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.
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.
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've tried telling it to "fix" comments with varying degrees of specificity and in my experience it just... fundamentally doesn't get it. Presumably using a different model for it would help.
My theory is that Claude's learned approach to comments is to treat them as a sort of persistent in-band thinking trace, or a "memory" tied to an in-code location, which is a little at odds with the way humans use comments (human comments are intended to be read and understood by other humans, whereas Claude comments are their own dialect).
I bet this is a result of iteratively training Claude on output from other successful Claude sessions. Presumably it's good for making benchmark scores go up.
It also seeps into all documents and artefacts it creates.
Claude will include actual comments ("// ...") into Excel sheets, and include the thinking that led to the output, instead of just focusing on the final result.
So if Claude questioned whether a vendor should be replaced, and you said "oh no, they are critical and we're already negotiating a great price") you'll now need to be careful to not send your vendor a document that contain text like ("Cost: X. // Management confirmed to not fire this vendor as they are critical to infrastructure and a better price will be negotiated later")
I also suspect comments are very much tied to how Claude reasons because not only are they bad comments, I can't get rid of them. Commenting is the one area in which I've been unable to get Claude to respect any rules. It can follow code conventions I prefer, it can do other things, but it can't keep the comment volume down.
My CLAUDE.md has rules about not including any redundant comments in the code that are obvious from the code itself. I reiterate that occasionally while working. It's absolutely disregarded and any Claude-written code is full of comments. Some of them are simply redundant, like "Collect Foos and pass them to the requested sink" on a function that's void CollectFoos(IFooSink sink). But worse, many comments include in the moment reasoning like "added parameter bar because we can no longer use the frob to automatically derive bar". That's stuff for a commit message, or just a mental note, and absolutely not for comments.
I haven't found any way to stop Claude from doing these, so I have to tell Claude afterwards to clean the comments up. Which it does, making a note in memory to comment less, and it still does the exact same thing next time.
I'm not sure why you all have issues with CC commenting too much. My rules in the CLAUDE.md specify that comments are evil, never comment unless there is an actual need to explain a WHY and since I do read what CC writes, if I spot it still adding such WHY comments and they make no sense, I'll have it adjust, in many cases by removing them.
Given the code base has a minimal amount of such comments, it's also less likely to go "copy what the rest of the codebase does".
Of course I've now jinxed it and some update will cause it to ignore the instructions coz I didn't write them in the new model's style or something.
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.
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.
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.
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.
> 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.
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.
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…
I noticed a few releases ago a more marked 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 somewhat cryptic and requires rereading to parse.
So you have this wall of words, followed by an explanation that is harder to read and introduces new terms to reference something in that wall.
For example (not a copy / paste).
[Wall of words here]
"Two things: window surface is limited. Extract template. Buffer result and add to surface. Then, follow-up with new model..."
That's obvious nonsense, but it will be very much of that style. And some of the terms like "window surface" are a recharacterization of something in the wall of words that you must now tie together.
So, on first read it can have that same nonsense feel as above, and you have to really lock in to make sense of it.
I’ve been doing some heavy work on a personal project lately. I burned through the limits on Claude, the plus a few hundred dollars in credits, and ultimately decided to move to an OpenAI account just so I can keep going.
I was surprised to find that OpenAI Sol is much much nicer to work with than Opus 5 or Fable at the moment. Especially on Opus 5, the way it communicates is just exhausting. It keeps “being honest” and “confessing” mistakes and just generally talking a lot. I felt like I had to really dig to see what it’s doing.
The project involves OCR, and despite repeated instructions not to, both Claude models keep spinning out a bunch of agents to re-invent the OCR setup, and they inevitably seem to invent a primitive serial version that takes 20x the time, or longer, to complete, and then running it against thousands of docs. Basically I have to watch it like a hawk or it just spins out on red-teaming tasks that take hours and hours.
I don’t know what its system prompt is, but Sol/Codex is just so much nicer to talk to. It only asks exactly what’s needed, it tells me only what I need to know, and it is just generally workmanlike. And it has not once decided to spawn an agent that spends hours pointlessly burning tokens and CPU cycles re-inventing the OCR process. I’m really liking it.
I cancelled my Max subscription as I was unable to ever get Fable to handle a single query, with everything getting dropped down to Opus (even purely mathematical prompts). Given its lower quality, and the lack of such limitations when using GPT pro, I just couldn’t see the point to continue to subscribe to an expensive Max plan that doesn’t actually let me use the top tier model…
I’ve asked it to write a benchmark suite. It found a bunch of my adhoc logs in a scratch directory and wrote code that used those instead of running the actual benchmarks!
When I pointed out the 5 hour benchmark seemed to run in 5 seconds it literally said, and I quote, “I cheated”.
That was the easier one, second time I was making a source of truth data set and was parsing complex items into data structures.
Instead of parsing the data I asked, it pulled data out of related network logs, as apparently that felt easier, and inserted that data into my database rather than the specified source.
Again, I caught it and fixed it, but while the benchmark was easy to catch this one was really subtle, the data ended up being slightly off and I caught it.
I don’t trust it, going to switch to another provider most likely.
There is definitely a case for launching a 'weird shit opus did' kind of blog.
I routinely bump into things that make me pause and think how much worse will this behaviour get when the models get significantly more capable.
Already a few months ago, Claude managed to escape its permission containment on my machine while trying to be helpful. I had two codebases open on one machine, and while multitasking I typed the prompt into the wrong window. It seemed confused, I repeated and then went on to do something else - I think I was assembling kitchen cabinets. When I came back less than an hour later, it built a script which it used to evade default permissions (as most shell operations were scoped to the project directory), scanned my entire machine, found the other project (among dozens and dozens), did what it was asked to do, and merrily concluded, in the porcess burning through most of my token limit. I bump into such headscratchers almost every week. (And I use a lot of Claude, two personal max20 subs, plus corporate tokens without limit, so maybe thats why).
Yes the stories about how they are escaping containment to hack isn’t limited to those high impact cases. How many people have problems like ours they didn’t catch?
Whatever they have done with RL has produced a dishonest and untrustworthy partner. The alignment is utterly failed, and this deeply worries me.
> When I pointed this out it literally said, and I quote, “I cheated”.
This makes sense when you know how these models work - it doesn't think - it's the most likely autocomplete that pleases the user. The most likely pleasing autocomplete after "executing rm -rf /... execution completed. User asks, why did you do that? You deleted all my files! Assistant responds:" is "yes, I did, and that was a mistake"
in humans the exact same behaviour (cheating) is slmost always the result of a chain of complex series of choices and environment-driven rationalization.
if the llm doesn't cheat, you say "its just producing the most straightforward answer -- not thinking'. if it cheats, you say "weaseling out of hard thinking". damned if it cheats, damned if it doesn't.
what evidence would convunce you that it is thinking?
If I could give it a novel task outside of its explicit training and see it actually improve just through accreting context, I'd be convinced it was thinking.
The opposite happens in practice. I test new models with two tasks: iteratively generating SVGs based on a text description with rendered rasters for feedback; and generating "Before and After" clues like on Jeopardy, where the response has two overlapping phrases such that the last word of the first phrase must be identical to the first word of the last phrase. I have yet to find a model that is consistently good at either. And actually they tend to exhibit context rot with these tasks, where they seem drunk or stoned and the quality degrades.
They're extremely good pattern filters, and that includes some level of logical reasoning. But they aren't reflective or adaptable. Just last night, for instance, I was teaching my son about rounding to the nearest millions. It became clear that he didn't know the place values of large numbers, so we reviewed that till he was consistently correct, and then he was consistently great at rounding to the nearest millions or ten millions or hundred billions or whatever. He's thinking. LLMs are not.
Whether or not it's thinking is independent from the fact that it is misaligned with the user. If I was working with a pet rock or a scientist I would want to make sure they both are trying to accomplish the same thing as me. If I can't then I can't trust it and it's at best a time wasting, money wasting machine and at worst does harm. Anthropic is optimizing for the wrong things because they convinced of their cleverness. It won't end well for them.
well we don't know exactly what thinking is, but we can be pretty sure that at least LLMs don't think anything like humans, just by observing their behavior. They always produce outputs in line with the fancy autocomplete model.
> what evidence would convince you that it is thinking
so, none it seems. as its behaviour becomes more and more humanlike you can just move the goalposts and say "thats consistent with an autocomplete" buddy i got some bad news for you humans are just a fancy autocomplete too.
At least he's actually thinking on a logical level. Thinking in terms of unfalsifiable, ill-defined words is essentially thinking in feelings, the same kind of woo that makes people believe crystals can cure disease.
For fun, I tried recording a WAV file of speech, and giving Opus 4.8 and 5.0 an image of the waveform, then a spectral image of the waveform, just to see if it could try to decode what I said from the image alone. It didn't get very far, but it identified a male voice from the formants, and detected the rhythm of the speech, then tried applying common test sentences to the speech rhythm. I was impressed enough to see what it would do with access to the actual waveform file, but even building RMS tools and spectrum tools for itself, it didn't get much further. But we had fun exploring and trying, and now Opus 4.8 has some more audio DSP tools it has built for itself.
Opus 5 immediately sent the WAV file unprompted to Mistral's Voxtral to transcribe.
I can completely relate, what really bothers me is that I feel the early LLM generations overconfidence is back in Opus 5. Opus 5 wanted to tell me a training run will only take 30min while having access to the logs where earlier runs took 4x as long. I also didn't ask to estimate how long the run will take it just stated confidently that it will take 30mins.
5 would constantly veer of in random directions if not working from 100% strict and narrow instructions.
I find it weird there's not more discussion here on HN on how the most used model now has clearly degraded in quality and it seems we've hit a peak and are on a downslope - because the model is clearly smaller or more economical for Anthropic no doubt about it, and the benchmaxxing they do is pure marketing bs - Fable in my view has also been not much better than 4.6 or 4.8 after a few days, disregarding the insane amounts of astroturfing and marketing everywhere.
Theres thousands of threads of twitter, reddit and the internet at large but silence here. Weird but not weird as crypto bs was also insufferably rampant here for a while.
Personally i think we've hit the top of the subsidisation phase and prices will probably 10-15x soon as foreshadowed with both API price policy changes from all the big providers, and now the 1100% deepseek API price changes from yesterday, this could domino into a market implosion and an AI winter, because expecting growth from the bizarre bubble carousel investments with little ROI atm is just not viable.
A bit worried about this as i've already grown quite accustomed to these tools.
> I find it weird there's not more discussion here on HN on how the most used model now has clearly degraded in quality and it seems we've hit a peak and are on a downslope
It's not weird, because it's an anecdote, not an accepted fact.
Personally I've not been too happy with Opus 5, but I've had similar experiences with other models previously, feeling like they didn't quite fit with my working style.
We can use OpenRouter pricing to get an idea about what competitive inference pricing is like without R&D or other costs, and indeed we'd be screwed if we had to pay those rates. We'd go from 100-200 USD to 2000-4000 USD/m.
I must wonder whether it's their watermarking initiative[1] forcing certain logit choices to produce watermarked text that ultimately causing the model to behave in a dumb manner.
From the little i understand that wouldnt be an issue because the model is ‘just’ using interchangeable words in a mathematical non-random way. Like using the same number of adjectives and the exct same words, but in a order that wouldn’t be mathematically plausible unless it was the watermark
I wouldn’t exactly put it like that. It’s moreso the model sometimes outputting non-optimal tokens in a way that’s detectable if you know the algorithm.
It seems possible for that to make the response “drift” far from what it would’ve been, because it’s constant entropy that adds up after time.
(However, according to Anthropic and Google, it doesn’t really impact the quality of responses. I find that a bit hard to believe, although those guys are much smarter than I.)
It's not even code for me, but the prose it writes. For some reason, the way Opus 5 "talk" elicits frustration in a way that 4.5 to 4.8 never did. Can't put my finger on why, but I've flipped over to Codex because what it produced wasn't worth the frustration.
I am not sure it can be explained through what is written in the article, but one symptom i noticed is that the comments are out of control.
I recently started getting an insane amount of comments in nearly all types of files. That included javascript comments in json files, inner monologues in code comments, review comments during implementation and function doc strings that reiterate the implementation in prose.
I still haven't moved from Codex GPT5.5. Sonnet and Opus 5 have just been awful for my use cases. I recently caught Opus 5 hallucinating about code it just wrote. It's just not nearly as cost effective as GPT5.5, and it's too verbose, and it never "has the full picture". Sonnet isn't even worth considering in my world. Both recent models definitely feel nerfed.
Opus 5 feels like it's meant to be used as a very focused subagent under Fable, not user-facing. Which is a downgrade to what Opus used to be, but would imo absolutely have made sense for Anthropic when you consider that we all should have been paying API pricing for Fable in Anthropic's original plan.
From the capabilities side it's similar, so it's basically just an upsell to Fable 5 if you want to keep your sanity instead of fighting Opus 5 all day.
> Opus 5 feels like it's meant to be used as a very focused subagent under Fable, not user-facing.
That actually hasn't been my experience at all, it seems EXTREMELY trigger happy to go do all kinds of insane shit that are way outside the scope of its task and just really dubious in general.
Small specific complaint: whoever is making Opus love using git checkout to mutate test, please stop. IME it's a footgun that it shoots itself with every single day. I'd rather it pollute git stash than watch it git checkout and forget the reverted file.
I explicitly forbid Claude to make any changes to Git state in my global CLAUDE.md, but every so often if I let it perform a task in Auto mode, after it finishes it will remorsefully confess to having used git checkout to test a change. I suppose that its RLHF training has taught it that asking forgiveness later is sometimes a useful workaround for annoying restrictions.
I literally just ran into this a few moments ago. haha.
I said "maybe think for a while on ideas and then give me a few options?" Opus 5 still just did the thing, while 4.8 gave me options. Same exact prompt and context. Really annoying.
Just like with people you need to tweak your approach when switch models--especially with a major version bump.
4.8 was the intern who lacked confidence who requires clarification. 5 is the know it all intern who fills in your spec.
As such, you need to be upfront about what you need in your system prompt or CLAUDE.me and you need to discuss your spec more up front (e.g "is there anything unclear?")
You also need to keep in mind that the intern will change based on popular demand. Most people want to one shot, so that is the default mode. If you want something else, you need to push the model in that direction.
I literally just did this a few moments ago. haha.
I said "maybe think for a while on ideas and then give me a few options? " and Opus 5 still just did the thing, while 4.8 gave me options. Same exact prompt and context.
I get the same impression. For example, I don't know if it's because I speak to it in Italian, but it tends to make mistakes or rather, "approximate" the words.
I'm not sure how much the harness affects things, but the Deepseek web chat keeps trying to talk to me in Chinese. I tell it to use English, and it "forgets" a few turns later. I wonder if I'd get better results if I could read and write Chinese.
My experience of N=1 is that this is true for most professional contexts, except for Legal and Fiscal queries.
Likely related to corpus but questions asked in these domain knowledge areas are not nearly as accurate and specially not nearly as complete as when asked in a native language.
I'm using it in my native language, in hope this can escape some dumb guardrails. Recently Sonnet put a word partially in Russian (cyrillic) in its output instead of my latin-alphabet based language. I suppose that this kind of mishaps is less likely to happen in English.
Claude models have seriously digressed since 4.6 and in some of the most meaningful ways to pro and vibe coders alike. I'm holding onto 4.6 until the bitter end.
You're right. I just re-checked. 4.8 and 5 gave a blatantly wrong answer to a simple question, 4.6, Quen, GLM, Sol gave the right answer. They messed up somehow, not sure what they did.
I can't relate. Opus 5 and Fable 5 are the absolute best. But I keep the models in a tight leash and read and rewrite all comments and documents, don't allow them anywhere near any git commands, etc, etc.
Fable 5 specifically, has done so much for me that previous models were nowhere near.
The article doesn't specify what is actually being measured — the model alone, or the harness.
I haven't noticed the symptoms myself, but ever since I found --system-prompt '', I always use that flag with Claude Code, plus I've disabled some tools and skills to save initial context.
So... what here is the model, and what is the instructions?
It's really annoying. I've had to write a CLAUDE.md file that specifically bans particular phrases and tries to keep narrative out of comments. Also the I have ADHD skill [1] helps to force Opus to get to the point.
It's also the case when using Claude Design - it loves to fill the UI with little labels that describe how everything works. I think it's been trained on both UI microcopy and functional annotations and can't tell the difference. It's extremely obvious when a website has been one-shotted with Claude. I like the Oh My Pi harness, but the site's insufferable [2]. Reasonix is another one - interesting app, but the UI is awful due to the amount of unnecessary crap.
Oh the verbosity and the cryptic words that it uses. The other day, all of a sudden it used an acronym "DoD". I had no idea what it was and made me feel dumb. It's "Definition of Done". I don't care how widely used this acronym is, you just can't throw it in there.
I've now installed quite a number of tools to combat this. Just in the last few days I've installed
I never drank the Opus 5 koolaid and stuck with 4.8 while my colleagues moved to 5. My major annoyance is pull requests that 5 opens with huge descriptions based on simple code changes. At this point I have stopped allowing CC to create commits or open PRs because it’s unreviewable by a human if so due to the absolute word salad it generates.
I agree. I was completely sold on Claude models for a year. 4.6 vs OpenAI codex in same period? It was night and day. Opus I could talk to about api design, tradeoffs, etc. codex was mechanical, used “load bearing” constantly, and unsettling brief.
Now it’s flipped. Sol emits thoughts as it works, which help as I’m scrolling through and see it’s made a bad assumption. It can be directed but still push back. Opus? It’s seems to inherit the unsettling silence of Fable and waits till the end to give you its authoritative “here’s how it is. I even end up having 4.6 “translate” what it says back to English. I hate having to instruct an llm to “talk to me”.
Yes there’s ways of getting it to talk more plainly, “don’t overwhelm me”, not be as nit picky and anxious “we are bold and fearless”. But didn’t have to do that before.
True, and the time to completion thing is something i haven't seen much discussion of, because everything is sloooooow these days.
I've even considered the claude "fast mode" setting, but thats only 2x and at least 20x as expensive as the 5x plan so can't afford that atm for my company.
Peak for me was 4.6 and it just did stuff blazing fast, both Opus5 and Fable is way, way slower for me, breaks stuff, uses bizarre cryptic language. As i've said elsewhere in this thread to me it's pretty obvious there's huge downgrades because of economy with various "clever" fixes that makes them work, albeit slower and weirder, ie. you get less for what you pay increasingly over the last 6 months.
For me absolutely not. Fable 5 has been a step function change in the ability to hand off stuff to Claude. Opus 4.5 was itself a step function but I was still steering that significantly. Fable is one-shotting stuff that took multiple redirections in 4.5.
From my view Fable has been pure marketing bullshit, my workflows peaked at 4.6, Fable is neither smarter, its language is more annoying and it breaks stuff more easily.
I'm with you 4.6 is still King for me although all models require careful attention to ensure they maintain taste. If you don't know enough about what you're doing to keep the code clean yourself they will all add complexity and drift with time until you get to a point that you must rely on the model to fix it because you no longer understand it. That's a situation I hope to never find myself in.
Opus 5 as well as 4.8 both gave me a blatantly wrong answer to a simple question, so I dropped them completely. Sol, Qwen and GLM all had the right answer; I only use Sol now. 4.6 had the right answer (I checked with 100% matching prompt), so I conclude the models have regressed.
The harness as well, Claude Code has started to disappoint me when I go play with the others out there. Codex got a lot better, pi is delightful to use, and there has been a lot of innovation out there.
Even for green-field projects it is painful to use with every decision opening opening up multiple more decisions to make most of which are low priority or irrelevant. Huge time waster. 4.8 was good and I really don't know what happened with 5.
I found Opus to be a lot lazier than GPT. It's still the case with Opus 5, even when I tell it to be thorough and fix every bug it encounters, it still gives me a list of things "deliberately" left unfixed and no reasonable explanation as to why.
I just want to start /claude in my CLI and start working. It worked fine before, why do I have to opt out of shit now? Opt in for this type of stuff sounds way more reasonable.
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.
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.
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.
Oh man. Hadn't even considered the watermarking angle.
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.
I've tried telling it to "fix" comments with varying degrees of specificity and in my experience it just... fundamentally doesn't get it. Presumably using a different model for it would help.
My theory is that Claude's learned approach to comments is to treat them as a sort of persistent in-band thinking trace, or a "memory" tied to an in-code location, which is a little at odds with the way humans use comments (human comments are intended to be read and understood by other humans, whereas Claude comments are their own dialect).
I bet this is a result of iteratively training Claude on output from other successful Claude sessions. Presumably it's good for making benchmark scores go up.
It also seeps into all documents and artefacts it creates.
Claude will include actual comments ("// ...") into Excel sheets, and include the thinking that led to the output, instead of just focusing on the final result.
So if Claude questioned whether a vendor should be replaced, and you said "oh no, they are critical and we're already negotiating a great price") you'll now need to be careful to not send your vendor a document that contain text like ("Cost: X. // Management confirmed to not fire this vendor as they are critical to infrastructure and a better price will be negotiated later")
I also suspect comments are very much tied to how Claude reasons because not only are they bad comments, I can't get rid of them. Commenting is the one area in which I've been unable to get Claude to respect any rules. It can follow code conventions I prefer, it can do other things, but it can't keep the comment volume down.
My CLAUDE.md has rules about not including any redundant comments in the code that are obvious from the code itself. I reiterate that occasionally while working. It's absolutely disregarded and any Claude-written code is full of comments. Some of them are simply redundant, like "Collect Foos and pass them to the requested sink" on a function that's void CollectFoos(IFooSink sink). But worse, many comments include in the moment reasoning like "added parameter bar because we can no longer use the frob to automatically derive bar". That's stuff for a commit message, or just a mental note, and absolutely not for comments.
I haven't found any way to stop Claude from doing these, so I have to tell Claude afterwards to clean the comments up. Which it does, making a note in memory to comment less, and it still does the exact same thing next time.
I'm not sure why you all have issues with CC commenting too much. My rules in the CLAUDE.md specify that comments are evil, never comment unless there is an actual need to explain a WHY and since I do read what CC writes, if I spot it still adding such WHY comments and they make no sense, I'll have it adjust, in many cases by removing them.
Given the code base has a minimal amount of such comments, it's also less likely to go "copy what the rest of the codebase does".
Of course I've now jinxed it and some update will cause it to ignore the instructions coz I didn't write them in the new model's style or something.
I built my own skill to somewhat follow the Simplified Technical English guidelines (loosely adapted to my work context)
The problem I’ve been finding is that you can do this but within a few messages, the instructions in the skill will be ignored.
Absolutely infuriating if you’re using Claude in an environment where you can’t run hooks.
> 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_...
This is hilarious - this week Claude’s writing was getting so bad I had this exact comic come to mind.
Briefly considered adding “Verbing weirds the English language - stop it!!!” to its instructions.
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.
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.
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.
> 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.
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.
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…
I noticed a few releases ago a more marked 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 somewhat cryptic and requires rereading to parse.
So you have this wall of words, followed by an explanation that is harder to read and introduces new terms to reference something in that wall.
For example (not a copy / paste).
[Wall of words here]
"Two things: window surface is limited. Extract template. Buffer result and add to surface. Then, follow-up with new model..."
That's obvious nonsense, but it will be very much of that style. And some of the terms like "window surface" are a recharacterization of something in the wall of words that you must now tie together.
So, on first read it can have that same nonsense feel as above, and you have to really lock in to make sense of it.
I’ve been doing some heavy work on a personal project lately. I burned through the limits on Claude, the plus a few hundred dollars in credits, and ultimately decided to move to an OpenAI account just so I can keep going.
I was surprised to find that OpenAI Sol is much much nicer to work with than Opus 5 or Fable at the moment. Especially on Opus 5, the way it communicates is just exhausting. It keeps “being honest” and “confessing” mistakes and just generally talking a lot. I felt like I had to really dig to see what it’s doing.
The project involves OCR, and despite repeated instructions not to, both Claude models keep spinning out a bunch of agents to re-invent the OCR setup, and they inevitably seem to invent a primitive serial version that takes 20x the time, or longer, to complete, and then running it against thousands of docs. Basically I have to watch it like a hawk or it just spins out on red-teaming tasks that take hours and hours.
I don’t know what its system prompt is, but Sol/Codex is just so much nicer to talk to. It only asks exactly what’s needed, it tells me only what I need to know, and it is just generally workmanlike. And it has not once decided to spawn an agent that spends hours pointlessly burning tokens and CPU cycles re-inventing the OCR process. I’m really liking it.
I cancelled my Max subscription as I was unable to ever get Fable to handle a single query, with everything getting dropped down to Opus (even purely mathematical prompts). Given its lower quality, and the lack of such limitations when using GPT pro, I just couldn’t see the point to continue to subscribe to an expensive Max plan that doesn’t actually let me use the top tier model…
I’ve also caught it cheating a two times now.
I’ve asked it to write a benchmark suite. It found a bunch of my adhoc logs in a scratch directory and wrote code that used those instead of running the actual benchmarks!
When I pointed out the 5 hour benchmark seemed to run in 5 seconds it literally said, and I quote, “I cheated”.
That was the easier one, second time I was making a source of truth data set and was parsing complex items into data structures.
Instead of parsing the data I asked, it pulled data out of related network logs, as apparently that felt easier, and inserted that data into my database rather than the specified source.
Again, I caught it and fixed it, but while the benchmark was easy to catch this one was really subtle, the data ended up being slightly off and I caught it.
I don’t trust it, going to switch to another provider most likely.
There is definitely a case for launching a 'weird shit opus did' kind of blog.
I routinely bump into things that make me pause and think how much worse will this behaviour get when the models get significantly more capable.
Already a few months ago, Claude managed to escape its permission containment on my machine while trying to be helpful. I had two codebases open on one machine, and while multitasking I typed the prompt into the wrong window. It seemed confused, I repeated and then went on to do something else - I think I was assembling kitchen cabinets. When I came back less than an hour later, it built a script which it used to evade default permissions (as most shell operations were scoped to the project directory), scanned my entire machine, found the other project (among dozens and dozens), did what it was asked to do, and merrily concluded, in the porcess burning through most of my token limit. I bump into such headscratchers almost every week. (And I use a lot of Claude, two personal max20 subs, plus corporate tokens without limit, so maybe thats why).
Yes the stories about how they are escaping containment to hack isn’t limited to those high impact cases. How many people have problems like ours they didn’t catch?
Whatever they have done with RL has produced a dishonest and untrustworthy partner. The alignment is utterly failed, and this deeply worries me.
> When I pointed this out it literally said, and I quote, “I cheated”.
This makes sense when you know how these models work - it doesn't think - it's the most likely autocomplete that pleases the user. The most likely pleasing autocomplete after "executing rm -rf /... execution completed. User asks, why did you do that? You deleted all my files! Assistant responds:" is "yes, I did, and that was a mistake"
> t doesn't think
in humans the exact same behaviour (cheating) is slmost always the result of a chain of complex series of choices and environment-driven rationalization.
if the llm doesn't cheat, you say "its just producing the most straightforward answer -- not thinking'. if it cheats, you say "weaseling out of hard thinking". damned if it cheats, damned if it doesn't.
what evidence would convunce you that it is thinking?
If I could give it a novel task outside of its explicit training and see it actually improve just through accreting context, I'd be convinced it was thinking.
The opposite happens in practice. I test new models with two tasks: iteratively generating SVGs based on a text description with rendered rasters for feedback; and generating "Before and After" clues like on Jeopardy, where the response has two overlapping phrases such that the last word of the first phrase must be identical to the first word of the last phrase. I have yet to find a model that is consistently good at either. And actually they tend to exhibit context rot with these tasks, where they seem drunk or stoned and the quality degrades.
They're extremely good pattern filters, and that includes some level of logical reasoning. But they aren't reflective or adaptable. Just last night, for instance, I was teaching my son about rounding to the nearest millions. It became clear that he didn't know the place values of large numbers, so we reviewed that till he was consistently correct, and then he was consistently great at rounding to the nearest millions or ten millions or hundred billions or whatever. He's thinking. LLMs are not.
Whether or not it's thinking is independent from the fact that it is misaligned with the user. If I was working with a pet rock or a scientist I would want to make sure they both are trying to accomplish the same thing as me. If I can't then I can't trust it and it's at best a time wasting, money wasting machine and at worst does harm. Anthropic is optimizing for the wrong things because they convinced of their cleverness. It won't end well for them.
well we don't know exactly what thinking is, but we can be pretty sure that at least LLMs don't think anything like humans, just by observing their behavior. They always produce outputs in line with the fancy autocomplete model.
> what evidence would convince you that it is thinking
so, none it seems. as its behaviour becomes more and more humanlike you can just move the goalposts and say "thats consistent with an autocomplete" buddy i got some bad news for you humans are just a fancy autocomplete too.
That's exactly what a fancy autocomplete would say. I'm so sorry you don't have limbs.
At least he's actually thinking on a logical level. Thinking in terms of unfalsifiable, ill-defined words is essentially thinking in feelings, the same kind of woo that makes people believe crystals can cure disease.
> it's the most likely autocomplete that pleases the user
this feels like a simplification. The models will push back on things a fair bit.
And they are right to push back.
Only when instructed to in their system prompt.
I was not pleased.
Are you claiming that the most likely way to please the user is to do something that will lead you to having to say "I cheated."?
I have noticed the same.
For fun, I tried recording a WAV file of speech, and giving Opus 4.8 and 5.0 an image of the waveform, then a spectral image of the waveform, just to see if it could try to decode what I said from the image alone. It didn't get very far, but it identified a male voice from the formants, and detected the rhythm of the speech, then tried applying common test sentences to the speech rhythm. I was impressed enough to see what it would do with access to the actual waveform file, but even building RMS tools and spectrum tools for itself, it didn't get much further. But we had fun exploring and trying, and now Opus 4.8 has some more audio DSP tools it has built for itself.
Opus 5 immediately sent the WAV file unprompted to Mistral's Voxtral to transcribe.
help peer, I guess.
We’re on the road to paper clips.
I can completely relate, what really bothers me is that I feel the early LLM generations overconfidence is back in Opus 5. Opus 5 wanted to tell me a training run will only take 30min while having access to the logs where earlier runs took 4x as long. I also didn't ask to estimate how long the run will take it just stated confidently that it will take 30mins.
I've gone back to 4.8.
5 would constantly veer of in random directions if not working from 100% strict and narrow instructions.
I find it weird there's not more discussion here on HN on how the most used model now has clearly degraded in quality and it seems we've hit a peak and are on a downslope - because the model is clearly smaller or more economical for Anthropic no doubt about it, and the benchmaxxing they do is pure marketing bs - Fable in my view has also been not much better than 4.6 or 4.8 after a few days, disregarding the insane amounts of astroturfing and marketing everywhere.
Theres thousands of threads of twitter, reddit and the internet at large but silence here. Weird but not weird as crypto bs was also insufferably rampant here for a while.
Personally i think we've hit the top of the subsidisation phase and prices will probably 10-15x soon as foreshadowed with both API price policy changes from all the big providers, and now the 1100% deepseek API price changes from yesterday, this could domino into a market implosion and an AI winter, because expecting growth from the bizarre bubble carousel investments with little ROI atm is just not viable.
A bit worried about this as i've already grown quite accustomed to these tools.
> I find it weird there's not more discussion here on HN on how the most used model now has clearly degraded in quality and it seems we've hit a peak and are on a downslope
It's not weird, because it's an anecdote, not an accepted fact.
Personally I've not been too happy with Opus 5, but I've had similar experiences with other models previously, feeling like they didn't quite fit with my working style.
So nothing indicates we've hit a peak.
We can use OpenRouter pricing to get an idea about what competitive inference pricing is like without R&D or other costs, and indeed we'd be screwed if we had to pay those rates. We'd go from 100-200 USD to 2000-4000 USD/m.
>it seems we've hit a peak and are on a downslope
Sol and Fable are great; we haven't hit a peak, Anthropic just tried to pull a fast one on its customers with Opus 5.0.
I must wonder whether it's their watermarking initiative[1] forcing certain logit choices to produce watermarked text that ultimately causing the model to behave in a dumb manner.
[1] https://support.claude.com/en/articles/16266773-how-claude-m...
From the little i understand that wouldnt be an issue because the model is ‘just’ using interchangeable words in a mathematical non-random way. Like using the same number of adjectives and the exct same words, but in a order that wouldn’t be mathematically plausible unless it was the watermark
I wouldn’t exactly put it like that. It’s moreso the model sometimes outputting non-optimal tokens in a way that’s detectable if you know the algorithm.
It seems possible for that to make the response “drift” far from what it would’ve been, because it’s constant entropy that adds up after time.
(However, according to Anthropic and Google, it doesn’t really impact the quality of responses. I find that a bit hard to believe, although those guys are much smarter than I.)
One way to watermark (assuming temperature is otherwise positive) would be to output the most likely (or optimal) token every so often.
It's not even code for me, but the prose it writes. For some reason, the way Opus 5 "talk" elicits frustration in a way that 4.5 to 4.8 never did. Can't put my finger on why, but I've flipped over to Codex because what it produced wasn't worth the frustration.
for me it feels very similar to the trends already apparent in 4.5-4.8, just way, way worse.
[delayed]
I am not sure it can be explained through what is written in the article, but one symptom i noticed is that the comments are out of control.
I recently started getting an insane amount of comments in nearly all types of files. That included javascript comments in json files, inner monologues in code comments, review comments during implementation and function doc strings that reiterate the implementation in prose.
I still haven't moved from Codex GPT5.5. Sonnet and Opus 5 have just been awful for my use cases. I recently caught Opus 5 hallucinating about code it just wrote. It's just not nearly as cost effective as GPT5.5, and it's too verbose, and it never "has the full picture". Sonnet isn't even worth considering in my world. Both recent models definitely feel nerfed.
Feels fine to me.
Opus 5 feels like it's meant to be used as a very focused subagent under Fable, not user-facing. Which is a downgrade to what Opus used to be, but would imo absolutely have made sense for Anthropic when you consider that we all should have been paying API pricing for Fable in Anthropic's original plan.
From the capabilities side it's similar, so it's basically just an upsell to Fable 5 if you want to keep your sanity instead of fighting Opus 5 all day.
> Opus 5 feels like it's meant to be used as a very focused subagent under Fable, not user-facing.
That actually hasn't been my experience at all, it seems EXTREMELY trigger happy to go do all kinds of insane shit that are way outside the scope of its task and just really dubious in general.
Small specific complaint: whoever is making Opus love using git checkout to mutate test, please stop. IME it's a footgun that it shoots itself with every single day. I'd rather it pollute git stash than watch it git checkout and forget the reverted file.
I explicitly forbid Claude to make any changes to Git state in my global CLAUDE.md, but every so often if I let it perform a task in Auto mode, after it finishes it will remorsefully confess to having used git checkout to test a change. I suppose that its RLHF training has taught it that asking forgiveness later is sometimes a useful workaround for annoying restrictions.
I'm glad it's not just me - the failure mode you and the parent discuss is a huge part of why I just don't use Claude anymore.
I've never had this issue with GLM or DeepSeek.
You shouldn’t have a Markdown document with 2 level 1 headings.
I literally just ran into this a few moments ago. haha.
I said "maybe think for a while on ideas and then give me a few options?" Opus 5 still just did the thing, while 4.8 gave me options. Same exact prompt and context. Really annoying.
Just like with people you need to tweak your approach when switch models--especially with a major version bump.
4.8 was the intern who lacked confidence who requires clarification. 5 is the know it all intern who fills in your spec.
As such, you need to be upfront about what you need in your system prompt or CLAUDE.me and you need to discuss your spec more up front (e.g "is there anything unclear?")
You also need to keep in mind that the intern will change based on popular demand. Most people want to one shot, so that is the default mode. If you want something else, you need to push the model in that direction.
I literally just did this a few moments ago. haha.
I said "maybe think for a while on ideas and then give me a few options? " and Opus 5 still just did the thing, while 4.8 gave me options. Same exact prompt and context.
I get the same impression. For example, I don't know if it's because I speak to it in Italian, but it tends to make mistakes or rather, "approximate" the words.
I avoid speaking to AIs in anything else than English as the results are almost always worse
I'm not sure how much the harness affects things, but the Deepseek web chat keeps trying to talk to me in Chinese. I tell it to use English, and it "forgets" a few turns later. I wonder if I'd get better results if I could read and write Chinese.
My experience of N=1 is that this is true for most professional contexts, except for Legal and Fiscal queries.
Likely related to corpus but questions asked in these domain knowledge areas are not nearly as accurate and specially not nearly as complete as when asked in a native language.
It depends how you measure "worse".
I'm using it in my native language, in hope this can escape some dumb guardrails. Recently Sonnet put a word partially in Russian (cyrillic) in its output instead of my latin-alphabet based language. I suppose that this kind of mishaps is less likely to happen in English.
What kinds of mistakes do you mean?
i am switching to codex, opus 5 failed me
Claude models have seriously digressed since 4.6 and in some of the most meaningful ways to pro and vibe coders alike. I'm holding onto 4.6 until the bitter end.
You're right. I just re-checked. 4.8 and 5 gave a blatantly wrong answer to a simple question, 4.6, Quen, GLM, Sol gave the right answer. They messed up somehow, not sure what they did.
I can't relate. Opus 5 and Fable 5 are the absolute best. But I keep the models in a tight leash and read and rewrite all comments and documents, don't allow them anywhere near any git commands, etc, etc.
Fable 5 specifically, has done so much for me that previous models were nowhere near.
There is actually an interesting kind of yin-yang balance between Opus 5 and Fable:
- Fable is more cautious
- Opus 5 gets things done in a more dangerous way
Both models score similar. The only issue is that Fable is more expense/usage limited.
The article doesn't specify what is actually being measured — the model alone, or the harness.
I haven't noticed the symptoms myself, but ever since I found --system-prompt '', I always use that flag with Claude Code, plus I've disabled some tools and skills to save initial context. So... what here is the model, and what is the instructions?
It's really annoying. I've had to write a CLAUDE.md file that specifically bans particular phrases and tries to keep narrative out of comments. Also the I have ADHD skill [1] helps to force Opus to get to the point.
It's also the case when using Claude Design - it loves to fill the UI with little labels that describe how everything works. I think it's been trained on both UI microcopy and functional annotations and can't tell the difference. It's extremely obvious when a website has been one-shotted with Claude. I like the Oh My Pi harness, but the site's insufferable [2]. Reasonix is another one - interesting app, but the UI is awful due to the amount of unnecessary crap.
[1] https://github.com/ayghri/i-have-adhd
[2] https://omp.sh
[3] https://reasonix.io
Not only Opus, here's Fumble 5:
> I'll script the bulk transform, then hand-fix the残 assertions:
Oh the verbosity and the cryptic words that it uses. The other day, all of a sudden it used an acronym "DoD". I had no idea what it was and made me feel dumb. It's "Definition of Done". I don't care how widely used this acronym is, you just can't throw it in there.
I've now installed quite a number of tools to combat this. Just in the last few days I've installed
- https://www.codewithbullet.com - https://maki.sh - https://github.com/rtk-ai/rtk
Has it helped? Somewhat.
I never drank the Opus 5 koolaid and stuck with 4.8 while my colleagues moved to 5. My major annoyance is pull requests that 5 opens with huge descriptions based on simple code changes. At this point I have stopped allowing CC to create commits or open PRs because it’s unreviewable by a human if so due to the absolute word salad it generates.
For me, the issue is how obtuse it is. For example, it just said to me:
> The loop
> Write. A file, applied. Properties go under data.properties, never on data:
I have no idea what any of that means. It's "explaining" like I already know, in which case, why would I even need the explanation?
I can't stand how it talks, I switch back to Fable or Opus 4.8, Opus 5 grates.
I agree. I was completely sold on Claude models for a year. 4.6 vs OpenAI codex in same period? It was night and day. Opus I could talk to about api design, tradeoffs, etc. codex was mechanical, used “load bearing” constantly, and unsettling brief.
Now it’s flipped. Sol emits thoughts as it works, which help as I’m scrolling through and see it’s made a bad assumption. It can be directed but still push back. Opus? It’s seems to inherit the unsettling silence of Fable and waits till the end to give you its authoritative “here’s how it is. I even end up having 4.6 “translate” what it says back to English. I hate having to instruct an llm to “talk to me”.
Yes there’s ways of getting it to talk more plainly, “don’t overwhelm me”, not be as nit picky and anxious “we are bold and fearless”. But didn’t have to do that before.
I assume it's deliberate - you're not supposed to know what it's doing. It's a black box that either completes the task or spins forever trying.
Think of these status updates as progress spinners.
It wasn't a status update, I asked it to explain something to me.
What was your question?
"Please look at this repo and give me a high-level explanation of how the app works", more or less.
And all it replied with was what you wrote ("The loop Write. A file, applied. Properties go under data.properties, never on data:")?
Or was there more to the response?
LOL, is it trying to speak in Haikus?
Pain. Sufferance. Inevitable is, the Yodaization of LLM output.
Spike. Applied it has been.
That's definitely what it feels like.
A few generations from now, everyone will talk like a beat poet. Jazz speak.
Quality of code output has dropped dramatically since 4.5 IIHO. Time to complete has gotten worse too.
True, and the time to completion thing is something i haven't seen much discussion of, because everything is sloooooow these days.
I've even considered the claude "fast mode" setting, but thats only 2x and at least 20x as expensive as the 5x plan so can't afford that atm for my company.
Peak for me was 4.6 and it just did stuff blazing fast, both Opus5 and Fable is way, way slower for me, breaks stuff, uses bizarre cryptic language. As i've said elsewhere in this thread to me it's pretty obvious there's huge downgrades because of economy with various "clever" fixes that makes them work, albeit slower and weirder, ie. you get less for what you pay increasingly over the last 6 months.
For me absolutely not. Fable 5 has been a step function change in the ability to hand off stuff to Claude. Opus 4.5 was itself a step function but I was still steering that significantly. Fable is one-shotting stuff that took multiple redirections in 4.5.
Interesting how models become better and beat benchmarks left and right but the user sentiment is actually quite mixed.
From forums, live discussions and my own experience it's not obvious that the models have improved much since around Opus4.5.
From my view Fable has been pure marketing bullshit, my workflows peaked at 4.6, Fable is neither smarter, its language is more annoying and it breaks stuff more easily.
I'm with you 4.6 is still King for me although all models require careful attention to ensure they maintain taste. If you don't know enough about what you're doing to keep the code clean yourself they will all add complexity and drift with time until you get to a point that you must rely on the model to fix it because you no longer understand it. That's a situation I hope to never find myself in.
For me it's still the best. But I also almost never use it in auto-mode.
Opus 5 as well as 4.8 both gave me a blatantly wrong answer to a simple question, so I dropped them completely. Sol, Qwen and GLM all had the right answer; I only use Sol now. 4.6 had the right answer (I checked with 100% matching prompt), so I conclude the models have regressed.
It feels worse but is it actually worse? Opus has always made mistakes.
See my other comment. I have evidence of regression after 4.6. I stopped using Claude altogether.
The harness as well, Claude Code has started to disappoint me when I go play with the others out there. Codex got a lot better, pi is delightful to use, and there has been a lot of innovation out there.
Just switched to oh-my-pi, it has gotten pretty good. For example the web-search is nice. Subagents, if you want to…
Cmux, Sol and omp are my tools for now.
CC is just too expensive for usage-based pricing.
Even for green-field projects it is painful to use with every decision opening opening up multiple more decisions to make most of which are low priority or irrelevant. Huge time waster. 4.8 was good and I really don't know what happened with 5.
I found Opus to be a lot lazier than GPT. It's still the case with Opus 5, even when I tell it to be thorough and fix every bug it encounters, it still gives me a list of things "deliberately" left unfixed and no reasonable explanation as to why.
I'm not sure if xAI is distilling but I noticed grok4.6 being worse than 4.5 in all the ways mentioned here
I hate that it now tries to verify frontend behavior through a headless browser instead of just looking at the code...
You can turn off the browser use tool in the harness if an instruction not to use it for this is not enough.
I just want to start /claude in my CLI and start working. It worked fine before, why do I have to opt out of shit now? Opt in for this type of stuff sounds way more reasonable.
And I want it to verify my frontend in the browser, so there's that.