Various LLM Smells

Original Article ↗ Hacker News Discussion ↗

Recognizing “LLM Smells” in Writing

  • Many commenters list repeated stylistic tics: triads of adjectives, contrastive negation (“not X, but Y”), “honest/genuine/real” qualifiers, “the thing to internalize,” “the smoking gun,” “quietly,” “inside baseball,” “load‑bearing,” “blast radius,” “smoke test,” “escape hatch,” “belt‑and‑suspenders/braces,” headers like “The Caveats,” and certain LinkedIn-style punchlines.
  • Short, punchy sentences, em dashes, and colon-tagged topic sentences are seen as strong tells.
  • Wikipedia’s “Signs of AI writing” page is cited; some worry that teaching these patterns publicly will just lead to prompt engineering around them.

UI / Web Design Tropes

  • People see the same “LLM slop” layout repeatedly: KPI cards, purple gradients, rounded cards, specific fonts, Tailwind-style palettes.
  • Debate whether this sameness is good (legible, better than median developer) or bad (signals low effort, scamminess, homogenization).

Quality of LLM Writing

  • Strong divide: some find LLM prose unbearable, hollow, and “soulless”; others note it’s still better than the average person’s writing, given falling literacy.
  • Concern that LLMs flatten culture: they optimize for mass, barely acceptable content rather than aspirational excellence.
  • Some use LLMs as stylistic critics, summarizers, or “phrase thesauri,” but avoid using generated text verbatim to keep human voice and avoid repetitive tropes.
  • Others argue you should practice writing instead of outsourcing it; skills and discernment can improve over time.

LLM-Generated Code

  • Clear camps:
    • “Camp 1”: LLMs massively boost productivity, write better code than many working programmers, and act like an always-on junior dev team.
    • “Camp 2”: output is often wrong, insecure, inconsistent, and increases maintenance burden; useful only with heavy supervision.
    • “Camp 3”: for throwaway tools and internal hacks, quality doesn’t matter much; speed does.
  • Discussion about code as end product vs mere means, and about how hard it is to judge code quality without deep experience.

Social and Behavioral Effects

  • Comparisons to fast food or “prison loaf”: cheap, filling, joyless.
  • Some now deliberately include typos, simpler structures, or avoid certain punctuation/phrases to not “look like AI.”
  • Others argue the AI-content witch-hunt is harmful, forcing people into suboptimal expression and stigmatizing common language patterns.
  • Several note that humans and models are now mutually influencing each other’s style, making detection and authenticity increasingly blurry.