Posted by Labo333 20 hours ago
I suspect, as we continue forward, humans will slowly start to adopt the language of LLMs, or at least certain language quirks that come from interacting with LLMs. Something I've noticed in my own writing is that I now present lists of examples in a consistent way: "... such as <example 1>, <example 2>, etc., ...". I started to notice I was using this pattern quite a bit somewhat recently, but I took a quick look at some of my social media posts and realized it's been occurring for a while. I had realized that I grown accustomed to this kind of language because, especially early on, LLMs would focus too much on the specific examples I'd provide when, really, I was just trying to give them a sense of what I was looking for. I just picked up that providing two examples then adding the "etc." worked to get the LLM to not focus so much on the specific examples and to understand that they need to consider more than what I explicitly presented. Of course, now I write like that in my social media comments, in Slack with my colleagues, etc. :>
I'd be interested to see if anyone can identify trends like this, since I think the human-language component of the adoption of LLMs is probably being somewhat neglected despite probably being surely dramatically affected.
Thank you for the compliment! I did spend a lot of time designing a nice experience on both desktop and mobile. Even the scrollbar to select words was non trivial as I wanted the words to be of different size, yet avoid flickering when scrolling!
I was focused on the data initially scrolling through until I suddenly realised, wow, this is really nice!
One very minor note: if your scroll device reports pixel-perfect deltas rather than discrete scroll-wheel ticks (e.g. logitech mx master, laptop touchpad, etc), the behaviour in the word search box is a bit weird. Arrow keys work fine though.
I did test it with my mac touchpad without issue. Maybe the problem is that I wanted to make discrete scroll-wheel ticks work. I just tried a fix by normalising deltaMode to pixels.
If you could just paste this in your console, I would have a better idea:
document.querySelector('.wall').addEventListener('wheel', e =>
console.log(e.deltaMode, e.deltaY.toFixed(2), e.defaultPrevented ? 'PAGE' : 'native'), true)I am the proud owner of several seams, and am considering giving them person-names. The empty space between my inventory APIs and their clients might be “Karen”.
I can’t say that Claude invented this; the same type of terminology cycling happens every few quarters based on what leadership is reading/being told by Gartner.
"load-bearing" I have never heard used for programming before Opus, and its incredibly annoying and over-used.
Seam is used liberally throughout because it captures the idea well (i.e. a place where you can cleave ball of mud code apart to begin refactoring efforts in an isolated way) and that takes many different forms throughout the book whether that be via methods and classes, source files as a single unit, linker seams, on and on.
"load-bearing" on the other hand is just a weird way to say "required" or "prerequisite" without drawing any attention to the fact that one cannot articulate what something is a prerequisite for, probably because that fact has since been lost from context.
https://martinfowler.com/bliki/LegacySeam.html
Claude is using it a bit liberally, but not totally incorrectly.
The kind of quirks you see came from crowd-sourced human-in-the-loop fine-tuning, with not very good work conditions or level of qualification (so resulting in "what non-writers thought good writing looked like", before people had developed the flair to detect these patterns) as well as feedback loops during agentic reinforcement learning and RLVR.
I'm already seeing it. A coworker said something like "<person> added the color to the ticket here" meaning that someone added details to a ticket.
I've started seeing Opus 5 talk about "hermetic testing" when it just means "unit testing", so I hope that doesn't catch on...
"Load bearing", not so much. Ick.
Would there be the same tendency of we looked simply at the number of words in a commit/pr?
In my case my commit messages are on average 10x shorter than Claude's.
I'm currently adding a search bar as well as increasing the data to 1000 PR per day.
A nice thing that is not obvious on the main page is that the dataset and analysis are updated daily using Github Actions (at least when they don't suffer from an outage ^^). I find it pretty cool to be able to build such apps without a "backend"!
I added the search bar, increased the data to 1000 PR a day (more than 50M words total) and added a feature to explore the other clusters as well. I hope the page is not getting cluttered.
The design is really impressive. Beautiful and dense, yet very understandable. How did you come up with this particular design? It's really nice and everything fits, the colors, the layout, all of it.
Were you inspired by anything in particular? I feel like this reminds me of something, but I don't remember what exactly.
I asked Claude for a few original designs, then iterated a lot for the UI. It called this design "Rasterfeld", which is a word that means "field of grids", a term used in swiss design https://docs.mew.design/blog/swiss-design-style/ as @alwa said.
The 3 primary colors refer more to pop art and Bauhaus.
I'm really eager for a nice book about "design" that would document all those styles, if someone has a recommendation.
It reminds me of Neue Grafik/Swiss Style, and Vignelli:
https://designreviewed.com/series/neue-grafik-new-graphic-de...
https://www.printmag.com/featured/swiss-style-principles-typ...
https://www.designculture.it/interview/massimo-vignelli.html
I've been scraping instagram posts recently to identify AI misinformation accounts that all repost each other's carousels and get hundreds of thousands of likes in engagement. Thinking of ways to present it and your dashboard looks very helpful. Did you experiment with any other types of visualizations before deciding on a stacked area chart for the clusters?
I did experiment and a stacked chart seemed the most clear, with the important cluster at the bottom.
Is there some feedback loop or compounding happening with each model generation? Maybe newer models are ingesting too much AI content? If the ratio of AI generated content in training data is getting higher and higher (because the amount of AI generated content is increasing in general), maybe this is a compounding bias, poisoning the training?
Its not the latter; its just excessively verbose wirh awkward word choices, the same as many poor writers. (And, like many such writers, the particular bad choices fall into recognizable, regularly recurring patterns.)
What RL does is narrow the variety generated by the model by steering the output towards the goal being rewarded. It's a bit like putting blinkers on a horse.
Of course RL is a very crude tool - it affects the entire model, even if you are just trying to make it better at some specific task(s), or trying to imbue a certain kind of personality (OpenAI's recent goblin problem).
If Claude understands Claude, Claude understands human, and human doesn't understand Claude, that doesn't argue well for "Claude is a caveman".
Maybe I am dumb and it IS talking down to me, but there have been many occasions where I’m reading AI generated docs / plans and it makes absolutely no sense, but looks really in depth at a glance.
Whatever that small corpus is, it contains some very specific grammatical tics, and that’s how we get Claudish.
Anyone who thinks a company/project as big as Anthropic/Claude wouldn’t make such a big mistake should take a look at how Azure cross-account federated login used to work.
A notable exception would be people like CEOs and managers higher up in big tech, who might be used to skilled engineers and domain experts reporting to them in unfamiliar lingo. Maybe that's why we don't hear as much on the everyday annoyances of Claude's language from that camp?
No ream of slides. No narrative. Just a lovely big painful conclusion.
What argument? I don't know what to take away other than "Claude likes certain words". Some of them are kind of amusing, but I'm not convinced the vocabulary is bad or that this is a problem, just from looking at this.
LLMs were not taught to say the phrase "load-bearing seam" from humans saying it, because humans have never said it. It's almost definitely an artifact of post-training and nothing more.
The search on this website suggests it is indeed 3.6x more likely in the claude cluster
I think using agents is just like speedrunning the whole experience of working with technical coworkers. Whereas you might have had a few coworkers at your company who used some of these phrases regularly, you now have a “coworker” who uses all of them regularly at a much faster pace.
so they might be RLHFing on these specific approaches and then it becomes the entire model
just an anecdote but I found it interesting how it went full on that it's from that book vs just "it's technical jargon"
While I agree the model doesn't have insight into how it was trained I do think the history of the term itself is interesting.
I might be wrong, but usually it'd be a lot less deliberate, and at least in my mind it wouldn't be surprising if they were heaving training these on these specific "best practices" books/methodologies and thus picking up lingo from them
Actually I have found the copy that Claude Design spits out is way better than using the same model directly. I have no idea why. It has its flaws but it sounds like it's written by a human who uses derivative language. But usually the models just soudn incoherent.