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Jev Hub — shiiivvaam
Independent GitHub repository behind jevhub.io, collecting ready-to-run Jev examples, real cost data, a free cost calculator, community builds, and dated news, plus plain-English explainers of Jev terms, request flow, question writing, and limits.
- Stars
- 1
- Reading time
- 2 min
- Published by
- Community
- Added
- Oct 2026
This is one of several Jev listings for the same project. The canonical entry is Jev Hub — mizzlelover.
Highlights
- Independent project; states it is not affiliated with TypeSafe AI.
- Holds ready-to-run Jev examples, cost data, and the directory of community builds behind jevhub.io.
- Explains Jev terms, request flow, question writing, confidence thresholds, and limits.
- Includes a free cost calculator plus dated news and comparisons.
- Documents Jev pricing at $0.042 per million input tokens with output free.
Questions about this entry
Short answers drawn from the linked project or page.
No. The repository states it is an independent project and is not affiliated with TypeSafe AI, the company that makes Jev.
Ready-to-run Jev examples, cost data, and the directory of community builds behind jevhub.io, along with explainers on Jev terms, request flow, question writing, and limits.
Yes. The project describes a free cost calculator alongside real cost data, and notes Jev pricing of $0.042 per million input tokens with output free.
Choice, which picks one of up to 255 options; Score, an ordered scale of 2 to 10 levels; and Noul, a yes/no statement that returns the probability it is true.
Up to 64,000 tokens per request in total, and 32,000 tokens for the state plus the longest single question.
It states Jev is not built for chat, writing, summaries, or code; it answers a defined question with a typed value and probability instead of generating text.
Through one POST request to the System One API endpoint, with a TypeSafe API key sent as a Bearer token; the repo also points to the Python SDK installable via pip.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: Simon Willison's overview of Noul, Choice, and Score, and where a decision model fits.
benwerd53 pointsJev introduces a new shape of LLMRead the thread on Hacker NewsHacker News: swyx posts the Latent Space interview with Diogo Almeida.
swyx4 pointsJev: System One Models for Prod, Not God – With Diogo Almeida, CEO, TypeSafe AIRead the thread on Hacker NewsX: Diogo Almeida in five minutes
X: LLMs vs. Jev, clearly explained
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Ian Maurer's ThinkThen analysis argues Jev's faster and cheaper are measurable today while its better is a bet on RLCD: unlike RLHF, which pleases humans, and RLVR, which optimizes benchmarks, Jev is trained to be reliable for programmatic use, in Diogo Almeida's own framing.
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A BIG CHANGE analysis of the Diogo Almeida Latent Space interview: Jev's most consequential proposition is the unit of automation, a bounded judgment inside an existing process, and the launch claim that Jev can't hallucinate needs a narrow reading because schema matching does not establish truth.
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Braintrust's provider page covers two ways to use TypeSafe Jev: instrumenting System One calls as decision spans, and running Jev as an LLM judge that turns typed choices into scores or labels.
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From the Jev guides
Original write-ups on what this entry is for.
From the Jev AI model community
Posts from builders shipping with Jev right now.