A single function Jev-like wrapper for LLMs, including vision models
I was intrigued by Jev and the self-hostable projects appearing around it, such as OpenJev and SemIf. Reading about them introduced me to a neat trick: reading an LLM's token probabilities. Apparently this is an old trick for some people.
- ▪I was intrigued by Jev and the self-hostable projects appearing around it, such as OpenJev and SemIf.
- ▪Reading about them introduced me to a neat trick: reading an LLM's token probabilities.
- ▪Apparently this is an old trick for some people.
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| Original publisher | Blogspot |
| Canonical URL | http://allanrbo.blogspot.com/2026/09/a-jev-like-wrapper-for-llms-including.html |
| Publication time | Sat, 26 Sep 2026 04:20:58 +0000 |
| Retrieval time | 2026-09-26T05:11:00.227Z |
| Last seen | 2026-09-26T05:11:00.227Z |
| 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 | bsTbqehQXcl2 · 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 |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
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| 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
I was intrigued by Jev and the self-hostable projects appearing around it, such as OpenJev and SemIf. Reading about them introduced me to a neat trick: reading an LLM's token probabilities. Apparently this is an old trick for some people. See e.g. OpenAI's logprobs cookbook. But it was new to me. I believe the basic idea is to write a prompt like this: State: My order arrived broken and I want a refund. Question: Which team should handle this? [A] billing [B] shipping [C] returns Answer with the letter of the best option only. Then add a few JSON request parameters to a compatible Chat Completions request: { "max_completion_tokens": 1, "logprobs": true, "top_logprobs": 20 } The LLM API will return the letter plus the model's log probabilities for alternative tokens.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Blogspot.
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