I wonder if people will start writing like this over time. “Order the fries with the burger. They go well together” becoming “the fries amplify the burger. One order. Together. Not separate — united ”. Haha man. The kids will think it’s normal and we’ll sit here like crotchety old men.
When I learned Spanish and had the opportunity to speak with native Spanish speaking friends I told them “if you love me, correct my Spanish”
I know of no other way, because there are lots of technically grammatically correct ways to say things in English, but sounds awkward to a native speaker. You can’t know really on your own.
There are lots of funny "why do you speak like a man/woman" or "why do you speak like a children's cartoon/radio announcer/train conductor" examples in language learning forums.
It always comes out that the learner speant to long with or only used one source for their learning.
As a counter-example, chinese ESL learners will often look for "American Business English" because our office speak is different enough from common speach and that is their intended use case for the language they're learning. They don't want to sound like they're announcing the news or acting in a movie, they want to sound like business professionals.
LLMs now will tell you that all your farts smell amazing - I kind of feel my English degrading over the past years.
Gosh I have to work on it more intentiaonnly
I actually don’t know what the concept is and I’m not going to look to see if it’s real. There is a lot of spam like this now on the Internet. Sorry, you might be a real Nigerian prince in urgent need of my bank account details but I can’t really know.
I use HTMX and like it so I might have read if it didn’t look like spam post.
Good luck with your project. Your English fluency is fine.
I meant the headline - because I understand that’s what you refer to.
I’ll try to make is less like spam post next time.
Just headlining as „built tiny js lib that works like HTMX live or datastar but also slightly different”
Anyhow, thanks for commenting - will try to improve for next time
Thanks!
PS: send your bank details via email, not here in public ;)
That second sentence you wrote there? That's a good, normal English-language sentence. But LLMs never generate that kind of sentence if they can help it; they break it up into a bunch of tiny "flat" top-level sentences.
If you think about how you speak in your native language, it probably has a certain rhythm of long and short sentences, with some "shallow" sentences that just say one thing, but then sentences that nest other clauses that could be whole sentence of their own, and then a hanging sentence fragment that makes sense in context, etc. As far as I know, every spoken language, English or otherwise, looks like that when humans are writing it.
The human mind's "buffer of verbalization" seems to be quite short, basically around one grammatical "clause" in size. So humans, when writing (or speaking) "off the cuff", generally only try to keep "a non-verbalized concept of what they want to say" plus "the verbal pattern for the current grammatical clause" buffered in their heads. A human speaker will only start deciding how to glue the next clause onto the current clause—whether to make it a new sentence, or use some preposition or conjunction, or to "verbally backtrack" / "interrupt themselves" to add detail "before" what they said—when they get to near the end of speaking/writing a clause. Much of the "reason" for the grammars of spoken languages to be structured the way they are, is to allow for this kind of narrow-buffered "streaming" composition.
Human written language can look different when someone has sat down and taken this "off the cuff" writing as a first draft, and intensively edited and rearranged and polished it. But the result of doing this still usually retains a lot of the original positive qualities of the "off the cuff" writing that went in. (Editors are told to not over-edit, because doing so will remove the "author's voice" from the writing. The particular grammatical gymnastics a speaker/writer uses to connect their thoughts can be a large part of this "author's voice.")
LLMs, despite "streaming" in a much more literal sense than humans do, seem to avoid "off the cuff" generation of successive grammatical clauses using "whatever grammatical glue works to get to the next thought." Instead, they seem to have been forced by their training into favoring particular sentence structures that allow them to never end up needing to reach for artful just-in-time grammatical connections in the first place. Mainly, they like using sequences of short sentences that each say exactly one thing.
(I hypothesize they like these forms because, in some internal layer of the model, these "simple" sentences can be represented all-at-once as plans [with that same plan getting reconstructed on each successive inference-step during emission of the sentence]; and so this kind of sentence can be emitted in a token order that results in the "polished, edited writing" style rather than the "off-the-cuff speaking" style. Much of the base-model training dataset — the stuff that made the model understand language and writing at all — came from polished, edited writing rather than casual/conversational writing. However much the model is trained to adopt a casual style, it's doing so on top of a language-generation "module" that learned to write by trying to emit "polished, edited writing" one token at a time. And the only way it managed to do that was by limiting itself to constructing sentences that could look like "polished, editing writing" despite a bounded ability to plan.)
Wooow.
I never really thought about it this way, but kind of it makes sense.
Humans more intend to stream concepts in form of words when LLM just stream words that relate to the current concepts.
It’s almost identical but the first approach makes the reader feel the writer wants to arrive somewhere
"This man took a burger, and then added some fries to it. He tasted it, and realised that this was what was missing from his life. He took a fry, and dunk it into the sauce, and then took a bite from the burger. People around him started looking. They couldn't believe it looked so tasty."
"If you take a fry, dunk it into a sauce container, add a burger, take a bite, chew it all together, and then take a big gulp, you will enjoy a solid meal!"
I hate whoever's come up with that shit. It's so annoying and it's friggin everywhere now.
Ai generate with human in the loop, so hard to say it’s not while also saying it’s terrible.
Definitely the fact that I have ai makes it more fesable for me to actually publish something rather than spednign years on copy I can spend time on code and outsource copy
The only way to get better at writing is by writing.
I’d rather read a less polished but clearly human written article than something clearly written by AI. If I can’t tell I can’t tell.
Use it for editing or as a review but the weird hyperbolic AI stuff makes me instantly close an article. I barely got past the intro:
ONE ATTRIBUTE. EXACT BEHAVIOR.
The markup says what happens.
Start with local behavior. Add the server only when the interaction needs to cross the wire. The attribute is moo= — in case you were wondering what the ox says.
Your onto something here, thanks - the title and the section where the things I’ve spent more time on than the rest
I mean this „ Behavior, right where it happens.
moo= for reactive signals and ordinary JavaScript on the element itself. Datastar SSE when the interaction has to cross the wire and patch HTML and state.”
Still feels bad but I know I went trhu it couple of times.
Not so much with the new sample you give here.
I only shared because it might very well be cool stuff but if people are tired of AI writing and close it like me then it’s a lost opportunity. Not publishing at all is a bigger loss though, so it’s a tricky balance.
Agree. AI generated is not bad if it's well directed and audited in a loop by a human in good detail. I won't discard a project because a tool has been used. That's extremism. I'll take a look at the project first.
Hope you’ll like it. At least concepts, I know htmx live or datastar are more mature so there’s no discussion about it.
Would you have any hints what to avoid for „next time”?
Should I just make this absurdly small and make much less polished approach even for this kind of project?
Honest question. Will try to work on anything you suggest <3
Yea.
Any particular thing you’d say is beyond terrible - in matter of concepts not just the visuals?
Actual library is fairly cool if you asked me - concept wise to learn a thing or two about how web works
I think this page is communicating something, but it's doing it in a very confusing and elliptical way. The page seems to assume the reader is highly familiar with both "Htmx" and "Datastar SSE", and understands implicitly that this project is (I gather) some kind of complement to using them.
This is a great example of one of the current failure modes of coding agents (which were almost certainly used here): the creator of this project probably described the project in these terms to the agent. Something like:
> I want to make a Javascript library that works like Htmx or Datastar SSE, enabling a web developer to add well-known behaviors to a page just by adding HTML attributes. This library will be for the cases those libraries don't cover: triggering purely-local state changes in the state of [elements? web components? not sure]; where because these state changes get persisted to the DOM in some way or another, they are visible to, the state these behavior-attributes mutate can be referenced by Htmx/Datastar/etc in their behavior-attribute DSLs.
Then, either because the agent is already briefed in these terms — or because the agent has then gone on to write all the code for this library in the same conversation, and so has that code in its context — the result is that the agent, when it moves on to the "generate docs and README" step, treats all this as assumed shared context for those docs, since (from the agent's perspective) the docs and README exist "in" the conversation "downstream" of the project brief and code; and, from its original base-model training, the model knows that things introduced early in a conversation shouldn't be re-introduced later on in the same conversation, but rather should be succinctly referenced.
(My hypothesis, that I haven't yet tested, is that you can work around this flaw by starting a fresh conversation before asking the model to write docs. The model should see info that enters the context through e.g. "read a file" tool-call responses differently than it sees things you or it "say", not treating that info as "real" conversation turns but more like e.g. source-code excerpts in a blog post, where the learned base-model expectation would be that everything that appears in the excerpted figure will be re-explained in plain language in following prose.)
But, of course, this is still a flaw in current models, and the "right" solution is still for the model providers to train models to be able to conceptualize of multiple "conversational reference graphs" co-occurring within the context, and compartmentalize linguistic/semantic referencing on a per-graph basis; such that top-level prose and inline code excerpted for explanation both exist in the default "internal" reference graph, while code and docs generated to be written into a codebase through tool-calls exist in a separate "external" reference graph.
I use datastar daily so yes there’s a lot of this contexts in my reasoning and conversations.
It’s also a project that kind of focuses on how they do things and how they work internally.
Maybe I should try to approach docs in non-htmx / Datastar user context - will think about it
I wonder if people will start writing like this over time. “Order the fries with the burger. They go well together” becoming “the fries amplify the burger. One order. Together. Not separate — united ”. Haha man. The kids will think it’s normal and we’ll sit here like crotchety old men.
Yea. The bad thing as non native English speaker and spending whole days with LLMs at this point I can’t event notice that’s something sounding bad.
Any hints what should I look at to avoid this?
I hope you like the concept of the ox at least ;)
When I learned Spanish and had the opportunity to speak with native Spanish speaking friends I told them “if you love me, correct my Spanish”
I know of no other way, because there are lots of technically grammatically correct ways to say things in English, but sounds awkward to a native speaker. You can’t know really on your own.
There are lots of funny "why do you speak like a man/woman" or "why do you speak like a children's cartoon/radio announcer/train conductor" examples in language learning forums.
It always comes out that the learner speant to long with or only used one source for their learning.
As a counter-example, chinese ESL learners will often look for "American Business English" because our office speak is different enough from common speach and that is their intended use case for the language they're learning. They don't want to sound like they're announcing the news or acting in a movie, they want to sound like business professionals.
I get a lot of that from my wife and in-laws who find my zài nǎr amusing rather than the zài nǎlǐ they'd say. You always know who's a Pimsleur boy.
„I am groot”
LLMs now will tell you that all your farts smell amazing - I kind of feel my English degrading over the past years. Gosh I have to work on it more intentiaonnly
I actually don’t know what the concept is and I’m not going to look to see if it’s real. There is a lot of spam like this now on the Internet. Sorry, you might be a real Nigerian prince in urgent need of my bank account details but I can’t really know.
I use HTMX and like it so I might have read if it didn’t look like spam post.
Good luck with your project. Your English fluency is fine.
I meant the headline - because I understand that’s what you refer to. I’ll try to make is less like spam post next time. Just headlining as „built tiny js lib that works like HTMX live or datastar but also slightly different”
Anyhow, thanks for commenting - will try to improve for next time
Thanks!
PS: send your bank details via email, not here in public ;)
That second sentence you wrote there? That's a good, normal English-language sentence. But LLMs never generate that kind of sentence if they can help it; they break it up into a bunch of tiny "flat" top-level sentences.
If you think about how you speak in your native language, it probably has a certain rhythm of long and short sentences, with some "shallow" sentences that just say one thing, but then sentences that nest other clauses that could be whole sentence of their own, and then a hanging sentence fragment that makes sense in context, etc. As far as I know, every spoken language, English or otherwise, looks like that when humans are writing it.
The human mind's "buffer of verbalization" seems to be quite short, basically around one grammatical "clause" in size. So humans, when writing (or speaking) "off the cuff", generally only try to keep "a non-verbalized concept of what they want to say" plus "the verbal pattern for the current grammatical clause" buffered in their heads. A human speaker will only start deciding how to glue the next clause onto the current clause—whether to make it a new sentence, or use some preposition or conjunction, or to "verbally backtrack" / "interrupt themselves" to add detail "before" what they said—when they get to near the end of speaking/writing a clause. Much of the "reason" for the grammars of spoken languages to be structured the way they are, is to allow for this kind of narrow-buffered "streaming" composition.
Human written language can look different when someone has sat down and taken this "off the cuff" writing as a first draft, and intensively edited and rearranged and polished it. But the result of doing this still usually retains a lot of the original positive qualities of the "off the cuff" writing that went in. (Editors are told to not over-edit, because doing so will remove the "author's voice" from the writing. The particular grammatical gymnastics a speaker/writer uses to connect their thoughts can be a large part of this "author's voice.")
LLMs, despite "streaming" in a much more literal sense than humans do, seem to avoid "off the cuff" generation of successive grammatical clauses using "whatever grammatical glue works to get to the next thought." Instead, they seem to have been forced by their training into favoring particular sentence structures that allow them to never end up needing to reach for artful just-in-time grammatical connections in the first place. Mainly, they like using sequences of short sentences that each say exactly one thing.
(I hypothesize they like these forms because, in some internal layer of the model, these "simple" sentences can be represented all-at-once as plans [with that same plan getting reconstructed on each successive inference-step during emission of the sentence]; and so this kind of sentence can be emitted in a token order that results in the "polished, edited writing" style rather than the "off-the-cuff speaking" style. Much of the base-model training dataset — the stuff that made the model understand language and writing at all — came from polished, edited writing rather than casual/conversational writing. However much the model is trained to adopt a casual style, it's doing so on top of a language-generation "module" that learned to write by trying to emit "polished, edited writing" one token at a time. And the only way it managed to do that was by limiting itself to constructing sentences that could look like "polished, editing writing" despite a bounded ability to plan.)
Wooow. I never really thought about it this way, but kind of it makes sense. Humans more intend to stream concepts in form of words when LLM just stream words that relate to the current concepts.
It’s almost identical but the first approach makes the reader feel the writer wants to arrive somewhere
"This man took a burger, and then added some fries to it. He tasted it, and realised that this was what was missing from his life. He took a fry, and dunk it into the sauce, and then took a bite from the burger. People around him started looking. They couldn't believe it looked so tasty."
"If you take a fry, dunk it into a sauce container, add a burger, take a bite, chew it all together, and then take a big gulp, you will enjoy a solid meal!"
I hate whoever's come up with that shit. It's so annoying and it's friggin everywhere now.
Holy AI generated everything.
HN needs an AI flag or something. Not sure how well it would work, but worth an experiment.
Ai generate with human in the loop, so hard to say it’s not while also saying it’s terrible.
Definitely the fact that I have ai makes it more fesable for me to actually publish something rather than spednign years on copy I can spend time on code and outsource copy
The only way to get better at writing is by writing.
I’d rather read a less polished but clearly human written article than something clearly written by AI. If I can’t tell I can’t tell.
Use it for editing or as a review but the weird hyperbolic AI stuff makes me instantly close an article. I barely got past the intro:
„You’re absolutely right” ;)
Your onto something here, thanks - the title and the section where the things I’ve spent more time on than the rest
I mean this „ Behavior, right where it happens. moo= for reactive signals and ordinary JavaScript on the element itself. Datastar SSE when the interaction has to cross the wire and patch HTML and state.”
Still feels bad but I know I went trhu it couple of times. Not so much with the new sample you give here.
Gotta work on that. Thanks!
I only shared because it might very well be cool stuff but if people are tired of AI writing and close it like me then it’s a lost opportunity. Not publishing at all is a bigger loss though, so it’s a tricky balance.
Agree. AI generated is not bad if it's well directed and audited in a loop by a human in good detail. I won't discard a project because a tool has been used. That's extremism. I'll take a look at the project first.
Hope you’ll like it. At least concepts, I know htmx live or datastar are more mature so there’s no discussion about it.
Would you have any hints what to avoid for „next time”? Should I just make this absurdly small and make much less polished approach even for this kind of project? Honest question. Will try to work on anything you suggest <3
Yea. Any particular thing you’d say is beyond terrible - in matter of concepts not just the visuals? Actual library is fairly cool if you asked me - concept wise to learn a thing or two about how web works
So many words and so few content.
Take a look at cookbook. Lots of examples and solutions. Landing page could be better, I agree
I think this page is communicating something, but it's doing it in a very confusing and elliptical way. The page seems to assume the reader is highly familiar with both "Htmx" and "Datastar SSE", and understands implicitly that this project is (I gather) some kind of complement to using them.
This is a great example of one of the current failure modes of coding agents (which were almost certainly used here): the creator of this project probably described the project in these terms to the agent. Something like:
> I want to make a Javascript library that works like Htmx or Datastar SSE, enabling a web developer to add well-known behaviors to a page just by adding HTML attributes. This library will be for the cases those libraries don't cover: triggering purely-local state changes in the state of [elements? web components? not sure]; where because these state changes get persisted to the DOM in some way or another, they are visible to, the state these behavior-attributes mutate can be referenced by Htmx/Datastar/etc in their behavior-attribute DSLs.
Then, either because the agent is already briefed in these terms — or because the agent has then gone on to write all the code for this library in the same conversation, and so has that code in its context — the result is that the agent, when it moves on to the "generate docs and README" step, treats all this as assumed shared context for those docs, since (from the agent's perspective) the docs and README exist "in" the conversation "downstream" of the project brief and code; and, from its original base-model training, the model knows that things introduced early in a conversation shouldn't be re-introduced later on in the same conversation, but rather should be succinctly referenced.
(My hypothesis, that I haven't yet tested, is that you can work around this flaw by starting a fresh conversation before asking the model to write docs. The model should see info that enters the context through e.g. "read a file" tool-call responses differently than it sees things you or it "say", not treating that info as "real" conversation turns but more like e.g. source-code excerpts in a blog post, where the learned base-model expectation would be that everything that appears in the excerpted figure will be re-explained in plain language in following prose.)
But, of course, this is still a flaw in current models, and the "right" solution is still for the model providers to train models to be able to conceptualize of multiple "conversational reference graphs" co-occurring within the context, and compartmentalize linguistic/semantic referencing on a per-graph basis; such that top-level prose and inline code excerpted for explanation both exist in the default "internal" reference graph, while code and docs generated to be written into a codebase through tool-calls exist in a separate "external" reference graph.
I use datastar daily so yes there’s a lot of this contexts in my reasoning and conversations. It’s also a project that kind of focuses on how they do things and how they work internally.
Maybe I should try to approach docs in non-htmx / Datastar user context - will think about it