Mini-Jev – typesafe's Jev implemented on top of an LLM locally
mini-Jev · read the letter What a Jev-style interface looks like on a frozen model: classify against a schema that arrives with the request by reading next-token logits, not by generating JSON. TypeSafe's Jev is trained for typed decisions without open generation. mini-Jev asks how much of that interface an ordinary frozen model already provides, and measures it against today's path, grammar-constrained JSON. Today the model writes a JSON object under a grammar, token by token.
- ▪mini-Jev · read the letter What a Jev-style interface looks like on a frozen model: classify against a schema that arrives with the request by reading next-token logits, not by generating JSON.
- ▪TypeSafe's Jev is trained for typed decisions without open generation. mini-Jev asks how much of that interface an ordinary frozen model already provides, and measures it against today's path, grammar-constrained JSON.
- ▪Today the model writes a JSON object under a grammar, token by token.
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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/r-ms/mini-jev |
| Publication time | Fri, 18 Sep 2026 00:21:58 +0000 |
| Retrieval time | 2026-09-18T00:38:44.963Z |
| Last seen | 2026-09-18T00:38:44.963Z |
| 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 | EbwIs-ne94vA · 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
mini-Jev · read the letter What a Jev-style interface looks like on a frozen model: classify against a schema that arrives with the request by reading next-token logits, not by generating JSON. Measured on Qwen3-4B. TypeSafe's Jev is trained for typed decisions without open generation. mini-Jev asks how much of that interface an ordinary frozen model already provides, and measures it against today's path, grammar-constrained JSON. A JSON schema arrives with the request. Today the model writes a JSON object under a grammar, token by token. This project measures the alternative for closed-choice fields: turn each field into a lettered multiple-choice question, run one forward pass, and read the model's scores for the option letters at the answer position. No token is generated.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.