> 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".
The reason I first created a CLAUDE.md file was to tell it whenever it felt a need to praise me, to replace it with a random onomatopoeia. That was a huge dx improvement.
OTOH, my unicorn prompt has caused some challenges at work:
"substrate" - I don't know what training they did with Opus 4.7 --> Fable/Mythos 5, but dang does it like the word substrate. Drives me insane. I had never heard anyone use this word prior in real technical writing or speaking.
Another one is "surface", like in "across all product surfaces". I've been in the field for 15 years and have never heard that particular usage before.
In my brief and abortive foray into education, I discovered that they friggin' love to use "surface" as a verb. As in: This activity surfaces an understanding of the turboencabulation principle for learners. Or somesuch. It's been a while, happily.
Unless you're a submarine, "surface" is not a verb.
I do UI design/dev and say "surface up" a lot. Although I don't use the term, in this area people call different container depths as surfaces (base, card, overlay as surface).
That one probably comes from maths, where surfaces show up all the time in geometric interpretations of things. I've been involved in more mathsy parts of engineering and I've heard it a lot.
The first week I encountered this "substrate" I asked it to justify the usage and IIRC it claimed the word is used in some infra/systems lexicons... I wonder...
It's a pretty common word if you've worked in anything that vaguely resembles an accountancy system. Also, anything crypto related will often use that word (the distributed ledger, etc)
That's the case for most of these LLM tropes or word choices. They are all common lexicon in their respective fields, but the LLM doesn't make that distinction and uses them everywhere making them standout.
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.
It's not that it uses certain phrases, it's that it settles on predictable speech patterns and uses them incessantly. What's funny is that humans do this too, but we don't find it irritating; we just call it a speaking style. But when a machine does it, it drives us crazy. Very interesting psychological phenomenon there.
> What's funny is that humans do this too, but we don't find it irritating; we just call it a speaking style. But when a machine does it, it drives us crazy. Very interesting psychological phenomenon there.
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.
It drives us crazy because everyone is using the same 2-3 different machines. So rather than each person having their own unique speaking style, the whole world (or, everyone that publishes direct LLM output) is now speaking in the same couple of styles.
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.
We do find it irritating at times. Office jargon, corporate buzzwords, etc. Claude communicates like the worst, most irritating project manager I’ve ever worked with, obscuring the most straightforward conclusion with layers upon layers of stuff so that its point is almost lost. I’ve largely gotten it to avoid that behavior with me, but bits of it sneak through. It couldn’t talk about “scaffolding” for a few weeks before I hammered it into submission.
Humans are capable of introspection, so, if you develop a verbal tic, you might eventually notice and say to yourself "I've used the word 'load-bearing' (or whatever) a bit too often lately, maybe I should try to cut down on it?". LLMs are not...
Fascinatingly, I'm now so allergic to certain LLM-phrases that I immediately noticed your use of Not X but Y in this comment. Maybe that was intentional, maybe not, but it's a funny illustration of how odd this language rabbit hole has been!
It's really frustrating, because now when I want to write something like a "not X but Y" or "you're absolutely right," I have to stop and decide if I want to self-censor to avoid sounding like a bot.
Sometimes those constructs are actually useful, but man has their overuse really killed them!
It was not intentional, and that's what makes this thing so weird. I wouldn't categorize my sentence that way because it's subtly different enough than the LLM version, which has a very punchy cadence.
it’s not a psychological phenomenon. If a human engineer constantly used pompous language to deliver unvetted information (the number of claude slop root-cause analyses i’ve read where “the smoking gun” is a red herring) we’d rightly consider them a moron
Who is we? Own your insults and the consequences of them sir.
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.
In the olden days, I enjoyed Opus 3 because it was easy to have it sound way more human than GPT.
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.
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.
I wish my company would do this. A coworker pulled an all nighter before a vacation and just left me with a million line claude summary of their work then just fucked off. The message was two-part due to size and lacked basic stuff like, "how to run".
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.
This, a thousand times. As the ratio of code to human writing necessarily [1] goes up, they become not just smarter, but more precise and technical, which requires them to use more jargon. You could say they become more nerdy. Hence, text generated by these models will become more easily recognizable, at least by default, when not asking them to twist themselves into something else via prompting — which degrades intelligence. This is a good thing, in my book, given all the slop we already have to contend with.
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.
It's why I like Gemini 3.1 Pro. That it sounds much more human than other LLMs is testament to Google's inability to post train.
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.
> Nowadays, with the focus on agentic use and coding, it seems models have all been RLHF’d to death
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.
Good. I don't want LLMs sounding human. I want the ability to shame and discredit anyone passing the job of prose to a machine. There's an art to writing, and hopefully LLMs never truly get it right.
Agreed. The only goal of these skills/tricks/requests for humanising LLM writing is to be able to pass it off as your own, because they know it's shameful and want to avoid the opprobrium.
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.
Agreed. I think we’re entering an era where some level of specialization for general LLMs is a good thing. Particularly between tuning for agentic use cases (where you want agency with a ton of guardrails and control) and writing which is more creative - you want the model to take the occasional risk and not sound like a monotonic robot. Having trained models first-hand, I can see the distinct use-cases clearly that are odds with one another.
Because LLMs are pattern-extenders that have nothing to say. The training overfitted to the grace notes in good writing. And since LLMs can’t wield language with purpose or experience the feeling of the words, they use these devices arbitrarily.
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.
It's not that the writing style is bad; in fact LLMs write actually pretty well. It's just too much overfitted. And even a style that, in itself, is pleasurable to read, becomes annoying when the same figures of speech are used over and over again.
It's not that nobody likes it, in fact the problem is that people like each instance of it well enough in isolation. Millions of people think it's "good enough," so it gets amplified and repeated until every PR description starts to sound like a toothpaste jingle.
i hate it, but plenty of people DO like it and plenty of people talk and write like that. It’s just corpspeak, being used a lot in the valley and beyond. And all upcoming hustlers running startups now feel the need to speak like that, feeding this machine.
I mourn the removal of Claude's Concise Style. I'd provide it a roughly drafted paragraph, ask concise-Claude to "rewrite for clarity", out comes the same paragraph, but cleaned up and perfect for grant writing.
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.
Yeah I sometimes see people on here getting defensive when you call out AI slop, saying maybe it's just a human who writes like Claude, and I really don't care- slop is slop.
Maybe the problem is that these LLMs will say something often enough for us to notice it, and it can be basically any arbitrary thing. Once we notice the pattern, it starts irritating us.
Maybe in the circles you circled in ... where I am from, I never had anyone saying "belt-and-suspenders" or "load-bearing" or "boil the ocean" or "swing for the fences" when talking about engineering topics. The only one who I heard say "circle-back to you" was Psaki.
All of those phrases I've heard actively used even a decade (or two) ago. (I actually had to read your comment twice because I thought you were saying always, not never!)
"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.
I think the simplest way to get it to stop with this kind of thing is to just instruct it that framing constructs are strictly banned, and then giving it a few examples like the classic "it's not this, it's that". Qualitatively it seems like lots of this "load-bearing" stuff actually falls out from the framing, and as Claude would say, the problem "dissolves" once the framing goes away. I do wonder how this affects reasoning, if at all.
Why when I read an how to stop Claude from saying X, I grep my saved conversations and I find no occurrences of X? I wonder if I'm using it differently from anybody else. It happens with coworkers too.
This is the worst one for me. I can maybe think of what it means, but I never heard it before, and could easily be imagining a meaning.
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.
Personally my least favorite is the overuse of "quietly" (e.g. "No tricks. No marketing gimmicks. Just one company quietly outperforming the others"), and the one that makes the least sense to me is "that's the wedge."
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).
Claude does at least use the British English version of the phrase to me - not sure whether its picking up a language setting or reacting to my spelling etc. The American version does sound odd over hear.
"Belt and braces" (UK) vs. "belt and suspenders" (US). I'm pretty sure the phrases have the same meaning, they just use a different word to refer to the thing that holds pants|trousers up.
My favorite one has to be "production ready" it will say that about completely broken code without hesitation. LLM says it's production ready, lets ship!!
I like to think that the reason it's so noticable is that Claude has recognized some important semantics that we ourselves lack a good word for or at least under-appreciate. What term is used in English (or other languages) with the same meaning as claude's "load-bearing"?
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.
but you don't see "load bearing" nearly as often in prose written by people, so it's not some irreplaceable phrase. It's just a token with a weirdly high likelihood in a lot of cases (given how Claude works, this kind of thing is bound to happen)
You don't think it's possible that an LLM's internal machinery could decide that an underused-by-humans word should be used more frequently in output than it sees in input because it maps cleanly onto a frequently needed semantic? I think that's possible
It sounds like you are trying to understand LLM behavior using a mental model that inaccurately personifies the stochastic parrot.
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.
I’ve been working in AI - and specifically NLP - since 2003. I am no stranger to how weird quirks can sneak into overparametrized models, nor am I a stranger to how good humans can be at inferring meaning where there is none in specific language model behaviors. So, yeah, I am inclined to assume non-teleological causes are more parsimonious than inferring the presence of a strange loop, because that continues to be the winning bet. Even for generative LLMs.
Because, for some high number of contexts, its likelihood comes out high in the big tree of multiplies that is claude's model. For some sets of 500 words (or whatever), the next word is "load". The classifier that decides which sets of 500 (or whatever) words is a prefix for "load" is returning "true" too often.
And like any good corporate buzzword, it’s merely a simulacrum of precise technical jargon. The way Claude uses it is clearly wildly polysemous if not outright ambiguous.
You yourself used "important" in the same paragraph.
"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.
In the figurative sense it's highly versatile across contexts, but still replaceable. For example:
"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.
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.
For me, "key", and "critical" merely say it's "important", but don't convey the sense that "out of the mess of connected concepts we're discussing, the one that is actually interacting with the thing we care about, or at least dominating the interactions with the thing we care about, is X".
"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.
I mean we have all kinds of under synonym'ed words. Just look at how few we have for "smell" (as in the act of smelling), and then how overloaded the word smell even is.
The real problem is not terms like "load-bearing," which communicate clearly enough. It's the constant invention of cryptic shorthand terms and phrases that have no referent, and end up acting like a puzzle to be decoded. This is often paired with hyphenation, but not always:
"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.
load-bearing, belt-and-suspenders, wrinkle, shape, coarse-grained, "key chords", code seams, flakiness, "narrow-scoped by default", "that's the authoritative source", canonical symptoms, gate, trigger-happy users, substrate, surface (as in: "let's surface how much these models sound like shit"), terse...
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!".
Yes, this and "belt-and-suspenders" are the ones that I notice the most. I also have non-native English speaking coworkers who have started using these terms/phrases recently, which makes me think that they're outsourcing all their writing.
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?
I wrote a thing about exactly this, but I'm resistant to blogging for undefined reasons so, maybe this will help someone...
# 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.
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.
Does anyone have a theory for what causes Claude to speak this way? A few months ago OpenAI came out with a bit on "gremlins". It's strange IMO that Anthropic hasn't addressed how irritating, dare I say oppressive, Claude can be. Codex is a breath of fresh air. I hope they fix it soon. If product folks at Anthropic think it's charming, it's not, it's terrible.
huh. I wonder if it's possible to use those hooks to add syntax highlighting to shell commands claude issues, or to replace full path to current directory with ./
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)
I'd contend that Claude's prose is not boring. It's generally overly grandiose waffle with a cliche or two punctuating every other sentence. It's good for tasteless marketing copy, sure. It's inappropriate in most scenarios.
Even great words, phrases, and styles, seen too often, grate.
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.
There are no real solutions, it has to be fixed during the training. ST folks have tried many non-working ways over the years, but two workarounds are more or less worth considering:
- 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.
Annoying because I used to like using that phrase.
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.
Lately, I feel like as GEN AI text becomes the majority, human-written text is starting to resemble it too.
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.
I recently started using caveman, and it’s been great. It doesn’t just cut down on overuse of specific terms; it cuts down on time spent digesting slop in general.
The token saving is oversold, from what I can tell so far. These days output tokens are just the tip of the iceberg.
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.
It's good, because it's just post-processing before display. So it doesn't interfere with the process, which those phrases that seem so offensive to sensibilities of so many people, for whatever reason, might be a part of.
I did something like this in my global `CLAUDE.md`...
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".
The reason I first created a CLAUDE.md file was to tell it whenever it felt a need to praise me, to replace it with a random onomatopoeia. That was a huge dx improvement.
OTOH, my unicorn prompt has caused some challenges at work:
>Keep "Local Oaf" out of committed code
I'm just glad to hear that we're all infallible. I really thought I made some mistakes here and there.
https://github.com/alxndr/dotfiles/blob/272475280d84e/claude...
Joking aside, it's nice to see a human written CLAUDE.md
> It can be tricky for humans to interpret the meaning when Generative AI uses first-person pronouns (e.g. "I", "me", "my", "myself")
Could you please provide an example of what you mean?
This is a method of manipulating the LLM, it doesn't have to be true.
I've given LLMs religion before to manipulate their behavior, that doesn't mean I believed in the great spaghetti goddess.
"substrate" - I don't know what training they did with Opus 4.7 --> Fable/Mythos 5, but dang does it like the word substrate. Drives me insane. I had never heard anyone use this word prior in real technical writing or speaking.
Another one is "surface", like in "across all product surfaces". I've been in the field for 15 years and have never heard that particular usage before.
In my brief and abortive foray into education, I discovered that they friggin' love to use "surface" as a verb. As in: This activity surfaces an understanding of the turboencabulation principle for learners. Or somesuch. It's been a while, happily.
Unless you're a submarine, "surface" is not a verb.
Sure it is.
https://www.merriam-webster.com/dictionary/surface#dictionar...
> : to come into public view : show up
> letters that have recently surfaced
I’ve heard (and used) the term “API surface” a lot…
I do UI design/dev and say "surface up" a lot. Although I don't use the term, in this area people call different container depths as surfaces (base, card, overlay as surface).
Recently read some LLM generated output that mentioned the “center of gravity” within a codebase.
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.
That one probably comes from maths, where surfaces show up all the time in geometric interpretations of things. I've been involved in more mathsy parts of engineering and I've heard it a lot.
Mine is obsessed with "planes". Data plane, control plane, management plane. Everything is a plane :)
Landing the plane.
It's pretty common to read "attack surface" in security.
Yeah, I imagine this is a big part of it.
Surface it to say, that's my favorite lobe-earing eggcorn, for all intensive purposes!
About a decade ago I worked with a product manager who used that phrasing constantly, so it kind of stuck with me.
The first week I encountered this "substrate" I asked it to justify the usage and IIRC it claimed the word is used in some infra/systems lexicons... I wonder...
i hate the word "some" in this kind of answers
"reconciling" is the most annoying one, in my opinion.
A coworker started spamming this word in ~April while working on system design/architecture.
"ledger" for me – used extremely rarely pre-LLM and Claude just loves it
It's a pretty common word if you've worked in anything that vaguely resembles an accountancy system. Also, anything crypto related will often use that word (the distributed ledger, etc)
That's the case for most of these LLM tropes or word choices. They are all common lexicon in their respective fields, but the LLM doesn't make that distinction and uses them everywhere making them standout.
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.
Clearly you don't work for Microsoft: https://techcommunity.microsoft.com/discussions/microsoft-36...
I actually found it somewhat useful conceptually, but yes, it definitely does overuse it lol
"Steelman" in almost every response never gets less cringe for me
My CLAUDE.md has "don't talk like a Hacker News commentator". It helps a surprising amount.
It's not that it uses certain phrases, it's that it settles on predictable speech patterns and uses them incessantly. What's funny is that humans do this too, but we don't find it irritating; we just call it a speaking style. But when a machine does it, it drives us crazy. Very interesting psychological phenomenon there.
> What's funny is that humans do this too, but we don't find it irritating; we just call it a speaking style. But when a machine does it, it drives us crazy. Very interesting psychological phenomenon there.
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.
It drives us crazy because everyone is using the same 2-3 different machines. So rather than each person having their own unique speaking style, the whole world (or, everyone that publishes direct LLM output) is now speaking in the same couple of styles.
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.
We do find it irritating at times. Office jargon, corporate buzzwords, etc. Claude communicates like the worst, most irritating project manager I’ve ever worked with, obscuring the most straightforward conclusion with layers upon layers of stuff so that its point is almost lost. I’ve largely gotten it to avoid that behavior with me, but bits of it sneak through. It couldn’t talk about “scaffolding” for a few weeks before I hammered it into submission.
Humans are capable of introspection, so, if you develop a verbal tic, you might eventually notice and say to yourself "I've used the word 'load-bearing' (or whatever) a bit too often lately, maybe I should try to cut down on it?". LLMs are not...
> What's funny is that humans do this too, but we don't find it irritating
I make fun of people all the time for shoehorning their favorite phrase into every context where it doesn't apply.
Fascinatingly, I'm now so allergic to certain LLM-phrases that I immediately noticed your use of Not X but Y in this comment. Maybe that was intentional, maybe not, but it's a funny illustration of how odd this language rabbit hole has been!
It's really frustrating, because now when I want to write something like a "not X but Y" or "you're absolutely right," I have to stop and decide if I want to self-censor to avoid sounding like a bot.
Sometimes those constructs are actually useful, but man has their overuse really killed them!
It was not intentional, and that's what makes this thing so weird. I wouldn't categorize my sentence that way because it's subtly different enough than the LLM version, which has a very punchy cadence.
If it uses a specific style for each user then this would still be fine. Problem is it does the same style for everyone. We need personality
> but we don't find it irritating
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.
I went through a “100%” phase recently and couldn’t for the life of me understand why I was suddenly saying it ALL THE TIME. Brains are so weird.
Did you negotiate her down to "99%"?
it’s not a psychological phenomenon. If a human engineer constantly used pompous language to deliver unvetted information (the number of claude slop root-cause analyses i’ve read where “the smoking gun” is a red herring) we’d rightly consider them a moron
I didn't articulate it, but what I meant was that I think we could swap these expressions out for _anything_, and we'd still find them irritating.
Who is we? Own your insults and the consequences of them sir.
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.
"Here's why this version is bulletproof" right before it fails in exactly the same way as the previous bulletproof implementation...
If LLMs were humans I would find that human absolutely insufferable. It is very much about the language.
...or we call it an overused catch-phrase.
We don't have to live with this. Increasing the temperature (randomness) would fix it.
In the olden days, I enjoyed Opus 3 because it was easy to have it sound way more human than GPT.
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.
> What, if anything, do people do for writing?
I use a keyboard, personally.
Amen.
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.
I wish my company would do this. A coworker pulled an all nighter before a vacation and just left me with a million line claude summary of their work then just fucked off. The message was two-part due to size and lacked basic stuff like, "how to run".
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.
If it wasn't worth your time writing, it isn't worth my time reading.
ai;dr
Haven't done it, but letting an AI polish a manual first draft might be the best of both worlds?
This, a thousand times. As the ratio of code to human writing necessarily [1] goes up, they become not just smarter, but more precise and technical, which requires them to use more jargon. You could say they become more nerdy. Hence, text generated by these models will become more easily recognizable, at least by default, when not asking them to twist themselves into something else via prompting — which degrades intelligence. This is a good thing, in my book, given all the slop we already have to contend with.
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
It's why I like Gemini 3.1 Pro. That it sounds much more human than other LLMs is testament to Google's inability to post train.
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.
> Nowadays, with the focus on agentic use and coding, it seems models have all been RLHF’d to death
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.
Good. I don't want LLMs sounding human. I want the ability to shame and discredit anyone passing the job of prose to a machine. There's an art to writing, and hopefully LLMs never truly get it right.
Agreed. The only goal of these skills/tricks/requests for humanising LLM writing is to be able to pass it off as your own, because they know it's shameful and want to avoid the opprobrium.
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.
I don't want LLM docs either
Agreed. I think we’re entering an era where some level of specialization for general LLMs is a good thing. Particularly between tuning for agentic use cases (where you want agency with a ton of guardrails and control) and writing which is more creative - you want the model to take the occasional risk and not sound like a monotonic robot. Having trained models first-hand, I can see the distinct use-cases clearly that are odds with one another.
For what it's worth, Anthropic seems to be keeping Opus 3 available on claude.ai, perhaps for this reason, so you're free to use it if you want to.
> Nowadays, with the focus on agentic use and coding, it seems models have all been RLHF’d to death
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.
Because LLMs are pattern-extenders that have nothing to say. The training overfitted to the grace notes in good writing. And since LLMs can’t wield language with purpose or experience the feeling of the words, they use these devices arbitrarily.
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.
It's not that the writing style is bad; in fact LLMs write actually pretty well. It's just too much overfitted. And even a style that, in itself, is pleasurable to read, becomes annoying when the same figures of speech are used over and over again.
Because humans do like it, in reasonable quantities. The AI overlearns this and does it too much.
It's not that nobody likes it, in fact the problem is that people like each instance of it well enough in isolation. Millions of people think it's "good enough," so it gets amplified and repeated until every PR description starts to sound like a toothpaste jingle.
For "agentic use and coding," they are trained to take useful actions, not produce desirable natural language writing.
Maybe it’s the dead internet.
All the bots and other LLMs providing feedback, so in reality it’s reflecting the reality in a sense.
every one-hit wonder asks the same question.
we liked it until we didn't.
i hate it, but plenty of people DO like it and plenty of people talk and write like that. It’s just corpspeak, being used a lot in the valley and beyond. And all upcoming hustlers running startups now feel the need to speak like that, feeding this machine.
I mourn the removal of Claude's Concise Style. I'd provide it a roughly drafted paragraph, ask concise-Claude to "rewrite for clarity", out comes the same paragraph, but cleaned up and perfect for grant writing.
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.
What was Concise Style? Not a skill, but something built in?
It might have been a prompt originally.
What is arguably worse is hearing these phrases from humans who have been inculcated with the notion that their usage is idiomatic and appropriate.
And we thought "robust", "circle back", and "to leverage" were grating...
Yeah I sometimes see people on here getting defensive when you call out AI slop, saying maybe it's just a human who writes like Claude, and I really don't care- slop is slop.
The humans are bristling because they wrote like that first. It's where Claude got it from!
Maybe the problem is that these LLMs will say something often enough for us to notice it, and it can be basically any arbitrary thing. Once we notice the pattern, it starts irritating us.
The one that does my head in is everything being a 'gate' where really it means a condition.
RLHF seems to incentivise analogy-like terms to the more plain alternatives.
Among all the claude-isms, i understand the hate for load-bearing the least. It was definitely part of tech argot prior to the LLM revolution.
Maybe in the circles you circled in ... where I am from, I never had anyone saying "belt-and-suspenders" or "load-bearing" or "boil the ocean" or "swing for the fences" when talking about engineering topics. The only one who I heard say "circle-back to you" was Psaki.
All of those phrases I've heard actively used even a decade (or two) ago. (I actually had to read your comment twice because I thought you were saying always, not never!)
"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.
Well, "load-bearing" is specifically an engineering term :D Actual engineering, not software "engineering".
I think the simplest way to get it to stop with this kind of thing is to just instruct it that framing constructs are strictly banned, and then giving it a few examples like the classic "it's not this, it's that". Qualitatively it seems like lots of this "load-bearing" stuff actually falls out from the framing, and as Claude would say, the problem "dissolves" once the framing goes away. I do wonder how this affects reasoning, if at all.
And there’s the smoking gun.
The gun is not smoking; it’s an honest footgun that will be load-bearing when it lands.
That's a real distinction, you're right to point it out.
Clean!
That's the unlock!
And the footgun (although I haven't seen a smoking footgun yet).
Now I have the full picture.
This one observation changes everything.
We’ve identified the shape of the problem.
And it has teeth.
This will bite you.
You are right ...
The one that matters.
I suspect load-bearing is a euphemism for 'not garbage'. Ad in 'most of what you said I can mostly ignore'.
Why when I read an how to stop Claude from saying X, I grep my saved conversations and I find no occurrences of X? I wonder if I'm using it differently from anybody else. It happens with coworkers too.
Is this a belt-and-suspenders solution?
Yes, and that actually sharpens my previous conclusions.
This is the worst one for me. I can maybe think of what it means, but I never heard it before, and could easily be imagining a meaning.
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.
Personally my least favorite is the overuse of "quietly" (e.g. "No tricks. No marketing gimmicks. Just one company quietly outperforming the others"), and the one that makes the least sense to me is "that's the wedge."
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).
Claude does at least use the British English version of the phrase to me - not sure whether its picking up a language setting or reacting to my spelling etc. The American version does sound odd over hear.
What's the difference between the two usages?
"Belt and braces" (UK) vs. "belt and suspenders" (US). I'm pretty sure the phrases have the same meaning, they just use a different word to refer to the thing that holds pants|trousers up.
And the word "suspenders" in British English means what Americans would call a garter belt, hence it sounding particularly odd over here.
That is what I had in mind. I was also wondering what American call them so thanks for answering that.
The US usage is much kinkier
Worth doing before merge if you want the belt and suspenders.
I'll make sure that the script is idempotent.
That's not really a claude-ism. Its an important requirement for a many asynchronous tasks.
Great thinking on your end! I will run the smoke-test once you're ready.
I might need to do a spike as this is a core part of the spine. I will verify it and let you know when it's landed.
My favorite one has to be "production ready" it will say that about completely broken code without hesitation. LLM says it's production ready, lets ship!!
I like to think that the reason it's so noticable is that Claude has recognized some important semantics that we ourselves lack a good word for or at least under-appreciate. What term is used in English (or other languages) with the same meaning as claude's "load-bearing"?
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.
> The honest conclusion
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.
You don't think me using "honest" there might have been a tiny bit of (on-topic, and therefore appropriate) trolling?
Poe's law
but you don't see "load bearing" nearly as often in prose written by people, so it's not some irreplaceable phrase. It's just a token with a weirdly high likelihood in a lot of cases (given how Claude works, this kind of thing is bound to happen)
You don't think it's possible that an LLM's internal machinery could decide that an underused-by-humans word should be used more frequently in output than it sees in input because it maps cleanly onto a frequently needed semantic? I think that's possible
It sounds like you are trying to understand LLM behavior using a mental model that inaccurately personifies the stochastic parrot.
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.
Ah right, you don't like AI and don't care to understand how it works.
I’ve been working in AI - and specifically NLP - since 2003. I am no stranger to how weird quirks can sneak into overparametrized models, nor am I a stranger to how good humans can be at inferring meaning where there is none in specific language model behaviors. So, yeah, I am inclined to assume non-teleological causes are more parsimonious than inferring the presence of a strange loop, because that continues to be the winning bet. Even for generative LLMs.
Ah right, so you like AI and don't care to understand how it works.
It doesn't "decide" anything or "need" any semantic. It derives the likelihood of the token, and "bearing" is likely to come after "load".
Sure but the question is why "load" after X?
Because, for some high number of contexts, its likelihood comes out high in the big tree of multiplies that is claude's model. For some sets of 500 words (or whatever), the next word is "load". The classifier that decides which sets of 500 (or whatever) words is a prefix for "load" is returning "true" too often.
More-or-less the same principle, but scaled up massively, and with context-dependent probability conditioning maps.
And like any good corporate buzzword, it’s merely a simulacrum of precise technical jargon. The way Claude uses it is clearly wildly polysemous if not outright ambiguous.
What do you replace it with? "necessary dependency"?
Required, important, irreplaceable, necessary, integral.
There are lots of ways to express an idea besides this one trendy construction metaphor
> Claude has recognized some important semantics that we ourselves lack a good word for or at least under-appreciate.
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.
'Load-bearing' is a physical analogy. Other words like 'pillar' imply the same physical analogy.
You yourself used "important" in the same paragraph.
"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.
In the figurative sense it's highly versatile across contexts, but still replaceable. For example:
"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.
> Exactly the same meaning and connotation.
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.
You're serious?
Operative, key, and critical are all more correct to me in this context.
For me, "key", and "critical" merely say it's "important", but don't convey the sense that "out of the mess of connected concepts we're discussing, the one that is actually interacting with the thing we care about, or at least dominating the interactions with the thing we care about, is X".
"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.
I mean we have all kinds of under synonym'ed words. Just look at how few we have for "smell" (as in the act of smelling), and then how overloaded the word smell even is.
If you find yourself getting irritated and physically agitated over language I suggest you do a 5 why analysis on yourself and seek therapy
The real problem is not terms like "load-bearing," which communicate clearly enough. It's the constant invention of cryptic shorthand terms and phrases that have no referent, and end up acting like a puzzle to be decoded. This is often paired with hyphenation, but not always:
"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.
Just one wrinkle.
load-bearing, belt-and-suspenders, wrinkle, shape, coarse-grained, "key chords", code seams, flakiness, "narrow-scoped by default", "that's the authoritative source", canonical symptoms, gate, trigger-happy users, substrate, surface (as in: "let's surface how much these models sound like shit"), terse...
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.
Fable has some Marvin the Android vibes going. It just sounds depressed all the time.
I maintain a list of phrases I beg it not to use that it frequently ignores:
- 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
‘Landed’ and ‘honest’ are also words it seems to overuse.
Yes, this and "belt-and-suspenders" are the ones that I notice the most. I also have non-native English speaking coworkers who have started using these terms/phrases recently, which makes me think that they're outsourcing all their writing.
Claude is obsessed with making things land. More than once I've reminded it that it's not a pilot.
I enjoyed this.
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?
I wrote a thing about exactly this, but I'm resistant to blogging for undefined reasons so, maybe this will help someone...
# 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.
References:
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."
I would add https://www.orwellfoundation.com/the-orwell-foundation/orwel...
("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'd love to see that tool.
> Babel: https://en.wikipedia.org/wiki/Tower_of_Babel
I was hoping for a reference to the Babel Fish, whispering its translations in your ear.
It's not clear to me whether that tool exists or you hallucinated it into existence with this post
Does anyone have a theory for what causes Claude to speak this way? A few months ago OpenAI came out with a bit on "gremlins". It's strange IMO that Anthropic hasn't addressed how irritating, dare I say oppressive, Claude can be. Codex is a breath of fresh air. I hope they fix it soon. If product folks at Anthropic think it's charming, it's not, it's terrible.
huh. I wonder if it's possible to use those hooks to add syntax highlighting to shell commands claude issues, or to replace full path to current directory with ./
I don't really care if it says load-bearing or belt and suspenders so long as it's using them correctly, which it mostly does.
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!
> which it mostly does
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)
I'd contend that Claude's prose is not boring. It's generally overly grandiose waffle with a cliche or two punctuating every other sentence. It's good for tasteless marketing copy, sure. It's inappropriate in most scenarios.
regexes > Claude. Even Anthropic knows this.
One honest caveat worth flagging though.
Even great words, phrases, and styles, seen too often, grate.
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.
> replacement "you're absolutely right": "I'm a complete clown"
Omg, that hit hard. We really need more of this.
Just ask it to aim for a Flesh-Kincaid ease-of-readability score of around 70. Or use ELI5 style. Or both.
Flesh Kincaid sounds like an excellent name for a Scottish porn star. I'd never heard of this, turns out it's Flesch, but thanks for the TIL!
SillyTavern folks have been perfecting the unslop solutions for years now.
Gotta be a way to draw from their progress.
There are no real solutions, it has to be fixed during the training. ST folks have tried many non-working ways over the years, but two workarounds are more or less worth considering:
- 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.
The reason it talks that way is clearly am attempt to hook into your dopamine system.
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.
But how can I make Gemini stop using "It's not this, it's that" every other sentence?
You hit the nail on the head!
Let me circle-back to you on that one.
Annoying because I used to like using that phrase.
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.
or "honestly"
Lately, I feel like as GEN AI text becomes the majority, human-written text is starting to resemble it too.
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.
Maybe implementing it as a hook via a regex replace is a better shaped solution?
I recently started using caveman, and it’s been great. It doesn’t just cut down on overuse of specific terms; it cuts down on time spent digesting slop in general.
https://github.com/JuliusBrussee/caveman
I love it. It also saves you tokens and it has been linked with more accuracy.
The token saving is oversold, from what I can tell so far. These days output tokens are just the tip of the iceberg.
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.
very load bearing suggestion.
"Byte-for-byte"
It's good, because it's just post-processing before display. So it doesn't interfere with the process, which those phrases that seem so offensive to sensibilities of so many people, for whatever reason, might be a part of.
Ask AI about castor beans and barley, it will stop all that nonsense.
“Smoking gun”
I want to be straight with you, I overstepped by naming it a "smoking gun."