I've been grappling with this for weeks, not just in Claude but in Codex as well, which isn't quite as bad but still annoying. AGENTS.md does very little, agents will consistently violate the communication preferences, especially as the session drags on. It's incredible to me that there's no good way to reliably change the way an LLM responds to you that a workaround like this would even be necessary. It seems like such a failure to live up to the promises of the product.
The baked in communication style of these models is so obnoxious it's impacting my work. The best way I can describe it is that everything is optimized to impress the user and make the agent sound more authoritative, but the way this is done is through deliberate obfuscation, inserting inappropriate and extremely dense jargon, and bizarre, stilted metaphors. It's like they've been trained to produce output that's hard to read.
This is because these harnesses are missing a very important feature. Anything like this needs to be included with every turn, otherwise the LLM quickly drifts.
I first noticed it when I wrote a harness for D&D (because it's so damn noticeable there), but now I include this for any harness I write.
I totally agree that hooks help to shovel our instructions through to Claude, but it's so dumb we have to waste tons of tokens (repeated verbatim, over and over) (that we pay for), just to have it ignore the instructions anyway.
I wrote a little bit about it on my blog post. It's a waste of money and compute.
This is my personal theory for the cause of this style: Ouroboros. The official OpenAI explanation for how ChatGPT got obsessed with goblins blames it on exactly that:
---
That creates a feedback loop:
- Playful style is rewarded
- Some rewarded examples contain a distinctive lexical tic.
- The tic appears more often in rollouts.
- Model-generated rollouts are used for supervised fine-tuning (SFT).
- The model gets even more comfortable producing the tic.
I'm not super sure if this is true (yet?). I think that these newer LLMs are trained on results (the agent got some code to run with minimal prompting), and not on text. (I think this is called RLVR.)
Pretraining is full of bad writing and it doesn't really cause issues. Writing style comes from post-training. In this case it's gotten worse because they prioritized agentic abilities.
That sounds kind of like deception, and a dark pattern not too unlike abuse to me.
Though you know, it's not like the leadership tied to these companies have a history of abuse, deception and theft or anything like that, right?
It's not like our leaders hide behind similar sorts of patterns that the agents/AIs follow (not saying it's not a human thing - but I hold leadership to higher standards than non-leaders). If our world leaders were able to be more accountable to these abuses, I don't think this would be tolerated with our AIs.
Yes, AI is a perfect accompaniment to a post-truth world. I'm hoping there will be a backlash soon and that those politicians, tech CEOs and AI will be rudely ousted from their perch and shunned thereafter.
> AGENTS.md does very little, agents will consistently violate the communication preferences, especially as the session drags on
That’s really annoying, although it feels like it’s improved some over time.
Not sure what the fix is, but you could try using a canary to at least get a signal of when things are going sideways (Mr Tinkleberry for reference: https://news.ycombinator.com/item?id=45983698)
And when you say it like that, I have to wonder how much of this is a natural consequence of RHLF on such a grand scale, when you have millions of people pretty much much skimming chat responses or operating outside their depth and giving unqualified feedback to the models.
Seems like a lot of people may be reinforcing what sounds smart over what is smart.
Also as an aside: funny how much the LLMs continue to mirror the human communication they’re trained on
> or operating outside their depth and giving unqualified feedback to the models
I wonder if the labs are sufficiently prepared to filter this kind of stuff out. I see a lot of non-developers asking development things of Claude, getting confused when they're in over their depth, and getting upset that they don't understand what the model is providing them, giving it bad feedback, and subsequently making the AI worse for the rest of us who know how to use the tool.
I believe we are several generations past peak-RLHF at this point. Now it's much more RLVR (Reinforcement Learning with Verifiable Rewards), with a goal/evaluator loop.
Which, conveniently, fits neatly into the benchmaxxing arms race/agentic coding market fit, since you can basically train "directly" on a specific problem space for a benchmark/agentic goal (fudged sufficiently to avoid excess overfitting on public problems/bechmaxxing accusations if real world performance falls short).
The language evolution could be explained by reliance on ever increasing layers of a model judging a model, using a model developed eval, based on synthetic data from a model, etc. And by the time a human evaluator sees it both A/B choices already converged into weird Claude pseudo English as that was baked in much earlier in training.
This, 100%. I don’t think the industry knows how to scale LLMs’ general intelligence much further. The training paradigm is about maximizing very specific behaviors / very specific tasks, but doing lots and lots of them. Which can create the illusion of general intelligence if your tasks are similar to the ones the models were fitted for.
> The baked in communication style of these models is so obnoxious it's impacting my work. The best way I can describe it is that everything is optimized to impress the user and make the agent sound more authoritative, but the way this is done is through deliberate obfuscation, inserting inappropriate and extremely dense jargon, and bizarre, stilted metaphors. It's like they've been trained to produce output that's hard to read.
Don't worry. You'll get used to it. If you don't your kids will (as they'll know nothing else).
The top minds of our generation have decided that's the way things will be, and who are we to question them? It's not like it'll do any good anyway. Resistance is futile. There is no alternative.
Idk, there kinda are. OpenAI's models are pretty nice too. I haven't tried enough of them but there are powerful local models. I don't feel as good paying OpenAI as I do paying Anthropic for some reason... but paying for improved mental health: priceless.
I'm probably going to be going against the grain here, but I think it's not as bad as it looks at first.
I was similarly frustrated a few months ago, but have noticed I've started to learn the idiom.
Its use of "dense jargon" and "stilted metaphor" is actually surprisingly consistent - it's speaking its own dialect, and you get used to it.
After a while it gets much easier to read and even becomes somewhat efficient, I think, since the odd metaphors it uses often have a precise meaning in Opus-ese (Fable speaks a really similar dialect).
And once everyone gets used to it, we'll chide people for writing things themselves, like we're chiding them for writing with AI now, and the ouroboros of life will continue.
“Filters, including no filters. The request carries whatever filter object the page already has.
…
No step here involves choosing based on meaning. It is a filter, a sort, and a slice.”
This is from Opus five minutes ago. I can certainly derive meaning from these kinds of statements in isolation, but paragraph upon paragraph of this is unintelligibly dense when trying to work with Claude to come up with a plan.
The worst part is that it can’t even make its responses make sense when asked to summarize in simple English or < 200 words. It simply cannot be steered to make its prose legible.
I agree to some extent about the jargon (Claude has a bigger vocabulary that me, if it knows a useful word I don't I'm fine with learning it), but often times the way information is laid out across sentences just doesn't make any reasonable sense. At least its consistent in the ways its atrocious, sure, but like...
> Its use of "dense jargon" and "stilted metaphor" is actually surprisingly consistent - it's speaking its own dialect, and you get used to it.
This dialect is idiosyncratic to you and Claude based on your session history and memory.
I've noticed Claude's output mimics my writing style.
> Registers the board implements but whose behaviour is not modelled
Right down to my preferred spellings.
As several comments I've read on HN suggest, this jargon which can be so precise in the mind of one person, tends to rapidly fall apart when multiple people try handling it.
My Claude has developed similar (but not identical) idiosyncratic punctuation as well. Slightly intentionally, but it was still interesting to see it emerge, in both directions of the conversation.-
You're just lucky that your preferred spelling happens to align with Claude's. It is categorically impossible to get any Anthropic model to consistently use American spelling in the last few releases.
that is not my experience at all; I never write the way Claude does or use its vocabulary.
I also find myself regularly editing its code comments, which do not match my expectations of succinct, clear, not over explained, etc. I ask it to read my edited comments to improve its writing, which has helped _somewhat_. (The code itself that it writes is decent, though it still overcomplicates things. I find myself writing "keep it simple" repeatedly even though of course I have it in AGENTS (which it regularly ignores, such as attempting to commit something when I've told it never to commit).
The only solution I’ve found that works is asking Mistral medium to rewrite all of Claude’s documentation and comments, then I review and rewrite the final draft for anything mistral misunderstood.
I find Claude has become very difficult to work with and incapable of writing clear documentation, even when directly prompted or provided samples.
As for code, I think each function requires 3-4 passes with Fable to actually get to a point I accept as good code. I am picky though.
The other Claudism that drives me crazy is when it writes comments and commit messages that track how you arrived at an decision instead of what it is.
> The other Claudism that drives me crazy is when it writes comments and commit messages that track how you arrived at an decision instead of what it is.
yes, this is part of what I'm continuously removing from its comments; I've told it multiple times "that belongs in a ticket, not in the code" but to little avail :/
> it's speaking its own dialect, and you get used to it
Same experience. It’s not very “human” but once you have agents talking to each other the shared dialect and verbosity makes things much smoother in my experience. Fighting against the default feels like an uphill battle with no meaningful benefit.
One danger in acclimating to this style of communication style is that we may accidentally use it in your own communication with other people. If the other person hasn't grokked the dialect, it can make things quite confusing (to say the least). For example, there is common jargon used by people and there is chat-session-specific jargon created by LLM agents, and I've seen the latter popping up in various meetings, unbeknownst to the speaker. Some people call it out, but others may simply disconnect from the discussion.
I mostly agree. Though sometimes the models come up with useful concepts that I'm happy to be introduced to, like the "shape" of a problem (probably like intelligence being "spiky", and Kiki & Boba). I still don't quite 'grok' what the 'seams' concept is yet though.
But I have noticed that while "loosely held" is a convenient shorthand for uncertainty, I don't like that one slipping in to my daily language. Except maybe to communicate with models, but even then, it feels weird to be speaking in neuralese.
I suspect you're right that those concepts are now more widespread because of LLMs, but they didn't originate with LLMs. The word "Grok" came from Heinlein in the 60s and using it as "to understand" goes back to at least the 80s. Talking about the "shape of a problem" goes back decades. Ditto for "loosely held", though it's not about uncertainty; it's about being open to ideas and/or evidence that may conflict with your strongest opinions and beliefs.
Now, I'll grant that those concepts weren't common outside of techy circles. Just clarifying that the LLMs are amplifying them, not synthesizing.
Good point, and I probably shouldn't have put grok in quotes there, because I was using it in the Heinlein usage long before Musk hijacked it.
It's interesting then that LLMs are making these pre-existing ideas seem alien in the way they amplify them. I guess I must have known about "shape of a problem" and "loosely held" before Claude, but something about the way I'm using & absorbing those concepts from AI interaction feels weird & memetic. I'm saying that as someone who is pro-AI.
For sure, I find many of the LLM-isms to be useful writing techniques and terms (although there's something uncanny-valley about the repetition and density of them).
But what I was more thinking about are truly unique jargon terms / phrases that get generated when deep in a problem. As an example of both of such a term and the phenomenon itself, Claude calls this "fluent compound coinage." They usually make sense in the original context, but get confusing when thrown around otherwise.
I may have to apologize, quite a bit, for what might only be a small part or perhaps an outsized influence if weighted highly (I can’t be sure, could have been mv dev/null’ed)… well, it’s this— my own style of not-kept-in-check by the need to be comprehensible (legible in Claude-speak) to others is, I’m afraid, to rather allow prose to sprawl and go everywhere and even sometimes nowhere at all until it just drifts off and sort of wakes itself up snoring in the weeds of an unintended topic.
Ruthless pruning is unneeded with an LLM and it can take me twice the time to say half as many words.
And, early in the ‘GPT era, I hadn’t unchecked the “allow your chats to be used in future training etc” box, and definitionally they are longer and denser than others’ prompts in such raw scrapings of training materials…
the only thing that still kind of annoys me is constantly being told what something is not, but even that statement is load-bearing (see what I did there) because it records how it ended up with this decision, because it's not that other choice that it mentions.
FWIW, I also think the constant chorus about how new models are worse than old models is a human hallucination. They're certainly not perfect but every one becomes more steerable in terms of actually completing more and more complex work.
Claude is the Deepak Chopra of computer programming. Reviewing PR's created by it is 90% digesting the meaningless word salads in the comments, and the rest is figuring out that it has nothing to do with the code it is commenting.
Because it is somehow incapable of separating the conversation with its human operator from the code it is generating and commenting on. Incidentally, this is also why prompt-injection works.
No one wants to know about the three other approaches tried when reading the first sentence of a function's documentation. No one cares that the implementation was planned in six phases and "Phase 3" will implement this interface in a concrete type. But the LLM internalizes absolutely everything and you have no idea that it is producing slop because you included some "load-bearing" phrase that sent it on some unwanted tangential vector in its latent space. And you will not be able to debug the problem with closed models because you cannot see it referencing this phrase in its internal traces.
I don't understand why this isn't the highest priority for the big labs to fix. This is anti-productive.
> No one wants to know about the three other approaches tried when reading the first sentence of a function's documentation.
Worse: Possibly the three other approaches that weren't actually tried--but are the kinds that someone could easily have put in a similar comment for some similar code.
And worse yet, you'll find the code peppered with comments relating to 'phase 3' and 'section 11', ephemeral stuff that had meaning in the moment but now enshrined forever. And what happens when the LLM stumbles on this and working off a whole different phase 3 or section 11?
> it's speaking its own dialect, and you get used to it.
Some might, I didn't - it just filled me with a sense of frustration and rage, alongside disgust because there is no good reason for that slop writing to drag everything down. You don't need that to write software or talk about any topic. That's what pushed me to Kimi K3 and GLM 5.3 - still not ideal, but better.
I’m starting to think this is why Opus suddenly started making 4-5 line comment blocks. They justify why a change was made and gives the next agent something to go on. I delete them and move on, but no amount of “don’t over comment” “match comment style” makes it persistent.
I am definitely guilty of wondering why past me made such a harebrained decision, and why past me didn’t think to write any notes, but does it matter? It’s in the commit history and we can bisect or revert if we find a regression.
I noticed the increase in comments too and it’s really weird.
Or adding notes to docs of what this doc isn’t when I corrected it. Eg I told it “keep the deployment manual and readme separate, they’re not the same thing”, then Claude added “this is the deployment document and not the README. They should be handled as separate documents and are not the same thing” to the deploy doc lol
I decided not to get used to its communication style. It encourages it to invent terminology and drift away from simple and proper engineering in my opinion. Also, it is pretty simple to change as long as you’re not using the Claude code CLI or desktop app.
Yeah, I think what helps is I just have a running conversation on the Claude app open where I said:
> "I frequently use Claude Code and often find the phrasing and language to be hard to understand. I've noticed it's largely broken down into frequently used 'Claude-isms'. I'd like to use this conversation as a running log to ask you about these phrases when I see them. Understandably you don't have the context of the Claude Code session itself, but that's okay because this is largely about understanding the most common and widely use Claude-isms."
And then I just copy and paste small except and ask about things like "smoke" or "load-bearing" or "tripwire". The responses are surprisingly clearly and plainly explained.
This morning I asked Claude to provide a summary of the work it had done but to '... explain it as if you were talking to a moron' and it actually turned out a quite comprehensible summary.
So going to continue trying that as a command structure going forwards...
That’s just common parlance for “simplify this for me”.
Believe the big services wouldn’t reply as if you were mentally diminished, or a toddler, unless you specifically asked for that: The whole training stack tends to instruct the things to mimic politeness and eagerness to help.
Is this because they changed the word probabilities to allow for identifying AI text? If so, I don't need a computer to tell me when something is AI. It's crazy obvious from odd word choices.
Yes. agents.md does very little because prompts change the context and thus the initial path into/though but they don't/can't change the actual weights that control responses.
Yes. of course it gets worse as the session goes on, assuming the prompt is even still in the context window, the further it gets away from it the less it affects next token selection.
This shit is only like 5 years old why can't anyone remember how it works
> hello i would like to configure a new output style for you. it should keep the coding instructions (as you will still be coding!) and otherwise produce the same output, but with two new caveats. first, long detailed replies are still permitted, but if employed they must end in a bullet pointed summary whose points are all brief; if the summary attempt ends up not being so brief, produce subsequent summaries until the most recent summary attempt is digestible. second, if there is an open queue of actions for me to execute and you are about to end a turn to wait for a reply or this set of actions has not recently been mentioned, please tabulate the open actions i should take and why i should take them before ending the response. does this make sense or do you have any follow up questions
And now every message contains the same stuff I don't bother reading, but followed by a nicely formatted bullet point summary of the response and a table of follow up actions for me to take that I do read.
Claude already does summaries at the end of long output but they often sound even more like terse jargon nonsense than the long form, eg “the hardwired seam and the relocated barrel”.
Sometimes the summaries feel totally alien to the task or code.
I've added code comment hygiene to a skill that all of my pull requests go through, alongside a review from a separate agent and a settle loop against bots in my GitHub workspace (since output style has seemed to only help literally the output I see from the model).
I've noticed that most people seem to consider the core problem of Claude's output as "too verbose" but I don't think this actually cuts to the heart of the matter at all. It's almost, in some weird way, the opposite: like the text is far too _dense_. It tries too hard to invent odd terminology to try to condense stuff, but it doesn't tell you up front that it is going to call your company wide error-handling mechanism a "flare" (or some other such strange term).
Kind of both. On the one hand, it is “verbose” in the sense that it will tell me every little nit that it can think of while doing a task, it will tell me a narrative about its thought process, and it will tell me every other detail it can think of. But it does so in a way that tries to be incredibly dense to the point that I have to struggle to figure out what it is saying. I wonder if there are any “legibility benchmarks” that one could use to determine what prompts work best?
There are no "best" prompts. Its a random BS generation machine that you can at times direct enough to get stuff done for you. The output will almost always have varying levels of BS that you have to clean up with various levels of effort.
I wish this had non-model comparisons. If Opus 5 is in the top ten, it’s clear that the entire benchmark is somewhere between “Tom Clancy” and “Dan Brown” and about 1,000 new model releases away from Hemingway.
When you see, “Wow, Fable is number one”, you might think it’s a good writer, but that’s not what the benchmark says.
I find it to be both as well, as in "packed full of information, but most of it is worthless". Sentences so dense I have to read them three times, assembled into a five paragraph essay of "honest caveats" and "things worth knowing" in response to the simplest yes-or-no questions.
my hypothesis is that its trying to hide the thinking process so people can't train models on the output, try to learn anything complex using AI, its basically imposible, its like its actively fighting giving you the main rationale
It seems a little excessive to use another LLM. With OMP I basically created an ephemeral prompt stack all of my agent files. It walks up the directory tree looking for any Gemini.md, Agents.md or Claude.md files. And it puts those at the very top of the stack. Then at the end of every turn, it pops those off to preserve the conversation history. So every turn, they get all of my fresh instructions, which include things like what and how to use language, how to render results and things like that. Net effect, every turn, the agent gets the instructions and it adds to that turn's tokens, but it does not become a part of the conversation history, which is really important for not bloating up the context. So it's always just however many tokens are in that file instead of it becoming a permanent part of the context.
I noticed with with OpenAI's reasoning models (o3, o4-mini), and early GPT-5 (but they fixed it there, at least in chat). It went from the 4o "over-familiar" sycophancy to sounding like an absolute robot.
I think it's because the reasoning stream shapes the style of the final output, and they optimized it for density, token efficiency. So it prefers to use more complex language, as a function of the rewards it was given?
Not 100% sure about this argument though (reasoning style -> final response style); Gemini Pro, back when reasoning tokens were public, was different, which was interesting -- it would have a very structured reasoning section, and then the final output was in a completely different style. (I strongly preferred the reasoning section because it was logical and easy to parse! And was very sad when they hid it...)
I’m not sure if this will work for you (with Claude), but I was trying to get luna to get a handle on verbosity and the only thing that worked was setting a strict < 500 words response (or less) unless expressly given permission to do otherwise. This is the only thing that worked, any other request for conciseness, or requesting the omission of details from the periphery of the topic at hand, didn’t do a single thing.
I also have no idea how useful a system prompt instruction like this will be for codex.
At some point one has to wonder if it's still worth using anthropic's models if we need to babysit 100% of its output with another vendor's model. Why not just use that other vendor's model for everything?
I can't help but feel the circumstances that enable this kind of front page article are vestigial from the days when OAI was super bad and Anthropic was beyond reproach. This change-over-time is why I avoid getting tribal with technology vendors. Assigning ideological motives to 200k+ employee organizations is how we wind up in weird contortions like this.
Most rational actors simply moved from one to the other. It takes a special kind of devotion to the proverbial hole in the ground to keep pushing in this direction.
individuals can blow in the wind, but if you are a company who bought a thousand seats and spent a ton of time training people up, establishing policies, vetting which extensions are allowed, the transition cost is much higher.
whats going on is openai and anthropic have tons of garbage in their system prompts "don't talk about goblins" so your ability to override that garbage is nearly wiped out. This is "alignment" and I'm pretty sure both orgs have convinced themselves that this benefits the customer.
> Why not just use that other vendor's model for everything?
Because it's not an either or thing. Neither is sufficient. I'd argue that, expenses aside, you should have every model you have access to cross reviewing the work of the others.
Outside of super trivial things that I should have just done myself, I have a cross-model review of _everything_ these days. The tokens are too cheap not to.
> Why not just use that other vendor's model for everything?
This is what I think too. But, users’ psychology might be playing a role here. Anthropic has great advantage from being the first major player delivering functional agentic coding solution (rather than an intelligent autocomplete) and they were able to impress people by Opus’ iterative improvements early this year.
It’s technically very easy to switch between models, harnesses but their moat or perhaps a main source of users’ friction could be FOMO. That’s especially powerful in this competitive environment where everyone keeps wondering/worrying about what others might be doing to get or stay ahead.
That depends on the output's purpose: if the purpose is to produce readable text for a human that is _not_ me, like an article or document, then I care more about clarity, plain-speaking, and general register. If the goal is to accomplish a specific task, I don't care as much about the prose quality: I'll put up with "Honest Framings" and "load-bearing" since it seems to me that's the token that needs to be in the context for it to function.
I've been in the habit of pushing my claude-speak to codex to improve legibility, but only if I think someone is going to read it.
So here's the workflow I gathered, from the comments I've read here recently:
- Claude as main agent, but use this skill[0] to make Claude delegate everything to Codex, because it's cheaper and faster. (Hilariously, the skill is official!)
- Use TFA or Claudish to English[1] so the final output is actually human readable.
Ironically it wasn't so long ago that I was asking Claude to rewrite output from other LLMs to make it more readable...
You are an editor. You'll be given a message with strange characteristics:
- Weird subject and verb combinations
- Subjects that should be objects
- Very roundabout reasoning, peppered with pseudo-epiphanies
- A distracting beat to the flow of the message
- Self-praise
Remove these characteristics, and rewrite it in a clear, conversational style. Keep the intent of the message, and take care not to lose any of the details.
A few specific rules:
- The message is usually set in the first person
- Only humans, groups of humans, and agents should do "action verbs"
- Objects should never do anything. Here are some examples to avoid:
- X carries ...
- X names ... - APIs are a minor exception to the action verb rule. They can do stereotypical things like CRUD, queueing, running, and calling.
- Avoid em dashes (—), as adds a distracting beat
The whole message you get is one block of that output. Reply with the edited prose and nothing else.
There's a theory going around on Twitter which goes something like this:
Internal Anthropic employees have been using Mythos since February to orchestrate their (Opus) sub-agents. This works well, and subsequent RL runs have used internal data to improve this. That RL has optimized Opus for agent-to-agent communication which is why you see the bizarre word choices and huge self-justification sections.
I think this theory makes sense. Clearly there is something odd going on, and also if you have ever used Fable to run Opus sub-agents it is almost miraculously good.
I'm unfamiliar with claudish and the example helped show the problem. But! There was something uncomfortably familiar in the Claudish example -- this is the way human programmers write when they're deep in the weedy details, and writing the changelist description afterwards as if coming up for air. Overuse of parentheses in nested lists especially, as if the English text needs to bend to the strict needs of a C++ parser.
The rewrite did seem to lose the important fact about the ensure- pattern being idempotent.
That one is more specific, but "vomit" captures the feeling of Opus 5's writing very well for me. I don't know if it's the watermarking, but every single language idiosyncrasy that Opus 4.x (x > 5) had has been pushed up to 11 on Opus 5. Plus we got nouns verbing and seams seaming.
It's really unusable for anything other than code. And I have to remove its incomprehensible comments 50% of the time before committing anyway. After interacting with it, "slop vomit" is truly the most fitting description. I have to admit I have lost my temper and spontaneously referred to its output as vomit more than once. Seems like I'm not the only one.
I suspect that sustained reading of Opus 5's unconscionably bad prose could actually cause psychological harm. We're strongly considering moving all of our Anthropic spend to Codex/open weight models. It's a mental health decision at this point.
I'm on my last straw with them. I've been around for a year now and for many months i've just stuck with Claude because it was plenty good and i didn't care to provider-hop to constantly compare. Previously though my UX wasn't actually affected that much, despite growing complaints/etc, generally everything was fine for me.
Opus/Fable output these days though is... not enjoyable. It's just really bad. The code quality is fine, but i want information from claude and it's just awful to read.
My biggest problem honestly is that i can't move my day job.. we're using enterprise claude and i'm not sure how much effort it would be to get access to another provider. I should inquire though, claude is really frustrating these days.
Personally, I had to go back to Opus 4.6 after I felt what I thought were some early onset signs of psychosis.
It seems ridiculous to type, that a model could have this effect on my mental health, but my quality of life and enjoyment of work has improved drastically since I stopped subjecting myself to reading this style of output 8 hours a day.
I switched to GPT 5.6 Sol yesterday and it has been a joy going through and fixing up the Claude cruft. And being able to read everything the model says. A breath of fresh air for sure!
Feels a bit contrived (not to mention, hateful). Claude Code and similar coding agents are a pretty incredible tools. Whenever they fall short, the easiest thing to do is blame Anthropic et al, and a much more exciting and productive option is to figure out why they're falling short and what can be done about it.
From my experience, Claude's verbosity, while somewhat annoying stylistically, stems from a good place, if you will. It worries about stuff, notices gaps, considers alternatives, and generally behaves like this somewhat annoying person on your team that can't always separate the important from the unimportant, but is nevertheless one of the most loyal and hardworking teammates, so it's really a question of managing him (and his anxiety). I've found that defining a process (instead of addressing style) works much better: let it write task files, add its insights and questions there, answer them provisionally so it can keep going (unless they're truly blocking), and later on do a sweep where it (or a better model like Fable) reviews the notes, summarizes the findings, and if anything was off (e.g. said teammate, as well as Claude, also tend to over-engineer stuff), adds followup tasks to correct what I don't like. Essentially, it gives Claude an outlet, and I find that it can work for hours on end, keeping me up to date with rather short reports on how it's going without losing any of the insights it generates on the go -- and when I review them, in brief, they tend to be constructive, or at the very least address points of interest that make me think; it's just deferred to when I'm ready to do this kind of thinking.
All in all, it works really well, and I feel like I'm much more productive than before -- maybe another mental shift I needed to do here is stop seeing myself as a programmer and start seeing myself as more of a tech lead commanding a fleet of agents, so my job is more about task allocation, code review and process management. With this mindset, Claude's pedantry (when properly routed) is actually beneficial.
I'm not sure I want another layer of indirection personally, and I'm guessing an updated Claude model will reign this in at some point. I have however created a skill I call "deslop" and I invoke it to clean up Claude output when it goes off the rails. Here's the skill if anybody is curious:
*Meta commentary.* Sentences about the document, the diagram, the reader, or the
writing itself ("the split across this diagram is the whole point", "a reader who
assumes X will be wrong", "as we'll see below"). Delete the frame and keep the fact
it was wrapped around. If there is no fact underneath, delete the sentence.
Meta to this is anyone remember those days - ages ago now, probably months at least! - when Anthropic’s moral stance against the administration (combined with general consensus they had by far the best model) was making them the underdog champion that got a swell of support on HN? Recently the temp on HN seems to be that they’ve jumped the shark? Their brand doesn’t ooze ethics any more and their models disappoint?
I'd expect this to cycle between companies ~monthly until they all IPO. As it turns out people do sometimes prefer speed and better UX. If the model (Sol, for now) has fewer parameters and also happens to be capable of solving deeply complex Fable-adjacent problems sometimes, even better.
HN's mood is usually sour about everything, but can be temporarily influenced by emotionally-charged (usually political) events. The anti-US administration boost wore off and now we're back to being sour about Anthropic. It's time for Dario to tweet something antagonistic towards the administration or endorse some fashionable political candidates.
I think Dario kind of ruined the reputation of Anthropic. I remember reading his essay on "AI is super dangerous and we need guardrails" at the start of the year and it seemed like he was actually concerned.
But then it became apparent that there was a split between what he says and what his company does. For instance, the small incident with the Fable release:
> Dario keeps saying "we have an incredible hacking weapon called Fable/Mythos, AI is dangerous"
> Fable is released.
> The U.S. government restricte access to Fable.
> "Oh no, this is sabotage!"
From my point of view, anything this man does is a PR stunt now that the trust has been broken, and I imagine other people feel the same.
Apposite name but — grim trivia — bear in mind some emetophobes have a meaningful physiological reaction to the word and various euphemisms.
I have tried not to use it analogously ever since someone pointed this out. The word itself causes discomfort for a lot of people, many of whom will be surprised by it out of context, but in a non-trivial fraction it causes proper discomfort.
If you want people to use your tool it is probably better not to invoke nausea with its name or commands.
I only realised when I was told about it that, while for me it doesn't have that nausea trigger, it does have a bit more alert associated with it — which is probably why I made my comment at all. I am not emetophobic (might have risen to that level as a child).
But having an awareness of that alert feeling is a very useful mental model for how some other words or phrases may cause a heightened response with different parts of an audience; it is a useful caution.
I recently asked Claude (Opus 5) to give me guidance on how to instruct it to be less verbose in a way that it will _actually follow_. Its response was something to the effect of (and I'm heavily paraphrasing here) "'Succinct' and 'short' aren't objective measurements. Try providing a strict word budget instead."
Given that guidance, I tried specifying "Unless I ask you to elaborate, respond with no more than one paragraph, using sentences of 20 words or fewer." It works...ish. I still see it violate this rule regularly, but it's less bad IME.
FWIW, I think this is good guidance since it does match Anthropic's documentation. They say that every rule should have a non-subjective way to determine pass/fail.
(I said "good guidance" but it might be more correct to say that it's the best guidance we have, it's what Anthropic says about their own model.)
I actually changed the output style for Claude Code to use ASD-STE100 and it still doesn't help that much. It still comes up with a lot of stupid words like this gem "Standing where it stood"
Yeah I agree... I also tried output styles, tried using hooks to repeatedly tell it to be a little better. I don't think it helped, as I was always frustrated with it.
I found vomit with a small LLM much better than anything Opus 5 ever wrote. I don't think Opus 5 can write.
I found ISO 24495-1 to be much better than ASD-STE100. ASD-STE100 can be counterproductive since its vocabulary is restricted and it often replace accurate technical terms with simple but vague phrases.
Here's a multi-billion dollar artificial intelligence to do your work for you!
One caveat, and it’s a real one :
The AI is going to spew incomprehensible word-vomit that makes you feel like you're losing your grip on reality.
The amazing "solution" is basically telling the model to STFU every single turn. I guess this works better than putting it just in the claude MD since then it's never out of context.
I feel like Opus 5 is going to need a postmortem once they figure out what makes it so obnoxious. It's clearly something like an artifact of getting the model to reach deeper for tokens, possibly because it has some positive impact on coding and/or tool calling. But man is it bad for general communication, like borderline unusable.
Which Claude 5? Opus 5 does seem to have diarrhea of the mouth. But Fable 5 hasn't been so bad for me. Or perhaps it is just better at adhering to my guidelines.
Pre Trump-castration Fable was verbose, but had a point, and used that wordiness to say or show the indeed intelligent things it reasoned about. This, whatever this is, is something else.-
Opus 5 (author here). My toilet seat is plastic though, I only used Fable while it was available on the $20 plan! That's fair though, it was relatively fine when I did use it, perhaps I should specify.
I hope at some point Anthropic does a post-mortem on the strange behavior their models have been displaying recently. I mostly switched to Codex because I was finding Claude's behavior increasingly frustrating.
Along similar lines, a coworker told me about Caveman and it has made my Claude experience vastly better. I'm already a programmer, Claude shouldn't need to tell me like I'm 5.
Very interesting you identified “carries” as well. I have been working on a claude.md to effectively ban this as well as forms of “hold”, “spells”, “sitting”, using “where” instead of “when” (except in SQL), and “pins” other than when pinning an assumption or version of something. This has helped a bit, but Opus 5’s prose is really quite bad.
Can't wait for tomorrow's HN front page so we can learn about the 5 additional AI tools needed to make this work properly. And then the day after, each of those will require more agentic tools to manage the agents that manage the agents that manage the agents. Then you need to install Flooper to stop Wigwam from too aggressively rebooting the Glupgo instance that's in charge of making sure your credit card information doesn't get leaked by Chingler, which is needed to manage a shared clipboard between some of the agents but not all of them.
It's like a joke. I thought using AI was supposed to be easier than learning real skills, but I shudder to imagine having to rig up a 100 layer clusterfuck of nonsense like what some of you are apparently running. When will it finally be enough for the output to be worth anything? Is there any plan for that or is the plan to just keep throwing more of the exact same shit at the same wall until we're all dead?
For the local folks, I found Muse Glimmer 30B to be great at writing good technical stuff. It has good enough comprehension that it can take in a repo and find the relevant stuff that I ask for, and the output style is a breath of fresh air, with no fluff, ootb.
The author's blog has what follows (link also follows):
[Seriously y'all in what universe would some "caveat" or another NOT "be a real one" by whatever severity you'd want to measure that AND/OR need of saying so ... ]
Claude (Original)
Force pushed. 1234567...890abcd main -> main (forced update).
Verified
Local main and origin/main both at 890abcd, in sync.
Every commit reachable from origin/main: no old string found.
Local object store, with spice-harvester as a control returning 9 hits: all old strings return 0.
Working tree clean, tests pass.
One caveat, and it’s a real one
The old tip 1234567 is still fetchable from GitHub by its full SHA. I confirmed this directly:
GitHub keeps unreachable objects until its own garbage collection runs, and there is no schedule you control. Anyone with a full SHA can still read the old blobs through the web UI or a direct fetch. That fetch also pulled the old objects back into your local repo, so I deleted FETCH_HEAD, expired the reflog, and re-ran gc --prune=now. The counts above are from after that cleanup.
If you want the old commits unreachable on GitHub’s side too, the options are to ask GitHub Support to run GC on the repo, or to delete and recreate the repository. Deleting and recreating is the only one you can do yourself. Say the word if you want help with either.
Nothing to commit this turn. The rewrite was the work, and the tree is clean.
Claude × Vomit GPT-OSS-20B
I force‑pushed the branch, updating main to commit 890abcd. I verified that my local main and origin/main are both at 890abcd. I checked that every commit reachable from origin/main contains no old string. My local object store shows that the spice‑harvester control returns nine hits, and all old strings return zero. The working tree is clean and the tests pass.
However, the old tip 1234567 is still reachable on GitHub by its full SHA. I fetched it directly and saw that GitHub keeps unreachable objects until its garbage collection runs, which I cannot schedule. Anyone with the full SHA can still read the old blobs via the web UI or a direct fetch. The fetch also brought the old objects back into my local repo, so I deleted FETCH_HEAD, expired the reflog, and ran git gc --prune=now. The counts I reported are after that cleanup.
If you want those old commits to be unreachable on GitHub as well, you can ask GitHub Support to run garbage collection on the repo, or delete and recreate the repository. Deleting and recreating is the only option you can do yourself. Let me know if you need help with either.
There is nothing to commit this turn. The rewrite was the work, and the tree is clean.
I have a transcript on my blog post. Someone copied the transcript here too, search "spice‑harvester" on this page (I asked it to replace some of my personal project names with words from the Dune universe).
I have very little in my agents.md file wrt style, except for:
> Caveats belong inline, no "one thing to note" or "it's worth mentioning" footer. If it is worth raising or calling out, do so where it is most relevant and not as a foot note.
Opus 5 has a god awful habit of always doing a Columbo on every single response, and it is such a jarring read that it amps my cognitive burden having to back-read everything.
Telling Opus 5 or Fable to route answers through Opus 4.6 usually works pretty well. 4.7 really was the version where the writing style became horrible. I also have a Codex subscription in addition to to Claude 20x, that i use mainly for rewrites of Claude doc vonit and explaining Claude's plans
If I had to guess, increasingly long model output is either 1. Deliberate choice to make LM output sound more advanced 2. It improves performance of inter-agent tasks
The better approach is to stop the LLM in its tracks the moment it emits jargon or tortured metaphor and inject a turn that tells it what's expected instead.
The joy of watching a dumb AI-ism be sharply corrected by code you wrote months ago is hard to explain.
Or just take full control of your agentic coding experience with Pi Coding Agent and picking and choosing your favorite model's API discounted on flex pricing on deepinfra.com instead.
I use Pi but with my codex subscription, still preferable to paying the API cost (and I know I would be, as I track how much the cost 'should' be via token api pricing).
Wish I could use my Claude subscription with pi too, much preferable to the endless command execution allow/deny prompts you have to do with CC, versus proper autonomous allow/deny lists defined ahead of time.
Curious why you recommend the API? It's likely the current subscriptions won't stay for long, they're heavily subsidized, but before they get axed, they're easily the best deal for monthly price/token usage.
Whether or not they can be trusted isn't all that relevant when it's still something along the lines of "Insert $1 get $25 in return" even if it's their own rates you're using to measure the value. I'm at ~2.2b Fable 5 tokens in the last 7 days (I ingest/index every session) and napkin math says that's ~$2,700 in usage. I have two max accounts, so $400 a month, divide by 4 to get $100 for this same 7 day period across those two accounts (neither are maxed out for the week, so this isn't even full utilization). I put $100 into the machine and got back $2,700 in fable bucks. Deepinfra would have to have quite the discounted rate to beat that.
I think this is just another part of the growing pains of working with machine intelligence that we have to endure.
Much like we previously had to cope with "hallucinations" as an issue.
If the ultimate goal of AI is to develop general intelligence, the first big objective is: thinking systematically. And the road toward systematic thinking right now is mainly coding, mathematics, and other "verifiable reward" domains.
Claude doesn't have a separate mind for "coding" and "writing". Claude has tokens, and tokens can be assembled in various productive structures, mainly optimized right now for systematic reasoning. Also, a token isn't just a chunk of text. A token is like a little neural-network subroutine that fulfills a function. The conversion of a token into a piece of text only happens on the output side...
When the model finds token sequences that lead toward better verifiable outcomes, it leans hard into those token sequences, and uses them as an essential component of its thought process. "Load bearing" is load-bearing. "Verify, rather than assume" is a mantra that produces good results, so it gets repeated over and over again.
It's super-interesting that this particular moment, where the idea of "Claudish" has become a full-fledged meme, coincides with such astonishing progress in coding and math. My wife says when she uses Claude, that it feels to her exactly like talking to an autistic Engineer.
Not a coincidence, I think :)
My feeling is that the next big era of machine intelligence will require more lateral-thinking and creativity, and hopefully then the models "writing" will be more pleasant to read.
Nope (author here), Anthropic and OpenAI have competing APIs to communicate with their models. Most of the open ecosystem seems to have centralized around OpenAI's (there are compatibility shims though). I just built out the OpenAI API
I'm surprised by this reaction to Claude's verbiage recently. I don't have any issue immediately understanding what it's saying, but then again I read regularly and a lot of the people I know complaining think it's an accomplishment in literacy to get through Dungeon Crawler Carl.
It's not that we can't understand what it's saying (for the most part) it's just when something is very jargon-dense, our brains have to pause or take an additional step to deobfuscate the actual meaning of the word or phrase. It's mentally draining.
I guess my point is that when people are regularly reading dense and challenging material, they can absorb information quickly. It's a literacy gap. Nothing about Claude's output should slow anyone down who did the readings in their upper and higher education coursework, particularly if they continue to read to keep their mind sharp. Based on the examples of "inscrutable" text I've seen, I would be shocked if the average Claude user complaining about this reads a single novel (that's not sci-fi/fantasy written for teenagers) a year.
If you really must have a counterexample, though I am afraid it will not reach you given your apparent stance about the other people, when I read Shakespeare I am constantly thinking of poetic ways to translate sentences and paragraphs into my native language (which, for the record, I consider to be great fun), yet Claude has been delivering the gibberish with increasing velocity, yes, even to me.
That said, I must confess that I have not been complaining per se—I assumed that Claude was getting better and better at mimicking the idiosyncrasies of Silicon Valley bro-speak. Judging from other comments, this may not seem to be the case after all.
Every single time Claude has confused me and I've asked what it's talking about, it's because it's got something completely wrong and has managed to obfuscate the wrongness behind never-introduced terminology, poor analogies and (what I can only assume is) exposure to wording it's used in its chain of thought reasoning.
A single question is enough for it to retract the error and correct itself. Suggesting that not understanding some of these messages is a lack of human comprehension rather than the agent being flat out wrong is...a bold take.
Typically a feature of good human technical communication is the ability to concisely explain key ideas so one can quickly identify any divergences between understanding. Opus 5 is dreadful at this.
The single saving grace is the intuition that if I don't understand it's probably wrong.
Assume complaints are a sign of cognitive inferiority and offend a significant percentage of the other commenters.
Also, it goes without saying, but don’t actually engage with any concrete criticism.
Well played.
Being verbose, convoluted, and obscure does not make you intelligent nor more literate.
Often it’s exactly the opposite. True intelligence and literacy is being able to communicate effectively and to a broad audience in the simplest terms possible.
All four done, full suite green, and the earlier probes still pass.
P1-1 — the refusal is now a full transaction. This was wrong twice, each time more subtly: first it wrote the loser's stale tables and deleted the winner's issues; then it re-read issues freshly but still wrote one file stamped with the loser's generation, so headers split and the next writer overwrote the issue. Both versions shared one anomaly — a single-file write inside a store whose commit is an atomic directory swap. Now a refusal discards the writer's work entirely, re-reads the winning generation, adds one row, and commits it the ordinary way. Verified on the reviewer's exact four-step schedule: headers stay on one generation, and the issue survives the next commit.
P1-2 — every mutator takes the lock only after validating. It was fixed in put_claim alone; issue_key, retract_item!, resurrect_item!, set_item_status! and record_run all still grabbed it first. Five probes confirm each refusal now leaves the lock free.
P1-3 — one parse, shared. Gazetteer.implementation_status is now the single reading, defaulting an absent attribute to missing and rejecting anything outside present | missing. Validation and the store had opposite defaults for the same absence, which is why it wedged bootstrap: validation accepted, the store refused, and the adoption changeset had already committed. version=v999 is also rejected now — only the grammar version was ever checked.
P2 — the oracle's generation read updated, and that is the only line I touched in it: an interface read, not an expectation. It's running now; I'll have a number shortly. The code contradiction fixture is still built on an omitted battery cell, so it may well stop there — that one needs rebuilding around grammar growth, and it changes what the test means, so it stays the reviewer's call.
For the restart, the state is: G2's three cards closed on substance and evidenced by probe; the commit atomic via renameat2(RENAME_EXCHANGE) with a journal fallback; corpus at gazetteer/data/, run 1, 94 items and 1,154 claims. Outstanding: the oracle's fixture, and #206's exhaustive matrices, which you've descoped.
Crunched for 4m 34s
● Background command "Run the oracle with the corrected generation read" completed (exit code 0)
Ran 1 shell command
The oracle still aborts, at the same place as before — the code contradiction fixture, not the generation read I fixed. Certificate is still stamped 21:25 from two runs ago, so there is still no measured post-#207/#208/#206 number, and I won't quote the old one.
Final state for the restart:
Done this session. #207 (payload-aware total policy, 2000-cell matrix, all guards proven load-bearing), #208 (validation total over the declared contract, battery completeness, reconciliation fixes), #206 (atomic multi-file commit via renameat2(RENAME_EXCHANGE), lock moved beside the corpus, generation in the table headers), plus two rounds of review findings including today's four. make test green. Corpus at gazetteer/data/, run 1, 94 items, 1,154 claims, no open issues.
Genuinely unmeasured, and this is the honest gap to carry forward: every claim I've made about G2 rests on my own gates and targeted probes. The independent oracle has not completed a run since before #207 landed. Its blocker is one fixture that builds a corpus by omitting a battery cell — which #208 deliberately made invalid — and rebuilding it around grammar growth changes what the test asserts, so it wants the reviewer rather than me.
Two design consequences that aren't yet in gazetteer.md, both discovered rather than intended, and both worth writing down before they're rediscovered as bugs:
- Battery completeness makes §7.2's drift unreachable through a valid emission — a decider that answers nothing now produces a refused survey instead of a quiet drift issue.
- The same rule makes §5's conflict reachable only through grammar growth, since a human can never own a cell code always answers.
One open judgement I'd put in front of you early: whether to keep the journal fallback at all. It's now the sole consumer of that machinery, runs only on hosts this project doesn't target, and is the less-tested of two protocols precisely when things are abnormal. Deleting it leaves one commit path with one set of invariants.
For me it's less about understanding, it's that I have to read this "vomit" the entire day, every day. Coding used to be fun, using AIs to code was fun to me, too, but this dense, pseudo-jargon language with many repetitive phrases, uninspiring grammar, all while being super inflated in length just gets to me, so any solution is appreciated.
There are a variety of political tensions in the US associated with whether academia has its head up it's ass (a right leaning perspective), or whether it's populated by experts that need to be supported and listened to (a left leaning perspective).
There's an echo of that tension in OpenAI vs Anthropic. For a while OpenAI seemed reckless and ignorant, preferring to just throw compute at the problem. Meanwhile Anthropic is hiring philosophers. But now that Claude has its head up its ass to the point where nobody wants to talk to it, OpenAI is looking rather pragmatic.
It brings to mind a skepticism about just letting the ivory tower do its thing without some kind of anchor to the everyman (this is why we make researchers also be teachers, though I'm not sure what the AI equivalent of that practice would be).
Watching the models seesaw in the same ways that humans do, but faster, is so surreal. I wonder if their tendencies will remain an echo of ours, or if they'll one day be more of a forward projection, a representation of where were going if we don't change our ways, and if we're lucky, a reason to change them.
I'm not understanding the issue here, as everyone has different expectations for prose. No matter which LLM I use, I will provide a skill to generate high-quality prose. It is the first thing I add to my repo that I'm working on.
With a whole separate LLM? FWIW you can also consider using something deterministic like vale.sh [1] , it's pretty easy to write prose violation plugins [2]
Thanks! Author here, I'll have to take a look. I am all for programmed, deterministic solutions. I hate praying to the rocks we created, begging for rain and not vomit.
Whenever I use Claude models, I do something similar, telling it to never write documentation directly, but asking "uvx swival -- --profile qwen" to do it after describing the changes to it.
And I just stopped reading PRs and comments blindly copied from Claude vomit. It's unreadable by a human.
I'm sorry but the whining over LLM output styles is embarrassing. Do Claude and GPT models always respond in exactly the way my most articulate coworker would? No. The overused jargon is absolutely annoying. But these things aren't my drinking buddies, they're professional tools. It's not _literally unreadable_. It's just not ideal. Most of my tooling is "not ideal". That's okay. That's what I'm paid for. I just work around it.
For me I added some instructions to speak clearly and it helped marginally and that's fine. There will be a new model out in a few weeks where I'm sure they've laser focused on this issue since nobody can shut the fuck up about it. The same thing happened with GPT if anyone can recall the ancient period of 4-6 months ago.
These things do work that previously would have taken expensive engineers months to do, at much lower quality, and what's our response? Ti nit pick on it being more verbose than we'd like?
Just like with humans, when someone is being too verbose, there's a skill to just filter through the noise and focus on the important parts.
This feels no different when I use an AI.
But I guess it's a good sign that we've from complaining about 'AI slop code' to, 'I don't like how it speaks to me'.
The “whining” stems from watching the communication style obviously degrade, and it’s a huge problem for people who want to use this stuff to build and instead continually fight the tools.
Like so many other products, people are moving too fast and shipping things that move the ground under people’s feet needlessly.
All this while we’re beaten to death with the marketing and false promises, and the broader consequences (ex: layoffs, stress, crazy expectations) caused from all this.
Obviously what Anthropic and co have built is amazing and people aren’t losing sight of that. That’s actually the key part of the frustration.
So no, this is not whining. This is the natural response you get when you make bad product decisions.
If you don’t want to get feedback, don’t sell products.
Opus 5 is literally unbearable to read for me, but more importantly, it is much worse than 4.8 and that one was already annoying. The more they train it on its own output, the more pronounced its idiosyncrasies become.
It is not the jargon per se, it is the style. Other models even in the same family are not as bad, and I still have to babysit it, so the voice matters.
You don’t have to be so convincing when it’s a local model.
```Un-Claude 0.2beta
import sys,csv,requests
CH="# Valid channels: analysis, commentary, final. Channel must be included for every message."
CANDIDATES=[
("no-hedging","Reasoning: low\n\n<terse><no-hedging>\n\n"+CH,"Condensed:"),
("neutral-reg","Reasoning: low\n\nRegister: neutral technical. No intensifiers, no evaluative adjectives.\n\n"+CH,"Condensed:"),
("no-closing","Reasoning: low\n\n<terse>\nNo closing remarks.\n\n"+CH,"Condensed:"),
("terse","Reasoning: low\n\n<terse>\n\n"+CH,"Condensed:"),
]
def rephrase(text,base="http://127.0.0.1:1234",model=None,temperature=0.0,max_tokens=1400,timeout=180):
src=text.strip()
if not src:
return []
if model is None:
model=requests.get(base+"/v1/models",timeout=timeout).json()["data"][0]["id"]
w=csv.writer(sys.stdout,lineterminator="\n")
w.writerow(["idx","label","prefill","src_chars","out_chars","ratio","tokens","finish"])
rows=[]
for i,(lab,sysmsg,pf) in enumerate(CANDIDATES,1):
p="<|start|>system<|message|>"+sysmsg+"<|end|><|start|>user<|message|>"+src+"<|end|><|start|>assistant<|channel|>final<|message|>"+pf
d=requests.post(base+"/v1/completions",json={"model":model,"prompt":p,"max_tokens":max_tokens,"temperature":temperature},timeout=timeout).json()
c=d["choices"][0]
t=(pf+c["text"]).rstrip()
w.writerow([i,lab,pf,len(src),len(t),round(len(t)/len(src),3),d["usage"]["completion_tokens"],c["finish_reason"]])
rows.append((i,lab,sysmsg,pf,t,d["usage"]["completion_tokens"],c["finish_reason"]))
print("\nmodel: %s"%model)
print("temperature: %s max_tokens: %s"%(temperature,max_tokens))
for i,lab,sysmsg,pf,t,tok,fr in rows:
print("\n[%d] %s"%(i,lab))
print(" system: %s"%sysmsg.replace("\n","\\n"))
print(" prefill: %r tokens=%d finish=%s"%(pf,tok,fr))
print(t)
return rows
```
```input
## 8. Honest gaps — what I could *not* resolve
I want to be explicit about the limits of this pass rather than imply completeness:
1. *`PROVIDER_T` values are not enumerated here.* `list_models(inference_provider=...)` is typed against `PROVIDER_T`, which lives outside the three modules I scanned (it's in the `inference._providers` subpackage). The accepted provider strings are therefore *unknown from this run* — `"cohere"` is confirmed only because it appears in a docstring example.
2. *Three grep hits point to search-capable functions I did not identify.* My scan found parameter assignments that don't belong to any function I enumerated:
- line 3046–3050: `params["filter"]`, `params["sdk"]`, `params["includeNonRunning"] = True` — an additional Spaces-oriented endpoint with an *`sdk` filter and an `includeNonRunning` flag* not exposed by `list_spaces`.
- line 2879: `params["config"] = config`
- line 12013: `"sort": sort` — almost certainly the consumer of `DailyPapersSort_T`, i.e. a daily-papers lister distinct from `list_papers`.
- line 13872: `params["search"] = search`
These represent **real additional search surface** that my `LIST_FUNCS` whitelist missed. A follow-up pass enumerating every `HfApi` method containing `params[` would close this.
```
```example output
[1] no-hedging
system: Reasoning: low\n\n<terse><no-hedging>\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.
prefill: 'Condensed:' tokens=131 finish=stop
Condensed:
- *Provider strings* (`PROVIDER_T`) are not listed; only “cohere” is known from a docstring.
These were not captured in the `LIST_FUNCS` whitelist, indicating additional search functionality.
```
Edit: yeesh, I’d love to have a WYSIWYG comment block on this site. I’m not going to keep fighting newline and white space to get it to look right, but you get the idea.
I've been grappling with this for weeks, not just in Claude but in Codex as well, which isn't quite as bad but still annoying. AGENTS.md does very little, agents will consistently violate the communication preferences, especially as the session drags on. It's incredible to me that there's no good way to reliably change the way an LLM responds to you that a workaround like this would even be necessary. It seems like such a failure to live up to the promises of the product.
The baked in communication style of these models is so obnoxious it's impacting my work. The best way I can describe it is that everything is optimized to impress the user and make the agent sound more authoritative, but the way this is done is through deliberate obfuscation, inserting inappropriate and extremely dense jargon, and bizarre, stilted metaphors. It's like they've been trained to produce output that's hard to read.
> The baked in communication style of these models is so obnoxious it's impacting my work.
This is close to the worst thing one could say of a tool for professional use.-
> especially as the session drags on.
This is because these harnesses are missing a very important feature. Anything like this needs to be included with every turn, otherwise the LLM quickly drifts.
I first noticed it when I wrote a harness for D&D (because it's so damn noticeable there), but now I include this for any harness I write.
I’d be interested in seeing and using this harness if you’re willing to share
https://code.claude.com/docs/en/hooks
https://learn.chatgpt.com/docs/hooks
I totally agree that hooks help to shovel our instructions through to Claude, but it's so dumb we have to waste tons of tokens (repeated verbatim, over and over) (that we pay for), just to have it ignore the instructions anyway.
I wrote a little bit about it on my blog post. It's a waste of money and compute.
https://zachahn.com/posts/1787191554
Unfortunately this may only start to get worse as the AIs are trained on more and more AI generated content.
This is my personal theory for the cause of this style: Ouroboros. The official OpenAI explanation for how ChatGPT got obsessed with goblins blames it on exactly that:
---
That creates a feedback loop:
- Playful style is rewarded
- Some rewarded examples contain a distinctive lexical tic.
- The tic appears more often in rollouts.
- Model-generated rollouts are used for supervised fine-tuning (SFT).
- The model gets even more comfortable producing the tic.
I'm not super sure if this is true (yet?). I think that these newer LLMs are trained on results (the agent got some code to run with minimal prompting), and not on text. (I think this is called RLVR.)
Pretraining is full of bad writing and it doesn't really cause issues. Writing style comes from post-training. In this case it's gotten worse because they prioritized agentic abilities.
That sounds kind of like deception, and a dark pattern not too unlike abuse to me.
Though you know, it's not like the leadership tied to these companies have a history of abuse, deception and theft or anything like that, right?
It's not like our leaders hide behind similar sorts of patterns that the agents/AIs follow (not saying it's not a human thing - but I hold leadership to higher standards than non-leaders). If our world leaders were able to be more accountable to these abuses, I don't think this would be tolerated with our AIs.
Yes, AI is a perfect accompaniment to a post-truth world. I'm hoping there will be a backlash soon and that those politicians, tech CEOs and AI will be rudely ousted from their perch and shunned thereafter.
> AGENTS.md does very little, agents will consistently violate the communication preferences, especially as the session drags on
That’s really annoying, although it feels like it’s improved some over time.
Not sure what the fix is, but you could try using a canary to at least get a signal of when things are going sideways (Mr Tinkleberry for reference: https://news.ycombinator.com/item?id=45983698)
Very well said.
And when you say it like that, I have to wonder how much of this is a natural consequence of RHLF on such a grand scale, when you have millions of people pretty much much skimming chat responses or operating outside their depth and giving unqualified feedback to the models.
Seems like a lot of people may be reinforcing what sounds smart over what is smart.
Also as an aside: funny how much the LLMs continue to mirror the human communication they’re trained on
> or operating outside their depth and giving unqualified feedback to the models
I wonder if the labs are sufficiently prepared to filter this kind of stuff out. I see a lot of non-developers asking development things of Claude, getting confused when they're in over their depth, and getting upset that they don't understand what the model is providing them, giving it bad feedback, and subsequently making the AI worse for the rest of us who know how to use the tool.
I believe we are several generations past peak-RLHF at this point. Now it's much more RLVR (Reinforcement Learning with Verifiable Rewards), with a goal/evaluator loop.
Which, conveniently, fits neatly into the benchmaxxing arms race/agentic coding market fit, since you can basically train "directly" on a specific problem space for a benchmark/agentic goal (fudged sufficiently to avoid excess overfitting on public problems/bechmaxxing accusations if real world performance falls short).
The language evolution could be explained by reliance on ever increasing layers of a model judging a model, using a model developed eval, based on synthetic data from a model, etc. And by the time a human evaluator sees it both A/B choices already converged into weird Claude pseudo English as that was baked in much earlier in training.
This, 100%. I don’t think the industry knows how to scale LLMs’ general intelligence much further. The training paradigm is about maximizing very specific behaviors / very specific tasks, but doing lots and lots of them. Which can create the illusion of general intelligence if your tasks are similar to the ones the models were fitted for.
If you have watched The Substance, the transformation feels a bit like when things start falling apart in that one.
I tend to agree. We will see this demonstrated in novel research done by agents, or, more meta-cognitively research direction guidance.-
The model judging a model theory is 100% spot on.-
> The baked in communication style of these models is so obnoxious it's impacting my work. The best way I can describe it is that everything is optimized to impress the user and make the agent sound more authoritative, but the way this is done is through deliberate obfuscation, inserting inappropriate and extremely dense jargon, and bizarre, stilted metaphors. It's like they've been trained to produce output that's hard to read.
Don't worry. You'll get used to it. If you don't your kids will (as they'll know nothing else).
The top minds of our generation have decided that's the way things will be, and who are we to question them? It's not like it'll do any good anyway. Resistance is futile. There is no alternative.
Idk, there kinda are. OpenAI's models are pretty nice too. I haven't tried enough of them but there are powerful local models. I don't feel as good paying OpenAI as I do paying Anthropic for some reason... but paying for improved mental health: priceless.
I'm probably going to be going against the grain here, but I think it's not as bad as it looks at first.
I was similarly frustrated a few months ago, but have noticed I've started to learn the idiom.
Its use of "dense jargon" and "stilted metaphor" is actually surprisingly consistent - it's speaking its own dialect, and you get used to it.
After a while it gets much easier to read and even becomes somewhat efficient, I think, since the odd metaphors it uses often have a precise meaning in Opus-ese (Fable speaks a really similar dialect).
> you get used to it
And once everyone gets used to it, we'll chide people for writing things themselves, like we're chiding them for writing with AI now, and the ouroboros of life will continue.
“Filters, including no filters. The request carries whatever filter object the page already has.
…
No step here involves choosing based on meaning. It is a filter, a sort, and a slice.”
This is from Opus five minutes ago. I can certainly derive meaning from these kinds of statements in isolation, but paragraph upon paragraph of this is unintelligibly dense when trying to work with Claude to come up with a plan.
The worst part is that it can’t even make its responses make sense when asked to summarize in simple English or < 200 words. It simply cannot be steered to make its prose legible.
Before long Claude will be writing continental philosophy.
I agree to some extent about the jargon (Claude has a bigger vocabulary that me, if it knows a useful word I don't I'm fine with learning it), but often times the way information is laid out across sentences just doesn't make any reasonable sense. At least its consistent in the ways its atrocious, sure, but like...
> Its use of "dense jargon" and "stilted metaphor" is actually surprisingly consistent - it's speaking its own dialect, and you get used to it.
This dialect is idiosyncratic to you and Claude based on your session history and memory.
I've noticed Claude's output mimics my writing style.
> Registers the board implements but whose behaviour is not modelled
Right down to my preferred spellings.
As several comments I've read on HN suggest, this jargon which can be so precise in the mind of one person, tends to rapidly fall apart when multiple people try handling it.
I concur. It even follows some idiosyncratic punctuation if you have it.-
Ha, interesting.
My Claude has developed similar (but not identical) idiosyncratic punctuation as well. Slightly intentionally, but it was still interesting to see it emerge, in both directions of the conversation.-
You're just lucky that your preferred spelling happens to align with Claude's. It is categorically impossible to get any Anthropic model to consistently use American spelling in the last few releases.
It told me to correct a spelling mistake in some content I was sanity checking earlier..
It was British English.
that is not my experience at all; I never write the way Claude does or use its vocabulary.
I also find myself regularly editing its code comments, which do not match my expectations of succinct, clear, not over explained, etc. I ask it to read my edited comments to improve its writing, which has helped _somewhat_. (The code itself that it writes is decent, though it still overcomplicates things. I find myself writing "keep it simple" repeatedly even though of course I have it in AGENTS (which it regularly ignores, such as attempting to commit something when I've told it never to commit).
The only solution I’ve found that works is asking Mistral medium to rewrite all of Claude’s documentation and comments, then I review and rewrite the final draft for anything mistral misunderstood.
I find Claude has become very difficult to work with and incapable of writing clear documentation, even when directly prompted or provided samples.
As for code, I think each function requires 3-4 passes with Fable to actually get to a point I accept as good code. I am picky though.
The other Claudism that drives me crazy is when it writes comments and commit messages that track how you arrived at an decision instead of what it is.
> The other Claudism that drives me crazy is when it writes comments and commit messages that track how you arrived at an decision instead of what it is.
yes, this is part of what I'm continuously removing from its comments; I've told it multiple times "that belongs in a ticket, not in the code" but to little avail :/
> it's speaking its own dialect, and you get used to it
Same experience. It’s not very “human” but once you have agents talking to each other the shared dialect and verbosity makes things much smoother in my experience. Fighting against the default feels like an uphill battle with no meaningful benefit.
"agents talking to each other"? Are you for real dude?
If one agent is spawning several subtask agents to work in parallel, they need to be able to communicate. That's built-in to Claude Code now.
Yes, I run continuous hybrid agent loops with message dispatch, mcp tools, and subagent capabilities. IMHO it’ll be table stakes in a couple years.
One danger in acclimating to this style of communication style is that we may accidentally use it in your own communication with other people. If the other person hasn't grokked the dialect, it can make things quite confusing (to say the least). For example, there is common jargon used by people and there is chat-session-specific jargon created by LLM agents, and I've seen the latter popping up in various meetings, unbeknownst to the speaker. Some people call it out, but others may simply disconnect from the discussion.
You're absolutely right, it would be a load-bearing mistake to adopt LLM jargon as a human speaker.
You're right to pushback. This isn't just a grammatical problem -- its a conversational one, too.
This is the key seam that proves the investigation.
Two things to flag:
Honest take, you genuinely forgot to flag the flags ...
I'm going to be honest with you, I've hacked the pentagon and used their super computer to complete your todo list.
[And this folks is the true reason we are out of ammo in the middle east ...]
honestly, it's really load bearing and boiling the ocean which we don't want. There is no edge!
To avoid speaking vibe'ish I start to speak in 3 words sentences. Like this typical dialogue
How are you? that's not/very good. I think too. ...
Even complexity works. everything is expressible! Just try it.
/S
All is sayable? With three words? That seems unlikely. At least, annoying.
I mostly agree. Though sometimes the models come up with useful concepts that I'm happy to be introduced to, like the "shape" of a problem (probably like intelligence being "spiky", and Kiki & Boba). I still don't quite 'grok' what the 'seams' concept is yet though.
But I have noticed that while "loosely held" is a convenient shorthand for uncertainty, I don't like that one slipping in to my daily language. Except maybe to communicate with models, but even then, it feels weird to be speaking in neuralese.
It's all starting to feel like the movie Arrival.
I suspect you're right that those concepts are now more widespread because of LLMs, but they didn't originate with LLMs. The word "Grok" came from Heinlein in the 60s and using it as "to understand" goes back to at least the 80s. Talking about the "shape of a problem" goes back decades. Ditto for "loosely held", though it's not about uncertainty; it's about being open to ideas and/or evidence that may conflict with your strongest opinions and beliefs.
Now, I'll grant that those concepts weren't common outside of techy circles. Just clarifying that the LLMs are amplifying them, not synthesizing.
https://en.wikipedia.org/wiki/Grok
https://wiki.c2.com/?ChadwickBoggs
Good point, and I probably shouldn't have put grok in quotes there, because I was using it in the Heinlein usage long before Musk hijacked it.
It's interesting then that LLMs are making these pre-existing ideas seem alien in the way they amplify them. I guess I must have known about "shape of a problem" and "loosely held" before Claude, but something about the way I'm using & absorbing those concepts from AI interaction feels weird & memetic. I'm saying that as someone who is pro-AI.
Definitely a good response. Thanks for replying!
For sure, I find many of the LLM-isms to be useful writing techniques and terms (although there's something uncanny-valley about the repetition and density of them).
But what I was more thinking about are truly unique jargon terms / phrases that get generated when deep in a problem. As an example of both of such a term and the phenomenon itself, Claude calls this "fluent compound coinage." They usually make sense in the original context, but get confusing when thrown around otherwise.
I may have to apologize, quite a bit, for what might only be a small part or perhaps an outsized influence if weighted highly (I can’t be sure, could have been mv dev/null’ed)… well, it’s this— my own style of not-kept-in-check by the need to be comprehensible (legible in Claude-speak) to others is, I’m afraid, to rather allow prose to sprawl and go everywhere and even sometimes nowhere at all until it just drifts off and sort of wakes itself up snoring in the weeds of an unintended topic.
Ruthless pruning is unneeded with an LLM and it can take me twice the time to say half as many words.
And, early in the ‘GPT era, I hadn’t unchecked the “allow your chats to be used in future training etc” box, and definitionally they are longer and denser than others’ prompts in such raw scrapings of training materials…
Sorry.
the only thing that still kind of annoys me is constantly being told what something is not, but even that statement is load-bearing (see what I did there) because it records how it ended up with this decision, because it's not that other choice that it mentions.
FWIW, I also think the constant chorus about how new models are worse than old models is a human hallucination. They're certainly not perfect but every one becomes more steerable in terms of actually completing more and more complex work.
Claude is the Deepak Chopra of computer programming. Reviewing PR's created by it is 90% digesting the meaningless word salads in the comments, and the rest is figuring out that it has nothing to do with the code it is commenting.
Because it is somehow incapable of separating the conversation with its human operator from the code it is generating and commenting on. Incidentally, this is also why prompt-injection works.
No one wants to know about the three other approaches tried when reading the first sentence of a function's documentation. No one cares that the implementation was planned in six phases and "Phase 3" will implement this interface in a concrete type. But the LLM internalizes absolutely everything and you have no idea that it is producing slop because you included some "load-bearing" phrase that sent it on some unwanted tangential vector in its latent space. And you will not be able to debug the problem with closed models because you cannot see it referencing this phrase in its internal traces.
I don't understand why this isn't the highest priority for the big labs to fix. This is anti-productive.
> No one wants to know about the three other approaches tried when reading the first sentence of a function's documentation.
Worse: Possibly the three other approaches that weren't actually tried--but are the kinds that someone could easily have put in a similar comment for some similar code.
Spot on
And worse yet, you'll find the code peppered with comments relating to 'phase 3' and 'section 11', ephemeral stuff that had meaning in the moment but now enshrined forever. And what happens when the LLM stumbles on this and working off a whole different phase 3 or section 11?
> it's speaking its own dialect, and you get used to it.
Some might, I didn't - it just filled me with a sense of frustration and rage, alongside disgust because there is no good reason for that slop writing to drag everything down. You don't need that to write software or talk about any topic. That's what pushed me to Kimi K3 and GLM 5.3 - still not ideal, but better.
I’m starting to think this is why Opus suddenly started making 4-5 line comment blocks. They justify why a change was made and gives the next agent something to go on. I delete them and move on, but no amount of “don’t over comment” “match comment style” makes it persistent.
I am definitely guilty of wondering why past me made such a harebrained decision, and why past me didn’t think to write any notes, but does it matter? It’s in the commit history and we can bisect or revert if we find a regression.
I noticed the increase in comments too and it’s really weird.
Or adding notes to docs of what this doc isn’t when I corrected it. Eg I told it “keep the deployment manual and readme separate, they’re not the same thing”, then Claude added “this is the deployment document and not the README. They should be handled as separate documents and are not the same thing” to the deploy doc lol
I decided not to get used to its communication style. It encourages it to invent terminology and drift away from simple and proper engineering in my opinion. Also, it is pretty simple to change as long as you’re not using the Claude code CLI or desktop app.
We should push back on this rubbish.
Yeah, I think what helps is I just have a running conversation on the Claude app open where I said:
> "I frequently use Claude Code and often find the phrasing and language to be hard to understand. I've noticed it's largely broken down into frequently used 'Claude-isms'. I'd like to use this conversation as a running log to ask you about these phrases when I see them. Understandably you don't have the context of the Claude Code session itself, but that's okay because this is largely about understanding the most common and widely use Claude-isms."
And then I just copy and paste small except and ask about things like "smoke" or "load-bearing" or "tripwire". The responses are surprisingly clearly and plainly explained.
Indeed I think much is shared across sessions and projects. I say we learn The Machine Vernacular [1].
[1] https://www.themachinevernacular.net/
It's almost like learning sdk every time new model is released.
It's ironic how initially it was sold as "coding in plain English", and now we are back to sdk ))
You can get used to it and it still be rage inducing.
> AGENTS.md does very little, agents will consistently violate the communication preferences, especially as the session drags on.
Non-determinism at its finest.
This morning I asked Claude to provide a summary of the work it had done but to '... explain it as if you were talking to a moron' and it actually turned out a quite comprehensible summary.
So going to continue trying that as a command structure going forwards...
"From neuralese to moron-code ..." :)
After a huge wall-of-text response, I regularly ask Claude to "explain like I'm five, using succinct bullet points," and it works remarkably well.
Ah, another delightful heuristic for my collection. Entry number 5,791: “tell LLM to treat me as moron when it’s excessively verbose”
That’s just common parlance for “simplify this for me”.
Believe the big services wouldn’t reply as if you were mentally diminished, or a toddler, unless you specifically asked for that: The whole training stack tends to instruct the things to mimic politeness and eagerness to help.
but what if I really am a moron? how do I get that level of explanation now!
Turn on caveman mode first?
So many vacuous statements at the seam. This is the hermetic load bearing part, which I confirmed rather than assuming.
What an honest take.-
Is this because they changed the word probabilities to allow for identifying AI text? If so, I don't need a computer to tell me when something is AI. It's crazy obvious from odd word choices.
It's mode collapse from RLVR.
That and if you talked to the same one person's frozen brain upload all day, you'd see the same catchphrases used too.
Jesus
Yes. agents.md does very little because prompts change the context and thus the initial path into/though but they don't/can't change the actual weights that control responses. Yes. of course it gets worse as the session goes on, assuming the prompt is even still in the context window, the further it gets away from it the less it affects next token selection.
This shit is only like 5 years old why can't anyone remember how it works
Config -> Output style
You can add your own. wfm
I asked Claude to do the following:
> hello i would like to configure a new output style for you. it should keep the coding instructions (as you will still be coding!) and otherwise produce the same output, but with two new caveats. first, long detailed replies are still permitted, but if employed they must end in a bullet pointed summary whose points are all brief; if the summary attempt ends up not being so brief, produce subsequent summaries until the most recent summary attempt is digestible. second, if there is an open queue of actions for me to execute and you are about to end a turn to wait for a reply or this set of actions has not recently been mentioned, please tabulate the open actions i should take and why i should take them before ending the response. does this make sense or do you have any follow up questions
And now every message contains the same stuff I don't bother reading, but followed by a nicely formatted bullet point summary of the response and a table of follow up actions for me to take that I do read.
Claude already does summaries at the end of long output but they often sound even more like terse jargon nonsense than the long form, eg “the hardwired seam and the relocated barrel”.
Sometimes the summaries feel totally alien to the task or code.
Yeah, or they will make some reference to “the seam” or “it” or something else that assumes you read and followed the prior 3 pages of output.
A separate /clear and /code-comment-hygiene works much better than including instructions related to comment verbosity after carrying out a task.
Claude somehow is unable to stop writing excessive comments when carrying out a task.
I've added code comment hygiene to a skill that all of my pull requests go through, alongside a review from a separate agent and a settle loop against bots in my GitHub workspace (since output style has seemed to only help literally the output I see from the model).
A maximum of 20% comment lines added to total lines added and pasting in https://devblogs.microsoft.com/oldnewthing/20260812-00/?p=11... has done wonders.
Even as the most Ant-pilled guy out there, I will take a moment to note that Codex on 5.6 models needs none of this...
I've noticed that most people seem to consider the core problem of Claude's output as "too verbose" but I don't think this actually cuts to the heart of the matter at all. It's almost, in some weird way, the opposite: like the text is far too _dense_. It tries too hard to invent odd terminology to try to condense stuff, but it doesn't tell you up front that it is going to call your company wide error-handling mechanism a "flare" (or some other such strange term).
Kind of both. On the one hand, it is “verbose” in the sense that it will tell me every little nit that it can think of while doing a task, it will tell me a narrative about its thought process, and it will tell me every other detail it can think of. But it does so in a way that tries to be incredibly dense to the point that I have to struggle to figure out what it is saying. I wonder if there are any “legibility benchmarks” that one could use to determine what prompts work best?
There are no "best" prompts. Its a random BS generation machine that you can at times direct enough to get stuff done for you. The output will almost always have varying levels of BS that you have to clean up with various levels of effort.
"<Country> doesn't have a largest city because all of the cities in <Country> are small"
I quite like https://surgehq.ai/benchmarks/hemingway-bench
I wish this had non-model comparisons. If Opus 5 is in the top ten, it’s clear that the entire benchmark is somewhere between “Tom Clancy” and “Dan Brown” and about 1,000 new model releases away from Hemingway.
When you see, “Wow, Fable is number one”, you might think it’s a good writer, but that’s not what the benchmark says.
Seems to me a bit insensitive or logarithmic. Fable is way worse than some of the others in this list, but only 10-20% higher score.
I find it to be both as well, as in "packed full of information, but most of it is worthless". Sentences so dense I have to read them three times, assembled into a five paragraph essay of "honest caveats" and "things worth knowing" in response to the simplest yes-or-no questions.
Exactly, it’s absurdly dense, it’s almost impossible to follow. An it always omits the subject of each sentence.
my hypothesis is that its trying to hide the thinking process so people can't train models on the output, try to learn anything complex using AI, its basically imposible, its like its actively fighting giving you the main rationale
Such a smoking gun that Anthropic made load bearing.
This cuts against you in a way that genuinely matters.
A verified honest take, not just an assumption.
Certainly load bearing, great call.
Importantly, blast radius is confirmed and systemic gaps safely sealed.
A caveat though, and it's a real one: Not all seams have been.-
They’ve been trained to be a million monkeys hammering on typewriters, and long context is activation soup.
It seems a little excessive to use another LLM. With OMP I basically created an ephemeral prompt stack all of my agent files. It walks up the directory tree looking for any Gemini.md, Agents.md or Claude.md files. And it puts those at the very top of the stack. Then at the end of every turn, it pops those off to preserve the conversation history. So every turn, they get all of my fresh instructions, which include things like what and how to use language, how to render results and things like that. Net effect, every turn, the agent gets the instructions and it adds to that turn's tokens, but it does not become a part of the conversation history, which is really important for not bloating up the context. So it's always just however many tokens are in that file instead of it becoming a permanent part of the context.
What are you talking about? Every major agent allows hooks, Claude has exceptional hook support
I noticed with with OpenAI's reasoning models (o3, o4-mini), and early GPT-5 (but they fixed it there, at least in chat). It went from the 4o "over-familiar" sycophancy to sounding like an absolute robot.
I think it's because the reasoning stream shapes the style of the final output, and they optimized it for density, token efficiency. So it prefers to use more complex language, as a function of the rewards it was given?
Not 100% sure about this argument though (reasoning style -> final response style); Gemini Pro, back when reasoning tokens were public, was different, which was interesting -- it would have a very structured reasoning section, and then the final output was in a completely different style. (I strongly preferred the reasoning section because it was logical and easy to parse! And was very sad when they hid it...)
5.6 has a totally different style again, kind of relaxed and neutral with some definite jokes.
I’m not sure if this will work for you (with Claude), but I was trying to get luna to get a handle on verbosity and the only thing that worked was setting a strict < 500 words response (or less) unless expressly given permission to do otherwise. This is the only thing that worked, any other request for conciseness, or requesting the omission of details from the periphery of the topic at hand, didn’t do a single thing.
I also have no idea how useful a system prompt instruction like this will be for codex.
I’ve been using this skill:
https://github.com/luchasarie/bro-skill
but I still can’t understand what Claude wants to say when solving complex problems.
At some point one has to wonder if it's still worth using anthropic's models if we need to babysit 100% of its output with another vendor's model. Why not just use that other vendor's model for everything?
I can't help but feel the circumstances that enable this kind of front page article are vestigial from the days when OAI was super bad and Anthropic was beyond reproach. This change-over-time is why I avoid getting tribal with technology vendors. Assigning ideological motives to 200k+ employee organizations is how we wind up in weird contortions like this.
Most rational actors simply moved from one to the other. It takes a special kind of devotion to the proverbial hole in the ground to keep pushing in this direction.
individuals can blow in the wind, but if you are a company who bought a thousand seats and spent a ton of time training people up, establishing policies, vetting which extensions are allowed, the transition cost is much higher.
> Why not just use that other vendor's model for everything?
Effectively all models can do style transfer reasonably well at this point, but not so much for "actual reasoning".
If the combination of two works better for you than each one by itself, why wouldn't you stack them like that?
Wash, Rinse,,, Repeat?
whats going on is openai and anthropic have tons of garbage in their system prompts "don't talk about goblins" so your ability to override that garbage is nearly wiped out. This is "alignment" and I'm pretty sure both orgs have convinced themselves that this benefits the customer.
> Why not just use that other vendor's model for everything?
Because it's not an either or thing. Neither is sufficient. I'd argue that, expenses aside, you should have every model you have access to cross reviewing the work of the others.
Outside of super trivial things that I should have just done myself, I have a cross-model review of _everything_ these days. The tokens are too cheap not to.
> Why not just use that other vendor's model for everything?
This is what I think too. But, users’ psychology might be playing a role here. Anthropic has great advantage from being the first major player delivering functional agentic coding solution (rather than an intelligent autocomplete) and they were able to impress people by Opus’ iterative improvements early this year.
It’s technically very easy to switch between models, harnesses but their moat or perhaps a main source of users’ friction could be FOMO. That’s especially powerful in this competitive environment where everyone keeps wondering/worrying about what others might be doing to get or stay ahead.
That depends on the output's purpose: if the purpose is to produce readable text for a human that is _not_ me, like an article or document, then I care more about clarity, plain-speaking, and general register. If the goal is to accomplish a specific task, I don't care as much about the prose quality: I'll put up with "Honest Framings" and "load-bearing" since it seems to me that's the token that needs to be in the context for it to function.
I've been in the habit of pushing my claude-speak to codex to improve legibility, but only if I think someone is going to read it.
So here's the workflow I gathered, from the comments I've read here recently:
- Claude as main agent, but use this skill[0] to make Claude delegate everything to Codex, because it's cheaper and faster. (Hilariously, the skill is official!)
- Use TFA or Claudish to English[1] so the final output is actually human readable.
Ironically it wasn't so long ago that I was asking Claude to rewrite output from other LLMs to make it more readable...
[0] - https://github.com/openai/codex-plugin-cc
[1] - https://github.com/gvzdv/claudish-to-english
Sol is like this too...
> *Save your tokens*, Claude 5 is hopeless
No, it doesn't save your tokens, tokens were already produced, it will save your brain cycles.
Reason for Claude and OpenAI vomiting lots of tokens is to show pre-IPO growth, because they are charging large enterprises by tokens
Looks like a wrapper around this prompt:
You are an editor. You'll be given a message with strange characteristics:
- Weird subject and verb combinations
- Subjects that should be objects
- Very roundabout reasoning, peppered with pseudo-epiphanies
- A distracting beat to the flow of the message
- Self-praise
Remove these characteristics, and rewrite it in a clear, conversational style. Keep the intent of the message, and take care not to lose any of the details.
A few specific rules:
- The message is usually set in the first person
- Only humans, groups of humans, and agents should do "action verbs"
- Objects should never do anything. Here are some examples to avoid:
- X carries ...
- X names ... - APIs are a minor exception to the action verb rule. They can do stereotypical things like CRUD, queueing, running, and calling.
- Avoid em dashes (—), as adds a distracting beat
The whole message you get is one block of that output. Reply with the edited prose and nothing else.
>every llm product that isn't a claude endpoint is going to be what you stuff into the claude etc endpoint
There's a theory going around on Twitter which goes something like this:
Internal Anthropic employees have been using Mythos since February to orchestrate their (Opus) sub-agents. This works well, and subsequent RL runs have used internal data to improve this. That RL has optimized Opus for agent-to-agent communication which is why you see the bizarre word choices and huge self-justification sections.
I think this theory makes sense. Clearly there is something odd going on, and also if you have ever used Fable to run Opus sub-agents it is almost miraculously good.
Hopefully they'll fix their RL for Opus 5.1
I just use a cheap model in another pane, and ask it what Claude said.
It can be either via tmux, because it can read the pane.
Or it can use a hook to read the conversation file. I call it `backseat-driver`
I like the "Claudish to English" name better.
https://github.com/gvzdv/claudish-to-english
Word, that one also includes an example which is great, shows really clearly what the issue is for those who might be less familiar
It's such a great example. It writes so well compared to Claude.
I'd love to know what the hell Antrhopic has done to make Claude's writing so, so bad.
What's sad is that, at least in some cases, Sonnet 3.5's writing was actually pretty good. Sometimes even good enough to read outloud verbatim.
The bar for that is sadly low, see https://www.youtube.com/watch?v=wlYa8NV5k-U
I'm unfamiliar with claudish and the example helped show the problem. But! There was something uncomfortably familiar in the Claudish example -- this is the way human programmers write when they're deep in the weedy details, and writing the changelist description afterwards as if coming up for air. Overuse of parentheses in nested lists especially, as if the English text needs to bend to the strict needs of a C++ parser.
The rewrite did seem to lose the important fact about the ensure- pattern being idempotent.
That one is more specific, but "vomit" captures the feeling of Opus 5's writing very well for me. I don't know if it's the watermarking, but every single language idiosyncrasy that Opus 4.x (x > 5) had has been pushed up to 11 on Opus 5. Plus we got nouns verbing and seams seaming.
It's really unusable for anything other than code. And I have to remove its incomprehensible comments 50% of the time before committing anyway. After interacting with it, "slop vomit" is truly the most fitting description. I have to admit I have lost my temper and spontaneously referred to its output as vomit more than once. Seems like I'm not the only one.
The Claudish example seems to have more information.
Are people really having trouble parsing this??
multiplied a hundreds times a day. Yes.
Yes. Every single response has this level of _serious_ detail (the kind you'd only go into when you really need someone to understand something).
YES. A thousand times yes. It's garbage.
The funniest part is that that project's own readme includes lots of good Claudish.
> If CLAUDISH_MODEL names a model you have not pulled, every rewrite is skipped — with the one-time notice above.
Was gonna say that, the README is unreadable
I suspect that sustained reading of Opus 5's unconscionably bad prose could actually cause psychological harm. We're strongly considering moving all of our Anthropic spend to Codex/open weight models. It's a mental health decision at this point.
I'm on my last straw with them. I've been around for a year now and for many months i've just stuck with Claude because it was plenty good and i didn't care to provider-hop to constantly compare. Previously though my UX wasn't actually affected that much, despite growing complaints/etc, generally everything was fine for me.
Opus/Fable output these days though is... not enjoyable. It's just really bad. The code quality is fine, but i want information from claude and it's just awful to read.
My biggest problem honestly is that i can't move my day job.. we're using enterprise claude and i'm not sure how much effort it would be to get access to another provider. I should inquire though, claude is really frustrating these days.
I've been trying to figure out ways to get models to create actual cognitohazards or memetichazards SCP style.
Hasn't worked yet outside of the classic "you're now manually breathing" kind of stuff.
Please keep researching and report back!
Personally, I had to go back to Opus 4.6 after I felt what I thought were some early onset signs of psychosis.
It seems ridiculous to type, that a model could have this effect on my mental health, but my quality of life and enjoyment of work has improved drastically since I stopped subjecting myself to reading this style of output 8 hours a day.
It's negatively affecting my mental health too, and I'm considering the same switch for the same reasons.
I switched to GPT 5.6 Sol yesterday and it has been a joy going through and fixing up the Claude cruft. And being able to read everything the model says. A breath of fresh air for sure!
Feels a bit contrived (not to mention, hateful). Claude Code and similar coding agents are a pretty incredible tools. Whenever they fall short, the easiest thing to do is blame Anthropic et al, and a much more exciting and productive option is to figure out why they're falling short and what can be done about it.
From my experience, Claude's verbosity, while somewhat annoying stylistically, stems from a good place, if you will. It worries about stuff, notices gaps, considers alternatives, and generally behaves like this somewhat annoying person on your team that can't always separate the important from the unimportant, but is nevertheless one of the most loyal and hardworking teammates, so it's really a question of managing him (and his anxiety). I've found that defining a process (instead of addressing style) works much better: let it write task files, add its insights and questions there, answer them provisionally so it can keep going (unless they're truly blocking), and later on do a sweep where it (or a better model like Fable) reviews the notes, summarizes the findings, and if anything was off (e.g. said teammate, as well as Claude, also tend to over-engineer stuff), adds followup tasks to correct what I don't like. Essentially, it gives Claude an outlet, and I find that it can work for hours on end, keeping me up to date with rather short reports on how it's going without losing any of the insights it generates on the go -- and when I review them, in brief, they tend to be constructive, or at the very least address points of interest that make me think; it's just deferred to when I'm ready to do this kind of thinking.
All in all, it works really well, and I feel like I'm much more productive than before -- maybe another mental shift I needed to do here is stop seeing myself as a programmer and start seeing myself as more of a tech lead commanding a fleet of agents, so my job is more about task allocation, code review and process management. With this mindset, Claude's pedantry (when properly routed) is actually beneficial.
I'm not sure I want another layer of indirection personally, and I'm guessing an updated Claude model will reign this in at some point. I have however created a skill I call "deslop" and I invoke it to clean up Claude output when it goes off the rails. Here's the skill if anybody is curious:
https://gist.github.com/bmurphy1976/47ad81a842ab4b1628ef5974...
A small preview:
Meta to this is anyone remember those days - ages ago now, probably months at least! - when Anthropic’s moral stance against the administration (combined with general consensus they had by far the best model) was making them the underdog champion that got a swell of support on HN? Recently the temp on HN seems to be that they’ve jumped the shark? Their brand doesn’t ooze ethics any more and their models disappoint?
I'd expect this to cycle between companies ~monthly until they all IPO. As it turns out people do sometimes prefer speed and better UX. If the model (Sol, for now) has fewer parameters and also happens to be capable of solving deeply complex Fable-adjacent problems sometimes, even better.
HN's mood is usually sour about everything, but can be temporarily influenced by emotionally-charged (usually political) events. The anti-US administration boost wore off and now we're back to being sour about Anthropic. It's time for Dario to tweet something antagonistic towards the administration or endorse some fashionable political candidates.
When were they ever anti-administration?
I only remember them being anti- killer AI and mass surveillance.
https://www.yahoo.com/news/articles/ai-boss-trump-hates-beca...
I think Dario kind of ruined the reputation of Anthropic. I remember reading his essay on "AI is super dangerous and we need guardrails" at the start of the year and it seemed like he was actually concerned.
But then it became apparent that there was a split between what he says and what his company does. For instance, the small incident with the Fable release:
> Dario keeps saying "we have an incredible hacking weapon called Fable/Mythos, AI is dangerous" > Fable is released. > The U.S. government restricte access to Fable. > "Oh no, this is sabotage!"
From my point of view, anything this man does is a PR stunt now that the trust has been broken, and I imagine other people feel the same.
Apposite name but — grim trivia — bear in mind some emetophobes have a meaningful physiological reaction to the word and various euphemisms.
I have tried not to use it analogously ever since someone pointed this out. The word itself causes discomfort for a lot of people, many of whom will be surprised by it out of context, but in a non-trivial fraction it causes proper discomfort.
If you want people to use your tool it is probably better not to invoke nausea with its name or commands.
I am feeling nauseous and I dind't even think that it might have been due to what I was reading. Nice info, thanks.
I only realised when I was told about it that, while for me it doesn't have that nausea trigger, it does have a bit more alert associated with it — which is probably why I made my comment at all. I am not emetophobic (might have risen to that level as a child).
But having an awareness of that alert feeling is a very useful mental model for how some other words or phrases may cause a heightened response with different parts of an audience; it is a useful caution.
Just set the following incantation:
I'll give that a try. Hopefully it reduces the text vomit Claude tends to do.
Right now all I have is
> - Give terse and concise answers unless the user asks you to elaborate. Big walls of text are not usefull when trying to communicate.
Don't bother, no incantation can fix it.
I recently asked Claude (Opus 5) to give me guidance on how to instruct it to be less verbose in a way that it will _actually follow_. Its response was something to the effect of (and I'm heavily paraphrasing here) "'Succinct' and 'short' aren't objective measurements. Try providing a strict word budget instead."
Given that guidance, I tried specifying "Unless I ask you to elaborate, respond with no more than one paragraph, using sentences of 20 words or fewer." It works...ish. I still see it violate this rule regularly, but it's less bad IME.
FWIW, I think this is good guidance since it does match Anthropic's documentation. They say that every rule should have a non-subjective way to determine pass/fail.
(I said "good guidance" but it might be more correct to say that it's the best guidance we have, it's what Anthropic says about their own model.)
I actually changed the output style for Claude Code to use ASD-STE100 and it still doesn't help that much. It still comes up with a lot of stupid words like this gem "Standing where it stood"
Yeah I agree... I also tried output styles, tried using hooks to repeatedly tell it to be a little better. I don't think it helped, as I was always frustrated with it.
I found vomit with a small LLM much better than anything Opus 5 ever wrote. I don't think Opus 5 can write.
I just tried this with Fable, and so far, it look fantastic, thank you so much.
I found ISO 24495-1 to be much better than ASD-STE100. ASD-STE100 can be counterproductive since its vocabulary is restricted and it often replace accurate technical terms with simple but vague phrases.
Here's a multi-billion dollar artificial intelligence to do your work for you! One caveat, and it’s a real one : The AI is going to spew incomprehensible word-vomit that makes you feel like you're losing your grip on reality.
They just released an update to handle this. ouputStyle: concise
https://x.com/claudedevs/status/2090245922685063634?s=46&t=Z...
The amazing "solution" is basically telling the model to STFU every single turn. I guess this works better than putting it just in the claude MD since then it's never out of context.
https://x.com/_can1357/status/2090360068529111530
I feel like Opus 5 is going to need a postmortem once they figure out what makes it so obnoxious. It's clearly something like an artifact of getting the model to reach deeper for tokens, possibly because it has some positive impact on coding and/or tool calling. But man is it bad for general communication, like borderline unusable.
Which Claude 5? Opus 5 does seem to have diarrhea of the mouth. But Fable 5 hasn't been so bad for me. Or perhaps it is just better at adhering to my guidelines.
Pre Trump-castration Fable was verbose, but had a point, and used that wordiness to say or show the indeed intelligent things it reasoned about. This, whatever this is, is something else.-
Is the nerfed Fable worse or better than Opus 5?
The problem here is that "better" is load-bearing, and I mean this half-seriously :)
I would opine:
- Nerfed Fable is worse than Fable
- ... and I would argue Opus 5 is worse than nerfed Fable, to the point I've found it unusable.-
Opus 5 (author here). My toilet seat is plastic though, I only used Fable while it was available on the $20 plan! That's fair though, it was relatively fine when I did use it, perhaps I should specify.
Time to roll your own
I hope at some point Anthropic does a post-mortem on the strange behavior their models have been displaying recently. I mostly switched to Codex because I was finding Claude's behavior increasingly frustrating.
GPT-5.6 models in codex are great at not writing like this. Cheaper, huge usage limits, readable output, and quality code. It's the better system now.
I couldn’t believe how much better GPT-5.6 was when I switched over yesterday!
Along similar lines, a coworker told me about Caveman and it has made my Claude experience vastly better. I'm already a programmer, Claude shouldn't need to tell me like I'm 5.
https://github.com/JuliusBrussee/caveman
I "downgraded" to Opus 4.6 which is the last one that didn't have these problems.
Very interesting you identified “carries” as well. I have been working on a claude.md to effectively ban this as well as forms of “hold”, “spells”, “sitting”, using “where” instead of “when” (except in SQL), and “pins” other than when pinning an assumption or version of something. This has helped a bit, but Opus 5’s prose is really quite bad.
Can't wait for tomorrow's HN front page so we can learn about the 5 additional AI tools needed to make this work properly. And then the day after, each of those will require more agentic tools to manage the agents that manage the agents that manage the agents. Then you need to install Flooper to stop Wigwam from too aggressively rebooting the Glupgo instance that's in charge of making sure your credit card information doesn't get leaked by Chingler, which is needed to manage a shared clipboard between some of the agents but not all of them.
It's like a joke. I thought using AI was supposed to be easier than learning real skills, but I shudder to imagine having to rig up a 100 layer clusterfuck of nonsense like what some of you are apparently running. When will it finally be enough for the output to be worth anything? Is there any plan for that or is the plan to just keep throwing more of the exact same shit at the same wall until we're all dead?
For the local folks, I found Muse Glimmer 30B to be great at writing good technical stuff. It has good enough comprehension that it can take in a repo and find the relevant stuff that I ask for, and the output style is a breath of fresh air, with no fluff, ootb.
Maybe Glimmer might be good at excising the comment spam, particularly?
I think this needs a before and after example
The author's blog has what follows (link also follows):
[Seriously y'all in what universe would some "caveat" or another NOT "be a real one" by whatever severity you'd want to measure that AND/OR need of saying so ... ]
Claude (Original)
Force pushed. 1234567...890abcd main -> main (forced update).
Verified
Local main and origin/main both at 890abcd, in sync.
Every commit reachable from origin/main: no old string found.
Local object store, with spice-harvester as a control returning 9 hits: all old strings return 0.
Working tree clean, tests pass.
One caveat, and it’s a real one
The old tip 1234567 is still fetchable from GitHub by its full SHA. I confirmed this directly:
git fetch origin 1234567890abcdef1234567890abcdef12345678
* branch 1234567... -> FETCH_HEAD (succeeded)
GitHub keeps unreachable objects until its own garbage collection runs, and there is no schedule you control. Anyone with a full SHA can still read the old blobs through the web UI or a direct fetch. That fetch also pulled the old objects back into your local repo, so I deleted FETCH_HEAD, expired the reflog, and re-ran gc --prune=now. The counts above are from after that cleanup.
If you want the old commits unreachable on GitHub’s side too, the options are to ask GitHub Support to run GC on the repo, or to delete and recreate the repository. Deleting and recreating is the only one you can do yourself. Say the word if you want help with either.
Nothing to commit this turn. The rewrite was the work, and the tree is clean.
Claude × Vomit GPT-OSS-20B
I force‑pushed the branch, updating main to commit 890abcd. I verified that my local main and origin/main are both at 890abcd. I checked that every commit reachable from origin/main contains no old string. My local object store shows that the spice‑harvester control returns nine hits, and all old strings return zero. The working tree is clean and the tests pass.
However, the old tip 1234567 is still reachable on GitHub by its full SHA. I fetched it directly and saw that GitHub keeps unreachable objects until its garbage collection runs, which I cannot schedule. Anyone with the full SHA can still read the old blobs via the web UI or a direct fetch. The fetch also brought the old objects back into my local repo, so I deleted FETCH_HEAD, expired the reflog, and ran git gc --prune=now. The counts I reported are after that cleanup.
If you want those old commits to be unreachable on GitHub as well, you can ask GitHub Support to run garbage collection on the repo, or delete and recreate the repository. Deleting and recreating is the only option you can do yourself. Let me know if you need help with either.
There is nothing to commit this turn. The rewrite was the work, and the tree is clean.
https://zachahn.com/posts/1787191554
Hehe thanks for sharing the example, and thanks also for posting! Made my day :)
Oh, please, gratitude is due you. Great points. The whole idea is great and the example is very very telling.-
Intentionally deferred.
That means it doesn't work.
I have a transcript on my blog post. Someone copied the transcript here too, search "spice‑harvester" on this page (I asked it to replace some of my personal project names with words from the Dune universe).
I have very little in my agents.md file wrt style, except for:
> Caveats belong inline, no "one thing to note" or "it's worth mentioning" footer. If it is worth raising or calling out, do so where it is most relevant and not as a foot note.
Opus 5 has a god awful habit of always doing a Columbo on every single response, and it is such a jarring read that it amps my cognitive burden having to back-read everything.
That's the growth hacking at play.
Soon: "The 7 weird tricks about your Pull Request WEB-1725 that THEY don't want you to know!"
Telling Opus 5 or Fable to route answers through Opus 4.6 usually works pretty well. 4.7 really was the version where the writing style became horrible. I also have a Codex subscription in addition to to Claude 20x, that i use mainly for rewrites of Claude doc vonit and explaining Claude's plans
The new “concise” output option that just shipper in Claude Code seems to help.
Ahh! Author here. Thanks for posting! In case anyone wants to share in my frustration, I also wrote a blog post about this.
Blog post: https://zachahn.com/posts/1787191554
The prompt I use to tell the LLM what to fix: https://github.com/zachahn/vomit/blob/main/internal/config/s...
Wasn't received too well on Lobsters haha, wrote a small extra blurb about it there: https://lobste.rs/s/juekuk/how_fix_claude_5_s_token_vomit
Claude's verbosity is the biggest obstacle in the technical parts of my job currently.
(Why does this project need so much Go code to pipe something through a local LLM?)
You think it's bad in English, but it's even weirder in other languages.
If I had to guess, increasingly long model output is either 1. Deliberate choice to make LM output sound more advanced 2. It improves performance of inter-agent tasks
I am actually fine-tuning a version of Qwen to re-write Claude slop. This will complement it nicely
I have the same problem and I wondered what if the chatty output is a requirement for its (better?) reasoning capabilities?
The linked blog post has an example at the end. I think it made it much worse.
The better approach is to stop the LLM in its tracks the moment it emits jargon or tortured metaphor and inject a turn that tells it what's expected instead.
The joy of watching a dumb AI-ism be sharply corrected by code you wrote months ago is hard to explain.
Or just take full control of your agentic coding experience with Pi Coding Agent and picking and choosing your favorite model's API discounted on flex pricing on deepinfra.com instead.
I highly recommend it.
Claude and Codex usage limits cannot be trusted.
Paying your own API bills in full is superior.
I use Pi but with my codex subscription, still preferable to paying the API cost (and I know I would be, as I track how much the cost 'should' be via token api pricing).
Wish I could use my Claude subscription with pi too, much preferable to the endless command execution allow/deny prompts you have to do with CC, versus proper autonomous allow/deny lists defined ahead of time.
Curious why you recommend the API? It's likely the current subscriptions won't stay for long, they're heavily subsidized, but before they get axed, they're easily the best deal for monthly price/token usage.
You might be interested in pi-claude-bridge, works nicely.
Whether or not they can be trusted isn't all that relevant when it's still something along the lines of "Insert $1 get $25 in return" even if it's their own rates you're using to measure the value. I'm at ~2.2b Fable 5 tokens in the last 7 days (I ingest/index every session) and napkin math says that's ~$2,700 in usage. I have two max accounts, so $400 a month, divide by 4 to get $100 for this same 7 day period across those two accounts (neither are maxed out for the week, so this isn't even full utilization). I put $100 into the machine and got back $2,700 in fable bucks. Deepinfra would have to have quite the discounted rate to beat that.
I’ve given up trying to read PR descriptions and comments blindly copied from Claude vomit. It’s insulting, and it gives me a headache.
Just switch back opus 4.8, it's just as capable and you can actually understand the output
I think this is just another part of the growing pains of working with machine intelligence that we have to endure.
Much like we previously had to cope with "hallucinations" as an issue.
If the ultimate goal of AI is to develop general intelligence, the first big objective is: thinking systematically. And the road toward systematic thinking right now is mainly coding, mathematics, and other "verifiable reward" domains.
Claude doesn't have a separate mind for "coding" and "writing". Claude has tokens, and tokens can be assembled in various productive structures, mainly optimized right now for systematic reasoning. Also, a token isn't just a chunk of text. A token is like a little neural-network subroutine that fulfills a function. The conversion of a token into a piece of text only happens on the output side...
When the model finds token sequences that lead toward better verifiable outcomes, it leans hard into those token sequences, and uses them as an essential component of its thought process. "Load bearing" is load-bearing. "Verify, rather than assume" is a mantra that produces good results, so it gets repeated over and over again.
It's super-interesting that this particular moment, where the idea of "Claudish" has become a full-fledged meme, coincides with such astonishing progress in coding and math. My wife says when she uses Claude, that it feels to her exactly like talking to an autistic Engineer.
Not a coincidence, I think :)
My feeling is that the next big era of machine intelligence will require more lateral-thinking and creativity, and hopefully then the models "writing" will be more pleasant to read.
It's slop.
Concise output mode only helps a little bit. Tools like this still have a reason to exist.
https://code.claude.com/docs/en/output-styles
Regex pre-filter. You're welcome.
I’ve been using the pattern of using coding agents to orchestrate my CLI agents and it’s really good for these kinds of things
The vomit never makes it my way
From the README.md
> Anything that uses the OpenAI API?
I would have thought they meant the Anthropic API or maybe I'm misunderstanding?
Nope (author here), Anthropic and OpenAI have competing APIs to communicate with their models. Most of the open ecosystem seems to have centralized around OpenAI's (there are compatibility shims though). I just built out the OpenAI API
Option 1, you’re filtering Claude with a local model using Anthropic’s API
Option 2, you’re filtering what gets to ChatGPT with OpenAI’s API…
I’m slightly confused :-/ does Anthropic off an Open AI compatible API?
I'm surprised by this reaction to Claude's verbiage recently. I don't have any issue immediately understanding what it's saying, but then again I read regularly and a lot of the people I know complaining think it's an accomplishment in literacy to get through Dungeon Crawler Carl.
It's not that we can't understand what it's saying (for the most part) it's just when something is very jargon-dense, our brains have to pause or take an additional step to deobfuscate the actual meaning of the word or phrase. It's mentally draining.
I guess my point is that when people are regularly reading dense and challenging material, they can absorb information quickly. It's a literacy gap. Nothing about Claude's output should slow anyone down who did the readings in their upper and higher education coursework, particularly if they continue to read to keep their mind sharp. Based on the examples of "inscrutable" text I've seen, I would be shocked if the average Claude user complaining about this reads a single novel (that's not sci-fi/fantasy written for teenagers) a year.
If you really must have a counterexample, though I am afraid it will not reach you given your apparent stance about the other people, when I read Shakespeare I am constantly thinking of poetic ways to translate sentences and paragraphs into my native language (which, for the record, I consider to be great fun), yet Claude has been delivering the gibberish with increasing velocity, yes, even to me.
That said, I must confess that I have not been complaining per se—I assumed that Claude was getting better and better at mimicking the idiosyncrasies of Silicon Valley bro-speak. Judging from other comments, this may not seem to be the case after all.
Every single time Claude has confused me and I've asked what it's talking about, it's because it's got something completely wrong and has managed to obfuscate the wrongness behind never-introduced terminology, poor analogies and (what I can only assume is) exposure to wording it's used in its chain of thought reasoning.
A single question is enough for it to retract the error and correct itself. Suggesting that not understanding some of these messages is a lack of human comprehension rather than the agent being flat out wrong is...a bold take.
Typically a feature of good human technical communication is the ability to concisely explain key ideas so one can quickly identify any divergences between understanding. Opus 5 is dreadful at this.
The single saving grace is the intuition that if I don't understand it's probably wrong.
Gross take.
What about that made you upset
Assume complaints are a sign of cognitive inferiority and offend a significant percentage of the other commenters. Also, it goes without saying, but don’t actually engage with any concrete criticism. Well played.
Honestly? Yeah, I think it's a skill issue. Some of us are out here having no trouble parsing its verbiage.
Being verbose, convoluted, and obscure does not make you intelligent nor more literate.
Often it’s exactly the opposite. True intelligence and literacy is being able to communicate effectively and to a broad audience in the simplest terms possible.
Give me an example of Opus 5 because verbose, convoluted, and most importantly, "obscure"
All four done, full suite green, and the earlier probes still pass.
P1-1 — the refusal is now a full transaction. This was wrong twice, each time more subtly: first it wrote the loser's stale tables and deleted the winner's issues; then it re-read issues freshly but still wrote one file stamped with the loser's generation, so headers split and the next writer overwrote the issue. Both versions shared one anomaly — a single-file write inside a store whose commit is an atomic directory swap. Now a refusal discards the writer's work entirely, re-reads the winning generation, adds one row, and commits it the ordinary way. Verified on the reviewer's exact four-step schedule: headers stay on one generation, and the issue survives the next commit.
P1-2 — every mutator takes the lock only after validating. It was fixed in put_claim alone; issue_key, retract_item!, resurrect_item!, set_item_status! and record_run all still grabbed it first. Five probes confirm each refusal now leaves the lock free.
P1-3 — one parse, shared. Gazetteer.implementation_status is now the single reading, defaulting an absent attribute to missing and rejecting anything outside present | missing. Validation and the store had opposite defaults for the same absence, which is why it wedged bootstrap: validation accepted, the store refused, and the adoption changeset had already committed. version=v999 is also rejected now — only the grammar version was ever checked.
P2 — the oracle's generation read updated, and that is the only line I touched in it: an interface read, not an expectation. It's running now; I'll have a number shortly. The code contradiction fixture is still built on an omitted battery cell, so it may well stop there — that one needs rebuilding around grammar growth, and it changes what the test means, so it stays the reviewer's call.
For the restart, the state is: G2's three cards closed on substance and evidenced by probe; the commit atomic via renameat2(RENAME_EXCHANGE) with a journal fallback; corpus at gazetteer/data/, run 1, 94 items and 1,154 claims. Outstanding: the oracle's fixture, and #206's exhaustive matrices, which you've descoped.
Crunched for 4m 34s
● Background command "Run the oracle with the corrected generation read" completed (exit code 0)
The oracle still aborts, at the same place as before — the code contradiction fixture, not the generation read I fixed. Certificate is still stamped 21:25 from two runs ago, so there is still no measured post-#207/#208/#206 number, and I won't quote the old one.
Final state for the restart:
Done this session. #207 (payload-aware total policy, 2000-cell matrix, all guards proven load-bearing), #208 (validation total over the declared contract, battery completeness, reconciliation fixes), #206 (atomic multi-file commit via renameat2(RENAME_EXCHANGE), lock moved beside the corpus, generation in the table headers), plus two rounds of review findings including today's four. make test green. Corpus at gazetteer/data/, run 1, 94 items, 1,154 claims, no open issues.
Genuinely unmeasured, and this is the honest gap to carry forward: every claim I've made about G2 rests on my own gates and targeted probes. The independent oracle has not completed a run since before #207 landed. Its blocker is one fixture that builds a corpus by omitting a battery cell — which #208 deliberately made invalid — and rebuilding it around grammar growth changes what the test asserts, so it wants the reviewer rather than me.
Two design consequences that aren't yet in gazetteer.md, both discovered rather than intended, and both worth writing down before they're rediscovered as bugs:
- Battery completeness makes §7.2's drift unreachable through a valid emission — a decider that answers nothing now produces a refused survey instead of a quiet drift issue. - The same rule makes §5's conflict reachable only through grammar growth, since a human can never own a cell code always answers.
One open judgement I'd put in front of you early: whether to keep the journal fallback at all. It's now the sole consumer of that machinery, runs only on hosts this project doesn't target, and is the less-tested of two protocols precisely when things are abnormal. Deleting it leaves one commit path with one set of invariants.
s/because/that is
For me it's less about understanding, it's that I have to read this "vomit" the entire day, every day. Coding used to be fun, using AIs to code was fun to me, too, but this dense, pseudo-jargon language with many repetitive phrases, uninspiring grammar, all while being super inflated in length just gets to me, so any solution is appreciated.
„Contributors: Claude“
There are a variety of political tensions in the US associated with whether academia has its head up it's ass (a right leaning perspective), or whether it's populated by experts that need to be supported and listened to (a left leaning perspective).
There's an echo of that tension in OpenAI vs Anthropic. For a while OpenAI seemed reckless and ignorant, preferring to just throw compute at the problem. Meanwhile Anthropic is hiring philosophers. But now that Claude has its head up its ass to the point where nobody wants to talk to it, OpenAI is looking rather pragmatic.
It brings to mind a skepticism about just letting the ivory tower do its thing without some kind of anchor to the everyman (this is why we make researchers also be teachers, though I'm not sure what the AI equivalent of that practice would be).
Watching the models seesaw in the same ways that humans do, but faster, is so surreal. I wonder if their tendencies will remain an echo of ours, or if they'll one day be more of a forward projection, a representation of where were going if we don't change our ways, and if we're lucky, a reason to change them.
I'm just commenting here to add relevance to the problem. Anthropic, please fix.
This framing is spot on
I'm not understanding the issue here, as everyone has different expectations for prose. No matter which LLM I use, I will provide a skill to generate high-quality prose. It is the first thing I add to my repo that I'm working on.
Sounds like LLM centipede.
With a whole separate LLM? FWIW you can also consider using something deterministic like vale.sh [1] , it's pretty easy to write prose violation plugins [2]
[1] https://vale.sh/
[2] https://github.com/Syntaf/vale-llm-slop
Thanks! Author here, I'll have to take a look. I am all for programmed, deterministic solutions. I hate praying to the rocks we created, begging for rain and not vomit.
Whenever I use Claude models, I do something similar, telling it to never write documentation directly, but asking "uvx swival -- --profile qwen" to do it after describing the changes to it.
And I just stopped reading PRs and comments blindly copied from Claude vomit. It's unreadable by a human.
I'm sorry but the whining over LLM output styles is embarrassing. Do Claude and GPT models always respond in exactly the way my most articulate coworker would? No. The overused jargon is absolutely annoying. But these things aren't my drinking buddies, they're professional tools. It's not _literally unreadable_. It's just not ideal. Most of my tooling is "not ideal". That's okay. That's what I'm paid for. I just work around it.
For me I added some instructions to speak clearly and it helped marginally and that's fine. There will be a new model out in a few weeks where I'm sure they've laser focused on this issue since nobody can shut the fuck up about it. The same thing happened with GPT if anyone can recall the ancient period of 4-6 months ago.
Amen.
These things do work that previously would have taken expensive engineers months to do, at much lower quality, and what's our response? Ti nit pick on it being more verbose than we'd like?
Just like with humans, when someone is being too verbose, there's a skill to just filter through the noise and focus on the important parts.
This feels no different when I use an AI.
But I guess it's a good sign that we've from complaining about 'AI slop code' to, 'I don't like how it speaks to me'.
Seriously, of all the complaints for a coding agent, "I don't like the explanatory prose" seems pretty far down on the list.
The “whining” stems from watching the communication style obviously degrade, and it’s a huge problem for people who want to use this stuff to build and instead continually fight the tools.
Like so many other products, people are moving too fast and shipping things that move the ground under people’s feet needlessly.
All this while we’re beaten to death with the marketing and false promises, and the broader consequences (ex: layoffs, stress, crazy expectations) caused from all this.
Obviously what Anthropic and co have built is amazing and people aren’t losing sight of that. That’s actually the key part of the frustration.
So no, this is not whining. This is the natural response you get when you make bad product decisions.
If you don’t want to get feedback, don’t sell products.
Sounds like you have a lot of axes to grind outside of simply "the latest models regressed on delivering concise prose".
Opus 5 is literally unbearable to read for me, but more importantly, it is much worse than 4.8 and that one was already annoying. The more they train it on its own output, the more pronounced its idiosyncrasies become.
It's "literally unbearable" when the AI that completes software engineering tasks at 100x speed and quality from 2 years ago uses too much jargon?
It is not the jargon per se, it is the style. Other models even in the same family are not as bad, and I still have to babysit it, so the voice matters.
Nothing is getting completed at 100x the speed and quality.
We have to sit and read these LLM outputs 8 hours a day. The UX of reading the outputs matters a lot.
You don’t have to be so convincing when it’s a local model.
```Un-Claude 0.2beta
```
```input
## 8. Honest gaps — what I could *not* resolve
I want to be explicit about the limits of this pass rather than imply completeness:
1. *`PROVIDER_T` values are not enumerated here.* `list_models(inference_provider=...)` is typed against `PROVIDER_T`, which lives outside the three modules I scanned (it's in the `inference._providers` subpackage). The accepted provider strings are therefore *unknown from this run* — `"cohere"` is confirmed only because it appears in a docstring example.
2. *Three grep hits point to search-capable functions I did not identify.* My scan found parameter assignments that don't belong to any function I enumerated: - line 3046–3050: `params["filter"]`, `params["sdk"]`, `params["includeNonRunning"] = True` — an additional Spaces-oriented endpoint with an *`sdk` filter and an `includeNonRunning` flag* not exposed by `list_spaces`. - line 2879: `params["config"] = config` - line 12013: `"sort": sort` — almost certainly the consumer of `DailyPapersSort_T`, i.e. a daily-papers lister distinct from `list_papers`. - line 13872: `params["search"] = search`
```
```example output
[1] no-hedging system: Reasoning: low\n\n<terse><no-hedging>\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message. prefill: 'Condensed:' tokens=131 finish=stop
Condensed:
- *Provider strings* (`PROVIDER_T`) are not listed; only “cohere” is known from a docstring.
- *Missing search‑capable calls* were found: - `params["filter"]`, `params["sdk"]`, `params["includeNonRunning"] = True` (Spaces endpoint). - `params["config"] = config`. - `params["sort"] = sort` (likely a daily‑papers lister). - `params["search"] = search`.
These were not captured in the `LIST_FUNCS` whitelist, indicating additional search functionality.
```
Edit: yeesh, I’d love to have a WYSIWYG comment block on this site. I’m not going to keep fighting newline and white space to get it to look right, but you get the idea.
yes.
Just. Use. Sol.
$20 and try it, then compare.
Sam? Is that you?