qingshungLI/everything-about-jev
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tell you everything about jev,TypeSafe AI's System One model for typed decisions.
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CommunityJev Tools & CLIs
Everything About Jev
A community-maintained Chinese guide to Jev: what the three typed judgments actually return, six runnable demos in Python, TypeScript, Node and curl, and a curated read of public discussion.
- Stars
- 3
- Language
- Python
- License
- MIT
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- Community
- Author
- qingshungLI
- Last verified
- Oct 2026
Works forJev for Classification
Used inJev for Software & DevTools
- Languages
- Python
Highlights
- Six demos behind one flag: command palette, ticket triage, research cross-check, model routing, tool guard and completion check.
- Runs against the jevai.org community API by default, with a separate documented path for an official TypeSafe key.
- Flags a real integration trap: confidence and the probability of the chosen option are different fields.
- The demos use synthetic data, with live API behaviour and rate limiting recorded separately in a validation document.
Watch out
It is community-maintained and states it has no affiliation with TypeSafe AI or jevai.org, and the quick start needs a jevai.org community key rather than an official one. The default demo returns an action name only and never touches a browser, so it is not an agent. The discovery index is described as candidate links gathered from five upstream lists and not yet screened item by item, and the community report says its material comes from 337 collected public posts. 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.
No, and the README says so plainly: the repository is community-maintained with no affiliation to TypeSafe AI or jevai.org. The quick start is written against the jevai.org community API and needs a JEVAI_API_KEY, with a separate document for readers holding an official TypeSafe key and a providers page comparing the two key types.
No. The guide is explicit that you attach a generative model when a reply needs writing, and that the application does retrieval first when fresh information is needed, then passes the result to Jev. Its own quick start matches a request to make the page darker onto a dark_mode action name and returns only that name, without operating a browser.
Mixed, and the README is careful about which is which. The demos run on synthetic data, with live API results and rate limiting recorded in a separate validation document. The community report draws on 337 collected public posts and says explicitly that it does not represent the community proportionally, and it preserves disagreement rather than smoothing it over.
The collected discussion pushes back, and that is the most useful thing here. Choosing one option is described as intuitive, while trimming long conversations, judging complex reasoning or planning multi-step tasks is flagged as needing whole-task evaluation, with an agent circling repeatedly in a maze offered as the concrete warning that single-step judgment is not multi-step planning.
Choice returns an option plus confidence and per-option probabilities, Noul returns the probability of yes, and Score returns a rating and distribution that may be fractional. Confidence and option probability are separate fields.
Python 3.10+ is the quick start, with a TypeScript demo on Node 20+, plus Node.js and Bash/curl entry points. An offline browser demo simulates the interaction without network on synthetic data.
Six Python cases cover a command palette, ticket classification, research-support checking, model routing, tool-guard checks and completion checking, selected with flags like --case model-route.
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