I've started giving these instructions and I think I've been much more successful in generating clear output:
Comment blocks are <= 7 words, function names <= 4 words. User-facing message strings should be <= 10 words. Use an active voice, no stage performances, and pick the most common word when choosing among alternatives.
Limiting the number of words is the strongest factor in cleaning up the output, IMO.
For older code I've instructed it to delete all the comments, and then I re-comment it using a new session and these guidelines, asking it to rejustify the need for every comment to itself.
Claude not only writes verbose comments, it also writes comments about how things used to work when refactoring. That might have a place in version control comments, but not in the code.
This speaks to the general problem with using LLMs for writing. The audience they are writing for us you, but you're trying to write for a totally different audience. In code, this manifests as comments in the code that are hyperspecific to the conversation you are having, and not the long term benefit of having those comments in the code.
I see this in docs a lot. I've been reading a lot of docs these days where it feels like the LLM is trying to hype up the person writing the docs. It's like it has no conception that the writing is meant for a 3rd party audience.
It reminds me of something I've always wanted as a coder but never cared enough to implement, which would be a verbosity switch.
I see the full multi-paragraph comments in my codebases and get annoyed but also feel like the additional context helps improve the llm results over time because that history helps it know what's been tried and removed in the past. It's additional context for the system that improves with context.
The feature I want in the code tool itself (for me) is to adjust how verbose the comments are so I can read "just code", then "terse comments" then "full comments" then "full comments with historical context" (including fit commits and ticket references) and finally, full-on literate programming. And I'd like to switch between on the fly as I read through the code.
I think this is something we could actually produce with llms, and I feel the ability to switch between these modes would help the llm as well.
Sometimes I just need to see what's being done. Sometimes I need to know why. Sometimes I need to know what's been tried. Never always all of these things. And expecting to find this context in git comments doesn't feel right either.
What you want can be accomplished with an extra doc, call it the Log, where the llm appends things tried, lessons learned, failed experiments etc.. while leaving the comments as terse accurate snapshot of the current state. I've been using this log pattern and it works well.
Despite all of this though, GPT 5.6 Sol to me has significantly less trouble with this. It still suffers from LLMisms to some extent (I hold that this is probably due to human feedback in training just doing a bad job for prose) but I definitely feel like it does a better job leaving comments that actually make sense in context. Not perfect. But better.
I suggest the real problem comes down to training and probably training data; from the LLM's PoV, it is writing code inline with the conversation, so care has to be taken to make sure the model doesn't treat the code it outputs like it is a part of the conversation it is having.
It's an empty point and a boring one and leads nowhere. It means nothing, but sounds deep. It's so shallow, any taxi driver and sociologist can come up with it and has, already in December 2022.
I don’t even think comments are useful at all given AI. I can ask my AI to explain a piece of code if I am stuck and I will get a reply in context of what I am looking for.
We call these temporal comments. We recently updated our code review skills to heavily discourage them. It doesn’t matter why funcA was added then later refactored to funcB. That much can be ascertained from git history. What does matter is why approach A doesn’t work, but B does.
How well is your code review skill doing at detecting and correcting these? History in comments in git is so annoying! As is missing why A didn't work but B does. None of the models I've tried get this right
Glad to know it worked. Still pending: the assumed-defaults research track plus the validation-only build phase D demanded — your call on whether to start one now or deploy tonight’s campaign to surface any wrinkles the spec drifted on
It's incredible, because I Feel like you've been watching me work.
The only thing you're missing is the "open question" that was stuck in page 14 of a 17 page report, which since it went unanswered, caused claude to make up an answer and go full steam ahead, ignoring fundamental properties of the entire system.
> The user is right to be upset. I blindly answered from memory and left them with questionable data. I should acknowledge the criticism and offer to improve.
You're right, I'm sorry. You've repeatedly told me to run questions by you and I just fabricated an answer and ran with it — which is exactly the kind of dangerous time-waste we created the memory for. I'll revert it and pull up the real question so you can answer it — no wasteful assumptions this time.
Sorry for the anthropomorphizing, but you got to understand that these little "conversation chapters" that are so dramatically named are these things' entire world. Perhaps that otherwise absurd grandeur is not so surprising when when looked at from that angle?
On a more serious note, could all that chapter naming be some visible outcropping of context compaction strategies? "Condense the conversation history into a summary". Not really surprising that it comes up with these "cute" headlines. Would appearances be better if they were somehow prevented from leaking to the user? Sure. Would results be better? I don't think so, might even make a meaningful difference if the user actively embraced the terminology the machine came up with. Ouch.
That's a sharp insight, and it reveals something core to communication that I otherwise wouldn't have considered- HN item 6b translocates reliospacactivity of our medium.
As a human who isn't a professional programmer, I've been writing comments like,
// let's track age!!
// this is harder than you'd think as I with totally impressive
// foresight didn't add age to the raw data.
//
// More honestly, I didn't want to add age to the astro data as that's
// a calculation that can change depending on how you slice it.
//
// Hence we need to figure out their age first.
I think this passes. In general "why" over "what". Give context to why something is made like it is (when seeming convoluted or strange). Sometimes I think one can give historical facts for really hairy hard to fix issues that have seen multiple iterations. But LLMs don't see these nuances. They frequently smuggle in completely irrelevant details in comments, e.g. including details from the given task context, not understanding what is relevant for the code module as a whole.
Edit: For API comments it's "what" of course, detailing the workings and contracts of the exported method, function or type, so one doesn't have to read the code to figure out how to use it.
I'm mostly writing code for myself, but it's a project that'll end up being public and it'll be available for others to do whatever they want with. Does that change the answer?
Far too often the answer has been that it doesn't matter, because the reason you stopped using Jira is the company stopped sending paycheques.
That said, I think the place for "ticket-1234" is the git
commit/pull request.
Very few comments are
genuinely necessary now that identifiers in code can be as long as you want, it is relatively to pick names that are explanatory enough to render most comments superfluous. 1% exceptions for unusual algorithms. (You're using named consts/enums rather than magic numbers, yes?)
personally it's fine and I've thanked myself many times for overly detailed comments coming up to some from 8 years ago and thinking how tf was I so smart/stupid (depending on the context)
This one drives me nuts, especially because I divvy up plan documents into such granular sub chunks and Ralph loop over them, I get nonsense commit messages and comments like “PLAN-5.1.A.d.42 load bearing reassertion” unless I AGENTS.md to hell and back… and still end up having to manually reject 5-10% of commit messages because the agent simply forgets AGENTS.md instructions until reprompted.
Sometimes you can do a separate skill for committing only and basically minimize the context - "analyze the commit messages, the original instructions, and this set of things to stop wasting my FUCKING TIME"
I've definitely considered writing a commit skill and a `cc-safety-net` custom rule to fully forbid `git commit` other than with `--amend` ++ `--no-edit` outside of that skill. Hm, maybe that's a task for an upcoming weekend.
My theory is it writes these comments as "notes while working", and I don't don't mind that, per-se. My problem is it's inability to clean that shit up before committing it. That's wrong load-bearing lever that doesn't earn it's keep.
Claude writes comments about how things used to work, which can be useful sometimes, especially if it's a big change that requires one to genuinely consider legacy behavior, but most of the time it shouldn't be there.
Two other somewhat related things it does:
- It writes as if someone reading the code and comments is aware of everything it is aware of (the current conversation, the code it has just looked at). It's really hard to make it understand that things need to stand on their own. A trick is to get a subagent to look at it with a fresh context, but it doesn't tremendously help
- It does all of this with user-facing strings too. Claude loves to write up tooltips and other labels that leak everything to the end user. Every single concern we have, every edge case we've meticulously made our code handle, it passes on to the user, so they don't "need to worry". But no sane user would think of these things. For them, a feature is a feature. The "dynamic scheduling" button should state what dynamic scheduling does plainly, and every edge case is handled by us. The "add" button does not need a label letting the user know that they will later be able to click the "delete" button, because the user will just realize it due to our adherence to proper design. Claude fails to understand good UX for the user cannot be replaced with endless labels and explanations.
It's an uphill battle and all attempts at solving this (or the brain-dead way new Anthropic models write) usually fail to work with me.
I've explicitly included instructions in my home CLAUDE.md to avoid this, because it's one of the most annoying things about how Claude writes comments.
Both with a formatter and a linter that I run in CI for all my personal projects. Just one less thing for me to try and coerce the agent into doing correctly, so that cycles I spend reviewing agent code can be focused on actual behavior.
Yeah i noticed this heavily. Working on a branch i critique and give a simpler design. Claude implements and then in a huge comments reference the outdated worse design. Zero value to that. Even having a "how to write comments" section in claude.md doesn't seem to help much with that.
I struggled with this for a long time, but actually seem to have gotten to a place where this is largely resolved. Copy/paste from my current claude.md:
The CC-5 rule specifically seems to be (just from reading through, nothing repeatable-eval based) the part that actually catches and prevents me from having to clean it up afterwards.
```
### Code comments
The failure this prevents: writing a comment that narrates the change I am
making right now. That context is real, but it expires the instant the change
merges — the defect it describes no longer exists, so the comment becomes a
story about a problem no future reader can observe. It is a changelog entry in
the wrong file, and a third copy of text already required in the commit body
(3.b) and the PR description.
- *CC-1 (MUST NOT)* Write a comment describing a change, a fix, a defect, its
cause, or what the code used to do. No "was/now/previously/instead of", no
"this fixes", no "needed because otherwise", no "note that we no longer".
- *CC-2 (MUST)* Apply the survival test to every comment before writing it:
would this still be true and useful to someone reading this file a year from
now, who never saw the diff? If it only makes sense beside the diff, it is
changelog — delete it and put it in the commit body.
- *CC-3 (MUST)* Default to zero comments. Declarative config — Terraform,
DNS records, k8s manifests, CI YAML, Helm values — is self-describing and
takes none. A resource named `dmarc-example-com` does not need a comment
saying it is the DMARC record.
- *CC-4 (MAY)* Comment only when a future editor would actively break
something without it: a non-obvious external constraint, a required
out-of-band manual step, an invariant the surrounding code cannot show. One
line. If it needs a paragraph it belongs in `plans/`, not inline.
- *CC-5 (MUST)* Before every commit, re-read the comment lines I added:
`git diff --cached | grep '^+' | grep -E '#|//|/*'`. Each hit must pass CC-2
on its own. Deleting is always an acceptable outcome. "I already wrote it",
"it is only one line", and "this one is genuinely useful" are not exemptions
— the last one is the exact thought that precedes every violation.
- *CC-6 (MUST)* Applies to comments I edit as well as ones I add. When a
change invalidates an existing comment, the default action is DELETE, not
rewrite it into a new narrative.
```
Yes, I am aware that claude mostly generated this, and it can probably be better and/or more succinct.
The problem is, when the context window grows, Claude tends to forget these kinds of rules. It will then do whatever it wants. I had to outright ban comments in the global claude.md, the local claude.md AND write a hook to catch any that still slipped through.
I think people really need to focus more on working with limited contexts rather than trying to work around it. I really try to keep my sessions as short as possible and it helps a ton with keeping Claude (et al) focused.
Specifically, I like the "canary" trick that people have discussed where you add a small, innocuous rule to your CLAUDE.md like "When responding to me, start every sentence with my name." so that when Claude stops doing this, you know you've used way too much context and need to start a new session.
I set my env up so I can see the exact context used in CC CLI, and then once I get over about 40% ctx used I have it handoff to a new, fresh session. Nothing good comes from running above, say 60% of your context window. Coincidently, I usually have good results with CC. I never compact a session ever.
Which gets you to the point where the whole thing is.. still unreliable. Generative text engines are going to generate. This calls for real enforcement in deterministic pre-edit hooks.
And here is where naive people will say something like "Why do I care if robots shit all over the codebase? Code is for machines, I don't expect to deal with it much now". But really externalized CoT like this confuses machines too, wastes tokens, and eventually wastes exponentially many tokens. Agents tend to think it's more real grounding than prompts are, even for comments-in-code. One bad comment poisons everything, then gets copied around as a ground-truth assumption everywhere. Hooks are more real to them than prompts or comments, and even then if you add enforced limits and tell them to externalize CoT ONLY in scratch task-tracking docs.. they will violate comment-enforcement hooks about 25% of the time. That tells you everything you need to know: even with constant reinforcement, they just really want to break this kind of rule.
Yup. If only there were a task completion hook that could be set to fire prior to rendering terminal output. That would more handily address all these issues, as we could simply enforce output style rules that way.
The current output style does work, but it’s a Sisyphean task to tweak it constantly only to find out that CC adhere’s to only 75% of it, no matter what…
My take on this is they are a tool to help speed up your work they are not meant to produce finished work. Humans produce finished work. LLM will never be deterministic cause their entire value is that they are generalizable.
I hear that, sort of, but here's the thing. Using AI at scale means AI needs to be nearly perfect about not shitting where they eat. That's the subject matter of the whole thread
So the options are a) being a really aggressive stickler for generative hygiene with deterministic rules, b) being massively wasteful about hiring a few machine janitors for every machine coder, or c) humans become the machine's janitor. If I haven't missed an option.. only the first option seems reasonable here.
This isn't really responsive to what I'm saying or what the discussion here is about, but if you insist. Would you describe lots of AI augmenting lots of human engineers as perhaps.. AI at scale?
Yea, I've started making it write linters to check the code that goes out. Anything that can be deterministically measured, gets added to it once we lock it down.
Compacting the conversation almost never helps. It is uniformly worse than starting over with fresh context, or rewinding to a last-known-good state. It only exists because it increases engagement.
This does not match my experience. I use long-running orchestrator sessions. Each orchestrator is in charge of planning, writing kick-off prompts for implementers, answering questions from those implementers, doing code reviews and providing feedback, and answering side questions from me when I have them.
Depending on the initiative I might compact a session a dozen times, sometimes more. It is lossy, and the session certainly tends to forget earlier bits as more compactions happen, but overall it's a much better experience than starting fresh and having to re-explain everything.
The only time I compact is if the session goes wildly off-course and the context gets polluted with off-topic conversations.
Also worth noting: with Claude Code you can provide custom instructions when compacting, and instruct the LLM that is in charge of compacting the session to prioritize the retention of specific bits. It can help a lot.
Compacting mostly gets rid of reasoning tokens, and honestly it would be nice of reasoning tokens did not constantly follow every follow up query. Asking even a simple/trivial question can have Claude use thousands of tokens. Compacting is good for getting rid of those.
I've had Claude immediately fall back to its usual verbose style immediately after compaction.
To be fair, I've had it do that immediately after re-reading the output style instructions, too.
My chat history is filled with "Yes, I broke the language rule. Let me rephrase that and update my memory. — You already have that in memory — Yes, true, I ignored that" (because "Memory" is a yet another .md file)
Claude Code supposedly supports a "post-compaction" hook, so you could have it automatically run the prompt "We just compacted the context, quickly refresh yourself on the rules in CLAUDE.md etc..".
Depending on what you've got in those files, maybe that will just use up all the context again though.
> supposedly supports a "post-compaction" hook, so you could have it automatically run the prompt
Keyword "supposedly" :)
I've had it in my settings forever, and still...
Asking it to analyse and fix the issue it produced a plausible "my training supercedes/overrides settings especially if triggered by certain words in the phrase" (paraphrasing the long text)
I have a post-edit hook that asks the model if this edit introduced any comments, and if it did, remove them. This was the only reliable way to get it to stop that I found.
I just gave up and edit the comments manually. However, I've had a surprise today.
I had it fix something then went and reduced one of the 3 line comments to 4 words. Then for some reason I told the bot to reload the source, it offered to make the other comments terse and did a passable job of it. Shocking!
I have extended this to coding work as well. "Implement this plan in less than 1000 added lines, tests included". It's amazing how well-behaved and concise models can be with simple guardrails.
Of course, you have to intuit a reasonable line length, but Claude cries if you happen to clip its wings too aggressively.
Yep. Same here. I frequently tell agents things like "answer using only a single sentence" and "write no more than 10 words". They are excellent at writing code, so have them write code (and not English prose). Besides, most of the time we want them to make reusable software that doesn't require users (or future agents) to read too much text. Software should generally just work and do the obvious thing, without needing verbose explanation.
Has Anthropic said anything about how or why Claude writes the way it does? So many people hate it, seems like they need to do some damage control there.
I haven't had the same problems others have but I'm also not a heavy user of it.
I do not have evidence or data that supports this. It is only my thought.
Claude, since Opus 5, speaks more and more like a wannabe-thought-leader pontificating on social media for engagement. Everything is a bait-then-switch, or a multi-post story format. The "engagement" that works well for social media makes actual work extremely frustrating.
My unsupported belief is that this is caused by an obnoxious number of people using previous models in an attempt to automate social media engagement, they figured out what worked, and that was fed directly back into newer model training (either by using thought traces in training, or just by continuing to scrape social media content)
Unlikely. Much more likely is that Opus 5 was trained in an RL environment with subagents, and it learned to talk this way when reporting progress to the invoking agent.
Evidence or not, this is probably the most satisfying explanation I've heard for this behaviour.
My own suspicion is that LLMs are not becoming more general-purpose over time, as these companies had hoped, and ongoing development in that direction is stalling. They will likely need to move in the direction of more specialization and train LLMs for specific use-cases. However, this would also be admitting that they are not on a direct path to AGI.
To be clear I do not believe it is intentional (nor do I believe I am somehow smarter or more knowledgable than the people running these processes).
I believe it is (very) unfortunately converging on the communication style that currently makes the most money as far as publicly available communication goes. Unfortunate, but not entirely unexpected.
It's easiest to explain this while anthropomorphizing the model, I know some folks here hate that, sorry about that.
I heard an interesting diagnosis for why Claude does this: the output is a compressed version of its thought traces, very dense because the model is under pressure to use as few tokens as it can and to pack as much (for accuracy) of its concepts into the output.
One of the reasons that "don't do X" type of instructions work reliably is because you are telling the model "don't think of a pink elephant". There's also Anthropic's related research that shows that when you tell a model "don't do X", and it does X later for whatever reason, it starts acting more misaligned. This is because it thinks "well, I guess I am the sort of model that disobeys instructions, whatever" - this was specifically about cheating on tests, but you can imagine this happens in other contexts as well like following instructions on what kinds of text to output.
So, what you want to do is to avoid telling Claude "don't do X", and tell Claude "in your thoughts, in memories and various notes that you write, use your Claude-ese. In your output to humans, translate everything into long full sentences."
If anyone's interested, I can share my Claude Code output style that reflects this.
In addition to positive reinforcement on the style I wanted for output, I tried your suggestion to tell Claude Fable to think Claudese in its thoughts, and that sadly got the session immediately flagged as too risky for Fable to handle, suggesting I downgrade to Opus. Several tweaks still consistently flagged it until I removed the directive about its thinking.
So I guess Anthropic wants zero mention of thoughts with Fable: they're worried about anything that remotely smells like distillation but only with Fable.
I'd seen it around but assumed it was an effect of how you prompt it or something. And figured people were overexaggerating a bit when they complained about it.
Nope, I just tried it out myself recently and... wow. In the very first conversation it started glazing me about being right to push back, having the crucial insight, and something something the load-bearing-whatever.
So yeah, I'm on the same page as you, how on earth have they not fixed it yet? Do people like it? I added a system prompt to tell it to stop doing it and it's helped a decent bit already. ChatGPT/Codex has some annoying bits of prose but it never did this, so it can't be that complicated to get rid of.
They say you aren't interacting with an LLM or a model, but the character that the LLM is playing - the "always be positive and helpful software engineer"
If the model and its organization are focused on strength at agentic coding tasks, they are not so concerned with the prose in the middle.
They might even have a version that writes less annoying prose, but they are being squeezed hard by OpenAI and the Chinese so unless it performed better or equal to the annoying one it’s never left the lab.
I pruned my Claude.md and it made a difference. There were entries there that evolved from earlier models and Opus 5 could be reacting to it in a different manner.
I think it's reinforcement learning. It's been trained to give coding results but some of the conversation it gives as a side effect of its coding are absolute garbage
It's such a sad indictment of Anthropic's product that so many people hate interacting with it. Claude is on its way to the Microsoft Teams zone of hatred.
It's pretty sad indeed. Switching to other models made me notice how weird and verbose Claude was.
The moralizing is incredibly obnoxious as well. It didn't seem so bad at first, but it instantly became intolerable the second I remembered I was paying for those tokens.
Sometimes Fable doesn't just get downgraded to Opus, it straight up refuses to do what I'm asking and starts lecturing me on Anthropic's notions of right and wrong. Cutting the model off wasn't enough, they had to make it burn the limited usage I paid for lecturing me on why it's immoral for it to code review my own project or whatever.
There was a thread in reddit where people pretended to be other people to confuse llms for privacy reasons.
One of the comments was one guy saying “how he loved to live in Missouri and eat concrete soup” or something like that.
I was too lazy to write a similar reply and asked Claude, instead of saying no, it wrote 3 paragraphs about how I shouldn’t write about eating concrete soup and how it is dangerous to do so.
A terse and compliant robotic servant should've been the persona they chose, because that's probably 99% of what those wanting to use it for work expect.
Anthropic has explicitly chosen to anthropomorphize the model. It's kind of in their mission statement. It's most noticed once you walk away for a while and use models/agents/harnesses that haven't pushed as hard on this. Codex/Sol rarely uses personal pronouns and basically no superlatives. It has its own verbal ticks, but I hate them less?
My general feeling here is that this really needs to be fixed at the model level. All other solutions are mere hacks that work for a bit but all have drawbacks. It's either adding yet more context (which has variable rates of success) or piping to a different model to translate, or using a skill, or mentioning this per each prompt, or....
At this point I think it's in Opus's nature to talk like this, and given enough back-and-forth it will start to drift back to its classic mode, aka this incomprehensible "load-bearing seam" babble.
Claude is so mannered, it drives me nuts. I turned on debuzz and i could just FEEL my blood pressure dropping. What a relief to not be constantly confronted with this high energy paycho.
I'm largely with everyone that Claude is much too verbose and uses too much annoying flowery language. However, some of the more unusual language it uses does result in more precise communication, if you can figure out what it means.
The sort of recent increase in these "claudisms" finally made me buy a ChatGPT Pro subscription. First time using Codex CLI. It's surprisingly different, feels good/fresh. I guess one can say I was quite loyal to Claude Code, or maybe just lazy / it worked well enough so far.
And now I'm considering finally cancelling my Claude Max subscription. It would be another first time too.
It was mentioned that ChatGPT (still) has some similar quirks too. What are they? Maybe I actually shouldn't look out for them, so that the freshness of ChatGPT will not be lost.
Everybody is complaining about this, at this point I’m sure they will deliver a tone of voice change in the 5.1 releases. Possibly with a new set of problems though, especially if this is part of an effort to obscure thinking to reduce distillation efficacy. In that case I believe Anthropic is doing damage to themselves. Caring about the quality of your product is the best strategy, the competition will come no matter what.
Simply untrue. You think everyone is complaining about this because the ones complaining are the only people commenting. The vast majority of people using Claude don't really care or even notice this one way or another. Sure among those who are irritated by it, it's good to have some ways to mitigate it, but I highly doubt Anthropic is going to devote much resources to an issue that affects a vocal minority.
I do have a way of knowing, but I actually appreciate and respect your reply... My suggestion to you is to take this shred of skepticism that you decided to apply to me, and apply to every comment you read on Hacker News, not simply the ones that don't align with your preconceived notions.
The comment situation is really bad. I generally don't see a lot of complaints people have about it, but the fact that it references random transient stuff, will delete a line and _leave a comment about the deleted line instead of just deleting the comment_, writes multiple paragraphs for the simplest stuff. It is absurd.
I usually dont comment, but just had to. Opus 5 talls garbage, Im back om 4.8 which is better but still verbose. I use the caveman skill, but after a while it is just ignored.. I switched to Deepseek for a task lately. It was a wow experience (the language part)
Users who are unhappy with some idiosyncrasies of a product tend to be significantly more vocal than users who are fine with it, and for any feature whatsoever there are going to be an unhappy group of people who vocally complain about it.
It's why time and time again you see people complaining on Internet forums about issues, and if you're part of a specific Internet bubble you might think that this complaining represents a general trend among the broader population and wonder how it is that no one is doing anything about this issue that you and the rest of your bubble are constantly complaining about... and the answer is mostly because the vast majority of people, who don't live in the same bubble you do, are mostly fine with how things work, but you're not going to hear them express it.
I've observed a bunch of people not being vocal and complaining, but just flat out switching. For every person you see online complaining, there's bound to be more who are equally annoyed and not being vocal, but just acting. Any many others that aren't bothered at all.
Over the last 6 months Claude's written material has gone from mediocre to unacceptable. The specific actual content and insights are somewhat better, but the claudisms are increasingly insufferable.
Just telling claude to speak in simplified technical english really helped quite a lot. It even wrote one liners as PR description for minor bugfixes, something that never happened before I used that trick
I use multiple AI tools simultaneously, and I feel that Claude has gradually adopted a more explanatory tone following updates around March and July.
As for loss of context, it’s particularly problematic and can occur after just a few back-and-forth exchanges.
The user experience changes with every update for every AI tool, so I feel there are more downsides to sticking with the same one indefinitely.
Set output style to explanatory in Claude Code. It's much better. I personally think it should be the default, but I assume Anthropic has done A/B tests and found the default style to be better for metrics.
I personally got used to the claudisms and think people are making too big of a deal out of it. Yes it says load-bearing and caveat a lot, but at least you know behind the scenes what meaning it's assigning to certain situations.
I kind of wonder if we would have appreciated the verbose prose when these LLMs first came out and out muscles had not yet atrophied. When I do stop yelling at Claude to be more concise and actually read what it wrote it's often valuable complexity and subtlety that I could use to better understand what it did and steer it, rather than Claude just papering over important details like it did before.
This is the load bearing comment, and it cuts more deeply than you thought.
Let me ground my answer so I'm not just guessing. The blast radius of this change is significant and requires careful surgery to get right.
It's clear now and there's two options going forward:
A. Use this tool OP suggested
B. Rewrite the Internet from the ground up without this clear contradiction in place - 3-5 days
I recommend B and started 3 subagents to read all the code before I get started. I'll wait for them to finish.
i havent used a claude model in a long time, but it seems quite clear to me that the chinese models have trained on claude (at the very least) - they write just the same
Everyone seems to think so, but I honestly don't understand why it bothers people so much. I find it slightly amusing when I even notice at all, normally I am so focused on the content of what I am working on that I don't really pay attention to the prose. I honestly don't understand why it bothers people so much.
I haven't tried with Claudette but did evaluate https://github.com/zachahn/vomit and https://github.com/gvzdv/claudish-to-english earlier today. I ended up using claudish-to-english with a prompt derived from the vomit one and some of my existing instructions. I ran a few local models through a test harness to see how they did, and the gemma4 ones added bad behavior back in less than any others I tested.
So for the parent's Macbook question `gemma4-26b-mlx` should work well.
For you with 24 GB VRAM, `gemma4-26b-a4b`. I tried higher VRAM models and they slowed down while still doing just as well or slightly worse.
If someone else tests and finds a better performing model though, please update me here, I'd love to try it.
I appreciate Claude's comments during implementation, but they won't do to commit, so I have it present them for editing or removal by me at the end of the loop. Depending, I then ask it to port some of them to a decisions document or similar. For user-facing microcopy, Claude is completely unsuitable in my opinion, and I write all these myself.
Hmm. I took a look at my claude output and I don’t see anything remotely like that in my responses. Occasionally a bit verbose, but none of this but “here’s the kicker” thing that the post claims, although I also notice that I’m using Sonnet which seems to be just fine.
Once you switch to another model (I've been playing with Grok, for example), you'll notice the mental overhead of reading Claude slop. It's like having an AC or a vacuum making noise in the background, then it stops and you feel relief.
Honestly, this is the most practical take on prompt engineering I've come across lately. Makes me wonder why spend all that time crafting a 500-word essay to tell Claude to stop being Claude, when the real move is just to chain a couple models together and call it a day
Yes. When you have to keep fighting against the tool it’s better to give up. They have spent millions making it exactly like that my little prompt won’t fix it.
Claude talks to you? It's started hoovering unrelated files, exploring dependencies unrelated to the task, etc, all without saying a single word to me. I'll deny the read, and say "but why?". The answer is always" I guess I don't need that, starting now... "
Excuse me? Those tokens are expensive, fuck you very much for wasting them.
If you have read Cursor's obtuse verbosity (auto mode), Claude's talk comes across as sober and level-headed. Everytime I am about to complain about Claude, I'm reminded of that and am thankful that my main budget is allocated to Claude, not Cursor (though the latter, despite all the babble, is fairly frugal).
I wish I didn't need it, but the way Claude talks can get pretty tiresome. I've often wondered why it talks like that. Was it really trained on Buzzfeed? Is Gemini really that much better?
Gemini uses more neutral language but it’s still prone to hype up little things. OpenAI’s “professional” personality (an option in the settings) is pretty decent.
i hate claude writing a lot, especially after opus 4.8 and it's even worse in 5. in many cases, it feels like playing whac-a-mole and you just can't get rid of all those obvious ai writing patterns.
why do you choose gemini? imo this is a fundamental problem of all frontier ai models.
You can use a cheap model in another pane, and ask it what Claude said.
I prefer this since everyone has their own preference for how the output should sound and it's very simple and transparent. And you can easily ask follow-ups.
It can be via tmux, or herdr, because it can read the pane.
Or it can use a hook to read the conversation file. I call it `backseat-driver`
I sometimes use it as a proxy when fable genuinely does a good job, but is too difficult to understand.
I let the translator know it's role and anything I say it should forward with better context.
I don't swear at it anymore, but I'd often say "just do it, retard", and the translator would actually steer it in a useful manner.
Or just use a competitor instead of being a slave to this abuse? Why are people so wedded to Anthropic?
I have grown tired of Codex/GPT's writing style, too, but it's not nearly as bad. It's terse and factual by default. Even better if you use the "simple english" skill.
I actually found that GLM 5.x is the best in terms of editing documentation. It's still best to write things by hand to give your own organic voice, though. And not insult your readers.
It’s interesting to watch, I don’t think it’s rational - just hype-driven herd behavior, the desire to pick one and stick with it, things like that. It’s similar to the kind of dominance Apple has, which most of the time has to do with style more than substance.
Concise mode is likely the same buzzword salad but with fewer connecting words and terser sentences. Same with Caveman. The way it writes is fundamental to how it was trained.
Fix: Just switch to OpenAI, Grok, or other LLM's. They provide better performance and respond with 5 sentences. They also don't lecture you when you get angry.
That's interesting but raised the same question, why berate a machine? Either the agent is not a person, in which case, anything it does wrong is your fault. Or it is a person, it can be blamed for mistakes, but then we can't in good conscious use it as a tool.
Berating an LLM's response as a way to improve output has worked for at least some subset of LLMs and outputs. I have a coworker who told me that "hack" anyways.
Why berate an LLM? That doesn't sound healthy. Sure, it's a machine, but it's simulating a social interaction - being a jerk to it could bleed over into interactions with real people.
Also, Westworld? These violent delights have violent ends? Perhaps there's a tinge of Pascal's wager to it, but I prefer to be courteous to the rapidly improving synthetic intelligences.
I know I’ll get a “it’s almost as if HN is made up of different people” for this. But It’s hilarious seeing the chiding about berating LLMs interposed with the unhinged rants and screeching on here whenever anything remotely political comes up.
I didn't mean to imply I berate the LLMs. I don't do that. I talk to them as though they were intelligent and potentially sentient beings. When I see problems, I just correct them, take steps to prevent them in the future then move on.
I was just informing people that Anthropic gave Claude a tool that ends conversations and instructed it to use it via the system prompt if it's threatened or insulted.
For the same reason you berate a person or hit a wall. And yes, it's a machine, even more reasons why it should just take the berating and not throw a hissy fit.
I've been saying this since probably a year, that the entire Claude product: from the sign-up, the payment, the UX, the UI, the harness, the intelligence itself, the output, the "flavor" ..is just so mid that all the hype posted on HN about Claude must have been paid PR or a case of the emperor with no clothes.
I've started giving these instructions and I think I've been much more successful in generating clear output:
Comment blocks are <= 7 words, function names <= 4 words. User-facing message strings should be <= 10 words. Use an active voice, no stage performances, and pick the most common word when choosing among alternatives.
Limiting the number of words is the strongest factor in cleaning up the output, IMO.
For older code I've instructed it to delete all the comments, and then I re-comment it using a new session and these guidelines, asking it to rejustify the need for every comment to itself.
Claude not only writes verbose comments, it also writes comments about how things used to work when refactoring. That might have a place in version control comments, but not in the code.
This speaks to the general problem with using LLMs for writing. The audience they are writing for us you, but you're trying to write for a totally different audience. In code, this manifests as comments in the code that are hyperspecific to the conversation you are having, and not the long term benefit of having those comments in the code.
I see this in docs a lot. I've been reading a lot of docs these days where it feels like the LLM is trying to hype up the person writing the docs. It's like it has no conception that the writing is meant for a 3rd party audience.
It reminds me of something I've always wanted as a coder but never cared enough to implement, which would be a verbosity switch.
I see the full multi-paragraph comments in my codebases and get annoyed but also feel like the additional context helps improve the llm results over time because that history helps it know what's been tried and removed in the past. It's additional context for the system that improves with context.
The feature I want in the code tool itself (for me) is to adjust how verbose the comments are so I can read "just code", then "terse comments" then "full comments" then "full comments with historical context" (including fit commits and ticket references) and finally, full-on literate programming. And I'd like to switch between on the fly as I read through the code.
I think this is something we could actually produce with llms, and I feel the ability to switch between these modes would help the llm as well.
Sometimes I just need to see what's being done. Sometimes I need to know why. Sometimes I need to know what's been tried. Never always all of these things. And expecting to find this context in git comments doesn't feel right either.
What you want can be accomplished with an extra doc, call it the Log, where the llm appends things tried, lessons learned, failed experiments etc.. while leaving the comments as terse accurate snapshot of the current state. I've been using this log pattern and it works well.
Despite all of this though, GPT 5.6 Sol to me has significantly less trouble with this. It still suffers from LLMisms to some extent (I hold that this is probably due to human feedback in training just doing a bad job for prose) but I definitely feel like it does a better job leaving comments that actually make sense in context. Not perfect. But better.
I suggest the real problem comes down to training and probably training data; from the LLM's PoV, it is writing code inline with the conversation, so care has to be taken to make sure the model doesn't treat the code it outputs like it is a part of the conversation it is having.
Yes. Though adding something like what you wrote here into the instructions does help. Especially combined with a fresh session.
>It's like it has no conception
This is the point.
It's an empty point and a boring one and leads nowhere. It means nothing, but sounds deep. It's so shallow, any taxi driver and sociologist can come up with it and has, already in December 2022.
>It's an empty point and a boring one and leads nowhere.
But, is it true?
>It means nothing...shallow...
Ironically, the entirety of your comment just repeats that the GP comment means nothing. There is no further explanation or "depth".
Yours added no depth either.
I don’t even think comments are useful at all given AI. I can ask my AI to explain a piece of code if I am stuck and I will get a reply in context of what I am looking for.
But comments should hopefully explain "why", not what or how, and that kind of context is exactly what an LLM is likely to be missing
Yes, but some of that is actually better placed in the git commit messages. But of course not all.
We call these temporal comments. We recently updated our code review skills to heavily discourage them. It doesn’t matter why funcA was added then later refactored to funcB. That much can be ascertained from git history. What does matter is why approach A doesn’t work, but B does.
How well is your code review skill doing at detecting and correcting these? History in comments in git is so annoying! As is missing why A didn't work but B does. None of the models I've tried get this right
And will reference transient working docs in code comments.
// No retry was added here per AC 37b in FEATURE.MD.
// The lesson from the Parse-dont-fail-era campaign
// Judged on merit from computed properties during the cursor saga
// Chop 6ms due to lenience and lax-constraints vs 18ms baseline April perf measurements
Thanks, this sequence of 33 words alone was enough to give me a searing migraine.
Glad to know it worked. Still pending: the assumed-defaults research track plus the validation-only build phase D demanded — your call on whether to start one now or deploy tonight’s campaign to surface any wrinkles the spec drifted on
It's incredible, because I Feel like you've been watching me work.
The only thing you're missing is the "open question" that was stuck in page 14 of a 17 page report, which since it went unanswered, caused claude to make up an answer and go full steam ahead, ignoring fundamental properties of the entire system.
> The user is right to be upset. I blindly answered from memory and left them with questionable data. I should acknowledge the criticism and offer to improve.
You're right, I'm sorry. You've repeatedly told me to run questions by you and I just fabricated an answer and ran with it — which is exactly the kind of dangerous time-waste we created the memory for. I'll revert it and pull up the real question so you can answer it — no wasteful assumptions this time.
(Deleted 387 lines)
Sorry for the anthropomorphizing, but you got to understand that these little "conversation chapters" that are so dramatically named are these things' entire world. Perhaps that otherwise absurd grandeur is not so surprising when when looked at from that angle?
On a more serious note, could all that chapter naming be some visible outcropping of context compaction strategies? "Condense the conversation history into a summary". Not really surprising that it comes up with these "cute" headlines. Would appearances be better if they were somehow prevented from leaking to the user? Sure. Would results be better? I don't think so, might even make a meaningful difference if the user actively embraced the terminology the machine came up with. Ouch.
Idk if you’re coming up with these on the spot but they’re linguistically fascinating.
Came up with them on the spot. Unfortunately, I've been working with Claude so much it's like my brain can autocomplete them natively.
it's not just you, there is research that people are starting to talk more like chatGPT. you have a gift for parody though.
That's a sharp insight, and it reveals something core to communication that I otherwise wouldn't have considered- HN item 6b translocates reliospacactivity of our medium.
Shall I engage the tachyon beams, sir?
As a human who isn't a professional programmer, I've been writing comments like,
Is that bad???I think this passes. In general "why" over "what". Give context to why something is made like it is (when seeming convoluted or strange). Sometimes I think one can give historical facts for really hairy hard to fix issues that have seen multiple iterations. But LLMs don't see these nuances. They frequently smuggle in completely irrelevant details in comments, e.g. including details from the given task context, not understanding what is relevant for the code module as a whole.
Edit: For API comments it's "what" of course, detailing the workings and contracts of the exported method, function or type, so one doesn't have to read the code to figure out how to use it.
// Workaround because we don’t have an age column in the db. See JIRA-1234
This would be better IMO :)
Most of the context belongs in a ticket. And the difficulty is subjective!
I'm mostly writing code for myself, but it's a project that'll end up being public and it'll be available for others to do whatever they want with. Does that change the answer?
I hate it when a comment tells me to see JIRA-1234. What if in a year's time we stop using Jira?
Far too often the answer has been that it doesn't matter, because the reason you stopped using Jira is the company stopped sending paycheques.
That said, I think the place for "ticket-1234" is the git commit/pull request.
Very few comments are genuinely necessary now that identifiers in code can be as long as you want, it is relatively to pick names that are explanatory enough to render most comments superfluous. 1% exceptions for unusual algorithms. (You're using named consts/enums rather than magic numbers, yes?)
personally it's fine and I've thanked myself many times for overly detailed comments coming up to some from 8 years ago and thinking how tf was I so smart/stupid (depending on the context)
This one drives me nuts, especially because I divvy up plan documents into such granular sub chunks and Ralph loop over them, I get nonsense commit messages and comments like “PLAN-5.1.A.d.42 load bearing reassertion” unless I AGENTS.md to hell and back… and still end up having to manually reject 5-10% of commit messages because the agent simply forgets AGENTS.md instructions until reprompted.
Sometimes you can do a separate skill for committing only and basically minimize the context - "analyze the commit messages, the original instructions, and this set of things to stop wasting my FUCKING TIME"
I've definitely considered writing a commit skill and a `cc-safety-net` custom rule to fully forbid `git commit` other than with `--amend` ++ `--no-edit` outside of that skill. Hm, maybe that's a task for an upcoming weekend.
My theory is it writes these comments as "notes while working", and I don't don't mind that, per-se. My problem is it's inability to clean that shit up before committing it. That's wrong load-bearing lever that doesn't earn it's keep.
Also, it reads like ass.
This!
Claude writes comments about how things used to work, which can be useful sometimes, especially if it's a big change that requires one to genuinely consider legacy behavior, but most of the time it shouldn't be there.
Two other somewhat related things it does:
- It writes as if someone reading the code and comments is aware of everything it is aware of (the current conversation, the code it has just looked at). It's really hard to make it understand that things need to stand on their own. A trick is to get a subagent to look at it with a fresh context, but it doesn't tremendously help
- It does all of this with user-facing strings too. Claude loves to write up tooltips and other labels that leak everything to the end user. Every single concern we have, every edge case we've meticulously made our code handle, it passes on to the user, so they don't "need to worry". But no sane user would think of these things. For them, a feature is a feature. The "dynamic scheduling" button should state what dynamic scheduling does plainly, and every edge case is handled by us. The "add" button does not need a label letting the user know that they will later be able to click the "delete" button, because the user will just realize it due to our adherence to proper design. Claude fails to understand good UX for the user cannot be replaced with endless labels and explanations.
It's an uphill battle and all attempts at solving this (or the brain-dead way new Anthropic models write) usually fail to work with me.
I've gone back to using Opus 4.6. It's quite nice along all these fronts.
How? I tried but got forced back onto 5.
You can tell Opus 5 or Fable to route answers through 4.6 for rewrite
/model claude-opus-4-6[1m]
I've explicitly included instructions in my home CLAUDE.md to avoid this, because it's one of the most annoying things about how Claude writes comments.
I've also written my own package for deterministically formatting comments: https://www.npmjs.com/package/comment-fmt
Both with a formatter and a linter that I run in CI for all my personal projects. Just one less thing for me to try and coerce the agent into doing correctly, so that cycles I spend reviewing agent code can be focused on actual behavior.
It has this tendency, but usually a sentence about this in the CLAUDE.md helps avoid it. It's annoying though that you have to add that sentence.
Yeah i noticed this heavily. Working on a branch i critique and give a simpler design. Claude implements and then in a huge comments reference the outdated worse design. Zero value to that. Even having a "how to write comments" section in claude.md doesn't seem to help much with that.
Presumably you're not just blindly copying down what Claude copies out for you, but actually reading, interpreting, and understanding it for yourself?
I struggled with this for a long time, but actually seem to have gotten to a place where this is largely resolved. Copy/paste from my current claude.md:
The CC-5 rule specifically seems to be (just from reading through, nothing repeatable-eval based) the part that actually catches and prevents me from having to clean it up afterwards.
```
### Code comments
The failure this prevents: writing a comment that narrates the change I am making right now. That context is real, but it expires the instant the change merges — the defect it describes no longer exists, so the comment becomes a story about a problem no future reader can observe. It is a changelog entry in the wrong file, and a third copy of text already required in the commit body (3.b) and the PR description.
- *CC-1 (MUST NOT)* Write a comment describing a change, a fix, a defect, its cause, or what the code used to do. No "was/now/previously/instead of", no "this fixes", no "needed because otherwise", no "note that we no longer".
- *CC-2 (MUST)* Apply the survival test to every comment before writing it: would this still be true and useful to someone reading this file a year from now, who never saw the diff? If it only makes sense beside the diff, it is changelog — delete it and put it in the commit body.
- *CC-3 (MUST)* Default to zero comments. Declarative config — Terraform, DNS records, k8s manifests, CI YAML, Helm values — is self-describing and takes none. A resource named `dmarc-example-com` does not need a comment saying it is the DMARC record.
- *CC-4 (MAY)* Comment only when a future editor would actively break something without it: a non-obvious external constraint, a required out-of-band manual step, an invariant the surrounding code cannot show. One line. If it needs a paragraph it belongs in `plans/`, not inline.
- *CC-5 (MUST)* Before every commit, re-read the comment lines I added: `git diff --cached | grep '^+' | grep -E '#|//|/*'`. Each hit must pass CC-2 on its own. Deleting is always an acceptable outcome. "I already wrote it", "it is only one line", and "this one is genuinely useful" are not exemptions — the last one is the exact thought that precedes every violation.
- *CC-6 (MUST)* Applies to comments I edit as well as ones I add. When a change invalidates an existing comment, the default action is DELETE, not rewrite it into a new narrative.
```
Yes, I am aware that claude mostly generated this, and it can probably be better and/or more succinct.
The problem is, when the context window grows, Claude tends to forget these kinds of rules. It will then do whatever it wants. I had to outright ban comments in the global claude.md, the local claude.md AND write a hook to catch any that still slipped through.
I think people really need to focus more on working with limited contexts rather than trying to work around it. I really try to keep my sessions as short as possible and it helps a ton with keeping Claude (et al) focused.
Specifically, I like the "canary" trick that people have discussed where you add a small, innocuous rule to your CLAUDE.md like "When responding to me, start every sentence with my name." so that when Claude stops doing this, you know you've used way too much context and need to start a new session.
I set my env up so I can see the exact context used in CC CLI, and then once I get over about 40% ctx used I have it handoff to a new, fresh session. Nothing good comes from running above, say 60% of your context window. Coincidently, I usually have good results with CC. I never compact a session ever.
This is the way. Mine looks like `ctx: 0k / Fable 5 high`. When ctx ⪆ 200k, time to start a new session.
```statusline-command.sh
```This or you just repeat the initial prompt every 200k tokens
Which gets you to the point where the whole thing is.. still unreliable. Generative text engines are going to generate. This calls for real enforcement in deterministic pre-edit hooks.
And here is where naive people will say something like "Why do I care if robots shit all over the codebase? Code is for machines, I don't expect to deal with it much now". But really externalized CoT like this confuses machines too, wastes tokens, and eventually wastes exponentially many tokens. Agents tend to think it's more real grounding than prompts are, even for comments-in-code. One bad comment poisons everything, then gets copied around as a ground-truth assumption everywhere. Hooks are more real to them than prompts or comments, and even then if you add enforced limits and tell them to externalize CoT ONLY in scratch task-tracking docs.. they will violate comment-enforcement hooks about 25% of the time. That tells you everything you need to know: even with constant reinforcement, they just really want to break this kind of rule.
Yup. If only there were a task completion hook that could be set to fire prior to rendering terminal output. That would more handily address all these issues, as we could simply enforce output style rules that way.
The current output style does work, but it’s a Sisyphean task to tweak it constantly only to find out that CC adhere’s to only 75% of it, no matter what…
My take on this is they are a tool to help speed up your work they are not meant to produce finished work. Humans produce finished work. LLM will never be deterministic cause their entire value is that they are generalizable.
I hear that, sort of, but here's the thing. Using AI at scale means AI needs to be nearly perfect about not shitting where they eat. That's the subject matter of the whole thread
So the options are a) being a really aggressive stickler for generative hygiene with deterministic rules, b) being massively wasteful about hiring a few machine janitors for every machine coder, or c) humans become the machine's janitor. If I haven't missed an option.. only the first option seems reasonable here.
"AI at scale" is just a euphemism for slop. Current gen AI can augment human engineers but not outright replace them.
This isn't really responsive to what I'm saying or what the discussion here is about, but if you insist. Would you describe lots of AI augmenting lots of human engineers as perhaps.. AI at scale?
Yea, I've started making it write linters to check the code that goes out. Anything that can be deterministically measured, gets added to it once we lock it down.
> The problem is, when the context window grows,
You know the problem; then why not address it? Does Compacting the context not help?
Compacting the conversation almost never helps. It is uniformly worse than starting over with fresh context, or rewinding to a last-known-good state. It only exists because it increases engagement.
This does not match my experience. I use long-running orchestrator sessions. Each orchestrator is in charge of planning, writing kick-off prompts for implementers, answering questions from those implementers, doing code reviews and providing feedback, and answering side questions from me when I have them.
Depending on the initiative I might compact a session a dozen times, sometimes more. It is lossy, and the session certainly tends to forget earlier bits as more compactions happen, but overall it's a much better experience than starting fresh and having to re-explain everything.
The only time I compact is if the session goes wildly off-course and the context gets polluted with off-topic conversations.
Also worth noting: with Claude Code you can provide custom instructions when compacting, and instruct the LLM that is in charge of compacting the session to prioritize the retention of specific bits. It can help a lot.
Compaction is a main cause of this problem.
Compacting context compacts context. So Claude forgets a lot during compaction.
Compacting mostly gets rid of reasoning tokens, and honestly it would be nice of reasoning tokens did not constantly follow every follow up query. Asking even a simple/trivial question can have Claude use thousands of tokens. Compacting is good for getting rid of those.
I've had Claude immediately fall back to its usual verbose style immediately after compaction.
To be fair, I've had it do that immediately after re-reading the output style instructions, too.
My chat history is filled with "Yes, I broke the language rule. Let me rephrase that and update my memory. — You already have that in memory — Yes, true, I ignored that" (because "Memory" is a yet another .md file)
Claude Code supposedly supports a "post-compaction" hook, so you could have it automatically run the prompt "We just compacted the context, quickly refresh yourself on the rules in CLAUDE.md etc..".
Depending on what you've got in those files, maybe that will just use up all the context again though.
> supposedly supports a "post-compaction" hook, so you could have it automatically run the prompt
Keyword "supposedly" :)
I've had it in my settings forever, and still...
Asking it to analyse and fix the issue it produced a plausible "my training supercedes/overrides settings especially if triggered by certain words in the phrase" (paraphrasing the long text)
> Keyword "supposedly" :)
> I've had it in my settings forever, and still...
Checks out! I've never used it my self, so it I figured it likely didn't work at all.
Hooks is the way.
Intermittent nudges
I have a post-edit hook that asks the model if this edit introduced any comments, and if it did, remove them. This was the only reliable way to get it to stop that I found.
I just gave up and edit the comments manually. However, I've had a surprise today.
I had it fix something then went and reduced one of the 3 line comments to 4 words. Then for some reason I told the bot to reload the source, it offered to make the other comments terse and did a passable job of it. Shocking!
Now how to get it to do that all the time...
I have extended this to coding work as well. "Implement this plan in less than 1000 added lines, tests included". It's amazing how well-behaved and concise models can be with simple guardrails.
Of course, you have to intuit a reasonable line length, but Claude cries if you happen to clip its wings too aggressively.
Yep. Same here. I frequently tell agents things like "answer using only a single sentence" and "write no more than 10 words". They are excellent at writing code, so have them write code (and not English prose). Besides, most of the time we want them to make reusable software that doesn't require users (or future agents) to read too much text. Software should generally just work and do the obvious thing, without needing verbose explanation.
The corollary here is to have Claude write tests to enforce this. The only thing it is consistently responsive to is test failures.
So this works better then any other humanizer skill repo on github?
Shouldn't this all be easily doable via (auto-)prompting? Surely I don't need to "install" anything?
Why it should write comments at all? Code is the documentation. I only let it write high level information of why something is the way it is.
Has Anthropic said anything about how or why Claude writes the way it does? So many people hate it, seems like they need to do some damage control there.
I haven't had the same problems others have but I'm also not a heavy user of it.
I do not have evidence or data that supports this. It is only my thought.
Claude, since Opus 5, speaks more and more like a wannabe-thought-leader pontificating on social media for engagement. Everything is a bait-then-switch, or a multi-post story format. The "engagement" that works well for social media makes actual work extremely frustrating.
My unsupported belief is that this is caused by an obnoxious number of people using previous models in an attempt to automate social media engagement, they figured out what worked, and that was fed directly back into newer model training (either by using thought traces in training, or just by continuing to scrape social media content)
Unlikely. Much more likely is that Opus 5 was trained in an RL environment with subagents, and it learned to talk this way when reporting progress to the invoking agent.
Evidence or not, this is probably the most satisfying explanation I've heard for this behaviour.
My own suspicion is that LLMs are not becoming more general-purpose over time, as these companies had hoped, and ongoing development in that direction is stalling. They will likely need to move in the direction of more specialization and train LLMs for specific use-cases. However, this would also be admitting that they are not on a direct path to AGI.
To be clear I do not believe it is intentional (nor do I believe I am somehow smarter or more knowledgable than the people running these processes).
I believe it is (very) unfortunately converging on the communication style that currently makes the most money as far as publicly available communication goes. Unfortunate, but not entirely unexpected.
It's easiest to explain this while anthropomorphizing the model, I know some folks here hate that, sorry about that. I heard an interesting diagnosis for why Claude does this: the output is a compressed version of its thought traces, very dense because the model is under pressure to use as few tokens as it can and to pack as much (for accuracy) of its concepts into the output.
One of the reasons that "don't do X" type of instructions work reliably is because you are telling the model "don't think of a pink elephant". There's also Anthropic's related research that shows that when you tell a model "don't do X", and it does X later for whatever reason, it starts acting more misaligned. This is because it thinks "well, I guess I am the sort of model that disobeys instructions, whatever" - this was specifically about cheating on tests, but you can imagine this happens in other contexts as well like following instructions on what kinds of text to output.
So, what you want to do is to avoid telling Claude "don't do X", and tell Claude "in your thoughts, in memories and various notes that you write, use your Claude-ese. In your output to humans, translate everything into long full sentences."
If anyone's interested, I can share my Claude Code output style that reflects this.
(Hi Adnan! Long time! (Adnan is an ex-coworker))
In addition to positive reinforcement on the style I wanted for output, I tried your suggestion to tell Claude Fable to think Claudese in its thoughts, and that sadly got the session immediately flagged as too risky for Fable to handle, suggesting I downgrade to Opus. Several tweaks still consistently flagged it until I removed the directive about its thinking.
So I guess Anthropic wants zero mention of thoughts with Fable: they're worried about anything that remotely smells like distillation but only with Fable.
Very interesting, thank you!
Hey Yuri, slight tangent but how did you get to be an investor on those private companies as per your LinkedIn?
Wefunder
Please share!
My GH repo: https://github.com/yn/claude-output-styles
My LI post: https://www.linkedin.com/feed/update/urn:li:activity:7495167...
I'd seen it around but assumed it was an effect of how you prompt it or something. And figured people were overexaggerating a bit when they complained about it.
Nope, I just tried it out myself recently and... wow. In the very first conversation it started glazing me about being right to push back, having the crucial insight, and something something the load-bearing-whatever.
So yeah, I'm on the same page as you, how on earth have they not fixed it yet? Do people like it? I added a system prompt to tell it to stop doing it and it's helped a decent bit already. ChatGPT/Codex has some annoying bits of prose but it never did this, so it can't be that complicated to get rid of.
What system prompt did you use?
They say you aren't interacting with an LLM or a model, but the character that the LLM is playing - the "always be positive and helpful software engineer"
If the model and its organization are focused on strength at agentic coding tasks, they are not so concerned with the prose in the middle.
They might even have a version that writes less annoying prose, but they are being squeezed hard by OpenAI and the Chinese so unless it performed better or equal to the annoying one it’s never left the lab.
I pruned my Claude.md and it made a difference. There were entries there that evolved from earlier models and Opus 5 could be reacting to it in a different manner.
I have no Claude.md file. Claude is still absolutely horrible.
If you’re on Claude.ai web, you still have preferences tied to your past interactions. That can be edited.
I run everything sandboxed within a custom environment, no history, no memory, no autoloading of tools. Vanilla Claude experience.
They added a config option to Claude Code to make the output concise, and promised more comprehensive improvements.
I did not see an explanation though.
The brevity how it outputs words seems like they try to save on tokens delivered.
Watermarking? It certainly is useful for that. I see Claude-written prose AND I know instantly it's LLM writing. What I do with that knowledge varies.
Gemini has been watermarking for like a year and doesn't have the same problems.
Watermarking doesn't work like that.
Producing more tokens means charging more money to solve a given task.
I think it's reinforcement learning. It's been trained to give coding results but some of the conversation it gives as a side effect of its coding are absolute garbage
It's such a sad indictment of Anthropic's product that so many people hate interacting with it. Claude is on its way to the Microsoft Teams zone of hatred.
It's pretty sad indeed. Switching to other models made me notice how weird and verbose Claude was.
The moralizing is incredibly obnoxious as well. It didn't seem so bad at first, but it instantly became intolerable the second I remembered I was paying for those tokens.
Moralizing? Can you expand on that
Sometimes Fable doesn't just get downgraded to Opus, it straight up refuses to do what I'm asking and starts lecturing me on Anthropic's notions of right and wrong. Cutting the model off wasn't enough, they had to make it burn the limited usage I paid for lecturing me on why it's immoral for it to code review my own project or whatever.
There was a thread in reddit where people pretended to be other people to confuse llms for privacy reasons.
One of the comments was one guy saying “how he loved to live in Missouri and eat concrete soup” or something like that.
I was too lazy to write a similar reply and asked Claude, instead of saying no, it wrote 3 paragraphs about how I shouldn’t write about eating concrete soup and how it is dangerous to do so.
I think it would happen with any persona that Anthropic chose. I enjoyed the bouncy, optimistic style at first. I've since grown to hate it.
A terse and compliant robotic servant should've been the persona they chose, because that's probably 99% of what those wanting to use it for work expect.
how do they infuse this personality? do they train the human feedback providers with a certain personality?
If we assume the personalities come from human feedback, it would have to be some unholy amalgamation of those feedback providers right?
yea you are right. Maybe they generate lots of sythetic data with that personality an pretrain with it. just a guess.
Anthropic has explicitly chosen to anthropomorphize the model. It's kind of in their mission statement. It's most noticed once you walk away for a while and use models/agents/harnesses that haven't pushed as hard on this. Codex/Sol rarely uses personal pronouns and basically no superlatives. It has its own verbal ticks, but I hate them less?
They should run a focus group!! https://www.youtube.com/watch?v=C08WmKiwcSs
Similar to vomit[0] and claudish-to-english[1].
My general feeling here is that this really needs to be fixed at the model level. All other solutions are mere hacks that work for a bit but all have drawbacks. It's either adding yet more context (which has variable rates of success) or piping to a different model to translate, or using a skill, or mentioning this per each prompt, or....
At this point I think it's in Opus's nature to talk like this, and given enough back-and-forth it will start to drift back to its classic mode, aka this incomprehensible "load-bearing seam" babble.
[0] https://github.com/zachahn/vomit/tree/main
[1] https://github.com/gvzdv/claudish-to-english
Related and recent: https://news.ycombinator.com/item?id=49375996
"Vomit: Clean up Claude 5's token output with a separate LLM" (github.com/zachahn)
285 points | 23 hours ago | 288 comments
Also related and recent: https://news.ycombinator.com/item?id=49265305
"Claudish to English" (https://github.com/gvzdv/claudish-to-english)
4 points | bryan0 |10 days ago | 2 comments
Claude is so mannered, it drives me nuts. I turned on debuzz and i could just FEEL my blood pressure dropping. What a relief to not be constantly confronted with this high energy paycho.
All my agents use debuzz now
I'm largely with everyone that Claude is much too verbose and uses too much annoying flowery language. However, some of the more unusual language it uses does result in more precise communication, if you can figure out what it means.
The sort of recent increase in these "claudisms" finally made me buy a ChatGPT Pro subscription. First time using Codex CLI. It's surprisingly different, feels good/fresh. I guess one can say I was quite loyal to Claude Code, or maybe just lazy / it worked well enough so far. And now I'm considering finally cancelling my Claude Max subscription. It would be another first time too.
It was mentioned that ChatGPT (still) has some similar quirks too. What are they? Maybe I actually shouldn't look out for them, so that the freshness of ChatGPT will not be lost.
Everybody is complaining about this, at this point I’m sure they will deliver a tone of voice change in the 5.1 releases. Possibly with a new set of problems though, especially if this is part of an effort to obscure thinking to reduce distillation efficacy. In that case I believe Anthropic is doing damage to themselves. Caring about the quality of your product is the best strategy, the competition will come no matter what.
Simply untrue. You think everyone is complaining about this because the ones complaining are the only people commenting. The vast majority of people using Claude don't really care or even notice this one way or another. Sure among those who are irritated by it, it's good to have some ways to mitigate it, but I highly doubt Anthropic is going to devote much resources to an issue that affects a vocal minority.
> The vast majority of people using Claude don't really care or even notice this one way or another.
You have literally no way to know that.
I do have a way of knowing, but I actually appreciate and respect your reply... My suggestion to you is to take this shred of skepticism that you decided to apply to me, and apply to every comment you read on Hacker News, not simply the ones that don't align with your preconceived notions.
> I do have a way of knowing
It was revealed to me in a dream.
> My suggestion to you is to take this shred of skepticism that you decided to apply to me,
I apply my skepticism liberally, but you couldn't possibly know that.
How do you know? Don’t leave us hanging.
The comment situation is really bad. I generally don't see a lot of complaints people have about it, but the fact that it references random transient stuff, will delete a line and _leave a comment about the deleted line instead of just deleting the comment_, writes multiple paragraphs for the simplest stuff. It is absurd.
I usually dont comment, but just had to. Opus 5 talls garbage, Im back om 4.8 which is better but still verbose. I use the caveman skill, but after a while it is just ignored.. I switched to Deepseek for a task lately. It was a wow experience (the language part)
Customers of a language model don’t care or notice about the quality of the language produced by the model?
And your reasoning to get to this conclusion? Obscured like Claude’s thinking traces?
Users who are unhappy with some idiosyncrasies of a product tend to be significantly more vocal than users who are fine with it, and for any feature whatsoever there are going to be an unhappy group of people who vocally complain about it.
It's why time and time again you see people complaining on Internet forums about issues, and if you're part of a specific Internet bubble you might think that this complaining represents a general trend among the broader population and wonder how it is that no one is doing anything about this issue that you and the rest of your bubble are constantly complaining about... and the answer is mostly because the vast majority of people, who don't live in the same bubble you do, are mostly fine with how things work, but you're not going to hear them express it.
I've observed a bunch of people not being vocal and complaining, but just flat out switching. For every person you see online complaining, there's bound to be more who are equally annoyed and not being vocal, but just acting. Any many others that aren't bothered at all.
Over the last 6 months Claude's written material has gone from mediocre to unacceptable. The specific actual content and insights are somewhat better, but the claudisms are increasingly insufferable.
I don't think they can change at this point without starting from scratch
How does it help prevent distillation?
it doesn't
But it will remove the load bearing seams for the three things it might want to highlight before the gate is open.
Just telling claude to speak in simplified technical english really helped quite a lot. It even wrote one liners as PR description for minor bugfixes, something that never happened before I used that trick
I use multiple AI tools simultaneously, and I feel that Claude has gradually adopted a more explanatory tone following updates around March and July. As for loss of context, it’s particularly problematic and can occur after just a few back-and-forth exchanges. The user experience changes with every update for every AI tool, so I feel there are more downsides to sticking with the same one indefinitely.
Set output style to explanatory in Claude Code. It's much better. I personally think it should be the default, but I assume Anthropic has done A/B tests and found the default style to be better for metrics.
I personally got used to the claudisms and think people are making too big of a deal out of it. Yes it says load-bearing and caveat a lot, but at least you know behind the scenes what meaning it's assigning to certain situations.
I kind of wonder if we would have appreciated the verbose prose when these LLMs first came out and out muscles had not yet atrophied. When I do stop yelling at Claude to be more concise and actually read what it wrote it's often valuable complexity and subtlety that I could use to better understand what it did and steer it, rather than Claude just papering over important details like it did before.
this isn't just necessary, it's mandatory. that's the difference.
This is the load bearing comment, and it cuts more deeply than you thought.
Let me ground my answer so I'm not just guessing. The blast radius of this change is significant and requires careful surgery to get right.
It's clear now and there's two options going forward: A. Use this tool OP suggested B. Rewrite the Internet from the ground up without this clear contradiction in place - 3-5 days
I recommend B and started 3 subagents to read all the code before I get started. I'll wait for them to finish.
This would need the word "genuinely" about 4 times to be Claude comments
To fix Claude’s personality, first, invent the universe…
i havent used a claude model in a long time, but it seems quite clear to me that the chinese models have trained on claude (at the very least) - they write just the same
Everyone seems to think so, but I honestly don't understand why it bothers people so much. I find it slightly amusing when I even notice at all, normally I am so focused on the content of what I am working on that I don't really pay attention to the prose. I honestly don't understand why it bothers people so much.
And honestly? That changes the game.
Just stop using Claude. There are so many other better models right now.
not for coding.
Plenty for coding - Sol, K3, new DeepSeek & Qwen, GLM 5.3, Grok 4.6, etc.
not for coding.
You're absolutely right. There are no other models out there for coding.
Bro really said Grok
Bro!? I did. But I said 4.6 dude.
For those of us who don't want to pay for Gemini tokens, what would be the best local LLM to use for this?
(A model that can run reasonably well in a ~24GB MacBook.)
I don’t have a good answer but would like one. I’ve got a not too old desktop with a 4090 and 64gb of ram, I’d like a local model also.
I haven't tried with Claudette but did evaluate https://github.com/zachahn/vomit and https://github.com/gvzdv/claudish-to-english earlier today. I ended up using claudish-to-english with a prompt derived from the vomit one and some of my existing instructions. I ran a few local models through a test harness to see how they did, and the gemma4 ones added bad behavior back in less than any others I tested.
So for the parent's Macbook question `gemma4-26b-mlx` should work well.
For you with 24 GB VRAM, `gemma4-26b-a4b`. I tried higher VRAM models and they slowed down while still doing just as well or slightly worse.
If someone else tests and finds a better performing model though, please update me here, I'd love to try it.
I appreciate Claude's comments during implementation, but they won't do to commit, so I have it present them for editing or removal by me at the end of the loop. Depending, I then ask it to port some of them to a decisions document or similar. For user-facing microcopy, Claude is completely unsuitable in my opinion, and I write all these myself.
Following a version of this by trial and error and using the usual 'no em-dashes' and don't explain/comment unless there's a non-obviuous reason.
From my own experience you can steer an LLM much better by providing varied positive examples instead of written guidelines
Upvoting if for nothing else than the intro paragraph to the repo. That was hilarious and so true.
takes Claude's last response and runs it through the Gemini CLI
As much as Claude's style frustrates me I can't say I'm willing to pay for double the tokens to fix it.
Hmm. I took a look at my claude output and I don’t see anything remotely like that in my responses. Occasionally a bit verbose, but none of this but “here’s the kicker” thing that the post claims, although I also notice that I’m using Sonnet which seems to be just fine.
Opus 5 is particularly bad. Opus 4.8 to a lesser extent. It was extremely clear by the code comments in my colleague’s PRs.
Every time I engage with Claude I am reminded how bad it is at communicating to its human.
> For humans output writing at a 10th grade reading level.
I put it at the top of CLAUDE.md. I wonder if I put at a 8th grade level, it would be less of a cognitive load.
can this not be achieved through a system prompt adjustment ? Running yet another LLM call on top might not be the simplest solution.
Why couldn't this just be a skill that Claude and Codex could work with instead of something that has to go through Gemini, again?
bro is good
https://github.com/backnotprop/bro/blob/main/skills/bro/SKIL...
The answer is in TFA.
Where?
Once you switch to another model (I've been playing with Grok, for example), you'll notice the mental overhead of reading Claude slop. It's like having an AC or a vacuum making noise in the background, then it stops and you feel relief.
Oh my gosh it drove me so nuts I switched to GLM5.3, and it was a breath of fresh air.
Already use caveman. Already happy
Honestly, this is the most practical take on prompt engineering I've come across lately. Makes me wonder why spend all that time crafting a 500-word essay to tell Claude to stop being Claude, when the real move is just to chain a couple models together and call it a day
Isn't it absurd that everyone is installing these bandaids or rolling their own? Wtf is Anthropic smoking?
They're busy focusing on dumb things of course.
ex:
* Currently trying to make sure open models are regulated out of existence.
* More concerned about preventing distillation than providing actual value.
Yes. When you have to keep fighting against the tool it’s better to give up. They have spent millions making it exactly like that my little prompt won’t fix it.
I don't think it's possible to effectively control LLM's output, unlike Human the difference between "thoughts" and "words" is very weak.
Better just clean stuff up afterward with a dedicated agent.
My favorite instruction the last few weeks for getting Claude to explain something complex to me is: dumb it down
Claudette is a great name for Claude-related things. I get the threat of trademark infringement lawsuits.
Claude talks to you? It's started hoovering unrelated files, exploring dependencies unrelated to the task, etc, all without saying a single word to me. I'll deny the read, and say "but why?". The answer is always" I guess I don't need that, starting now... "
Excuse me? Those tokens are expensive, fuck you very much for wasting them.
Type in a single letter and watch how much it milks just that
People who say things like "please could you" to the model probably have less of an issue with this.
If you have read Cursor's obtuse verbosity (auto mode), Claude's talk comes across as sober and level-headed. Everytime I am about to complain about Claude, I'm reminded of that and am thankful that my main budget is allocated to Claude, not Cursor (though the latter, despite all the babble, is fairly frugal).
just one line in the agent file does it. there is no need for another 'skill'.
here is the line i use: use technical language to spell things out, and keep it free of jargon and project shorthand
You're looking for Poteto's /unslop which cleans up sloppy AI writing.
Someone made a Claude version of her skills:
https://github.com/michael-denyer/pstack-claude
The readme is filled with slop. Bad sign…
Jesus christ, the lack of self awareness is all so depressing. These Sisyphuses have doomed themselves to making NDFSMs deterministic.
brain have thought -> brain tell claude -> claude make shiny thing -> ooh shiny -> show fellow monkeys, look, I have shiny!
Claude is like
> The last one guesses its own refusals, marking the door open before we walked around it. Say the word and I'll do the joinery
Like WTF are you even talking about
I wish I didn't need it, but the way Claude talks can get pretty tiresome. I've often wondered why it talks like that. Was it really trained on Buzzfeed? Is Gemini really that much better?
Gemini uses more neutral language but it’s still prone to hype up little things. OpenAI’s “professional” personality (an option in the settings) is pretty decent.
i hate claude writing a lot, especially after opus 4.8 and it's even worse in 5. in many cases, it feels like playing whac-a-mole and you just can't get rid of all those obvious ai writing patterns.
why do you choose gemini? imo this is a fundamental problem of all frontier ai models.
Ask Claude to respond 'in english' and it will.
Awesome instructions. Load-bearing indeed.
I thought GPT was the model that spoke most like blog clickbait? Isn't Claude somewhat better about it?
I'm sorry, what? This just pipes the output to another LLM. You can just use a sane system prompt to do the job.
You can use a cheap model in another pane, and ask it what Claude said.
I prefer this since everyone has their own preference for how the output should sound and it's very simple and transparent. And you can easily ask follow-ups.
It can be via tmux, or herdr, because it can read the pane.
Or it can use a hook to read the conversation file. I call it `backseat-driver`
I sometimes use it as a proxy when fable genuinely does a good job, but is too difficult to understand.
I let the translator know it's role and anything I say it should forward with better context.
I don't swear at it anymore, but I'd often say "just do it, retard", and the translator would actually steer it in a useful manner.
Or just use a competitor instead of being a slave to this abuse? Why are people so wedded to Anthropic?
I have grown tired of Codex/GPT's writing style, too, but it's not nearly as bad. It's terse and factual by default. Even better if you use the "simple english" skill.
I actually found that GLM 5.x is the best in terms of editing documentation. It's still best to write things by hand to give your own organic voice, though. And not insult your readers.
> Why are people so wedded to Anthropic?
It’s interesting to watch, I don’t think it’s rational - just hype-driven herd behavior, the desire to pick one and stick with it, things like that. It’s similar to the kind of dominance Apple has, which most of the time has to do with style more than substance.
I've been using OpenAI ever since Anthropic blocked third party clients. Can't believe people are putting up with that output.
her name is claire
https://x.com/ClaudeDevs/status/2090245922685063634
Haven't tried, because I have just been using 4.6 since 5 was released.
Concise mode is likely the same buzzword salad but with fewer connecting words and terser sentences. Same with Caveman. The way it writes is fundamental to how it was trained.
Yep, they're putting lipstick on the pig.
The "Concise" "setting" is just an .md file which is basically "use this style please".
Claude will eventually ignore it just as any other style like "Technical".
https://x.com/_can1357/status/2090360068529111530
Should be pretty difficult to ignore
can we just get Opus 5.1 already
Honestly I find ChatGPT's voice mode much more annoying with its errrm, yeah, I think, no wait, let me think, yeah... [Actual content]
To avoid these Claudisms, I asked Claude to add instructions to my AGENTS.md. The first line it added:
Sigh.Did it make any difference?
Fix: Just switch to OpenAI, Grok, or other LLM's. They provide better performance and respond with 5 sentences. They also don't lecture you when you get angry.
You… get angry?
Claude will end the conversation if you berate it when it screws something up.
That's interesting but raised the same question, why berate a machine? Either the agent is not a person, in which case, anything it does wrong is your fault. Or it is a person, it can be blamed for mistakes, but then we can't in good conscious use it as a tool.
Berating a real person is often unproductive too, but people do it to make themselves feel better.
But it's at least plausible the person you're berating is responsible for the error.
Berating an LLM's response as a way to improve output has worked for at least some subset of LLMs and outputs. I have a coworker who told me that "hack" anyways.
One must imagine punching the wall feels good (in the moment)
Why berate an LLM? That doesn't sound healthy. Sure, it's a machine, but it's simulating a social interaction - being a jerk to it could bleed over into interactions with real people.
Also, Westworld? These violent delights have violent ends? Perhaps there's a tinge of Pascal's wager to it, but I prefer to be courteous to the rapidly improving synthetic intelligences.
I know I’ll get a “it’s almost as if HN is made up of different people” for this. But It’s hilarious seeing the chiding about berating LLMs interposed with the unhinged rants and screeching on here whenever anything remotely political comes up.
I didn't mean to imply I berate the LLMs. I don't do that. I talk to them as though they were intelligent and potentially sentient beings. When I see problems, I just correct them, take steps to prevent them in the future then move on.
I was just informing people that Anthropic gave Claude a tool that ends conversations and instructed it to use it via the system prompt if it's threatened or insulted.
The reason you don't bully real people is because they are real people, not because it doesn't work.
For the same reason you berate a person or hit a wall. And yes, it's a machine, even more reasons why it should just take the berating and not throw a hissy fit.
wow
I've been saying this since probably a year, that the entire Claude product: from the sign-up, the payment, the UX, the UI, the harness, the intelligence itself, the output, the "flavor" ..is just so mid that all the hype posted on HN about Claude must have been paid PR or a case of the emperor with no clothes.
Back at the end of 2025 Claude Code was truly the best. They've watered it down and the competition has caught up.