I'm not anti-AI, but I have QUALLMS
I'm not anti-AI, but I have QUALLMS August 10, 2026 — I haven’t been shy about voicing my negative opinions of the current large language model hype. But I’m hesitant to describe myself as “anti-AI”. And it’s not because I have some sort of super-nuanced position like “LLMs are fine in some cases” or “I’d be OK with LLMs if only we could solve these problems”.
- ▪I'm not anti-AI, but I have QUALLMS August 10, 2026 — I haven’t been shy about voicing my negative opinions of the current large language model hype.
- ▪But I’m hesitant to describe myself as “anti-AI”.
- ▪And it’s not because I have some sort of super-nuanced position like “LLMs are fine in some cases” or “I’d be OK with LLMs if only we could solve these problems”.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,338 of its stories.
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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 | Cholling |
| Canonical URL | https://cholling.com/posts/quallms/ |
| Publication time | Mon, 10 Aug 2026 20:11:14 +0000 |
| Retrieval time | 2026-08-10T20:20:48.044Z |
| Last seen | 2026-08-10T20:20:48.044Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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
I'm not anti-AI, but I have QUALLMS August 10, 2026 — I haven’t been shy about voicing my negative opinions of the current large language model hype. But I’m hesitant to describe myself as “anti-AI”. And it’s not because I have some sort of super-nuanced position like “LLMs are fine in some cases” or “I’d be OK with LLMs if only we could solve these problems”. I’m in fact adamantly opposed to the very assumptions on which the hype is based: that a statistical model of word co-occurrence frequency is a good foundation for a multi-purpose tool for answering questions or generating meaningful text or code. Train a local model on text acquired with the creators’ express permission and run it on a machine fueled entirely by renewable energy, and I still don’t think it’s a good idea.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Cholling.
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