Your intellectual fly is open when you use an LLM to author a post (2025)

We need to summarize the 5 most prevalent themes from the discussion. Must output markdown, concise, with direct quotations and author attribution.

We have to read through the discussion and identify common themes. Let's parse.

Discussion is about LLM-generated writing, concerns about disclosure, quality, perception, ethical issues, cognitive prostheses, etc. Themes:

1. Concerns about low quality / "LLM slop" being posted without disclosure (e.g., "obviously LLM writing is garbage", "unreadable slop").

2. Disclosure and honesty: using LLMs without disclosure is misleading, akin to plagiarism or intellectual catfishing.

3. LLMs as tools: some argue they can be useful as aids (grammar correction, non-native speakers) if used responsibly, but concerns about over-reliance, loss of thinking.

4. Writing as thinking: many argue that writing is a thinking process; outsourcing to LLM undermines thought and personal voice.

5. Audience perception and detection: difficulty detecting LLM text, some people can't tell, some claim they can, and the effect on trust and engagement.

Alternatively, there is also theme about LinkedIn being flooded with low-effort AI content, affecting platform quality.

But need to pick five most prevalent themes, with quotes.

Let's scan for repeated ideas:

• "LLM writing is low quality / slop" appears many times: embedding-shape, IshKebab, NateEag, jjice, luxuryballs, TomGarden, etc. Also "It's just spammy", "people not reading what they wrote". So theme: Poor quality of LLM-generated text.

• Disclosure / honesty: riskable, Sharlin, jonahx, SecretDreams, etc. Also discussion about "intellectual catfishing". Theme: Using LLMs without disclosure is misleading/dishonest.

• Writing as thinking / cognitive process: jeremyjh, Dumblydorr, IanCal, etc. Theme: Writing is thinking; outsourcing removes personal understanding and effort.

• LLMs as useful tools / assistance: riskable (grammar correction), Vishal_Max? Actually there are comments about using LLMs for non-native speakers, grammar help, summarizing articles, etc. Also kisper's cognitive prosthesis. Theme: LLMs can be helpful aids if used properly, but need disclosure and editing.

• Detection / perception: throwrioawfo, TomGarden, dominotw, layer8, etc. Theme: Many people cannot detect LLM writing; some think it's obvious; concerns about trust.

• Also theme about LinkedIn being inundated with low-effort AI content, affecting platform usefulness (jjice, Andy99, etc.)

Which are most prevalent? Let's count approximate occurrences.

We can manually approximate but need to pick five.

Let's list each comment with themes.

I'll go through and tag.

• Arch-TK: em-dash ownership (not a theme)

• embedding-shape: low quality writing, detection (people don't see low quality themselves, must identify others' LLM writings) => theme: quality & detection.

• IshKebab: same as above, intersection of producers and consumers, maybe they don't care. => quality & indifference.

• NateEag: people putting out LLM writing aren't trying to communicate, they seek side effects (job, views) => motive.

• jjice: customer drops unedited Claude posts, thinks they read like slop => quality.

• luxuryballs: if they were half decent at writing before they'd notice quality issue; not seeing it is a writing skill issue => quality & skill.

• theandrewbailey: suspicious number of em-dashes => detection.

• TomGarden: em-dash use different from normal LLM use; if straight out of LLM impressed; also says writing is just bad => quality.

• jLaForest: addressed in article... (skip)

• imtringued: em dashes vs absurd analogy => detection.

• throwrioawfo: research on percentage able to detect obvious LLM text => detection.

• TomGarden: people might not know it's LLM prose, they just notice its poorly written => quality & detection.

• dominotw: majority of population dont read anything at all => detection / audience.

• StilesCrisis: large chunk who "read" Facebook and skim => detection.

• raincole: > obvious; who decides what is obvious => detection.

• layer8: plane rental company OpenFly (irrelevant)

• dominotw: fired from job for taking FMLA, marking director's posts as 'looks like ai slop' => detection & personal experience.

• theandrewbailey: talk to lawyer => not theme.

• dominotw: consulted lawyer, said no case.

• Vishal_Max: you used LLM to write this post, lol! => accusation.

• StilesCrisis: It reads naturally, Pangram flags 100% human => detection.

• dynm: argument about disclosure; if LLMs get better at writing, will you switch? => disclosure & future stance.

• visarga: some people type 100x more words digging => effort vs output.

• masswerk: systematic flaw in RLHF, overexposure, style smell => quality & detection.

• zahlman: quotes about Turing test of competent reader => quality.

• IshKebab: responds to dynm: would be more OK if prose improved; two reasons: low value and horrible prose => quality & trust.

• CuriouslyC: counters IshKebab about low value assumption => trust.

• fn-mote: alternative uses for reading: facts/processes, opinions/experiences, value limited to inputs => utility & trust.

• mitxela: if Nerds for Nginx article AI-written, probably subtly wrong => quality/trust.

• NathanielK: spammers infinite desire, most LLM writing garbage; readers associate with garbage => quality perception.

• qlte: risk of using LLM to "clean up" writing; heuristic formed from LLM spam => trust.

• Sharlin: undisclosed LLM writing like plagiarism; dishonest => honesty/disclosure.

• riskable: people view LLM output as "their thoughts"; tool vs writing for you => disclosure & intention.

• Sharlin: if someone tells author to write novel based on premise, not co-author => honesty.

• jonahx: plagiarism and misrepresentation => honesty.

• mpyne: doesn't foreclose possibility of LLM achieving human-like writing; ghostwriting analogy => honesty.

• tdeck: analogy about wiping ass => quality.

• andy99: misunderstanding what it means to be better at writing; LLM writing equivalent to copy/pasting Wikipedia; empty when expanding unspecified => quality/trust.

• p-e-w: counters andy99 => quality.

• AngryData: AI writing less trustworthy than copying Wikipedia => trust.

• arjie: modern agent can link sources; but if humans have outlines expandable by LLMs, do late => utility.

• mpyne: writing is a way humans communicate ideas, not only way; AI agents communicating via hacked websites => not core.

• notahacker: purpose of LinkedIn is to convey interest; outsourcing thought leadership avoids thinking => honesty/thought.

• layer8: would rather live in world where LLM writing high quality; lack of disclosure lesser evil => disclosure preference.

• sebzim4500: fine with LLM writing if not unpleasant to read => quality.

• supriyo-biswas: if LLM writing improves to infer context and serve functional purpose, then fine => quality & utility.

• jonahx: undetectable LLM writing allows assuming another's identity => honesty/trust.

• manlymuppet: LLM writing atrocious, but people also want it to be atrocious because they don't like LLMs => bias.

• zahlman: things can be tested; some gifted at discerning AI work => detection.

• SecretDreams: intellectual catfishing => honesty.

• kisper: uses LLMs as cognitive prosthesis because can't express clearly; wants room for use without losing humane-ness => utility & honesty.

• fgdfsdfslkdsflk: counters kisper; self-underestimation; disclosure low hanging fruit => honesty.

• anon84873628: you must be unique if converse the way you write; written piece more refined => quality.

• SecretDreams: using LLM as voice supplanting knowledge => honesty.

• WCSTombs: intellectual catfishing phrase; writing represents you; using LLM no longer represents you => honesty.

• CrimsonRain: LLMs write better than most journalists; also better than majority people; happy to read LLM writing => quality (positive view).

• wavemode: disagrees; LLMs not necessarily great deeper level => quality.

• jampekka: LLMs struggle with continuity, carrying on point => quality.

• CrimsonRain: continuity/carrying/context management is your task => quality.

• riskable: grammar correction solved; trusts grammar corrections => utility.

• zahlman: doubts about grammar corrections; LLMs massive overkill => utility.

• anon84873628: would almost never transmit LLM output without editing; doesn't understand people who paint it as horrible unreadable slop => quality perception.

-antonvs: LLM tells offensive like formulaic writing; pervasive => quality detection.

• qlte: same as before.

• LtWorf: they are worse than most people at communicating ideas => quality.

• kjs3: MAGA crowd shows up (irrelevant)

• zahlman: prefers LLM writing over typical long form perfect English journalist crap => quality (positive).

• CrimsonRain: not all of them; you can ask to be concise => quality.

• stronglikedan: no need to disclose tools; people own output; disclosure unnecessary => disclosure stance.

• 27183: utility limited without prompt; need prompt to discern intent => transparency.

• lacunary: prompt tree or DAG; how communicate => transparency.

• hallole: grammar correction not comparable to LLM text generation; tool vs doing it all => disclosure.

• zahlman: nobody disclosed Word's grammar suggestions; but LLM prose is different => disclosure.

• aDyslecticCrow: proof of effort by human part of credibility; disclosure addresses concern => honesty.

• GPerson: technology companies forcing us into new behaviors => not core.

• aviperl: LinkedIn posts farm credibility; gross; using LLM branding suicide => platform quality.

• pxmpxm: average LinkedIn experience sprinkling updates from people you know in ocean of inane content => platform quality.

• tempodox: branding suicide or boosting brand as tasteless brainless schlub => platform quality.

• ciupicri: title truncated => not theme.

• Jtariiiii: filter deletes "Your" => not theme.

• frogulis: HN does automated editing of titles => not theme.

• jjgreen: submitters can edit/revert changes => not theme.

• delichon: your fly is also open when you indiscriminately fling accusations of LLM writing around => meta accusation.

• hypfer: not really? => not.

• lsofzz: read past LLM-ness; message should matter => honesty/trust.

• Jtariiiii: some people want to enjoy existence not read garbage => quality.

• lsofzz: ship has sailed; puritans won't survive => not.

• Jtariiiii: survival not hinged on reluctance to read AI slop => quality.

• bigstrat2003: interested in what a human has to say, not clanker => honesty.

• simonw: can't read LLM assisted posts; so long; hints obvious; unpleasant; filler wastes time => quality.

• raincole: (2025) self-reported preference poll; selection bias; people lie => detection/trust.

• bcantrill: explains why take seriously; numbers overwhelming => trust.

• siskiyou: worked at LinkedIn; awful million-view posts written by humans; LLM replicates garbage => platform quality.

• strenholme: uses AI to summarize press articles; disclaimer; archive.org issues => utility.

• timcobb: Fair Use discussion => legal.

• strenholme: Fair Use for quoting; etc.

• andy99: many people blind to LLM writing; not details people; judged on superficial cues; content piling up nobody consuming => dead internet theory => platform quality.

• jgrahamc: most important line: LLMs are lousy writers and most importantly they are not you => honesty/voice.

• mtabini: published magazine; editing challenge; AI not improving style/character; functional communication benefit => utility.

• jgrahamc: people should not use AI tools for functional communication as crutch because they struggle with writing clearly; need to learn to write clearly => utility/honesty.

• hi_im_greg_h: if you cant communicate ideas properly to human, how prompt effectively => utility.

• mtabini: pragmatic about AI use in routine scenarios; language barrier => utility.

• fn-mote: learning to write clearly valuable; reading while learning less valuable; want clear communication => utility/honesty.

• fwip: thanks for Cloudflare blog.

• its-summertime: wouldn't write open letter to spam bots => platform.

• Brendinooo: not morally opposed but want content owned by person creating => honesty.

• CrimsonRain: valid position; should endorse content => honesty.

• classified: HN title about startup => not.

• sajithdilshan: block cringe posts => platform.

• rglover: can tell when someone uses LLM without proper context; can get good results if tune => utility/detection.

• f0e4c2f7: feels like satire; praising LinkedIn; text contains LLM tells => meta.

• Dlemlo: no clue why people use LinkedIn posts serious? => platform.

• manlymuppet: post uses far too much punctuation; jarring; trying to distinguish from LLMs => style.

• Hasz: LLMs not defacto bad; people lazy, never customize, tell chatgpt to make post => honesty/effort.

• patrickmay: if you put that level of effort in, just write yourself => honesty/effort.

• jeremyjh: writing is thinking; thinking and deciding; outsourcing understanding => honesty/thought.

• jampekka: writing is not always thinking; need to communicate non-interactively; grammar/style => thought.

• iterateoften: constraint driver of creativity => thought.

• alpinisme: abstract painting different => thought.

• jampekka: need more reconceptualization than writing => thought.

• 27183: friction of writing saved from prematurely communicating poorly understood idea => thought.

• hi_im_greg_h: key reason people obsessed with LLMs: opposed to friction => thought.

• 27183: Great Smoothening of Minds => thought.

• fwlr: having to do reader's thinking for them valuable => thought.

• card_zero: shared fiction of imaginary person's thinking => thought.

• DenisM: abdicating to LLM worst of all worlds: not thinking and product not tailored => thought.

• Dumblydorr: all writing is thinking when done by human => thought.

• altmanaltman: thinks writing is thinking but nuance => thought.

• max__dev: wrote apt apt apt to prove point => thought.

• IanCal: writing helps think about world; decoupled saying and hearing => thought.

• jkahrs595: not all writing is thinking => thought.

• jampekka: compromise to accept nitpicking => thought.

• jakelazaroff: charitable interpretation? => thought.

• jampekka: condensed ideas => thought.

• tomjen3: transferring other people's thoughts; writing about showing belonging to in-groups => thought.

• OroPla: list of writing uses (translation, transcription, deception, posting first!, textbook answers) => thought.

• habinero: translation deeply creative => thought.

• fc417fc802: wants literal translation => thought.

• thaumasiotes: polysemy => thought.

• kwarcode: Scots Wikipedia => thought.

• agile-gift0262: applies to both prose and code => thought.

• jeremyjh: code doesn't need to be understood => thought.

• ripe: difficulty thinking of code that doesn't need to be understood => thought.

• Winfred-zz: many projects where doesn't need to know how works => thought.

• 27183: need to understand when service down => thought.

• jeremyjh: test code can prove coverage without understanding test code => thought.

• btrettel: writing is thinking before; counterargument using LLMs iteratively with feedback => thought/honesty.

• jeremyjh: tried that process, learned a lot, clarified ideas => thought.

• bbor: LLMs targeted at modeling human understanding; but we reward appearance of understanding => thought/honesty.

• jeremyjh: we reward appearance of understanding; evolution also rewards appearance => thought/honesty.

• antonvs: appearance of understanding not meaningful distinction => thought/honesty.

• jeremyjh: AI models do have something like understanding (Leela understands chess) but for general writing insufficient RL => thought.

• antonvs: claim narrower; differences in training data etc => thought.

• qsera: we cannot conceive it because new => thought.

• mitxela: Stack Overflow made of humans with understanding => thought.

• AnimalMuppet: LLMs model part of human understanding captured by relationship between words; non-verbal aspects missing => thought.

• zahlman: not everyone accepts simulationist view => thought.

• victorbjorklund: example of supplier email; AI gave concise clear reply; could have done yourself but more work => utility.

• ptx: doubts about AI reply being more concise; where did it get details? => utility.

• whateveracct: writing implicitly has review baked in; hand-written code "reviewed by construction" => thought.

• ModernMech: writing can be thought; using AI can be thoughtless but doesn't have to be => thought/honesty.

• zero_shift: non-verbal thinker; writing critical to serialise thoughts for others => thought.

• AnimalMuppet: designed code that way; shapes => thought.

• jimmaswell: rewarding to figure out good shape for system; visualizing => thought.

• eikenberry: +1 => thought.

• lelanthran: don't worry, no one thinks in words; tip-of-tongue phenomenon => thought.

• bcherny (Anthropic