Humanising LLM Outputs Is Dumb
The author argues that humanizing LLM outputs is not the right approach, as it can lead to lossy compression of information and hide failures. Instead, the author suggests that agents should keep detailed state and exchange precise, machine-facing information, with human-readable summaries generated only at the boundary. This approach would allow for more accurate and reliable information exchange between agents and humans.
- ▪Humanizing LLM outputs can lead to lossy compression of information, resulting in a loss of detail and accuracy.
- ▪The use of Simplified Technical English and other style rules can hide failures and make it difficult to diagnose issues.
- ▪Agents should keep detailed state and exchange precise, machine-facing information, with human-readable summaries generated only at the boundary.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,275 of its stories.
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Story provenance
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Record
| Original publisher | ᨒ MindDump |
| Canonical URL | https://kuber.studio/blog/Reflections/Humanising-LLM-Outputs-is-Actually-Dumb |
| Publication time | Mon, 10 Aug 2026 13:35:40 +0000 |
| Retrieval time | 2026-08-10T13:45:45.214Z |
| Last seen | 2026-08-10T13:45:45.214Z |
| 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 | jjgCJqYbANTk · 1 stories |
| 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
The largest tell for me to tell where culture and sentiment is shifting for AI tools is usually X, viral GitHub repositories and Hacker News. One of these tells I’ve been seeing a lot lately is skills like I have ADHD and Agents.md instructions such as giving outputs in only ASD-STE100 Simplified Technical English. I understand the appeal, none of us really like the verboseness and specific quirks of LLM outputs, but I really think fixing that by humanising the model is the wrong abstraction.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at ᨒ MindDump.
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