Various LLM Smells
The article discusses the author's experience with using LLMs to enhance writing, noting a distinct style that emerged from AI-generated content. The author identifies recurring patterns, termed 'ai-smells', in both writing and website design that are recognizable across various platforms. This observation raises questions about the originality and uniqueness of AI-assisted creative tasks.
- ▪The author began using LLMs to improve their writing for a math blog.
- ▪They noticed specific sentence structures and styles appearing widely across the internet.
- ▪The term 'ai-smells' refers to recognizable patterns in AI-generated content.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,048 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Shiv After Dark |
| Canonical URL | https://shvbsle.in/various-llm-smells/ |
| Publication time | Thu, 28 May 2026 19:02:18 +0000 |
| Retrieval time | 2026-05-28T19:14:58.152Z |
| Last seen | 2026-05-28T19:14:58.152Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | n8jeoAqLlYTM |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Various LLM smells 28 May, 2026 Late last year I started writing a math blog and decided to use LLMs to polish/enhance my writing. The LLM generated writing obviously felt significantly better than my own writing. It had better vocabulary, interesting sentence structures etc etc. I swear it did not seem like AI-slop to me at the time. Then about 3 months later, I see the exact sentence structures appearing ACROSS THE ENTIRE F***** INTERNET. And what is fascinating to me is that ai-smell seems like an artifact that emerges across various AI assisted tasks that you can now easily recognize. A few examples that I've collected so far to show the "ai-smells" across two domains: 1.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Shiv After Dark.
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