Reverse-engineered Jev-like model
Jevlike Train a small model that chooses among a changing list of text options. A Jev-like model takes a piece of text and a list of N text options. It does this in one pass instead of writing an answer word by word.
- ▪Jevlike Train a small model that chooses among a changing list of text options.
- ▪A Jev-like model takes a piece of text and a list of N text options.
- ▪It does this in one pass instead of writing an answer word by word.
Hacker News (Front Page) files mainly under programming. We currently carry 1,734 of its stories. Top-voted stories on Hacker News.
Story provenance
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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/vinnylarouge/jevlike |
| Publication time | Wed, 16 Sep 2026 18:49:57 +0000 |
| Retrieval time | 2026-09-16T21:08:41.681Z |
| Last seen | 2026-09-16T21:08:41.681Z |
| 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 | Yhubq0NiPyta · 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
Jevlike Train a small model that chooses among a changing list of text options. A Jev-like model takes a piece of text and a list of N text options. It returns one probability for each option. It does this in one pass instead of writing an answer word by word. Jev is TypeSafe's commercial model for this kind of task. TypeSafe has not published its design. This repository is an independent starter model with the same input and output shape. Demo The same option-attention head can score controller buttons from image patches. This ten-second film joins two selected five-second windows: live deadly_corridor combat on the seven Doom buttons, then a chess controller walking to and playing moves with five keys. The diagram shows the tensors used for each decision.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.