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What you can do with Awesome Jev

A curated list of open-source projects, SDKs, and integrations built around Jev, TypeSafe AI's model for typed, probabilistic decisions.

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cobanov/awesome-jev on GitHub

Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk.

What do you want to do?

5 use cases

  • Rank a shortlist by weighted criteria in CI

    Score each item on weighted dimensions from a YAML spec and gate builds on the result.

    View prompt
    Use the jeff-cli project at github.com/saembit/jeff-cli with a YAML spec for <target candidates>. Run its noul or score command and report the ranked list and any threshold failures as exit codes. Verify that three items match a manual check of the spec weights.

  • Label DataFrame rows with closed-set Jev answers

    Ask Jev a closed-set question for each row, cache the answers, and join them back to the table.

    View prompt
    Using the steven-shoemaker/hunch Python library, classify each row of <dataset path> into the closed-set labels <label list>. Return the labeled table and a count per label. Verify twenty random rows by hand before reporting results.

  • Find repository files by meaning, not keywords

    Score live repository files and code ranges with Jev probabilities, without an index.

    View prompt
    Use the jegrep tool at github.com/can1357/jegrep on <repository path> with the query <plain-language description>. Report the top ten files and ranges with their probabilities. Verify the top three by reading the code.

  • Add typed routing to a LangChain pipeline

    Insert a Jev classifier Runnable that routes requests and checks risky tool calls.

    View prompt
    In the langchain repository at github.com/langchain-ai/langchain, wire the TypeSafeClassifier Runnable to route <request type> into the fixed routes <route list>. Report the route chosen for each of twenty labeled requests. Verify that every risky tool call is blocked or flagged by the middleware.

  • Rerank retrieved documents with Jev Noul scores

    Send candidate documents as Noul questions in one request and sort by the returned scores.

    View prompt
    Using the Jev reranker adapter in the milvus-model repository at github.com/milvus-io/milvus-model, rerank the candidate documents for <query> from <candidate set>. Output the sorted documents with original indices and scores. Verify that the top result matches a hand-judged best match for three queries.

How to use Awesome Jev

  1. Check you have what it needs: Access to Jev through the TypeSafe early-access entry point at typesafe.ai; A small labeled sample of your own data to check Jev answers before relying on them.
  2. Open cobanov/awesome-jev on GitHub and read the README and license.
  3. Open the jeff-cli repository at github.com/saembit/jeff-cli and read its YAML spec format.
  4. Write a YAML spec that lists your weighted dimensions for the items you are ranking.
  5. Run the noul, choice, or score command described in the jeff-cli README against your items.
  6. Set the threshold so a failing result returns a non-zero exit code, then wire that into your shell script or CI step.

These steps come from our top use case, Rank a shortlist by weighted criteria in CI, which also has a copy-paste agent prompt.

Awesome Jev alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Access to Jev through the TypeSafe early-access entry point at typesafe.ai
  • A small labeled sample of your own data to check Jev answers before relying on them
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Questions about Awesome Jev

What is Awesome Jev?

A curated list of open-source projects, SDKs, and integrations built around Jev, TypeSafe AI's model for typed, probabilistic decisions. It is an open-source project on GitHub: https://github.com/cobanov/awesome-jev.

Who is Awesome Jev for?

Developers and AI agents choosing a Jev client, framework integration, or tool for routing, classification, ranking, or guardrails.

What can I do with Awesome Jev?

5 use cases so far, for example: Rank a shortlist by weighted criteria in CI; Label DataFrame rows with closed-set Jev answers; Find repository files by meaning, not keywords. Each has steps and a copy-paste agent prompt.

What do I need to run Awesome Jev?

Access to Jev through the TypeSafe early-access entry point at typesafe.ai; A small labeled sample of your own data to check Jev answers before relying on them.

What are alternatives to Awesome Jev?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Awesome Jev safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.