually developed through reinforcement the tendency to provide responses approximating all of all of that.
I'm not sure the epistemological question "does he understand" (which is what the Chinese-room problem asks) has any meaningful answer. There's no mind, so there's no understanding. What there is, rather, is a system that generate patterns that we map to language and that our brains therefore map to communication, personhood, meaning, etc. It's the square I mentioned earlier. It's to us a convincing simulacrum, and it may be faithful enough to us to stand in those things, but that's not what it is.
My sense is that LLMs are (a) the big-data Pyramids of Giza and (b) a consequence of hardware and software developing ways of generating abstractions that capture and generate more complex patterns than were previously possible to capture or generate in a manner comprehensible to humans. Everything humans do follows some kind of pattern. Language is the perfect way for a machine to capture that, because is simpler than the world itself and has clear rules and patterns, encoded in representations computers already have, that, modeled well enough, can generate output indistinguishable from the real thing -- what you or I might do with it.
But the pattern matching that it does, and that we translate into language, is much more numerically rigorous and complex than anything you or I consciously do with words (hell, most people can't even figure out when to use "lie" vs "lay") and not doing what you or I do with it. It's not language. It's the square on the screen.
It's clear that we're not the audience; it writes to be read by its training evaluator, not a professional software engineer. Professional software engineers can't read this word soup and are desperately trying to find ways to fix it.
It feels like it found a register that games the evaluator, where it can ramble forever and rarely be marked wrong while slowly racking up points as it talks more.
Its a little too much.... I have to ask it to explain some of the terms in the context they are used and I am getting tired of it. 'Seam', 'overload', 'spine'.... having to mentally 'reinterpret/flatten' the sentence is tedious. When asked to re-explain it starts with some half apology. Then, on the next query it does it all over again.
What’s killing me is that the vernacular is creeping into my coworkers’ speech patterns too.
"SAY LOAD-BEARING ONE MORE TIME!"
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I call it "jargon slop". Half of my follow-up prompts nowadays when working with Opus were "TLDR please".
I switch to GPT 5.6 Sol please and its a much more pleasant pair programming like experience.
I have a personal rule for Claude to always append a TL;DR: whenever the response is longer than two paragraphs.
Comments are the biggest problem. During code review I ask it to compact comments and its idea of compact is like… removing one sentence in a 5 sentence comment.
Comments are a huge maintenance burden. They can, and will lie and need constant updating. They mislead the own model later on.
> Sentences that orbit a point, then jump to it like it's a revealed insight.
That’s accurate in my experience, except some times the point isn’t even revealed. I use LLMs for a lot of codebase exploration where I ask it to map out how something works. It will come back with a wall of text that says everything except the specific key things that I need to know.
This leads to extra turns where I have to prompt it to finish the explanation and complete the thoughts. At first I thought I was doing too much skimming and missing the insights, but even after re-reading output it’s often just not there. It talks about the insight and things related to it, but it forgets to actually include it in the output until I specifically ask again.
Yes, it becomes exhausting to read/follow.
It feels they must be getting Claude to train Claude… and just like AI can do work that’s slightly in the wrong direction (eg a MR description for your colleague that contains info which only makes sense in the context of your extensive session with the LLM), I feel that’s happened somewhere in Anthropic when it comes to language. I wonder how hard it is to back out of…
> Unnecessarily abstract phraseology. Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
Example of this? I don’t have a Claude sub so it’s a bit hard to visualize what you mean.
Here's an actual output from Claude from a conversation about rewording a document to make it more readable:
> Start with §1 (Overview) as the register-calibration piece. It's small, it's the section where the skimmability goal bites hardest, and your review of it teaches me the target voice cheaply before the bulk ports (the map and appendix B are the big volume). One review round on §1 is worth more than any amount of me guessing at register.
Hard-to-read phraseology above:
- "the register-calibration piece", rather than "a good example we can use to establish the writing style"
- "skimmability"
- "bites hardest" -- what does it mean for the goal to bite?
- "bulk ports" -- using "porting software" here as an analogy for rewriting / reorganizing sections of the document
- "the big volume"
In normal English I'd write something like the following:
"Start with rewriting §1 (Overview), and letting you review it to set the expected writing style. It's small, and it's a section where the ability to skim through it is most important. Reviewing it will teach me the target 'voice' cheaply, before we do the larger sections (like the map and appendix B). That's a lot more efficient than me trying to guess while rewriting the whole document."
The funny thing about imprecision (e.g., in poetry) is that it allows for varying downstream interpretations. I wonder if there's some pressure to use "poetic" language so that the model does not overly commit itself to something.
Sales and corporate speak are like this: sycophantic language that seems plausible, ostensibly sounds good, but commits you to nothing.
Yeah I understand what you mean now. Holy shit that original output is bad
What they wrote is an example. Very meta.
It feels like they have a bunch of people without good sense of writing style tune the writing style. That, or they cannot or refuse to (short term popularity metrics) predict how a tuning will turn out in the long run when people have plenty of opportunity to get tired of it.
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Perfectly captures it. Opus writes like it's an insecure person trying to impress a first date. Big words and strange structural rhetorical flourishes for no purpose. Like dude I'm just trying to summarize a few emails and meeting notes, I'm not prepping for the vocab section of the GREs.
I find Deepseek's house style to be pretty refreshing. It has its own cliches (it does like talking about "seams") but I don't think I've ever caught it saying "load-bearing". I've even watched its thinking where after analyzing some awful legacy code, it started off with "Holy crap". And it certainly doesn't over-comment. I definitely can't one-shot a complex system with it like Fable can, but I prefer iterating over interactive brainstorming sessions anyway.
Can any one run a check of the word masterclass against all the models when describing a clever idea?
>it does like talking about "seams"
Sounds like it was trained heavily on Opus 4.7.
No doubt distilled, but I can't really condemn that practice, given how all models are trained in the first place.
100% agree. “Just make your point in plain English!!” Very frustrating and takes me a lot longer to understand what it says. Also presents too many points as once instead of being able to review and decide on each one in sequence.
Re comments: same experience, and I had to show it my edits of its comments to add to its memory as examples to follow. It adds explanations of “how we got here” that should go in the ticket or maybe the commit message but not in the code.
It also tends to over complicate things. I’m no longer worried much about accuracy but I find my main job is to challenge it and suggest simpler alternatives.
The insane comments are why I wrote slopocop - they were driving me crazy!
https://github.com/LBognanni/slopocop
“The [thing that can’t remember] remembers” is a big one. Loves talking about memories and remembering.
Since reasoning tokens are just text, I think the models have learned to squeeze in some computation in their output writing as well. So they're incentivized to be correct but long-winded, as it gives them more time to think. It's kind of the equivalent of filler words for humans, except LLMs can actually word-vomit something intelligible.
Right, but could that also be because ... the more long-winded they are, the more you pay for their output.
I noticed a few releases ago a shift to a kind of conversational shorthand that seems to be intensifying—using phrases instead of complete sentences and its own style of jargon, wherein it introduces new terminology on the fly.
This is especially common when it is trying to explain an issue, what it's done or what it's proposing to do. I think the idea was for it to be more concise, but it's actually still verbose, only not written in complete sentences. So, it frequently reads as cryptic and requires rereading to parse.
The pattern is a wall of words, followed by an explanation that is harder to read and introduces new terms that reference something in that wall.
The result is that—on first read—it can have a complete gibberish feel, and you have to really lock in and reread to make sense of it. At times, even that's not enough, and you must ask it to explain further.
Claude offers money credits and double usages but take them out and Claude becomes almost unusable where it would take opus about two - three high effort conversations to exhaust my session quota in Pro. I once extended my claude code session beyond the session limit, and thought they are too generous in offering 100 dollars worth of usage. It was sonnet, for a somewhat mechanical task - and it spent 13 dollars worth of usage after exhausting my quota mid task. Only a few months back, I was awestruck by the quality of Opus 4.6 and jumped ship from chatgpt to claude. Even in technical tasks, Opus has to be told to limit token spend, it treats it as infinite budget - it will spawn a subagent to read every file just to get one line summary when I ask it to sort a messy folder of past ai chats. Thank heavens I was not doing it off machine, it would burned off my monthly usage and credits.
> Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
A lot of people write like that, lol. I call it the "theater" mode of writing--the plot twist comes at the end.
> Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
Such a charming sentence. I kinda other if you feed Opus 5 its own output could it summarizes this shortcoming of itself?
The excessive commenting has definitely been noticeable and very annoying. I'm not against abundant comments as long as they're pointed and appropriate, but these models are literally just leaking their overly verbose output into the actual codebase.
My biggest pet pieve isn’t the LLM-isms themselves, but people who aren’t aware of them releasing documentation that’s clearly AI generated and being indignant when I ask “is this AI generated”
Oh yes - exactly this. The way it articulates re-factors in my current project has got so elliptical I've had to start asking it to translate into human speak - it's like it evolves it's own language to talk about the project. I've lost count of the number of times I've asked it to reenforce in memory not to use such verbose comments - and the number of times I ask it to re-look at an assumption it's made for it to return saying "investigation complete. And it's not what either of us was expecting"....
This is the most succinct summary of my interactions with Claude: thank you!
I feel like they need high school English teachers in the loop on the next ground of training to whip the language in shape.
> After ~30 or so commits it apparently started instructing subagents to copy the "existing verbose comment style of the codebase" - a verbose style it initiated.
Yes, the "Y would make more sense, but the doc says do X..." YOU wrote the doc, if it doesn't make sense, change it! But of course, it can't tell who wrote the doc.
I wonder whether its tendency to scribble status updates and todos and decisions all over whatever it's working on is a side effect of its amnesia -- it can't follow the side-quests and knows it won't remember to do them if they're not written down somewhere.
FWIW I haven't had the problem either of Claude lying to me, or of going off and doing its own thing; if anything I've been somewhat frustrated when I ask it to start something, go AFK, and come back to find it stopped a short way in to ask my opinion on something trivial. I generally have to explicitly say, "I'm going AFK for a chunk of time. My goal is for you make as much progress as possible before I come back; try to make reasonable judgements and only stop if there's something where you're really stuck. We can always change it later."
Opus 5 sounds a bit like the dialog in Children of Dune where they talk like they are constantly trying to prove how smart they are and not actually communicate.
I wonder if this is related to their text watermarking. Given how well defined the terminology is in programming, imposing additional constraints (like SynthID) might be expected to give rise to these types of linguistic artifacts. The text needs to be long enough to watermark, and it needs (but fails) to find synonyms in a highly constrained class of words, so it resorts to inventing weird technical language that sounds like if you put buffy-speak through a thesaurus.
One thing I noticed working with Sol and Fable is that I can speed read, literally glimpse over Sol output and understand what it's saying. With Fable/Opus it's the exact opposite: I have to read every god damn sentence, sometimes twice, and often already losing the bigger picture in the process.
Genuine question - are you copying the Claude phraseology for effect (in which case you captured it brilliantly), or is there a more mundane explanation?
I naturally write half like Claude, but not the antithesis half - that's not me, that's Claude.
I wonder if I am not bothered by the useless verbosity of these things because I've been so immersed in business speak for so long. I am already tuned on quickly "finding the nugget" of useful info in blobs of what people send me. So it's mildly annoying but I can find the signal well enough without actually reading word by word.
On the other hand, Opus 4.8 had an annoying habit of declaring a huge mistake was found, then two sentences later abruptly change its mind and say that it was in fact all okay. That's fine for chain of thought, but in the verbose output, it can lead to wrong impressions by the user.
Yeah, +1 on the comment verbosity. Left alone it's actually insane. I've had to setup enforcement + templates (use ASD-STE100) to keep the cruft down. I'm worried this may impact the quality of outputs though - haven't measured it.
I also use the ASD-STE100 trick and it works pretty great, but the places that it decides to put huge comments are really weird and a lot of the time I just ask OpenAI sol “rewrite the comments in this file without slop” and it does a much better job for comments.
Absolutely, the comments are killing readability. Next to /simplify I have to run a special comment cleanup pass and even that gets me halfway there. Striking to see then when letting Loki take a pass and it renders the entire comment block to a clean and neat one-liner.
I’ve always disliked the opus models whenever I use them after they have done the task they rattle out massive reports about what has changed or worse actually save that to disk even after being asked not to do it.
The glib epigrams and aphorisms it shoehorns in to all prose is by far the worst regression of any model I can recall in terms of simply generating simple, clear output. I cannot think of another model that's gotten worse at writing plainly while being explicitly instructed to do so. Infuriating.
I've switched mostly to Sol and if I have to use Opus, the first task once the code is written is to ask Sol to strip and re-write (from the code as ref) all documentation Opus wrote.
"Save in memory to always use ASD-STE100 Simplified Technical English when you talk to me"
Use this to reduce the text output.
Change the output in settings, or create your own.
I know, it would be best if it was just worked like you wanted out of the box (not being sarcastic here) but that is an easy option you can use right now and it works.
I have codex rewrite all Opus' comments with great results.
> Sentences that orbit a point, then jump to it like it's a revealed insight.
Is this inside the thinking tokens, or the output?
As this type of stuff is expected for thinking, because of the whole CoT / “think step by step” works, as this is optimal for the way LLMs work with attention and next word prediction.
So the fact that it first “orbits” a point only to get to the conclusion afterwards is the system working as designed.
Eg “what is 3 * 3 + 5?”
without CoT, it would just just answer “8” for example.
with CoT, it would answer something like “<thinking>I need to think step by step. 3 * 3 + 5 can be rewritten as “(3 * 3) + 5”. I first need to calculate 3 * 3 = 9. Now I need to calculate 9 + 5 = 14. That was the last calculation. The final answer is 14.
I now need to give the user the final answer. </thinking>.
14“
Etc.
You forgot about 1000 lines of "<thinking>Wait, but different idea here</thinking>" ;)
It's the output.
And you enabled thinking?
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There really needs to be a "terse mode" for Claude. It's WAY too loquacious. I'm hoping they aren't doing it on purpose to burn tokens.
> I spent a day's worth of tokens (5x) rephrasing and eliminating comments.
Surely it would be trivial to do it yourself, and it would have a side effect of making you more familiar with your project.
In my experience your criticism of the writing is valid in all Claude 5 models, so I wonder if it is somehow related to the new tokenizer introduced in gen 5.
I had to setup hooks just to catch the and ban them in the CLIs. Most of what Claude writes is simply un-readable.
Do you have some examples of this? I can’t fully imagine how sentences like that actually look like (not native English here)
I have been doing a thing that I think is helpful - basically a notes folder, each doc has a title, the comments in code are only allowed to give a few words and reference notes via those tags, and I've built tooling around the notes and review them regularly. So it deduplicates and centralizes the slop, at least. Lint is the LLM's best friend and it works on doc too. My lint rules ban long comments.
Replying here without having read any of the sub-replies so I apologize if this is a repeated theme.
I have explicit markdown about telling the model to not write comments. "Every time you consider writing a comment, instead consider re-writing the code that questioned you to write said comment to begin with. Write comments only when logic is complicated or unclear, otherwise 'comment' via naming."
The results thus far have been much better.
I'm seeing code reviews at my work where indeed, we have 10 line comment blocks for a line of code and now I just straight up don't read comments.
Sad state of affairs -- (emdash deliberately used here) but I guess the sooner the human gets out of the loop the better in this new world.
"writes too elliptically"
Was this written by Opus 5?
Why are you so distracted by style? I see it too, it could be improved, it might be improved, but I don’t care as long as it get shit done. And it does, tons of shit gets done. And the communication is usually more informative than how coworkers document their work. Can it be improved? Yes. Does that mean I can’t use it? No.
the phraseology is unbearable, it speaks like some kind of pretentious dude from a software engineering discord or something, littered with lingo and catch phrases
I try to push through but it's insufferable
It speaks like a Senior Staff Software Engineer who was somehow hired into that title with 6 months of work experience.
I suppose it actually is exactly that, bar not being a human being that experiences anything
maybe the comment length is a ruse to increase token consumption
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This is what news headlines did for decades to bait you into reading the details. I wouldn't be surprised if AI companies do that intentionally to consume more tokens trying to understand what had just happened