No standard says what to record about an LLM call, so I built the record
I build an investigations engine where every finding has to trace back to the source bytes it came from, which turned out to be an awkward promise once a language model started helping produce the findings. In a trust accounting case in New York, an expert witness told the court he had used an AI assistant to cross-check his damages calculation. The judge asked him what he had typed into it.
- ▪I build an investigations engine where every finding has to trace back to the source bytes it came from, which turned out to be an awkward promise once a language model started helping produce the findings.
- ▪In a trust accounting case in New York, an expert witness told the court he had used an AI assistant to cross-check his damages calculation.
- ▪The judge asked him what he had typed into it.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,428 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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 | Johannes Stillig |
| Canonical URL | https://stillig.net/posts/tamper-evident-llm-calls/ |
| Publication time | Tue, 11 Aug 2026 12:07:44 +0000 |
| Retrieval time | 2026-08-11T12:10:47.911Z |
| Last seen | 2026-08-11T12:10:47.911Z |
| 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 | yl1j_PgTlPtk · 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
A judge ran the same prompt three times and got three different answers 5 Aug 2026 19 min Contents Reproducibility is the wrong thing to want first Nobody has specified what to record What I built Three claims wearing one word What broke What is still unsolved The strongest argument against all of this What to take from this I'm Johannes. I build an investigations engine where every finding has to trace back to the source bytes it came from, which turned out to be an awkward promise once a language model started helping produce the findings. In a trust accounting case in New York, an expert witness told the court he had used an AI assistant to cross-check his damages calculation. The judge asked him what he had typed into it. He could not remember.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Johannes Stillig.
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