Eric S. Raymond take on AI
Raymond@esrtweetThis is a certain kind of talk around LLMs that I find increasingly puzzling. That is all of the people bitching that LLMs constantly generate crap code and hallucinate solutions, and are worthless for programming. This has almost never happened to me, and never during the last two model generations I have used (chat GPT 5.4 and 5.5).
- ▪Raymond@esrtweetThis is a certain kind of talk around LLMs that I find increasingly puzzling.
- ▪That is all of the people bitching that LLMs constantly generate crap code and hallucinate solutions, and are worthless for programming.
- ▪This has almost never happened to me, and never during the last two model generations I have used (chat GPT 5.4 and 5.5).
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,311 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 | X (formerly Twitter) |
| Canonical URL | https://twitter.com/esrtweet/status/2074889702381953222 |
| Publication time | Thu, 16 Jul 2026 14:30:11 +0000 |
| Retrieval time | 2026-07-16T14:34:49.297Z |
| Last seen | 2026-07-16T14:34:49.297Z |
| 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
Eric S. Raymond@esrtweetThis is a certain kind of talk around LLMs that I find increasingly puzzling. That is all of the people bitching that LLMs constantly generate crap code and hallucinate solutions, and are worthless for programming. This has almost never happened to me, and never during the last two model generations I have used (chat GPT 5.4 and 5.5). Occasionally a model used to get a little deranged when I pushed its context limit, but under codex that doesn't happen anymore; instead I got a red-highlighted warning when the limit has been exceeded and I need to clear my session. I've applied AI to feature changes, refactoring, and debugging over 63 different projects written in C, Go, Rust, Python, and shell. I've written documentation with it.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at X (formerly Twitter).
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