LLM smells are the tiny tells that make AI-assisted writing or AI-built websites feel oddly familiar. A short post by Shiv After Dark put a useful name on the pattern: punchline-heavy prose, repeated sentence shapes, monospace-heavy pages, badges, cards, and step sections that keep appearing across unrelated work.
Table of Contents
The short version
- LLM smells are not proof that a piece of work is bad. They are signs that the draft may still be too close to the model’s default style.
- The clearest writing tells are punchline sentences, repeated short sentences, “X is the Y of Z” metaphors, and tidy contrast formulas.
- The web design tells are just as visible: JetBrains Mono, step layouts, badge dots, familiar cards, and generic call-to-action buttons.
- The useful editorial move is to treat AI output as a draft, then add concrete details, uneven human rhythm, and product-specific design choices.
- Hacker News readers mostly pushed the argument toward code quality: AI output looks strongest when you do not yet know enough to judge it.
What happened
Shiv After Dark published “Various LLM smells” on May 28, 2026, after noticing that prose once polished by an LLM had started to resemble a lot of other writing on the web. The post is short, but the examples are sharp: aphoristic one-liners, strings of clipped sentences, metaphor templates, and the familiar “not merely X” style of contrast.
The second half moves from prose to AI-generated websites. The author points to the same stack of visual habits showing up again and again: monospace typography, step sections, cards, buttons, blinking badge dots, and footnote-style flourishes. None of those choices are wrong by themselves. They become LLM smells when they arrive as a bundle, without much relationship to the product or audience.
If you follow AI writing and web tooling, this fits a larger pattern. Models are good at producing plausible defaults. Plausible defaults are useful for a first pass. They are also easy to recognize once enough people publish them unchanged. For more English briefs on AI tooling and product craft, see the IT & AI archive.
Why this is worth watching
LLM smells are worth watching because they are an editing problem, not a purity test. The author is not arguing that people should stop using AI for creative work. The better reading is more practical: if a model gives you a draft in seconds, you still need to remove the model’s house style before the work feels like yours.
For writing, that means checking whether a sentence adds information or only adds mood. Punchy lines can work, but a whole page of them starts to feel assembled. The same goes for neat metaphors. “X is the visible signature of Y” may sound elegant the first time. By the tenth version, it reads like a preset.
For web teams, LLM smells are a useful QA category. A landing page can be clean and still generic. If the typography, cards, steps, icons, and microcopy could belong to any AI startup, the page probably needs one more design pass. App builders should pay special attention here, because store listings, onboarding screens, and extension directories reward clarity, but punish sameness.
What Hacker News readers are arguing about
The Hacker News discussion quickly widened from writing to competence. One of the strongest recurring points was that LLM output looks best in domains where the user is least able to judge it. That explains the split many people see in coding threads: beginners may experience the model as a dramatic productivity boost, while experienced engineers see the rework, missing context, and bad abstractions.
Several commenters gave concrete coding examples. One described an assistant proposing a security-dangerous approach that would have bypassed a WebAssembly sandbox and executed submitted Python in the application container. Others complained about agent-generated codebases growing too large because each feature gets built in isolation: every modal is different, every button drifts, and business logic ends up scattered.
There was a more positive camp too. Some readers said LLMs are genuinely useful for format conversions, API mappings, learning unfamiliar concepts, or getting past small obstacles. The practical distinction was not “use AI” versus “do not use AI.” It was whether the user has enough taste, tests, and domain knowledge to catch the smells before they harden into the final product.
LLM smells checklist
Before the final edit, look for the repeated shapes: punchline stacking, metaphor templates, tidy contrast lines, generic cards, and typography that says more about the model than the product.
The practical read
Use LLM smells as a checklist before publishing. In prose, look for punchline stacking, repeated short sentences, decorative metaphors, tidy contrast formulas, and abstract claims that do not name a real example. Replace them with specifics. Add the thing you actually saw, measured, built, shipped, or changed.
In interface work, scan for the default AI landing page kit: monospace labels, gradient cards, step grids, badge dots, identical buttons, and generic hero copy. Keep the pieces that fit. Cut the ones that only make the page look “AI polished.” The goal is not to hide the tool. The goal is to make the result specific enough that the tool is no longer the most visible author.
The same rule applies to code. AI can get you moving, especially on routine or verifiable tasks. But if you cannot review the output, you are outsourcing judgment. That is where LLM smells stop being cosmetic and start turning into maintenance work.