Pangram – AI Detector
Our classifier uses a traditional language model architecture. Then, the model turns each token into an embedding, which is a vector of numbers representing the meaning of each token. The input is passed through the neural network, producing an output vector.
- ▪Our classifier uses a traditional language model architecture.
- ▪Then, the model turns each token into an embedding, which is a vector of numbers representing the meaning of each token.
- ▪The input is passed through the neural network, producing an output vector.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,428 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 | Pangram |
| Canonical URL | https://www.pangram.com |
| Publication time | Mon, 03 Aug 2026 22:35:35 +0000 |
| Retrieval time | 2026-08-03T22:50:43.345Z |
| Last seen | 2026-08-03T22:50:43.345Z |
| 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
Our classifier uses a traditional language model architecture. It receives input text and tokenizes it. Then, the model turns each token into an embedding, which is a vector of numbers representing the meaning of each token. The input is passed through the neural network, producing an output vector. A classifier head transforms the output vector into a prediction of human, AI, or AI-assisted. We train an initial model on a small but diverse dataset of approximately 1 million documents composed of publicly licensed human-written text. The dataset also includes AI-generated text produced by GPT-5 and other frontier language models. The result of training is a neural network capable of reliably predicting whether text was authored by human or AI.
Excerpt limited to ~120 words for fair-use compliance. The full article is at Pangram.
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