Show HN: Factlabel: catches AI agents lying about the data they're reporting on
factlabel A nutrition label for AI-written content. factlabel checks what an AI agent writes about data against the data itself, blocks what doesn't hold up, and shows readers exactly what was checked and why. The problem AI agents now write the updates people read: fundraising reports, financial summaries, dashboards, briefings. Three failure modes show up again and again: Invented figures.
- ▪factlabel A nutrition label for AI-written content. factlabel checks what an AI agent writes about data against the data itself, blocks what doesn't hold up, and shows readers exactly what was checked and why.
- ▪The problem AI agents now write the updates people read: fundraising reports, financial summaries, dashboards, briefings.
- ▪Three failure modes show up again and again: Invented figures.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,888 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 | GitHub |
| Canonical URL | https://github.com/generallymatthew/factlabel |
| Publication time | Mon, 21 Sep 2026 19:07:55 +0000 |
| Retrieval time | 2026-09-21T19:28:48.970Z |
| Last seen | 2026-09-21T19:28:48.970Z |
| 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 | B-aBLfusmJck · 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
factlabel A nutrition label for AI-written content. factlabel checks what an AI agent writes about data against the data itself, blocks what doesn't hold up, and shows readers exactly what was checked and why. The problem AI agents now write the updates people read: fundraising reports, financial summaries, dashboards, briefings. The numbers usually come from real records. The story around them often doesn't. Three failure modes show up again and again: Invented figures. "More than 2,000 new donors joined." The ledger shows 9 donors in all. False attribution. "Driven by an explosion of grassroots momentum." 90% of the money came from one corporate gift. Spin. "Individual giving grew 40%." It fell 30.6%. Each sentence sounds reasonable, and a reader has no way to check it.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.