I had Gemini train its own replacement for $9
← petervijeh.comI had Gemini train its own replacement for $9This article was written with the assistance of AI. The numbers are real: every score comes from the ten training runs described below, and the full run log is in the linked knife.day write-up.The knife.day write-up, with the full run logI like to cook, and somewhere along the way that turned into an obsession with high-end chef's knives. So I scrape the Reddit threads where people argue about them and pull out every brand, model and steel they mention, to see what is getting bought and argued about.Picking product names out of text is a job called named-entity recognition, and small models have done it for a decade.
- ▪← petervijeh.comI had Gemini train its own replacement for $9This article was written with the assistance of AI.
- ▪The numbers are real: every score comes from the ten training runs described below, and the full run log is in the linked knife.day write-up.The knife.day write-up, with the full run logI like to cook, and somewhere along the way that turne
- ▪So I scrape the Reddit threads where people argue about them and pull out every brand, model and steel they mention, to see what is getting bought and argued about.Picking product names out of text is a job called named-entity recognition,
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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 | Petervijeh |
| Canonical URL | https://www.petervijeh.com/projects/reddit-ner |
| Publication time | Thu, 17 Sep 2026 13:17:16 +0000 |
| Retrieval time | 2026-09-17T14:03:44.992Z |
| Last seen | 2026-09-17T14:03:44.992Z |
| 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 | XxEHrf0yUhKA · 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
← petervijeh.comI had Gemini train its own replacement for $9This article was written with the assistance of AI. If that bothers you, stop reading here. The numbers are real: every score comes from the ten training runs described below, and the full run log is in the linked knife.day write-up.The knife.day write-up, with the full run logI like to cook, and somewhere along the way that turned into an obsession with high-end chef's knives. So I scrape the Reddit threads where people argue about them and pull out every brand, model and steel they mention, to see what is getting bought and argued about.Picking product names out of text is a job called named-entity recognition, and small models have done it for a decade. I was doing it with Gemini 3.1 Pro, one paid API call per comment.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Petervijeh.
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