Rerun the analysis when new data lands and send the figures that changed
For a standing research question: the dot watches a data folder, reruns the notebook on each new file, flags anomalies and sends only the figures that moved.
Jev judge eval (1 score) over a fixed state : runnable via experimental_evaluate.
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Jev judge eval (1 score) over a fixed state : runnable via experimental_evaluate.
Copy this into Muse and replace anything in [square brackets] with your own details.
// Grade: marketing translation fluency — Jev judge eval (typesafe-ai/jev).
// An agent translates a tagline into German; the judge scores fluency and fidelity.
// Model: typesafe-ai/jev via the Vercel AI Gateway ($0.042/1M input tokens).
// Auth: set AI_GATEWAY_API_KEY (vck_…) in the environment — never paste the key here.
// For zero-data-retention / no-training workloads, add
// providerOptions: { gateway: { … } } to the evaluate call below.
import { experimental_evaluate } from 'ai';
const result = await experimental_evaluate({
model: 'typesafe-ai/jev',
state: "Source: Ship calm software, every single week.\nTranslation: Liefere ruhige Software, jede einzelne Woche.",
questions: {
"fluency": {
"type": "score",
"instructions": "Score this German marketing translation. Higher is better.",
"criteria": [
"Wrong meaning or ungrammatical.",
"Understandable but awkward or overly literal for marketing copy.",
"Fluent and accurate, though slightly flat as a tagline.",
"Fluent, idiomatic, and punchy — reads like native marketing copy."
]
}
},
});
console.log(JSON.stringify(result, null, 2));
Source / inspiration: View credited source
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Sign in to join inFor a standing research question: the dot watches a data folder, reruns the notebook on each new file, flags anomalies and sends only the figures that moved.
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Reported by a Muse user on vercel.com (original post). Collected by the open-source EveryAI Muse field guide under the MIT licence. Sundog has not tested this workflow; results people report here appear on this page.