How to stop Claude from saying load-bearing

Article URL: https://jola.dev/posts/how-to-stop-claude-from-saying-load-bearing

Comments URL: https://news.ycombinator.com/item?id=48905248

Points: 569

# Comments: 583

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569 points · 583 comments · 查看原帖

doctoboggan
I do not mind when I am coding with Claude and it uses all the typical claudisms. I am much more bothered when I am reading a blog post, email, or other form of prose and I see those same claudisms. 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.
infogulch
Lots of people have their own voice and tend to prefer certain phrases. This has been the case for a long time and is generally not a big issue. 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.
alxndr
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".
demosthanos
The biggest consistent tell for LLM writing is when the conversation leaks through into the final prose. 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.
Arathorn
I've been keeping a record of the increasingly opinionated vocab it fixates on: * Projection (it seems to love to describe one data structure as a projection of another) * Strand (if some data gets isolated/stuck, it's "on a strand" or simply "a strand") * Load-bearing (obviously) * Frontier (the leaf on a tree) * Quiescence (waiting for an algorithm to settle - I guess this one is legit) * Honest (obviously) * Residuals (any kind of data which hasn't been consumed by an algorithm) * Rescission (something which has been rescinded; rather than saying "a rescinded offer" it enthusiastically calls it A Rescission!) * Supersession (it's not a session which is a superset of another session... it's the word supercession; something that supercedes; similar to preferring the participle form of rescind). I wonder how much of this is due to it mirroring proximate things to my code's own weird voca
d4rkp4ttern
I've been looking around for skills that help with improving AI prose, and found these: (1) Avoid AI Writing - https://github.com/conorbronsdon/avoid-ai-writing (there is a similar "humanizer" skill that (1) subsumes) (2) Agent style: https://github.com/yzhao062/agent-style (3) Journal-adapt (style transfer from your writings): https://github.com/WantongC/journal-adapt-writing-skill I find it useful to set up dynamic workflow loops with (1) and/or (2) as checkers. On (3) - I think "style-transfer" that was all the range in image-gen is going to be very useful for prose-generation. Curious if there are LLMs specifically trained for this.
infogulch
LLMs are far from great writers. They struggle to form long coherent sentences and lean on punctuation like emdash and semicolon to ensure grammatical correctness when splicing together short phrases. 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.
pocketarc
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.