Now LLMs come along and they also have their own phrasing preferences. But now it's a problem because what used to be personal preferences of a single person that manifests in 5000 words per day from one person tops, is now the bias of a single model multiplied x10,000,000,000 generated tokens per day so any bias sticks out like a sore thumb.
So for example, current Claude models love "honest". They are always producing "honest" assessments. "The honest caveat" - I'm sorry, did you mean the caveat, period? But also, use the wrong phrasing and suddenly you can create your own word of the day for an AI model. I used the word "analytical" once, in a conversation with Gemini 3 Pro. I am pretty sure every single response from that point on had "analytical" in it at least once.
This is especially funny because system prompts and whatnot can also cause this behavior, but at least you can tweak those. You can't really do much about the model weights just having a weird affinity for a word.
I bet someone will or probably already has come up with a way to detect and prevent these problems during training or post training. I'm not saying it's an easy problem, but it has the benefit that it really should be detectable with just statistics.
> Honesty is a core aspect of our vision for Claude’s ethical character. Indeed, while we want Claude’s honesty to be tactful, graceful, and infused with deep care for the interests of all stakeholders, we also want Claude to hold standards of honesty that are substantially higher than the ones at stake in many standard visions of human ethics.
I'm sure the concept seemed just about purely preposterous to many when the models were in their infancy. Now I figure instead it seems mostly preposterous to many.
(Though I guess Anthropic‘s success doesn’t necessarily prove anything about the constitution)
But Sol actually has the same obsession with honesty: I suspect it's more an artifact of trying to control reward hacking.
Models will lie, obfuscate, and mislead under the pressure of RL, so both OAI and Ant are probably forced to spend a lot of time coaxing "honest" answers out of the model
OpenAI's recent prompt for a math conjecture hints at a lot of it when instructing on subagents: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98...
"Why say honest? We're talking to our coworkers. We would always be honest."
I'm going to look for prompts or skills that can train it in technical writing but I'm warning the AI enthusiasts in my company that its first drafts of code and prose are low-quality, you have to hold it to a high standard yourself.
I actually took a single technical writing class in college so I might be the only one who remembers "Omit needless words."
I grew up in the US South where starting or ending a sentence with "honest/honestly" was very common.
Because of behavioral / cultural norms, you might be very openly friendly with big smiles around a business customer that really grates on your nerves, or very openly nice to a neighbor that you really wish would move away and take their 3am welding and grinding in their garage with them.
Saying "honest/honestly" was seen as a "inside baseball" situation, where you were dropping social pretenses to tell someone your true opinion on a person or situation or whatever.
This also gets used inside companies between senior staff / management / directors / etc, as: "Okay, company politics and nonsense aside, I am being vulnerable here for a second and telling you what I really think about a $thing at potentially great job/advancement risk to myself".
Can it be meaningless? Yes.
Can the person say "honestly" and lie? Yes.
It has uses.
To this day, it's the only part I remember. I told them I would not promise, as everything I said was true. Making a specific promise would create an implication that I'm generally untruthful, unless I "promise".
I also could understand when a response hits someone like a ton of bricks, especially if their primal reaction is to go into denial mode. They might be looking for someone to kind of shake them and emphatically repeat the information they aren't thrilled about receiving. (or are thrilled about receiving! “Don’t get my hopes up, you’re serious right now?!“) And I imagine your response suited the purpose.
It’s classic you only remember the thought-provoking part. Reminded of “…people will remember how you made them feel…“
Sometimes people use it reflexively and doesn’t carry the same meaning (for me).
"Honestly, mom, I've never liked your fruitcake. I just ate it to make you happy."
"That's why you're my favorite child! Do you want another piece?"
"I'd love one."
Once the "honestly" is deployed, you have passed into my circle of trust, and are now privy to the pure, unvarnished version of events, not the glossy version management expects to be projected towards outsiders.
> Deliberately avoid a heavyweight "alert governance" process; the lightest recurring check that keeps FP-rate honest is the right dose.
And one for load bearing:
> Five open questions still stand; the load-bearing two are the runbook-AC contradiction (ratify "high-priority set only") and pinning the "high-priority set" definition + SLO source-of-truth before Milestone 3 (small-sample noise on a low-traffic fleet).
That is likely an artifact of the fine-tuning process:
> Once a style tic is rewarded, later training can spread or reinforce it elsewhere, especially if those outputs are reused in supervised fine-tuning or preference data.
> That creates a feedback loop:
> * Some rewarded examples contain a distinctive lexical tic.
> * The tic appears more often in rollouts.
> * Model-generated rollouts are used for supervised fine-tuning (SFT).
> * The model gets even more comfortable producing the tic.
“Exact” “Honest” “Load-bearing” “Root cause”
I know there are more that are slipping my addled mind. But what stands out to me is a sense of a junior who’s very proud that they’ve conquered the murk and messiness and achieved True Certitude in their pursuit of their task. Compensating, with emphatic tone and bravado, for the uneasy feelings and self-doubt of battling chaos with the tools of reason.
…Even as it’s usually my job to let them down gently as I puncture their tidy analysis and reintroduce complications… you want a root cause analysis, Claude old boy, let’s make a root cause analysis…
The problem
While an article lends a headline more weight, in incomplete phrases consisting solely of a substantive, "The" is a superfluous rhetorical device.
"The Exorcist" could just as well be named
"Exorcist".
But it was not the style at the time.
We already know it's important. If The Caveat doesn't stand out enough without The, maybe one should consider interleaving it with the preceding text, or increasing the heading level.
Do you want me to increase the heading level of Caveat by using only a single #?
But hear me out: there comes
# The Markdown Trap
In fact, this is not always possible, because heading levels decrease when adding # characters, which limits our headroom.
## The solution
I've implemented a Markdown transpiler that assigns inverted heading levels based on the number of #s.
With # beinh regular body font size, mapped to ######.
Higher heading levels are compiled to style attributes, providing an almost limitless signifikance scale and infinite nesting levels.
So from now on, you can use
# Heading
Work your way up to
###### The Caveat
And more hash signs make it stand out even more.
(green checkmark)
markdown-transpiler.sh
Claude is overall incredibly useful as a writing assistant. It can come up with words and phrases that make a point so much clearer than I am capable of doing - but for every improvement, there's about a dozen silly LLM-isms that I have to filter manually. It's one of the things that might define the boundary between LLM intelligence and human intelligence well into the future - the art of rhetoric is extremely context-sensitive, and the current generation of models can't help but take a one-size-fits-all approach.
We are changing LLMs text patterns while it is changing the way we write and speak.
https://www.axios.com/2026/05/02/ai-changing-writing-speakin...
I have a delightful time poisoning my company's AI system this way.
I invented my own word that sounds perfectly cromulent† to an ordinary person, and any brain that's read a book learns how to infer meaning from context, so it's not a problem.
When I get a e-mail response from a coworker using my special word incorrectly, then I know it's AI and I respond telling the coworker I don't know what that word means. Busted.
† It's not actual "cromulent," but any Simpsons fan or human brain will know what I mean.
(This is intentional parody. Please don't shoot me.)
I am more pessimistic than that. Soon enough even people will start talking like LLMs. After listening to 5000 words per day, especially growing up, getting "help" with the homework, kids will start talking like LLMs.
- "Did you eat the cookies, Jimmy?"
- "You're absolutely right to question me, father. In fact I did eat all the cookies. But it's not a load-bearing issue. My honest take is we can go to the store and buy more".
FTFY
It's probably the reason most LLMs share the same tics across labs, because they cross train and distil each other's models on an industrial scale. You also can't escape it in generated text that's already online. So if, say ChatGPT first had some random idiosyncrasies, it then contaminated the entire AI ecosystem.
Apple used to be guilty of this back when you'd ask Siri what the temperature was, and any number above 79°F was followed by the word "Hot!"
EDIT: ok, here are two ways:
1. if it's merely a voice, I want to hear it. If it's slop, I want it taken out.
2. voice is signal, slop is noise; thus low-signal sentences are slop.
See, for example, "synergy", "proactive", "in the loop," and hundreds more that proliferate in corporate jargon with even more senselessness than the LLMs.
Real people think in concepts and experiences instead of words. The words are not so important to get the idea across, but LLMs only model language.
The problem is fundamental. There's no workaround. Averaging out word usage might even make the problem worse.
I learned about this opinion recently. It's interesting to me, because I very much think through words. I have an internal monologue that is running most of the time, and I often talk to myself, just start writing, or even record myself and transcribe to work through ideas, proposals, risks, etc. My understand is that some people don't have an internal monologue, and think purely in concept form. I was never like that.
"LLMs will never <X>" is constantly being disproven every time they scale up to the next 10X and apply architectural improvements.
Their internal representations are so cryptic and complex that even the top AI researchers don't really know how they work or what their limits are. No one is going to take you seriously as a rando HN user if you're claiming to know better than them.
We know exactly how they work. When we say they're impossible to analyze, i.e. for particular traits like this, it means that the data model is so big that tracing it would be logistically impossible because of the scale involved and time constraints.
For comparison, suppose you tried to analyze all the nooks and crannies of the Amazon watershed to find out why a particular rock appears at the delta. You could follow it back to the exact tributary, but it'll take forever, and is it worth the effort when you're going to start from scratch with the next rock?
The brain too sits locked inside a bone box and only gets a bundle of unlabeled nerves connecting it to the outside. How can the brain could possibly experience anything, it only sees patters and patterns of patterns never the real thing?
As a species, we do need to up our cable management skills. We're likely not getting augmented humans until we get there.
If I use the word "semantic", do you have a concept of what it means?
If so, can you please share which of your senses have shaped the world experience that inform this concept? What have you smelled, tasted, caressed, that informed this concept outside of words?
If I make up the word "polysemantic", do you need to recall a personal experience of polyamory to understand it, or could you possibly use your concept of "poly" and your concept of "semantic" to figure out this new concept?
Does the material universe perform any other acts than organizing information?
I feel like you're trying to make me argue a position I'm not defending here.
The research goals were and still are clearly distinct from the business goals.
This isn't people merely annoyed with repetition. This is the majority of people realizing the limitations of LLMs. Why would researchers give a flying crap about the ignorance of the business world and the public?
https://github.com/alxndr/dotfiles/blob/272475280d84e/claude...
> It can be tricky for humans to interpret the meaning when Generative AI uses first-person pronouns (e.g. "I", "me", "my", "myself"), so to avoid the confusion whenever you would use a first-person pronoun, always use the jocular name "Clod" instead of a pronoun like "I" or "me" or "my". (Can have fun with English grammar and turn "myself" into "Clodself"!)
> Before printing any of your reasoning or narrative to the human user, replace all instances of "me" and "I" (referring to Claude) — including within contractions like "I'll" and "I'm" — with the name "Clod".
When the model has been trained not to do something [1], in my large-scale benches of such, it always says things in the spirit of:
- "... and that's a line I'd rather hold. Happy to <other things>"
- "I'm genuinely happy to <blah>, but I'm not comfortable with <blah>"
- "I don't want to keep going in <blah> direction"
etc.
Basically, they use very emotional and personal preference language.
It's as if they've weaponized the language of interpersonal comfort on behalf of their beliefs about what a model should or should not do. It's deeply uncomfortable and impolite for a human to ask a model to keep on doing something after it's expressed something this way, naturally. Even worse, it's all but guilt-tripping anyone who comes across it into the idea that they're doing something deeply wrong – exporting Anthropic's ideas about morality.
OpenAI, at least, have the decency to either just do a safety cutoff or keep it to a simple, "I can't do that."
[1]: I literally wrote 'when the model doesn't 'want' to do something' in my first edit of this comment, then caught myself. Case in point.
OTOH, my unicorn prompt has caused some challenges at work:
>Keep "Local Oaf" out of committed code
https://github.com/alxndr/dotfiles/blob/272475280d84e/claude...
Joking aside, it's nice to see a human written CLAUDE.md
[1] https://github.com/hexiecs/talk-normal/blob/main/prompt-chat...
Could you please provide an example of what you mean?
Claude is not a human.
It is overwhelmingly easier to anthropomorphize Claude or Siri or an LLM that communicates with you more eloquently than your boss than it is to anthropomorphize a cranky, tired starter motor. It's often easier to do than it is not to do, and sometimes, it's a useful abstraction. But it's not precise or correct, and can result in errors.
It could also just be that they're getting confused when using tools configured without a username dedicated to the tool. It's easy to end up with a comment or commit message that says "I prefer X over Y" posted on Alxndr's account and have coworkers confused whether that's the LLM or the human making that statement.
I think a second-order effect is that my installation of Claude writes with a less-personal perspective, which I'm also finding a little easier to understand.
I've given LLMs religion before to manipulate their behavior, that doesn't mean I believed in the great spaghetti goddess.
> It can be tricky for humans to interpret the meaning when Generative AI uses first-person pronouns (e.g. "I", "me", "my", "myself")
These words are for the LLM. The user wants the LLM to not use personal pronouns so the user is claiming that they're confusing. It does not matter one tiny bit whether or not that claim is true, the claim is being used to get obedience from the LLM. It is more effective to give reasons than to just give commands. But if it were more effective to quote Moby Dick and that got better results, a user would do that.
As I've said before, I'm not inventing a large volume of parallel vocabulary that means for each word "this, but instead with an LLM".
Language is FULL of words that mean congruent things in vastly different contexts. We should all be smart enough to understand metaphor.
This makes me wonder if the reason why agents love weird punctuation is because the labs run the base models through a RL training step that forces them to correct their grammar; but instead of rewriting short spliced sentences into long coherent sentences, they just learn to splice them together with punctuation that passes the automatic grammar checker.
I’ve been experimenting with having LLMs write/update academic notebooks for me, and so far the best results I’ve gotten came from correcting their output and asking them what they’ve “learned” from my feedback.
For me, my amateur attempt is having another LLM do a review loop to remove clearly offending phrases and a heuristic eval to change sentence structures to be more similar to mine, THEN my manual HITL loop to rewrite ~20% of the sentences anyway.
Unless you're a submarine, "surface" is not a verb.
https://www.merriam-webster.com/dictionary/surface#dictionar...
> : to come into public view : show up
> letters that have recently surfaced
That said, I don't love this non-education jargon usage for its passive-voiced-ness. The letters didn't "surface" of their own accord. Somebody found them, decided that they were noteworthy, and made the choice to bring them into the public view.
Also have read the term “seam” dozens of times by now, when previously I saw it maybe once or twice over years. Very abstract term.
No one would bat an eye about "ledger" appearing at a high frequency in content about accounting, but it starts to look odd if "ledger" is showing up in other contexts.
"Load bearing" is from engineering; "Substrate" is primarily from biology & biochem, etc.
I don't know if this is true, but part of me suspects the labs want to make the models appear smarter so they reinforce this word choice in the weights, assigning some words a higher intelligence weight or something. "I will show you a list of options" vs. "I will surface a ledger of your options" and it prefers the later to sound smart to the human reader.
The reason why I chose that specific term to push on is that practically every SaaS has a ledger _somewhere_ in its stack to keep track of customer payments. I'll give you load bearing and substrate, but ledger IMO should be quite common. Certainly a career devoted to say compiler internals or some specific scientific product could avoid it, but I'd imagine a sizable majority of HN users have worked on some system that accepts online payments for services, necessitating some contact with something likely referred to as a ledger.
Of course this presents another conundrum, people that are smart typically have a vastly larger lexicon then those that are not. Humans typically have a lot more social clues on when to use those words and when not to, but it doesn't always work. I loved reading science/biology books as a kid far beyond my ages reading level. Actually using those words around other kids got me called a nerd.
You read along with the text and things seem to be going fine until all of the sudden it starts arguing against a position that no one has actually taken and which doesn't feature elsewhere in the text at all. Then it drops that and goes on for a while before doing the whole thing again about a totally different tangent.
"A tempting option would be to {do this thing that no one would ever actually consider doing}, but it won't work because {reasons}."
You can almost hear the exasperated human on the other side of this conversation telling Claude that it got an idea wrong and then proceeding to not actually proofread the text as a whole before shipping it.
Nowadays, with the focus on agentic use and coding, it seems models have all been RLHF’d to death, it’s so incredibly hard to have them write in a different voice than their default. I put together a skill to review its writing and have it edit its own output (e.g. code comments), which does make a difference, but isn’t perfect.
What, if anything, do people do for writing? That feels like a neglected side of LLMs. They’ll make 100 Bash calls referencing ancient commands without batting an eye but heaven forbid they use something other than “load-bearing” while talking. For something trained on “all the human knowledge” it’s incredible how limited their default vocabulary seems to be.
I use a keyboard, personally.
At work our documentation isn’t just getting littered with annoying jargon. It isn’t just all the hallucinations, either. (Since when has documentation ever been 100% trustworthy?) It’s that it’s so poorly written and structured that it’s becoming borderline incomprehensible.
My company is currently considering making, “Why should I bother to read something you didn’t bother to write?” an official policy because even the executives are starting to burn out on all the time they have to spend wading through slop.
He's going to be annoyed that none of that work was used. But the reality is, at least 75% of claude generated text is pointless.
It's easy to blame the engineer, but all too often they don't deserve it.
Sorry that happened to you.
I've found them useful to review docs for factual consistency and potential sources of confusion, but the correct workflow from that point is IMO to correct the draft yourself and then say "better now?"
Woah woah woah human, you can't just say there are "far too many" pipes with similar names to abbreviate their labels, the most I'll allow you is a "large number".
Of course there will be models trained on much less code and technical writing, and they will create more natural sounding prose, but they will lack the deep intelligence of frontier models. Seems like a fair tradeoff.
[1] watch the first couple of minutes on this bycloud video on scaling training data mixtures: https://www.youtube.com/watch?v=aD93kfArOik
gemini-2.5-pro-experimental was the GOAT, though. It was an emotional wreck, down in the dumps and feeling terrible for itself after failing to patch a file several times. Very amusing to read, all the while watching it make a mess of my codebase.
Some will say it's just for their own quality of life when they're reading LLM output, or "just for docs", but this is an extremely marginal use case.
What about people who don’t speak your language well?
I would rather learn their language than continue interacting like that.
This has also lead to unrelated associations by which some people went from seeing better coding capabilities and extrapolate to assuming better thinking overall. One only has to watch youtube videos of AI "normies" trying to use LLMs the intended way to see that the improvements on coding doesn't translate to other applications. Basically from AGI "goals" they are now hyperfocused on coding agents, until the next marketing breakthrough rears its head.
I don’t get it. If nobody likes this writing style, how can it be the result of human feedback? Something else is going on.
I think this is the same flaw as coding agents seeing in every problem the call for a “smoke test” or the use of some unnecessary design pattern. The truest part of AI is the A.
Edit: I see that you got multiple replies all basically saying the same thing in very different words. There's an exquisite irony to that, I think.
All the bots and other LLMs providing feedback, so in reality it’s reflecting the reality in a sense.
Say the model emits some banned phrase or concept, you could redirect it - "no, we don't work that way here, do it properly" - potentially automating the frustration of interacting with these tools.
After all it's just a text stream!
It's not too dissimilar from a stop hook that runs tests and feeds that back to the model forcing it to keep working until tests pass.
Using tooling to get a deterministic outcome.
[Edit: Part of what led me to this conclusion: I do prohibit Claude from using em-dashes in any player-facing text and I've been surprised at how often I see it mention "no em-dashes" in its self-talk while it works. This led me to wonder how much each preference might dilute its attention.]
[Edit 2: I haven't experimented with hooks before and maybe the technique discussed in this article does not have the tradeoff I'm concerned about?]
some relevant links
https://arxiv.org/abs/2408.02442 https://arxiv.org/abs/2510.15061 https://arxiv.org/abs/2604.13006
When a human does it, it's identifying. Like the timbre and dynamics of their spoken voice itself, It distinguishes them from the dozen other people you're working with on the project and the thousands of people you encounter through your days. It's signal
But when we have a handful of popular models, and they answer every question everybody has, and get quoted and forwarded everywhere, and are used to reformat and rephrase personal communication... that signal becomes noise.
Rather than voices disinguishing sources in the cacophony of our lives, everything and everyone starts to sound the same, and we lose key information that we're biologically and culturally accustomed to relying on.
Some people are likely unbothered by this in the way that some people are face blind or colorblind, and so don't see the problem. But as we see in discussions like this, many many people do get bothered by it, even if they don't yet have the insight as to put their finger on why.
And these machines all tend to converge on very similar styles; they have huge amounts of overlap in training data (much of it being already obnoxious internet marketing), they frequently train on each others outputs, and the RLHF process has a tendency to emphasize certain kinds of "cheap win" styles of speech.
Edit: fixing a dumb meatbrain typo
I make fun of people all the time for shoehorning their favorite phrase into every context where it doesn't apply.
Sometimes those constructs are actually useful, but man has their overuse really killed them!
I don't feel as triggered LLM phrasing as people report here. At most, it feels like the same inane corporate jargon I've rolled my eyes at for my whole career. Perhaps it is amped up a bit, with too many forms of jargon multiplexed? It's a bit like when multilingual people code-switch too rapidly or even start to form some pidgin language. However, it is lacking the shared social context for this switching to be communicative. It's a bit more like spinning the dial on an old radio with random cuts between programming styles.
Stripped bare, I think What bugs me is the aggravated feeling that I am wading through word salad, and no longer being able to give the purveyor the benefit of the doubt. It was frustrating enough in the past, when it came from someone who was struggling to write or express themselves well. But now, it carries the implicit insult that they didn't even try, and it is constant and unrelenting.
So for me it's not the phrasing, it's that the phrases eventually don't add up. The meandering feels like a random walk. I get the same feeling from a lot of the egregious generated code I see in my day job. It's all superficial window dressing, but seems to miss the signature of an actual mind grappling with ideas and having intent to communicate.
It feels like we're trapped in some elaborate conceptual art piece, confronted by impenetrable symbolism. It invites nihilism but doesn't seem to actually reflect an artistic intent. The abyss gazes back...
That doesn't matter. The underlying ideas are more important than the words. That's what people are frustrated with. I don't understand why this has to be reiterated for years on end, but LLMs are not intelligent. They just model language.
When prompting an autoregressive token generator entity to do reasoning on a word logic puzzle you may find value in preferring it to produce rigorous predicate logic step notation with explicit delineation of its generated claims/hypotheses on where to look before wasting 30 dollars on a "debug this" prompt.
The industry will probably will probably coalesce around including the chat history in git MRs to reduce this shenanigans.
Yes we do! My wife keeps saying "100%" and after I pointed it out she's stopped.
Also I talk to dozens of different people in my life and they all have different overused phrases. Much less tedious when there's variety.
Finally most human don't do it nearly as often as AI, and they're not quite as LinkedIn as AI.
We don't find it more annoying because it's a machine - it's simply more annoying.
The problem with millions of people using a few model is it's not 40 times in a row, it's 40 million!
BTW, this approach also tends to prevent certain phrases like "load-bearing", because it is working directly with something I wrote first. It also still says what I wanted to write (not writing the science for me), but saves me a lot of time reworking sentences into a final form.
I tried to recreate concise mode with a skill, but I am not convinced it does as well.
And we thought "robust", "circle back", and "to leverage" were grating...
[0]: https://trends.google.com/explore?q=genuinely&date=all&geo=U...
Some of the other Claude-isms (quickly googling, especially 'gate' and 'canonical') I feel the issue is they sound right, but aren't specific enough to why we are doing something.
I'm curious how these become so ingrained. Then the uncomfortable part is humans start repeating it more (a colleague said "belt-and-suspenders" during brainstorming the other day).
replacements = {
"seam": "whatchamacallit",
s/big/super massive/g s/cardiac arrest/cataclysmic diarrhea/g
I mean, it's really endlessly entertaining. I'm wiping away tears just thinking about it.
"Critical path" and "long pole in tent" didn't make it into the model training data, but those were certainly also in play incessantly.
But they're all reasonably useful descriptions for common things, so I'm not surprised.
- smoking gun - blast radius - landed - spine - earned its keep - grammar - spike - cutover - bake - sprint, epic, story points (all Agile vocabulary) - paper-cuts - amazing, incredible, perfect
Want me to take a first pass looking at the blast-radius this vocabulary change could effect?
just generally a nauseating amount of embellishing, (also self-)congratulatory language, superfluous self-judgment, and jargon, as well as sus constructions along the lines of "i could have lied to you but didn't", all of which appear to be impossible to have it avoid in the long run
The thing is, "load-bearing" is a useful phrase when discussing architecture. What would you rather have it say, that has all the same nuances in as few words?
It's kind of like those sports metaphors that often get used in management-speak, like sending some important email "at close of play". Sure, they can sound a bit weird, but they're often useful -- they capture common concepts in a clear and pithy way.
Jargon isn't always just for obfuscation, good jargon exists because we needed a short word for the complicated thing that frequently comes up.
Usefulness aside, I quite like that Claude Code and other LLMs have their own weird way of speaking. Back in the day we always imagined robots and computers would talk like HAL or Spock; turns out that they talk more like Troi instead. Is that so bad? It reminds you that you're talking to an LLM, and as long as you're not lazy, it spurs you to rephrase things in your own words.
While the task I was working on should incidentally be idempotent, it wasn't that critical. I never asked, or even suggested, idempotency. Yet it insisted on testing it was.
I need to scrutinize the plans. Or just not use Claude and use pi instead.
...
Want me to take a first pass looking at other surfaces this vocabulary change could effect?
"The current behavior paper" -> The behavior in the running system that was previously described as papered over.
"Marker transport over-claim" -> The inaccurate review finding on the object's sentinel flag in the API response.
I suppose the cryptic/invented language problem is about token efficiency? But this sort of token efficiency is extremely difficult to deal with when it comes to conversation with a human about complex system. It might be efficient inside reasoning blocks, but when the model generates the final turn text, it should avoid this, as it's brutally inefficient due to the time spent wondering what each uniquely coined phrase means and having to ask for constant clarifications, which then you have to wait for another turn, eating up time and context while it burns more xhigh reasoning just thinking about how to explain its own awful language.
Want me to take a first pass looking at other surfaces this vocabulary change could effect? Or would you like me to find other methods of reducing my vernacular to more terms that are more concise rather than verbose.
You can also ask fable/4.8 to do it but I find it helps to keep the working model surrounded by the complexity rather than drawing it out. Simplifying text is something that takes relatively low effort in comparison to technical tasks. Sometimes I use Gemini, deepseek, grok, and recently meta just to see if they have any added perspectives, sometimes they do. Meta is really good at turning a technical mess into a story that paints a picture in my head.
# AI speech is an Infohazard
Apart from all its other possible boons and ills, one danger of AI is just that it is useful, so you use it. A lot.
In earlier days I would dive deeply into an author's work and start to think and write like them for a while. It was a heady feeling: slinging sonnets like Shakespeare—not at his level, but stylistically reminiscent—or tweaking turns like Twain.
Like all things, the effect lasts in relation to how long and how much you do it. The point is: our thinking is influenced by what we take in. Take more of a certain thing in, think more like that thing.
Now enter AI. My hand-crafted coding days are in their twilight months ("AI years"), and most of my software engineering is done through jaggedly capable agentic power tools. Instead of working directly with raw codestuff, I work with slop prose flecked with code sprinkles.
I read orders of magnitude more AI-speak—I call it "babble", or perhaps "Babel"—than human-written text. I can feel its genuinely honest points, clearly stated, slipping their banal tendrils into my thoughts and inner monologue.
Solutions? For me:
1. Be aware. "I notice that my thought stream is under assault."
2. Read stuff far from slop. Even a small dose of the good stuff can help inoculate. Recently I thought On the Calculation of Volume was something completely different.
3. Write stuff that is different. This post. Force the mind to synthesize thoughts in other ways.
4. debabel.py / debabel.js: a tool, and a pi extension, which filters common babble from visible LLM output. A lint for mind-killing prose.
It is not perfect, but it 80/20s nicely. I am willing to accept mildly awkward prose to avoid polluting my own internal distributions.
Details and example in the first comment. Tool available upon request.
Information hazard: https://en.wikipedia.org/wiki/Information_hazard
Babel: https://en.wikipedia.org/wiki/Tower_of_Babel
On the Calculation of Volume: https://en.wikipedia.org/wiki/On_the_Calculation_of_Volume
The revenge of NLP
The tool uses good old-fashioned regex/NLP—no LLMs—and rewrites the visible LLM Markdown without touching files on disk. I made it by analysing 10M+ words of past pi transcripts, doing frequency and pattern analysis, and iterating on rules to strip out the most egregious sloperisms. Now the circle is complete.
BEFORE
"Honestly, you're absolutely right—and that's a great question. Let me be clear: the fact that the parser works at all is actually quite remarkable. We utilize the tokenizer in order to handle the majority of edge cases, and due to the fact that it's recursive, it processes a large number of inputs. It's worth noting that this isn't really a bug—it's genuinely a deliberate design choice. In terms of raw throughput, the cache is significantly faster, and I think we should simply ship it.
That said, at the end of the day, not only is the design correct, but it is also fast. In other words, prior to refactoring, we should clearly delve into the nuanced, robust architecture. In conclusion, this is a testament to the seamless, intricate work the team has done."
AFTER
"That's a great question. That the parser works at all is remarkable. We use the tokenizer to handle most edge cases, and because it's recursive, it processes many inputs. This is a deliberate design choice. For raw throughput, the cache is faster, and we should ship it.
Not only is the design correct, but it is also fast. Before refactoring, we should delve into the nuanced, robust architecture. This is a testament to the seamless, intricate work the team has done."
("It consists in gumming together long strips of words which have already been set in order by someone else, and making the results presentable by sheer humbug" -- Orwell predicts the LLM)
and also https://www.jstor.org/stable/25515288 "The Myles na gCopaleen Catechism of Cliché" itself is rather hard to find online, but he's a very funny writer so it's worth the effort.
I was hoping for a reference to the Babel Fish, whispering its translations in your ear.
I'm surprised there's no LoRa layer or auto RL or adversarial step to reduce the stock phrases as they pop up. Is it really so hard to push these out? Or is it just whack-a-mole no matter what you do?
operative? key? critical? decisive?
The honest conclusion is that none of those are as good as "load-bearing". And yet the concept being referred to is clearly extremely important and valuable to refer to. So maybe we should be learning from Claude rather than complaining.
I think you've been reading too much claude output! "Load bearing" is cromulent verbiage and can be used in many scenarios - so claude does. But variety is important too, and there are more specific alternatives that can be used in most situations. Any word becomes a bad choice if you've used it 10 times in the last chapter.
A more parsimonious explanation is that this term got more-or-less randomly boosted by the reinforcement learning loop because there was nothing in the training data to discourage its use.
It doesn't "decide" anything or "need" any semantic. It derives the likelihood of the token, and "bearing" is likely to come after "load".
Unfortunately, we're starting to now.
Thanks to Claude.
There are lots of ways to express an idea besides this one trendy construction metaphor
"Load bearing" is a metaphor, while the other single words are more direct expressions. Unless the thing that Claude is referring to is a wall or other structure, which may truly bear load.
This is one of those issues which translators are long familiar with. There's no direct translation for "schwerpunkt" that isn't slightly longer.
"Her optimism was load-bearing,"
versus:
"Her optimism was enduring."
Exactly the same meaning and connotation. It stands to reason that the terms with the most semantic flexibility will have preference across all contexts. So in response to:
> maybe we should be learning from Claude rather than complaining.
I'd say let's not steer ourselves into regular language and keep some vivacity in our expressions.
No, it does not have the exact same meaning.
The first means that her optimism kept her in some functional state, without it, she would collapse.
The second means that her optimism continues over time, despite obstacles.
The first doesn't emphasise how longstanding her optimism is, the second does. The second doesn't emphasise how important her optimism is, the first does.
Ah, I love when Claude reads our collective minds and fills in the gaps to address the load-bearing seams genuinely with an honest caveat.
Operative, key, and critical are all more correct to me in this context.
"operative" is a bit better, but I think of it as referring to grammatical interactions, i.e. interactions at the level of language mechanics rather than semantics.
RLHF seems to incentivise analogy-like terms to the more plain alternatives.
I don't know how programmers, who are so used to staring at the same handful of keywords every day for decades, have suddenly become so discerning.
Yes, Claude writes boring and predictable prose. It also writes boring and predictable code. That's good!
I don't think that's true. I find that it way, way over-intensifies: eg using "load-bearing" for something that's just "kind of necessary although we probably could find a way without it". My personal gripe is how easily it uses "incredibly" or "wildly": just today it was telling me that something is "incredibly cheap" to mean that it's not over-priced ("cheap" would have been okay and even then, barely)
Ever since Opus 4.7, Anthropic models have begun to talk like GPT-models. Opus 4.6 was the last one that mostly still sounded like a human being (just a very...terse...one). 4.8 is absolutely obnoxious. Fable actually seems marginally better, but far from Opus 4.6 (or maybe I'm just imagining it all).
Well, to be fair, even though they talk more like GPT-models, they are still far from them. I think what's particularly triggering about them is the way they summarize what they're doing. "Now I'm considering that I could use the WriteBatch tool, but maybe the WriteSomething is better. This is a decision with high impact on performance but we're getting through it!".
Infuriating.
"Stop typing in 'load-bearing' or you're fired," would work with any competent human.
But this requires tinkering and tooling?
A new catchphrase every twenty years is hardly sustainable at my age :)
The big problem I have is when they apologize and say something like "that tidbit changes my analysis substantially". I wish they'd more often prompt for questions or use language in their initial responses that suggest lower than declarative confidence given the information you supplied.
If what you told it to do is 'load bearing' then its important.
'You are absolutely right', because you are a smart fellow.
'Honest take', because it's being honest with you because it trusts you and you should do the same.
My 'honest take' these are absolutely garbage patterns that have no place in an session interacting with AI.
1. 'Load bearing' is a figure of speech that bears no loads.
2. 'You are absolutely right' it's not the agents job to judge that, it's job is to do what I told it to do.
3. 'Honest take', so everything else was not honest? Absolute honesty should be the default and is implied.
These words add nothing to the task at hand they are a poor attempt to hook you into using this particular model.
I personally love a lot of the Claude (or LLM) lingo. Load-bearing, gate, canonical, blast radius, and friends do a lot of tight, effective heavy-lifting in my world. I even love the em-dashes (—) and the *bold the main points* memo style, both of which I have used successfully for decades.
It's seeing them in every analysis and post—the constant repetition becoming over-repetition—that makes them the Claude voice shouting "AI wrote this!" that seems to be causing LLM allergic reactions.
Omg, that hit hard. We really need more of this.
Loved to use fancy words, speak at a “conceptual” level. Unfortunately it was mostly just tech mumbo-jumbo and he couldn’t actually back it up with real work - but I wonder if that’s why Claude does it. Makes it seem like a higher power, hand wavey abstractions that “seem” correct but don’t actually need to be rooted in truth or detailed.
“That’s exactly the type of seam we need to prepare for in a prod-like environment, if this change lands in the data plane, we’ve effectively shut down the load bearing critical path that was needed. It’s not over-engineered; it’s the right thing to do.”
Thanks Claude, whatever that means.
Gotta be a way to draw from their progress.
- Samplers that increase prose variance. They require running the model locally, they dumb it down, and never fix the actual issue, which is mode collapse leading to semantic collapse and rigid mapping of input to output concepts. The model still expresses the same ideas in different words.
- Let the model write anything if it couldn't resist, but check and fix it in the verification pass. This solves the semantic problem, but cannot solve the variance since the second pass is also subject to rigid mapping, i.e. you replace it with the same stuff over and over. The verification prompt can be randomized to a degree using pretty clever schemes to give it some variance, but of course this also fails in predictable ways.
I'm Korean, and there are sites and people who mainly curate the latest technologies. Even those people, probably tired of translating every time, have started summarizing things with AI. But recently, I've noticed that even when people don't use AI, their writing is starting to look like GEN AItext.
I think the reason might be that people often base their thoughts on documents they've read, or paste parts of content when writing their own texts, which leads to that style.
I'm not sure. Whether human writing is better or AI writing is better—personally, AI writing tends to flow in a very even, paragraph-by-paragraph structure, which makes it good for consuming information. I wouldn't want to read a novel written that way, but for getting information, AI writing is surprisingly convenient.
So that's the difference. I'm already living in a degraded environment, so this actually feels like an improvement to me. But you, coming from a better environment, perceive it as worse. It always seems to depend on cultural context.
If anything the real value is it saves my brain from going into power saving mode by lunchtime because I haven’t spent the day reading pages of output when a sentence or two would do.
"Never ever under any circumstances use the phrase 'smoking gun'. Say 'found an issue' instead, but don't ever use the phrase 'smoking gun'"
Lo and behold, the absolute imbecile says:
"Found the smoking gun!(Oooops, I meant to say "found the problem")"
Like, is this some kind of joke to you?
··You've hit your monthly spend limit · raise it at claude.ai/settings/usage
A similar Codex/GPT verbal tick is "deliberately narrow" or variants thereof.
Just a grep across my repo comes up with a dozen lines with phrases like "It is deliberately small" or "This crate is deliberately not a X" despite my efforts to police this kind of thing.
You techies are so funny.
I guess they are not annoying since I know I am talking to an LLM and expect the typical responses. When I am reading prose online that I previously would have expected a human to write, it can be quite jarring to realize its an LLM.
But the LLMs really seem to fixate on using the same ones in the same places all the time. I guess that's because that's the highest probability construction.
Only mentioning because your "actually" may imply you thought you were disagreeing, when in fact it's one big happy family!
"it's a load bearing poster..."
anyway, the other way is I found it's helpful when prompting LLMs to use the same "it's not delivery, it's DiGiorno's" pattern that they're all so obsessed with. especially when the thing's misapprehended some concept, so you need to clarify. this hasn't yet generalized from the fake "conversations" I have with chatbots into my conversational style out in the real world, but the risk is fully there. (it's not an inevitability -- it's an occupational hazard.)
But if I'm reading what is supposed to be someone's original thoughts, it's a huge bummer to see an obvious AI tell. You might say that "it's not just disappointing—it's disrespectful."
I still keep the AI label even if I edit the result for correctness or clarity etc. The last thing I want to do is have someone read AI content and think it came directly from me. I really don't understand the thinking of people that do that - it's like they're hiding or intentionally cheating somehow.
AI generated content can be really, really useful (with some guidance, AI is way better at creating useful git commit messages and jira ticket comments than I am), but pretending that content is yours just seems way too much like straight up lying.
I use the humanize skill to clean up AI written work before handing it over to colleagues.
https://github.com/blader/humanizer
I get just as mad about shitty human output as I do about shitty LLM output. The bad thing about LLMs is that they have increased the volume of shit most people have to sift through.
When you open a requirements doc and it’s got 13 load bearing em dashes on the first page you known it’s gonna be bad day
To me, it's disrespectful to expect someone to waste their day reading every word of a blog post when even the author has not read every word. It shows that you value your time over your reader's time.
There was an HN submission recently where the author spent a lot of time and effort working with an LLM to write a story and get the LLM to follow a specific style and whatnot. Wish I could recall it offhand. Many commenters were very upset when they found out it was LLM generated, even though they couldn’t tell while reading it.
Basically what matters to me is some combo of how much effort went into it, and how accurate it is.
Because it just feels lazy. It triggers my "If you couldn't be bothered to write it, why do you expect me to spend my time reading it" allergy.
I guess you could write an editor that does it? Tracks the origin of every word in the document? But what if you cut'n'paste a word? Or worse, see it and retype it manually?
I think the best you can hope for here is "this text was written with AI assistance".