ES version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
63% Positive
Analyzed from 18819 words in the discussion.
Trending Topics
#llm#information#bits#writing#more#don#read#write#human#something
Discussion (424 Comments)Read Original on HackerNews
Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
Giving someone the text output of a LLM is very similar to publishing a summary without links to the referenced material. When you were querying your LLM, you could have asked specific questions or asked for a custom focus or point of view. Your intended audience might have questions or different concerns, but they're unable to interact with your LLM. What you have delivered is static and unresponsive. It has all the disadvantages of being machine output without the advantage of being interactive, the way your LLM was for you.
It may have to wait until compute is cheap enough that tokens are essentially free, but we need a system to pass "hyperlinks" to LLM's primed with context, ready to be interactively queried on a chosen context. It's being overly generous to assume that people are putting even 300 bits into a LLM for every 1000 bits of regurgitated writing they try to pass off as their own. When people post LLM output as if it were their own, I have no choice but to assume they had zero knowledge of the subject, but this query taught them what they wanted to learn, and now they're sharing that. That's fine, but please pass an interactive LLM link rather than static text.
Once we have "hyperlinks" for LLM sessions, perhaps we can share LLM output a little more usefully and honestly.
This has been happening for decades; I still see it happening today*. My cynical suspicion is that words like "maybe" and "suggests the possibility" don't sell enough papers.
* Worst offender I can remember was actually from the summary of a paper published on the research institution's own website, so I couldn't blame it on "Oh, the journalist misunderstood what the scientist wrote". Summary said "Exposure to X can, on average, cause a 40% higher chance of Y" (where Y was a negative health outcome). I clicked through to the study and read it. Turned out the confidence interval on that chance of Y was so wide, all you could say with 95% confidence was that exposure to X could do anything from reduce your chance of Y by 5 percent, or increase it by 85 percent, or somewhere in between. They had averaged -5 and +85 to get the scarier-sounding 40% number that they published in the summary, but the truth would have been far closer to "this confidence interval is so wide that we really can't conclude anything from this data". But that wouldn't be nearly as likely to get them grants, so they tortured the data in their summary so that it would look better.
I still remember a recent example where one of those trivia accounts on Twitter posted an interesting story about some guy whose life completely changed after an accident, but neither linked to a source or named the person in question.
The only way I was able to verify it was true was through someone in the comments asking the platform's AI chatbot, and the chatbot providing context that I could research and verify...
I agree, this drives me crazy. Ironically, one of my favorite uses for Claude is to ask, "What study is this news article talking about?"
It's pretty good at digging up the source and related sources. And most of the time, if you read the source, the article is nonsense and gets everything wrong.
I’m a big fan of this approach.
The "$CITY_NAME Business Journal" websites are the absolute worst with this. They'll refer to something specific, for example "$BIGCO's 2025 10-K filing" and it will be a link. That link will go to the 10-K, right? Nope! It goes to another page at the same business journal. Maybe that page is a summary of the 10-K, but probably not. Maybe it's just the general index page for all the articles about $BIGCO at that journal. What it links to, it definitely won't be the specific thing described by the text of that link.
It's the opposite, all big news websites do this. Fairly sure it's part of the policy.
I would say only small, niche websites link to sources.
Communication only works if you have multiple levels of representation and abstraction, including but not limited to - letter shapes, grammatical structures, style and register, stylometry, and subtext.
All of that is learned, and writers usually assume they can rely on that learning as the context for the text.
So you don't write to 'transfer information' like a network cable, you write to trigger experiences in the human version of latent space.
Factual information is one kind of experience. But even when that's the goal, there are always layers of implied relationship, social register, role, status, and other implications in everything that's written.
In normal communications the context - business emails, personal messages, mainstream journalism, fiction, and the rest - defines what acceptable language looks like.
The content fits inside that. But it has to fit the context, otherwise it lands in a semantic and psychological uncanny valley - like sending LinkedIn speak to a spouse on a wedding anniversary.
The real problem with LLM writing is that it's good at the technical layer - the grammar and spelling - and has some insights into the rest.
But the default content style is marketing and ad speak. And recently it's developed a weird and unique hybrid style which applies marketing fluff and pretension to technical content like code comments.
So you get one register instead of all of them. It can attempt others, but it's still too limited to generate them fluently. Sometimes the results are outstanding, but often it defaults to mechanical clichés.
So that's why it sucks and sounds so hollow.
Can it be fixed? Yes, but it's very hard work, most people don't have the skills, and it takes time - often too much time to be worth the effort.
When LLMs eventually get good at writing in the correct style for a given context, I'll admit that they have value in that way. But they aren't good at that yet. And even when they do get that good, I'll still dislike it for reasons that are more emotional than rational.
If the seed of intent is "convey XYZ details so they know them" then I can choose to go and learn those details any way I see fit - maybe even ask an LLM to summarise some data for me! - rather than having to ingest whatever their LLM use poops out and trying to digest the intent and content and figure it out.
It is about empowerment, rather than eating shit.
Sometimes Claude's problem, such as when I ask it to summarize a long, complex session back to me, is it's too information dense. It uses weird invented terms to gloss over complex parts of the architecture instead of explaining them.
But no matter what - too dense or too sparse - it always sounds like Claude.
It's essentially the tower of babel. Each person will devolve to speak their own internal language only they understand. Each language will need to be encoded down to its meaning to be reinterpreted. None of us will know if the transformers are accurately decoding, or if the other person is accurately interpreting the decoding (which is arguably already a feature of human language without the computers in-between.)
I feel like a lot of this is a problem when someone technical is attempting to communicate a complicated technical subject to a less-technical audience.
I can only dumb a thing down so much before the description is useless (when you zoom out too much you lose the details). Even technical people who could understand it but are lazy / "in a hurry" use the summary, without thinking about what detail they are losing.
Even more infuriating is when they then reply to my email, having only read the AI summary, and ask a question that was already answered by my message.
This is the exact same thing that happened pre-AI, with the added step of wasting energy/resources on the AI summary in the middle.
They knew it was going to be like that from the beginning.
https://web.cs.ucdavis.edu/~rogaway/classes/188/materials/th...
I see this all the time now with LLM generated output. It's easy to have an LLM generate a chunk of content that can be dropped into a chat or comment, and when it took you 20 seconds to have something written up based on the shared understanding you and an LLM have about the context of the situation, but it takes other people 3-5 minutes to read and understand that content, that fundamentally doesn't scale. It's bad enough when one or two people are doing it, but if the whole team is doing it, the only way to keep up with the stream of information is to also consume it through an LLM. At that point you're likely to be missing much of the nuance, and the amount of errors will explode.
This can be alleviated by people reviewing the output of an LLM and making sure it both includes fundamental information that might be assumed by context and reducing it to the parts that are essential for the new context it's in. This takes time, but is extremely important.
Having an LLM write gobs of text to send to other people instead of doing it yourself is the equivalent of a low yield cognitive zip-bomb. Don't do it.
No, I don’t want to read LLM writing because it is BAD at it. It doesn’t really understand how humans think (because it thinks differently), and doesn’t seem to understand core principles very well (presumably due to the lack of world model), so it can’t write something humans enjoy yet.
TLDR the length was the same curtesy of tl;dr before, just now with a different name.
The issue isn’t that a 300 bit idea is padded with 15 KB of content. You can take any human-written article and reduce it by 90% with next to no information loss. What you lose is what makes the article a compelling read instead of a fact table.
I think the reality is that we will see quality long form AI-written content at some point. It doesn’t even feel like labs are particularly interested in chasing that now; code sells way more tokens. Right now the trend is that subsequent models degrade in writing quality as long as that pulls them up on coding benchmarks.
You've unintentionally circled the error here. The "purpose" of an article extends beyond "convey this essential information".
By analogy, a textbook contains far more words than a spec sheet, but attempts to train the human to be able to easily interpret spec sheets. The so-called "information" content of both might be equivalent, yet one does a better job of teaching students.
All that being said, I acknowledge that people (me included) love to be sloppy in their comms (with or without AI) and then blame others for misunderstanding. It's also unlikely we'll change soon. What can you do.
LLMs do inference or computation among other things, so the remaining 700 bits can be something like that. The hidden implication in your claim is that computation adds no information content, which leads to an interesting philosophical discussion.
So for example, if I ask an LLM to give a proof or derive a new theorem from a set of axioms, according to your assumption, if it answers correctly, then I haven't learned anything new.
I am not really sure how to resolve this paradox in information theory.
The primary reason is that human language is becoming a proof of work, that speaking out loud or writing directly indicates that the idea is important enough for a human to express. This is more costly than llm output, which is often just botspam.
From a realistic perspective in the context of people copy-pasting LLM output, my thoughts are that asking an LLM to research for you is more defensible, but it's still better to read the LLM's research results and write the important parts in your own words (partly because the LLM probably used way more words than necessary for the context).
But then I realized that the reader can prompt the LLM with the same prompt for the same or equivalent expanded text. Most people don't do this as it's extra effort, but it's interesting to imagine a world where this is the default way of engagement with a text, assumed by both writers and readers alike.
That's basically what I've been asking my colleagues (so far a losing battle): Please don't send me AI-generated text. Send me your prompt instead. It is highly likely that I will understand it without needing an LLM, and if not, I can do it myself.
I’ll often put a long stream of consciousness on the page, or jot down rough meeting minutes, then ask ChatGPT to “summarise this for an email”. The result is shorter, clearer and easier to read.
AI amplifies the habits of the person using it. If they’re lazy or dim, then it's like giving a monkey a gun.
Rather than send 300+700 bits, like you said, send 300 (or less!) and let the human intelligence on the other side generate the result. Which supports the even older perspective: “If I had more time, I would have written a shorter letter.”
I’m not sure if this lands on anything very profound, but what about a pattern where, instead of codifying agent output at all, the only artifacts we share are the prompts. And the rewards (respect) accrue to those who generate the most generative among people and AI
Sure you can. LLM doesn't know what those 700 bits are, but you do. You may not realize it, and may not even know it at the time of prompting, but you do by the time you're sending.
Typical case is like this: you have 500 bits of semantic information to transfer. You give 300 of them to LLM, and get back the 500 bits you knew you have, and extra 500 you can quickly confirm are correct and relevant. Some of them are just dereferences of your input - where you recalled a pointer, but not what it pointed to. Some of it is information you never had before, but are able to easily validate.
You send that to me. I likely immediately realize the message was AI-assisted, but I trust you to be a decent human being, and not an asshole that lobs unverified LLM vomit over the fence for others to deal with. End result: you communicate 1000 bits of information to me, instead of planned 500, and you yourself learn extra 500 bits.
This is the optimistic scenario, but it does happen when LLM operator is not an asshole.
(Excuse the strong language, but I spent a lot of effort every day on both dealing with inconsiderate people lobbing LLM output at me, and making sure never to act like one myself, so it's a topic close to my heart.)
If the receiver wanted these 200 added bits, she could infer them either herself or even use an llm to do it.
My brain does not contain all the information that can be added by an LLM; a human brain could contain it, even the biggest LLMs are about 1% of the (if you approximate synaptic count ~= parameters) parameter count of a human brain, but none actually will.
What my brain may actually contain is the information necessary to verify the (in this example) more than 200 bits the LLM claims to have added and trim out the parts which are false, retaining the (in this example) 200 "new" bits of new information added by the LLM.*
Concrete example: I am a software developer by training, though not a web developer. If someone who does not have any developer experience asks me to make a web app, I am forced to use an LLM as I do not know enough JS etc syntax to get it done myself. But as we all know, LLMs are only "ok" but not "good" at making software, so there are a lot of rough edges and outright mistakes. My experience as a software developer extends to detecting such failures and I can usually correct them.
The original person, someone who has no developer experience, can also prompt the LLM. Right now, this would result in something that retains all the errors, because they didn't have someone like me intermediating between them and the LLM.
I add bits by removing noise, the LLM adds bits but they contain noise.
I do not know for how long this will remain true, but today it is true.
* Feels like P versus NP to me. The answers AI generate are at their best when they're easy to verify. Then again, when they're easy to verify, they can be RLed to get good at this quickly and the need to verify goes down, leaving them still pretty bad at things that are hard to verify.
No, they're not. Getting those bits takes energy.
SOTA LLMs know way more than any individual on approximately anything there is to know (and what they don't, they can look up faster than people can). It's very easy for them to make the "missing" 200 bits explicit, rather than implicit, which in practical terms is the same as adding 200 bits that weren't there before.
Theoretically, an idealized omnipotent mind / AGI could derive the unifying theory from reading your HN comment on a phone screen. There is enough information there, if you were able to extract every bit of evidence available from it. But you are not. Neither am I. It would take us practically infinite work to try, solving this most cruel mathematical riddle.
Me specifically, I never send anyone LLM output I haven't give at least a quick read (not skim, read) to make sure it's reasonable and there is no obvious bullshit there. And then I still mention it's LLM-sourced.
> If it’s less than 50% of the time, it means that by not modifying it, you have added at most one bit of information to what you originally wrote. (...) Instead of sending the LLM response, you could send the prompt and one extra bit indicating whether the LLM response to the prompt should be modified, followed by the modifications.
It's not the case, though. Prompts are not interchangeable with output. There is no guarantee that if you send a prompt, and recipient passes it to their LLM, they'll receive anything similar to what you did. It may have mistakes - different mistakes - or just spend focus differently.
The extra bits I claim LLMs can add to the message hinge strictly on you vouching for the response. Of course, you can just prompt an LLM, learn from the response, and then write your message clean, containing both the bits you originally had, and the bits you gained. But at that point, the LLM already gave you text containing all those bits - if you can vouch for it, you may as well copy it over and save yourself the trouble.
This is exactly the use case an LLM might (huge emphasis on might, depends on workflow, agentic vs. relying on contextual which can hallucinate) be good at and yet humans are notoriously bad at, because we are swayed by emotional responses and it is easy to have an emotional response to text that is programmed to look good for you and you alone.
> you communicate 1000 bits of information to me, instead of planned 500, and you yourself learn extra 500 bits.
Extremely optimistic. If this were the ideal scenario, you would USE the LLM to garner information ABOUT those 500 bits and then reframe them in a way that you yourself would put it. If there is insight, your "word" in your mental register now expands from the original 1000 bits to 1500 or 2000, and then are "processed" by your human brain that includes subconscious choices that are meaningful to the end result. There are tons of hidden semiotic data in your diction and wording (think resource forks in classic MacOS/HFS, only visible to the filesys) that is lost when you rely on another source to put together words for you; it's as if it is a game of Telephone. These are subtleties which you may intend for your recipient to receive and which are crucially important to your recipient and are irretrievable, it is intrinsically lossy. You have an alphabet soup of words, they cannot be put together by an LLM in exactly the way your brain did. We must rely on the fact that we ourselves put this together, the "aha" moment when an LLM does it for you is illusory and does not itself provide meaningfully important confirmation that you indeed say what you mean to say. Of course, humans say things and put things in way we do not intend to all the time. I still fundamentally believe this is more honest than relying on a third party that is not capable of understanding human emotional nuance to put together language for you, when language is and always has been a manner in which to dictate human emotional nuance.
> (Excuse the strong language, but I spent a lot of effort every day on both dealing with inconsiderate people lobbing LLM output at me, and making sure never to act like one myself, so it's a topic close to my heart.)
Does not negate the fact that LLM output itself, at least when used to convey human emotions or thoughts, is lossy. A very highly compressed JPEG with added interpolation from an upscale algorithm might come up with cool details that were never present in the original, and may look cool to both you and the recipient but are not honest to the source material. You receiving 240p JPEGs on a day-to-day basis is irrelevant to this. For the purposes of communication, it is a massive error which has the potential to compound, regardless of whether or not you or the recipient believe this to be the case.
Yes, but at that point in practice we're getting into over-optimizing territory. In this optimistic case I presented, you could learn those 500 bits yourself and formulate a clean message yourself, with all 1000 bits in it, but since LLM already gave you the text, and you feel you vouch for, you may as well send it over and save yourself the effort.
In reality the numbers are probably lower, and writing the message yourself is IMO also a good way to be truly sure you vouch for the "extra" 500 bits, as it forces you to actually pay attention. There's a chance you'll find inconsistency in output, or in your own understanding. I don't begrudge people for eventually cutting the process off here, for practical reasons - it's the fuzzy line between accuracy and perfectionism.
> A very highly compressed JPEG with added interpolation from an upscale algorithm might come up with cool details that were never present in the original, and may look cool to both you and the recipient but are not honest to the source material.
Again, I think it's a wrong take. LLMs aren't pulling the information out of their asses, and you are also not able to express every information directly. LLM can "upscale" information and you can take a look and recognize, "yes, this is exactly as it was", even without being able to write out that "upscaled" version by yourself. Verification is often easier than direct recall.
> This is the optimistic scenario
So is it typical or optimistic?
> I spent a lot of effort every day on both dealing with inconsiderate people lobbing LLM output at me, and making sure never to act like one myself
So why are you so eager to defend your fantastical scenario? It doesn’t matter how considerate you are, truth is the overwhelming majority of people aren’t and won’t be. We’re discussing reality here, not “what could be if we lived in a utopia which will never come to pass”.
The optimistic case is "having 500 bits, giving LLM 300, getting back 1000, and learning extra 500 in the process". Real numbers are lower. People don't vouch thoroughly and don't catch all mistakes.
But reasonable people don't send every output from LLMs to others without giving it a cursory glance (obvious hallucinations or nonsense would paint the sender as incompetent or inconsiderate), and that alone eliminates the worst levels of noise. A cursory read and cutting out obvious bullshit before sending is enough to make the message carry more bits of information than the propmpt.
> the overwhelming majority of people aren’t and won’t be.
In my experience, the "overwhelming majority" are giving something between a cursory glance and cursory edit; whether the resulting message has more or less information than prompt then depends on how much noise LLM added on top. The inconsiderate people I deal with, they often send "net more bits than in prompt" outputs, but those outputs are also verbose and not fully filtered for bullshit, thus it's effortful to tease out the signal from noise.
A math teacher only has "this class is about math" to transfer, the rest is known. ;-)
Jokes aside, I think we're in a weird transition now where AI is used to generate text that looks good but is bad. In a few years people will know that and be more critical.
I think we went through a similar phase when DTP had it's breakthrough. Suddenly school papers were laser printed 300ppi times new roman and got more attention than better papers written by hand. But eventually that became the baseline.
I think Ai will make it so that well-written texts with clarity, good layout, correct illustrations, callouts etc become the norm, and will no longer impress anyone unless the information itself is actually good.
And if the information is good, it won't matter if it's AI generated or not.
It depends on the teacher. I’ve had good and bad math classes, and LLMs today are quite a bit better than the bad ones. The worst human teacher in my memory didn’t offer interactions. He walked in, turned his back to the class, wrote equations on the board for 45 minutes, and then left. The best math teachers, the ones better than LLMs, are the ones who share the joy and sense of discovery and history of math, and not just the mechanics. But there aren’t that many teachers of that sort.
Learning by using the internet without LLMs is rarely very good, but more often than not in my experience sucks much worse than using LLMs. If you include using public LLMs in internet usage, then it’s not very different from just using LLMs that search the internet. I have heard that a huge swath of today’s high school and college kids are reaching for chatGPT before Google (which is incidentally OpenAI’s goal), and that many of them would rather talk to chatGPT than talk to a teacher. I’m going to refrain from making any claims, but I believe there are a lot of people who disagree with your ranking of the options.
Foreign language learning is one case where I love using LLMs, because it’s not typically an option otherwise. You can practice non-stop and have conversations with someone fluent in a language who will be infinitely patient with your mistakes. This is true of math and other subjects too; using LLMs to practice, so that the human teacher isn’t the bottleneck, to supplement and reinforce the human interactions, is usually better than using the internet without LLMs. The other reason many people prefer talking to LLMs is the lack of judgement. If you aren’t getting it and ask the teacher one too many basic questions, they treat you differently. Sometimes it’s necessary and helpful, and sometimes it’s harmful and takes a long time to change. LLMs don’t do that, they just explain and explain. That lack of judgement is a big reason many people prefer LLM interaction to human interaction.
It's not an either-or though, LLM generated text already provides lots of value in many situations right now. It's also used for fluff, yes, but much of what humans write is fluff too, reporters often get paid by the word.
The point is that the value in a text has nothing to do with whether it was generated by an LLM or not. What matters is if it's useful or not.
It doesn't actually follow, because maybe the LLM is smarter than the original writer (at least in the domain the writing is about) and hence really is able to complete the ideas in a way the writer can't. As an existing example, consider formulating a conjecture and having an LLM prove it. But I agree; if I wanted to read an LLM's output I'd simply ask it myself rather than read someone's supposedly-human writing.
You are stranded in a desert island. You start writing a message "Help, I am..." and pass at that point.
Somebody finds the message. They can no doubt come up with plausible continuations like "Help, I am Robinson Crusoe" or "Help, I am hungry" but they cannot create information. No matter how smart and how long you stare at the message, that is not going to tell you what the original person would have written.
Isn't it from Claude Shannon that information lowers uncertainty? Infinite regurgitation or massaging of data does not create new information. You will get the information form the LLM, not from that original person.
Consider, for example, that if somebody doesn't know English at all, then before receiving the message, their best guess at what it is is some probability distribution over all English characters (or sounds, depending on what we assume them to know), and after knowing the first part is "Help, I am" that distribution might not change much at all. Therefore, they derived very little information from this message.
Going in the opposite direction: keeping fixed the knowledge someone starts with, there is an upper limit to how sure they could be (even if they are logically omniscient) in completing the message (that is, a lower limit on the entropy of their probability distribution) - this is what you're talking about in your example. But this limit only becomes important under these constraints - for example, knowing more about the person who wrote the message can let you predict it better, and if predictor A isn't logically omniscient, predictor B can do better than it with the same prior knowledge, just by being smarter than A.
Obviously this doesn't really apply to super simple questions that the LLM can just spit out the answer to right away.
What I'm talking about is if you add "database foo" to your prompt, the LLM may then add text describing what that database is, where it is, etc. But that's not new information, it (hopefully) already exists in your team's public docs, slack convos, etc. You should just say "database foo" directly to your reader, and if they want to learn more about that database, they can do that themselves, or you can give pointers to them based on what you consider important.
But I can most of the time, and correct it if it chooses the wrong thing. That's the whole reason LLMs are faster. Its the reason we can give a paragraph prompt and get a kLOC PR back but only need to correct about 5% of it.
I bet no one would like to get direct rude "source" instead.
You bet wrongly. Rudeness carries information.
“Fucking hell, how many times have I asked you to XYZ” is different from “G’day gov’nor, terribly sorry to bother you. May I remind you to XYZ? Would you mind doing so at your earliest convenience? My deepest regards, toodeloo”.
The former conveys urgency and annoyance while the former conveys that you can keep ignoring it (and straining the relationship).
The latter (with its twisted mix of Australian, Cockney and Kings English) carries a calm sarcastic tone which indicates ones displeasure far more than the former, more vulgar statement, could ever hope to achieve.
One is however, reminded that Americans simply don't get our sarcasm, frequently leading to some amusing cultural clashes.
But there is a reason no one is talking that way...
It is often about persuasion and that sometimes benefits from framing effectively, which I think an LLM can help with given the key points you’ve got.
In other words: If I didn't reduce the 500 pages of text for you, you wouldn't know what I mean or what is relevant, or how to filter it yourself.
I get vague statements thrown at me with people expecting me to understand it.
Same with writing, setting up whole context to properly transfer 300 bits is always orders of magnitude bigger then just additional 700 bits.
I am not ready to use the Americanism, 'teh telephone game', since nobody knows what that means, yet everyone over a certain age knows what 'Chinese whispers' means.
Exactly this. Just send me the prompt! ;)
If you consider that what humans are doing during conversation is a form of compressed encoding / decoding from some latent representation through a quantized signal then if you interpret it that as a compressed sensing problem you absolutely can infer to a very close approximation the original latent representation using far fewer than those 1k bits.
- people writing in a non native language
- people insecure in their writing
- people not used to writing in industry terms
- people with the curse of knowledge that are aware that they can’t write for a general audience well
Surely others too. None of those mean you have to read it, but I have gotten immense value from reading some things people have had ai write (and I’ve seen a ton of junk as well)
> people insecure in their writing These people can grow up, I don't care. Not a good enough reason to send a slop grenade.
>people not used to writing in industry terms Similar to the non-native speakers, but slightly less in magnitude. They can educate themselves though.
- people with the curse of knowledge that are aware that they can’t write for a general audience well These people probably can get some value out of it but they should take care
Still not really a good enough reason in the end
I disagree, but I think neither of us are going to benefit from continuing this discussion.
1. Transliteration - roughly keeping the number of characters or bits, but translating to a different lingo, language or mental model (e.g. metaphors). Roughly the safest mode, but can still yield catastrophic results - it's safest if the author still provides taste and editing.
2. Compression - taking out redundancy to make the text more dense and more salient. The LLM chooses what to take out - and might take out the wrong things. More dangerous - but if you're happy with the salience and you believe the reader won't have time to read the uncompressed - it's probably safer than having the reader LLM compress without the benefit of your editing process.
3. Decompression - using the salience of your idea to add detail to the reader who wants to understand it fully, by utilising knowledge that is common to you and not common to the reader. This can be very powerful when there's no time to fully write the thing by a human - but it's the easiest to get wrong and to create slop. As an example - you could try explaining concept X + illustrate it through 3 examples. You know the examples are in public memory and easily retrievable - so you write your explanation of concept X, list the examples you want - and the LLM can take all of them, synthesise and bring the full package from your 300 bits to 1000 bits.
You are right that those are not the exact 1000 bits from the original brain, but they could contain 900 of the 1000 - which is still better communication efficiency than transferring 300.
I am however, more and more in the camp of fleshy brains writing everything, as my slop allergy rises.
Overall, ideas are ideas. I'm not overly concerned with the fact that it was you who had the idea, as long as the idea is interesting. I don't know most of the people who write the things I read, so it seems to be of no consequence to me at all if they wrote it, as long as it is interesting. LLMs are notorious at creating things that are bland and vacuous, but they by no means have a monopoly on it.
Be the source human, machine, or dolphin, if they write a good article, I'm prepared to read it.
The real benefit is that the use of an LLM allows me to convey to it 1747 bits of scrambled information in an order that fits what's actually inside my head, and then have it unscramble that and convert it down to the 1000 you need. It can do that better and faster than I can.
It massively reduces the time it takes to make a short letter.
- The assumption that both parties know about the same as an LLM does. An LLM know orders of magnitude more.
- The assumption that the output of the LLM is not refined over a few cycles.
The point is that you might give 300 bits of semantic information to an LLM, it fills it to a 1000 with perhaps 400 wrong bits. You correct it half a dozen times. It's now 950. You do the final touch ups. It's now at 1000. And it still took you 20% of the time to do it.
- If you're giving additional prompts to the LLM to refine its output, then you're the one adding real information, not the LLM. The LLM is just rephrasing the information and adding noise.
- You are adding real information. The LLM is also adding real information. That's the entire point. It happens very often that an LLM suggest something to me that I did not know or simply did not think about. An LLM solved Navier-Stokes recently. That was most certainly not just adding noise. That's real information purely generated by an LLM. Information that was worth a million dollar price. Information that man centuries of mathematicians were not able to do generate.
That means folks using LLMs starting off with 300 bits KNOW that they lack the full payload of information to transfer to you. IOW, they know they need to transfer much more than 300, so they use LLM to fill those gaps. That's the crux of the slop universe out there. Folks are using LLMs for the 700 bits on top of their 300 bits and passing off the full 1000 bits as their own.
I echo the writer's sentiment. "I don't want to read the clanker's 700 bits. I want only your synthesis." (I can get the clanker to generate those 700 myself. Unless ... unless this whole LLM slop market is all about saving you the time to get an LLM to generate those 700!)
This of course has the potential to change with personal LLMs that can have shared private context with me. However, that isn't a defense for sending people AI slop, it just turns it from "LLMs don't add value" to "LLMs may add value when used judiciously."
Furthermore not all writing is for another to consume; nor even for the author themselves to consume. That is to say it has meaning ipso facto, not dependent on transference, as ritual.
When you talk about something you're wondering about, you're saying that you're missing information. Your ponderings are dancing around the void in your knowledge, defining its boundaries, and maybe imagining what answers might be able to fill that void.
When you put your thoughts into words, they're insufficient. You have so many ideas swirling around in your head, and you can never put them all on a page in the fidelity at which they exist internally. But words are the best we have. Whatever words you write are your best attempt to convey your thoughts to me (barring other media). You're distilling your inner voice that speaks a language only you can understand, into an outer voice that others can understand.
I don't think I'm exactly refuting you here. I think what you've written makes sense, and caused me to think about many things, more so than any other reply to me today. But I also don't think your comment is refuting the point I was trying to make, mainly that LLMs rarely add value in human-to-human communication.
I could probably have pasted my comment and yours into an LLM, and it would have come up with a clearer thread connecting my words to yours. But that thread probably wouldn't have been any of the ones either of us saw, would it?
Thanks for adding a new perspective to the conversation :)
Surely the ability to do that is worth taking note of.
I am not advocating for letting LLM's write for you, to be clear. Sentiment wise, I largely agree with you. Just not with your total writing off of the possibility that it could serve.
It's easy to imagine an LLM aiding the communication between a mentally disabled person and their parent/caretaker.
Or, perhaps, some day, between animal and man. Who cares if the mediating component "hallucinates" some particulars of expression if it achieves the goals both want, which were previously impossible?
I have a bunch of CLI utils I run for various clients and their peculiar setups. They now have man pages with descriptions and examples in them because the LLM went and read my code and did the needful.
I no longer have to re read my own code, rather I can just use the manual page.
Format and description came from semantics and context that (barely) existed elsewhere and I was not going to retain or transmit, but I have now.
You're making a big assumption that the code is what is being executed, and not a compiled binary.
Where is the code: My repo? the clients? If it's in mine, the client does not have access and the CLI is a first stop to debugging. They arent in the context of written docs, more likely a production error from a log (thats now spitting out a message to check the CLI).
Less steps, less tools, more context in line and available in an interface your already using.
> they can ask an LLM to analyze it, within the context of their specific use case and your personal thoughts if any.
Or I can skim the man page it generated and make sure it looks good. The "work" (the tokens) dont have get spent over and over again.
AIT tried solving it? But AFAIK it's a lot of pretty results with not much real application.
A better approximation is something of a "shared model"; then you can actually state things like, the transfer of information sometimes is "trivial" because, well, it's right there in your compressor/decompressor.
An Outline of a Theory of Semantic Information by Carnap was the early attempt.
Fred Dretske wrote Knowledge and the Flow of Information in 1981.
Luciano Floridi has a few recent books.
I couldn't find much else. I don't think AIC really solves the problem of meaning either.
I think the Dretske book was the first time I really understood where Shannon was coming from but I gave up when it got to his actual semantic ideas.
I think I ran across a recent paper that motivated trying to back track what work had been done in this area but I don't recall the name of the paper.
I've have shelved all this for now as over my head.
Here's a clearer example - would you rather learn a concept from a research paper or a textbook or blog? You say the research paper but they're dense and hard to wade through where-as blogs and textbooks are more wordy but hold your hand, which is something that helps humans learn.
> would you rather learn a concept from a research paper or a textbook or blog?
I pretty much always read blogs first, and then move to a research paper only if I want more details or care enough about the subject to verify with the original source. Typically this is because research papers have too much information to be approachable.
You absolutely can if that information is in the code, which it often is.
There should not be that much in the code that needs further elucidation.
Some stuff definitely - but not much.
Usually you need the code and architectural summary + that stuff.
The AI is not very good at it but it will get better.
I think the debate here is about a few different things.
(This is all under an information model that assumes the LLM and your readers have equal access to knowledge, which I probably should have made more explicit in my original comment.)
My instinct says that these systems will expand their complexity to fully fit the cognitive budget of the agents that coded them and then atrophy the same way human-built systems do at lower cognitive budget. Only this time, because of the larger up front budget, the complexity ceiling will be higher, and the potential depth of the problem may be much much larger. It may mostly manifest as increasing cost over time - the agents grind for longer and longer, iterating over and over to fix all the failing tests, and the breaking point will be where it never converges and you come back to millions of dollars in budget spent and still tests are failing and effective gridlock on system changes.
But this may be all my human-biased fantasy that justifies still taking a role in software development.
wow this is a beautiful way to put it
To use the wooley term “quality”, the top 20% might stand a good chance of making huge strides. But the remaining 80% of projects (in particular the bottom 20%) will atrophy extremely quickly. Yet, these will be the project that many push LLM’s too as their domain/technology is complex and/or outdated. Digital transformations that can be done quickly will be tantalising but ultimately unsatisfactory long term (as you describe).
I'm old enough to remember using CVS and then subversion in companies. People would commit straight to main (which was then called "trunk"), because making feature branches and merging them was cumbersome. And, on regular intervals, the person responsible for some corner of the codebase would do a show-and-tell presenting it to peers, but without the sharply defined boundaries of what the code looked like before vs. after some recent set of changes. People might remember some things from the previous show and tell or from first hand experience with that code, but that kind of memory is necessarily fuzzy, and diffs weren't an artefact that was typical to look at. So, these reviews didn't block people, and any comments that came from reviews defined a direction that things should go from here on out. If a corner of the codebase was deemed to be in a bad shape, the blame around that was equally fuzzy.
Everyone is fatigued by endless code review which you get no credit for and has become massively more of a burden.
All PRs are superficially fine now. There are no typos, there is unit test coverage, but there are deeper issues that require massive amounts of effort and time to spot.
Lots of review comments about various conditions that wouldn't feasibly happen (same shit with claude now).
But then I'd see these same reviewers approving PRs where the bigger design was just fundamentally broken. Oh, we're adding a blocking call on our hot path, but at least the method name makes it very clear that it is blocking.
In general I agree that the current AI reviews are creating too much noise and it is masking these bigger design issues.
How many are you seeing / estimating?
In the meantime, for business communication, I use AI to shorten my text, to make it more concise.
Until I fully understand what's going on, the PR doesn't move and my interrogation of the LLM doesn't end. My interaction is littered with "Explain X" and "How does this square with Y?" and "What if Z happens?"
The interrogation is the point, without me having to wade through hundreds of lines of irrelevant code to get at the meat of the matter.
We have this at work : fully AI-generated code and description. People will give review comments generated by AI which the "author" replies with an AI-generated response, all with LLM wording full of jargons no one understands not even the person who sent it. When you ask them what they meant, yeah idk Claude said so
So far I haven't had a reason to go back through commits to isolate any issues but if I do hoping the 'why' messages may come in handy for my LLM lol
https://github.com/josephmisiti/awesome-machine-learning
It's helped a lot. Agents haven't figured out how to do that yet, or sendgrid, sns, etc are doing the hard work for me.
One of the instructions I've hammered into Claude is "Write like a human. I don't want this to sound like AI content. Your standard style of writing would fail miserably if it was reviewed by an English teacher. I want you to write prose that is nice to read. For example, write full sentences instead of bullet points."
It works wonders. Suddenly, my plan documents are something I actually understand, and something I'd be happy to share as an RFC. They still need iterating every time before they're ready to share, but I no longer have "the honest truth is" and other BS in my output. It's so refreshing for my brain to be able to actually focus on the content.
I've noticed this with my partner, too. She used Claude to draft a strategy document and felt completely overwhelmed. A classic moment of "AI did all the thinking for me, but now I don't know what I'm presenting". Once I helped her prompt Claude into writing the strategy in proper English, she understood what the AI was proposing, rejected large parts of it, iterated several times and ended up with a draft document where she edited the finishing touches herself and felt that it was truly hers. The AI was still incredibly useful: it helped her with the blank slate problem, and tremendously sped up her workflow.
So yeah, if you proofread and iterate on your AI's output until you feel you'd be proud if you had written it yourself, I'm happy to read it, too.
Of course Claudeish or GPTish ("unusually") will easily push me away. Just like any repetitive or obnoxious tendencies that might appear in human writing.
I agree, using LLMs is disproportionately frowned upon, but what matters is if you invested your own attention in the process. I use LLMs a lot for sparing, usually ask it to assume some opposing persona or use web search, not relying on its defaults.
If this actually works, it's absurd. The implication is that Anthropic could trivially make Claude sound less obnoxious, but chooses not to. I don't see a way this could be justified as a safety feature or anything, so.
> A pattern I see is that people use AI to build something new, then they use AI to retrospectively summarize what they have already built into a design document. Reading a document like this isn’t just difficult—it is punishing.
All of the posts in 2026 on that blog area about AI, by the way.
I've only just discovered pangram, but I've seen it referred to a few times in HN recently wit nothing obviously pejorative about it. Take it with all required grains of salt, though
0: https://www.pangram.com/dashboard
We've had AI output all these decades, but never recognized it. Is this evidence of time travel?
\s
TFA's use is more common in "normal" language: "it's not just [minor], it's [major]". (But, as others have pointed out, it was probably deliberately parodic anyway.)
these posts are beginning to make me wince. ai is giving voice to a lot of folks we probably wouldn't even be hearing from otherwise - because not everyone does their thinking in articulate prose, and extracting their realizations as shareable language takes effort, or long, embarassing iterations with ai "workshopping" to get to something they can read back and go yes this is what I am trying to say.
now the sentiment is that whatever these people had to say they could have just come out and said it - and that would be more passionate and less disjointed - no, not necessarily, and the more it gets repeated the more it's sounding elite and pompous to me. for example this:
> I had someone write me a personal message about a sensitive topic that was clearly workshopped with AI in an effort to nuance the conversation and not offend me. But the message became impersonal, dispassionate, and disjointed. It had all of the parts, but it didn’t make sense as a whole. I wasn’t interested in reading it, or responding.
this reads as someone choosing to ignore a personal message about a sensitive topic - because of an assumption they made about their writing: that they workshopped it when they should not have - because whatever it was they were struggling to say they should have isntead trusted the author to be able to understand the precise meaning of their raw, inelegant thoughts no matter how embarrassed they might be to share them in that form. they're complaining about a message they probably weren't otherwise going to see.
That's clearly not the type of people they are talking about. For some people expressing their thoughts in words is really, really hard. It doesn't mean they don't give a damn. Just like being in a wheelchair doesn't necessarily make you lazy.
I suspect it is for everybody, but I might be blowing smoke up my own ass.
My attention is a finite resource.
Everyone is an elitist, when you've enplaned do you assume the pilot is qualified to fly it or do you hope everyone gets a turn? Admittance to the canon is no less a responsibility, would you trust the yoke of human culture to the artless, craftless, and naive?
not everyone does their thinking, if you dont I dont want you to have voice
What do you mean? Are you a "Write-Not"? https://www.paulgraham.com/writes.html
I still want the imperfect human.
Because while I dislike AI writing, it matters in context. If someone is trying to formulate a coherent thought of work tasks required of me then I don't really care if it reads like AI as long as the point they're trying to get to me is clear.
If there's an attempt at something human, or emotionally important I will quickly balk at anything spat out through an LLM. If you're trying to communicate at the human level with me I want your input, flaws warts and all.
A card with just...nothing personal on it has as much value as machine text. I understand that you took the effort to think of me, but that's the bare minimum. If trying to express yourself isn't worth the time to make it personal then I'd almost appreciate if you didn't try at all because it hurts more to believe I'm not worth the effort.
As for second paragraph, the writing "had no overall meaning". There is nothing to respond to if there is no overall meaning.
What we are seeing here is a Luddite reaction from artisans. A skill has been automated and those that have the skill are not happy about it.
For many writing is painful and the machines ease the pain. And like all machines it takes a while to use them skilfully. In particular remembering that like code it is write once and read many. What the reader needs should be at the forefront of a writers mind - both for code and prose.
Nobody gets on a motorbike and wins the TT in the first weekend.
Along with removing the possibility for them to ever actually learn to write better.
This is not a good thing.
When I read AI-enhanced writing, I always feel like I have to peer through the blinds to see the writer's true intention. This feels very dishonest and partly offensive, because it can sometimes take several paragraphs before I realize that I'm reading AI output, rather than someone's processed thoughts.
Seriously? Expecting basic school literacy is now being a Luddite? What else? Writing by hand is an ancient craft resorted to philosophers long gone?
I swear this AI polarization turns off brain on both sides.
Bollocks. We managed to communicate before LLMs, but now suddenly everyone needs to write like a lobotomised Hemingway?
> What we are seeing here is a Luddite reaction from artisans.
I suggest reading up on and understanding the history of the Luddites, instead of repeating the same tired thoughtless meme.
> For many writing is painful
Then do something else! Draw, dance, sing, sculpt. Figure out what isn’t painful to you and express yourself that way.
> Nobody gets on a motorbike and wins the TT in the first weekend.
And no one wins ever by getting others to run for them.
They are hardly artisans. They are simply the vocal few who repeatedly conflate communication with art simply because the written word can be used for both.
Writing has always been incredibly difficult for me, both due to incredible difficulties with handwriting and finding my own voice. That difficulty I find in writing is the friction that identifies what my voice is, the words I choose and the meaning I attempt to convey through my own understanding.
I cannot read AI-generated crap for the life of me because all of it simply has no meaning. I will go back and read through paragraphs, trying to grasp at the meaning of something that does not exist. If your argument is that AI-generated writing eases the pain then I will make the opposite argument: reading AI-generated slop increases the pain 10x. I would sooner read through an essay with endless punctuation issues and spelling mistakes than one passed through an LLM because one is an earnest attempt to communicate while the other is offloading all of the mental burden to me. Which gets to the root issue of a lot of the complaints around people slinging slop: you make it marginally easier for you, and then far worse for everyone around you.
Grammar check is one thing, but AI mangles your thoughts.
Nobody cares what your English grade is. Everyone would prefer to just hear you speak in your own voice. This isn't Luddism, this is the equivalent of people pushing their selfie through five Instagram filters until they look like an Oblivion character because they have no confidence in their own appearance.
This is not done in the service of readers, nobody wants the slop, it's writing dysphoria
I can't tell if this essay was written in earnest or as a subtle troll.
To me, it also seems like they AI digestion is getting actively worse? As best as I can tell, all the agentic nature and reasoning for code is now making writing actively worse, as the agent pulls across your whole knowledge base and will take that one thought and eagerly join and context it thinks is relevant, with the reasoning spread throughout the page.
IMO it is a costly/goodhart-resistant way to “show your work” and help other people understand or challenge your mental model. (IE a justification for something you believe to be true). Overly polished writing is performative, it’s hard to take seriously once you’ve read The Economist/LW enough to see how poorly “well written” correlates with truth
To a certain extent are all wrong or ignorant about almost everything because our knowledge/time are very limited. But it is really important to understand what other people think in order to coordinate with them/align human goals and understanding.
It’s good that the average person taking the lowest-friction path to using an LLM in bad faith is easy to identify now. The more obvious and disliked it becomes, the more they’ll be hit with the stick to actually know things and not bother people. It’s so much worse to be “bad and stupid and not care” than “possibly cringe or wrong”
I feel the same. I've actually started to appreciate things I used to dislike. Like typos, or grammatical errors. I used to see it as a lack of attention to detail. But more and more it now feels like "hey, something written by a fellow human!".
Same thing for video voice overs. Things like a bad quality microphone, or someone who doesn't pronounce things very clearly. Now I go: for sure a human!
Do a search on "Claude Sonnet 4.5" on Reddit and you'll see lots of disappointed users [1]
If I could give out ratings,
Average human with a degree: 5/10
Sonnet/Opus 5: 2/10
GPT 6 Astra, 5.4 Sol: 3/10
Sonnet 4.6: 8/10
Claude Sonnet 4.5: 9/10
GPT 4o: 6/10
GPT 4.5: 10/10
GPT 3 Davinci (with a lot of coaching): 7/10
[1] https://www.reddit.com/r/claudexplorers/comments/1ta6f9c/i_s...
There's a reason ASD-STE100 is mentioned everywhere now. They're at the point where people can't understand what they're saying.
What I often notice in pull request descriptions isn't necessarily a problem with them being written by AI. It's the lack of nuance. They don't tell me exactly what the author expects the reviewer to pay attention to, what might be risky, or where they would particularly value another set of eyes. Being able to articulate those things is far more important to me than whether AI was involved in writing the text.
I would also push back a little on the author's point about imperfections. Being able to say that imperfections are fine somewhat ignores the elephant in the room: bias. Bias can have very real consequences. The author may personally be more tolerant of variations or imperfections in writing, but that's not something we can realistically expect from everyone. I'm also not sure it's fair to put the burden of dealing with those biases entirely on the person doing the writing.
That's also why I keep coming back to the expectation of having a distinctive "voice." Not everyone has the same attachment to their writing voice in the first place. For some people, getting their ideas across clearly and being understood matters much more. If voice isn't something they particularly value or identify with, then insisting that they preserve it feels somewhat moot.
We do not experience the world as words. Words and language are already an hallucination on top of reality and LLMs are an hallucination on top of language.
There are many use cases for LLMs that will help humanity progress. Putting AI in between yourself and the people you want to build productive relationships with is not one of them.
I'd always rather read an actual person's thoughts, clearly communicated and written down in their voice. But, there's clearly a subsection of people who don't want to (or aren't confident in / etc) writing their ideas fully.
If they have a bullet point list, and they want to use AI to neaten up the bullet point list and present it back, that's fine by me. I wouldn't have minded reading their thoughts in a messy list, but AI is serving to make it more readable.
What I think everyone hates, is AI being used to convert the list into pseudo-thought-out-prose. It just contains a bunch of bloat that wastes people's time.
So my advice if you use AI to write and don't want to change: keep going, just stop seeing the desired output as human prose. Just neaten up your bullet point list / promot / whatever, and send me that.
[0] myself included https://www.google.com/goto?url=CAESbQHrOzAViiJizn8otPF3AAeE...
The same goes for articles. I want the author to distill the material using their own voice and hard earned expertise. I don't want a generic text which sort of tells the tale.
> I would much rather someone be themselves and write with their own voice, or with some passion
I recently had a fairly successful article here on HN. The common thread among comments was appreciation for the passion and it being "hand-crafted". I am thrilled my passion shone through despite the dry subject. Still, it's sad that something simply being written by a human has become such a rare compliment.
(And I try to be judicious about what counts as "of substance" so they can't accuse me of flooding them with messages)
“Reading a document like this isn’t just difficult—it is punishing.”
Also we have reached the point where LLMs are pubkishing more contents than humans so we have probably reached a point where humans will inconciously start copying and using LLMs style too.
1) negative parallelism: "It's not bold. It's backwards."
2) em-dash addiction: "The problem -- and this is the part nobody talks about -- is systemic."
again, i'm not saying the author wrote this sentence using AI. i was just tickled by how stereotypically AI it sounded. for better or for worse, those characteristics are just going to set off the AI detector in people's heads now, as evidenced by the fact that there are like 10 separate comments on this post that picked up on the exact same thing.
Opus 5 and Fable 5.1 commit/PR messages are incomprehensible garbage.
However, Astra messages are nearly perfect for a copy/paste to less-technical stakeholders. I maybe fix a line or two.
Give it a year, and I suspect that I won’t even need to make those fixes.
The only problem right now is I can’t get my team 20x OpenAI accounts due to supply constraints. We’re all stuck waiting, hoping Anthropic either ups their game or OpenAI gets more capacity.
We would easily pay $1000 a month per developer/PM for a business tier ~30x account or similar that let everyone use Astra all week without running out of tokens. And that is entirely because of the writing and communication improvements.
My question recently has been how to broach this subject with colleagues who really enjoy producing prose with AI. There is not yet a better cultural shorthand for this sort of thing than "slop" which is a harsh-sounding word and itself sort of a thought-terminating cliché. "I don't want to read what you didn't write" is maybe closer — but it needs a pithier and somewhat more encouraging encapsulation, like "I want to hear it from you".
Has anyone had good experiences setting up professional boundaries or team norms around AI-written docs?
For the team I lead, my guideline is AI generated is fine but it needs to be human-edited and/or summarized. You want me to read what you're offering? Put some effort into it and meet me halfway. I don't want AI generated gibberish with made-up terms. You'd better also understand what you are presenting as your work. It's been fairly well-received though we're still working on it.
I have some co-workers on other teams who use AI to generate responses to literally everything. Ask a simple question? Get pages of AI generated nonsense in response. They are proving a tougher nut to crack.
You may find that brings a better and more human focused discussion. Who is the audience and what is best for them? Don't frame it about your preferences but about the real persons that your colleagues seek to talk to.
Everyone else, including me, not so much. I envy his talent and ability to so successfully use the new tools. It's just something to think and talk about regularly. Maybe one day we will all be able to use the tools as well as that guy.
Thats said, i don't know any professional way for this but currenlty trying to communicate with peers in order to work without a problem.
As much as you want to say "actually do your job, jackass." Being goal oriented has had the best results for me.
- https://news.ycombinator.com/item?id=49767937
- https://news.ycombinator.com/item?id=49784816
At a broad level, the issue isn’t generated content, it is the ratio of verification capacity to generation capacity (V/G). Your pain is because generation capacity has increased significantly, while verification is laborious and capacity has not (and can not) catch up.
Unlike spam which is from external sources and can be ignored, messages from other employees have to be responded to. I guarantee this is creating bottlenecks all across the firm, outside of the individuals who are feeling productive.
For fixes, theres theoretical approaches that might work?
If you need leadership to help you, then this issue has to become something that is on their radar, which means that something needs to go wrong or costs need to be registered.
The shortest conversation for that is to make people aware that generation has improved individual productivity, while moving the costs of that production to the rest of the firm.
If leadership is not at the stage to listen, then you need to move the costs you are incurring to the people who are sending them to you. Maybe set time aside to sit down with whoever sent a PR and then read what they sent together, to understand it.
It also makes a difference if tokens are being subsidized or not. If the firm doesn’t care how many tokens are being used, then you are naturally going to have over production.
But so many READMEs and design docs are degraded to this level by uncritical/unmonitored use of Claude. You make it sound like it’s an exception, not a rule.
In my experience, that brain damage inducing Claude-style technical writing is everywhere. It sucks not only because of its verbosity, but because it includes completely pointless tidbits extracted from random extended LLM sessions, with no attempt to prune redundant or gratuitous details.
I write almost every line but I have one or more agents always running against the changes alongside me evaluating correctness, suggesting approaches, auditing test coverage, etc.
I’ll also use it for grunt work refactors, scaffolding, resolving merge/rebase conflicts, etc.
I hope this was an in-joke but my fingers still reflexively closed the tab as soon as I saw it and I had to go reopen it again…
If you don't get to the point and continue to go on and on, I will use a LLM to summarize what you wrote and then decide whether to spend time listening/reading what you wrote.
What should worry us is that AI may introduce a slant that the prompter assimilates during their conversation with the tool, which could introduce a subtle control lever over public opinion.
At this point I can't tell if it's a pun or the anti AI article is AI written as well. This sucks.
You’re giving them too much credit.
Only a privileged person can say this. Not everybody is a native speaker or good communicator. As an IT guy somewhere on the spectrum, I have damaged my career on multiple occasions by clumsy writing.
So there it is: cruel people who don’t even read their own prompts while happily believing others read their text load and pat the genius on the shoulder for being such a great guy in writing, while in essence it turned the RTFM movement on its head: UUOAI - “Use your own AI.”
People cannot communicate better by using the digital equivalent of logorrhea.
One minor addition: when a (conscious) human writes a proposal, a question or a request, they'd instinctively choose words with which they can express their uncertainty in some of the details. I as a reader can detect these uncertainties and I can choose to do more research assert or correct them, if it feels appropriate. AI-generated texts miss those cues, therefore they are much harder to read and connect to.
My own solution for this (for now at least) is to add a preamble section “AI;DR” to PR descriptions, filling what a TL;DR section would have done in the past. I’ve socialized to my peers that no part of such a section will be written by AI, and it should contain exactly this missing high-level content described by the author.
I have a large and complicated project at $DAYJOB where I am the solo dev with a large team of domain experts. They AI dump me with suggestions (rather rarely, thank god) for features with complicated implementation details despite having no insight into the codebase. What is the point of that? I can't discuss it with them. If I jump on a call, they'd have to say "oh sorry — I don't know, I'll get back to you".
When I need to send them a message about something domain specific I have no knowledge of, I'll spend hours understanding it as far as I can. And then wording it in a way that makes sense to me. I use AI A LOT in the understanding and proof reading of messages, but the wording and writing is mine.
I'd rather understand something partially, write a bit poorly, and send an imperfect message than to AI dump a perfectly formed message which takes everything into account but not really understand everything that I'm sending. It closes the door to actual communication between two humans.
The juxta-positioning of the ambi-dextrous personification of the meta-sematicism is going to be both rich and soul transpiring....
It's gonna be basically a fingerprint in your soul, from my soul...
One thing that has markedly helped is tweaking our AGENTS.md prompt and prose-heavy skills like `/pr` or `/ticket` to specify that all technical output needs to be written in ASD-STE100 Simplified Technical English. It has helped us so much. We still have to `/decomment` or minimize to remove unnecessary stuff that's been written in ASD-STE100, but the output is a lot easier to digest.
OTOH, we have a guy in marketing that has taken it upon himself to use Claude to draft these wildly complicated and dense implementation specs that he doesn't understand for various things that he just hands off to my team to implement. He has no context for how our product is built, the various layers and their interactions, etc. so they're often flat out wrong and overly complex, invent systems that do not exist, all of that. Then he gets mad that we're not moving as fast as he wants, because he gave us the implementation spec! I have been struggling to come up with a way to diplomatically tell him that his actions are slowing us down, and if he'd just describe what he needs instead of speccing it himself, we'd be twice as fast...
And yet, every person high up in business that I talk to, including my wife, advises me to use Claude for this sort of thing. I don't get it. Claude text sucks, and it hurts my eyes when I'm forced to read it.
Sure, I use precise and technical language, but I'm still a human and I can understand what people are getting at when they use imprecise terms. But if the text passes through an LLM I can't tell whether the result actually matches the original problem or whether the LLM has made guesses or filled in blanks.
On Mac, it's option-shift-hyphen. Option-hyphen gives the en dash.
On Linux, you may be able to type Ctrl-Shift-U to get unicode entry and type 2014 and Enter. 2013 is the en dash. Again, I will never remember that.
— (em dash)
Option+- gives you an "en dash": – which is longer than a normal hyphen.
Moreover, people are finding it hard to differentiate what is an is not AI-generated with newer models, often attributing original work with those of LLMs. It has just become an easy scapegoat for lazy comprehension and a desire to do less. You are jumping at AI boogeymen.
Just about the only thing here I can level with you on is, yes, AI is far from perfect and will continue to advance. Otherwise, so much of this reads as fruity prose to excuse apathy.
Rather than entertain the idea of reviewing the code I just send it back to them until they work out how to run it. And almost always they submit a new change because the last one didn't actually work, despite how confident and articulate claude was to them.
Do you read our AI output end-to-end and decide that it’s the accurate content communicated concisely? If yes, this is not an issue.
If you are sending people generated text you haven’t read, how do you know what you sent doesn’t fall in the category of writing the author describes?
Let us say someone had few good ideas, and seeded them into a prompt, and after few back and forth, web searches via agent, feedback from the author, a piece of work was produced and the author decided to share it as a blog.
This is not much different than how people are producing original work LLM in areas such as math.
Would you object to reading their work because it was a byproduct of collaboration between AI/Humans? What about songs? movies? math proves? and software produces as such?
In my mind the issue isn't that it was crafted by AI, but that it wasn't crafted with the intention of getting the point across in a way that respects the reader's time or energy. The result is often needlessly wordy with information buried in a sea of flourish. This might be forgivable if the author had a certain entertaining style, but LLMs are aggressively mediocre by their nature, making wading through it painful.
Its certainly possible to get an LLM to write concise prose but thats not how most people are using them. We can talk all day on HN about the ideal LLM user that is conscientious, reviews the output, puts time and effort into stylistic concerns, etc. But none of that actually matters because thats not what people are doing; the entire reason they're using an LLM in the first place is so that they don't have to do that.
I don’t like doing either of these things. I don't want to do your job and I don't want to lecture about how it isn’t “your work” if you don’t touch the content after an LLM spits it out. It is rude and selfish to put me in that position.
There was just something about them, like they were written by someone obsessed with detail but without a clear idea of the audience they were writing to.
The result always ended up being a document that was way too long, way too detailed, without context for those details, speaking over the head of the reader.
If you don't like AI slop. Don't read it. But wasting your time generating human slop to complain about AI slop is so obviously futile that it immediately identifies the writer as lacking the capacity for reason or emotional clarity, or merely seeking attention for their self-promotion with clickbait.
I'm sorry but that's the last thing (almost last) I want to read.
I want an 'excellent summary' of the design including motivations and concerns.
Yes - the AI is bad at that but hopefully it will get better.
What the humans would see would be what is filtered up to them after all of this evaluation is done. And content generation would be the reverse. People would provide a seed of an idea and then their agents would turn that into potentially useful work.
I’d be curious whether the author composed this sentence himself or it was the output of AI. Personally I often find myself “it’s not X it’s Y” and then recoiling in disgust and rephrasing it simply because AI has made it so grating from overuse.
This becomes problematic when it turns out people haven't even read what they sent me.
To clarify: this is about voluntary projects that do not win them extra points.
It doesn't fundamentally change the equation if I use AI to prepare and then write it myself. If I'm using AI effectively, it's likely that you won't be able to tell.
This sort of post is increasingly coming off as high and mighty, where the user thinks they are being exceptionally creative and other people who are using AI are using it mindlessly.
"I don't like the way other people use AI, but the way I use it is okay."
I'm going to stop reading right there. I don't want to read that either.
I stopped reading here. I’ve become so sensitive to LLMism, I don’t want to go on. Tip; omit the needless ”it’s not X, it’s Y” and just write ”it’s Y”.