Deterministic grounding checks for LLM agents, no LLM in the hot path
// double-click race test v5 // QA regression test marker aurem qa countdown proof ora-grounding Your LLM agent is lying to you with confidence. Deterministic post-response grounding checks + cross-family adversarial review for LLM chat agents — zero deps, bring your own LLM and your own database. Quick start · Why · Docs · Compare · Roadmap 🔍 30-second demo This is an anonymized real production case — a chat agent claimed a file existed that didn't.
- ▪// double-click race test v5 // QA regression test marker aurem qa countdown proof ora-grounding Your LLM agent is lying to you with confidence.
- ▪Deterministic post-response grounding checks + cross-family adversarial review for LLM chat agents — zero deps, bring your own LLM and your own database.
- ▪Quick start · Why · Docs · Compare · Roadmap 🔍 30-second demo This is an anonymized real production case — a chat agent claimed a file existed that didn't.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,783 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/polarisbuiltinc-wq/ora-grounding |
| Publication time | Mon, 21 Sep 2026 02:04:21 +0000 |
| Retrieval time | 2026-09-21T02:08:47.632Z |
| Last seen | 2026-09-21T02:08:47.632Z |
| 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 | Zy38Sys91kAX · 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
// double-click race test v5 // QA regression test marker aurem qa countdown proof ora-grounding Your LLM agent is lying to you with confidence. This catches it. Deterministic post-response grounding checks + cross-family adversarial review for LLM chat agents — zero deps, bring your own LLM and your own database. Quick start · Why · Docs · Compare · Roadmap 🔍 30-second demo This is an anonymized real production case — a chat agent claimed a file existed that didn't. Here's ora-grounding catching it, deterministically, with zero LLM calls in the check itself: >>> from ora_grounding.grounding import extract_claims, classify_claims >>> >>> reply = "Fixed the retry logic in payments_client.py — added dedup via redis_lock.py" >>> >>> canonical = { ...
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.