Are the preview percentages shown on the gallery real Jev measurements?

No. The guide calls the preview billing split, refund probability, and frustration score illustrations, not measured outputs, and states no benchmark winner is claimed.

Does the side-by-side setup prove Jev beats the language model baseline?

No. Both sides receive the same state and question definitions, the baseline uses constrained structured output rather than forced prose, and cost and latency are reported per live run only.

What will a live comparison cost and where do my keys actually go?

Each live comparison makes one paid request to each provider with keys held on the local loopback server, and the response details include the token-based cost math instead of a fabricated zero.

Can I run just one use case from the terminal without starting the gallery?

Yes. Every numbered folder runs standalone from the terminal in preview without keys, with flags for live mode, scenario selection, and the plain-text baseline variant.

How do I run the gallery and see previews?

Use Node.js 22.9 or later, then git clone, npm install and npm start, and open http://127.0.0.1:3000. Choose a demo and click Run comparison; preview mode uses clearly labeled hand-authored fixtures and needs no key or API call.

What does the OpenAI baseline use by default?

Structured Outputs, with plain text prompted to return JSON as the alternative. Both modes call client.responses.create() and read response.output_text, and the output is validated before it drives the visual.

How are live costs displayed?

Each panel shows estimated USD cost per run from provider-reported tokens and published rates, including OpenAI cache reads and writes, with tiny costs kept to six decimal places. Preview runs show $0 because no call occurs, and missing usage or unknown pricing shows Unavailable rather than a fabricated zero.

CommunityJev Demos & Experiments

Awesome Jev Use Cases — whyashthakker

Fifty minimal runnable demos pairing Jev's choice, score, and yes-no primitives against a structured-output language-model baseline, each with synthetic scenarios and a small 2D visual.

See highlights

Open github.com

Stars: 16
Language: HTML
License: MIT
Published by: Community
Author: whyashthakker
Last verified: Oct 2026

whyashthakker/awesome-jev-use-cases

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Awesome list of Jev use cases. Compared with GPT Models (LLMs) across on cost and speed.

Forks: 3
Last pushed: Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.Homepage

This is one of several Jev listings for the same project. The canonical entry is Awesome Jev use cases — walidboulanouar.

About this project

50 minimal, interactive examples of TypeSafe Jev’s Choice, Score and Noul primitives, compared side by side with OpenAI’s Responses API (gpt-4o-mini). Each use case has its own folder, two synthetic scenarios, a small 2D visual and a runnable JavaScript example. No frontend framework. No database. Preview without keys; add your own keys when you want real responses. Quick start · Three primitives · All 50 use cases · Beam CLI integration · Methodology · Research sources.

Quoted from the project's README. Read the full README.

Tagscommunity jevbest catalog-import html

Works forJev for Classification

Used inJev for Software & DevTools

Highlights

One ticket worked through all three primitives showing routing, flagging, and graded scoring together.
Preview mode with labeled fixtures needing no keys, plus live mode with explicit per-provider requests.
Same-state comparison with validated outputs, native distributions, and disclosed cost math.
Numbered folders covering routing, triage, ranking, and judging, each runnable on its own.

Watch out

Preview outputs are hand-authored fixtures shared by both panels, so they illustrate the interface rather than measuring either provider; live runs record timing and cost per run only. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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OpenJev

Python decision client exposing Choice, Noul, and Score primitives with isolated order-invariant scoring, a deliberative flywheel that crystallizes hard cases into fast paths, and local or hybrid deployment.