Introduction
The mental model in two pages: state plus typed questions in, typed answers with probabilities out.
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24 entries
The mental model in two pages: state plus typed questions in, typed answers with probabilities out.
First request in Python, TypeScript, or curl.
Choice, Score, and Noul, and when each one fits.
How to package what Jev judges, and why less is more.
The request and response contract.
Aliases, current version, price, and rate limits.
Known failure modes, straight from the vendor.
What the category means and how it differs from a chat model.
Decompose a judgment into atomic questions and keep the control flow in code.
What the confidence field means and how to turn it into act, review, or fall back.
Speculative fan-out, confidence routing, composite scoring, intent routing.
The vendor's own catalogue of where Jev fits and where it does not.
Worked recipes, starting with batching many questions into one request; the sidebar has the rest.
The vendor's benchmark on four workflows, with the caveats printed on the page.
The product thesis, summed up as build prod, not god.
Every documentation page as plain Markdown, for feeding to an agent.
Waitlist, API keys, and usage.
Model id `typesafe-ai/jev`, billed through Vercel, no TypeSafe waitlist.
Call `typesafe/jev` from a Worker through `env.AI.run`.
Beta listing on the general-purpose gateway, model id `typesafe/jev-1.13`, billed on your OpenRouter key.
OpenRouter cookbook: a cheap model answers, Jev checks the answer, only the failures escalate.
Zero-config access from Netlify Functions, no separate TypeSafe key.
Route the System One endpoint through a LiteLLM proxy for key management and cost tracking; no streaming, since TypeSafe has none.
Official server; builder demos live in the show-and-tell channel.