Skip to content

Cairn Jev Lab

Memory admission evaluator that asks Jev three typed Choice questions on support, durability and commitment, then applies a versioned policy to recommend save, skip or defer.

Stars
4
Language
JavaScript
License
MIT
Published by
Community
Author
Cairn-ink
Last verified
Oct 2026

About this project

Test what your AI should remember. Browse published results and replay recorded examples. No setup, account, or API key needed. Public demo · Quick start · Bring your own cases · Policy · Jev vs Luna results · Contributing · 繁體中文. Cairn Jev Lab is an experimental memory admission evaluator. Give it a source passage and a proposed memory. Jev evaluates the evidence; a small, inspectable policy recommends save, skip, or defer.

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

Highlights

  • One request asks three independent Choice questions on source and candidate.
  • Deterministic admission-v1 policy maps judgments to save, skip or defer.
  • Local reports keep choices, probabilities, confidence and policy reasons.
  • Node.js 22 with no runtime dependencies and a reusable judgeMemory entry point.

Watch out

Labels are AI-authored without independent human review and the public lab is recorded-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.

More Jev resources like this

4GitHub stars
OpenAI-compatible proxy that holds streamed tokens in a sliding buffer and scores the full criteria matrix before release, cutting the stream before a violating token reaches the client.
Jev Practices & Patterns#community#awesomejev#catalog-import
3GitHub stars
A single-file port of jev-ultrafast that lets Jev drive an Ego Lite browser on macOS: an in-page script indexes visible controls and one typed-choice request returns both the operation and the target index.
Jev Practices & Patterns#community#awesomejev#catalog-import
An eval that puts Jev two questions about shadcn-ui/lint: whether each finding is a real rule violation, and whether the linter's message tells you what to change. It runs the real linter over 131 rule test cases and scores both the detections and the messages.
Jev Practices & Patterns#community#jevbest#catalog-import
24GitHub stars
Unofficial laptop study of parallel constrained decoding: prefill once, share the KV cache across fields, and decide all schema fields in one batched forward pass without writing JSON.
Jev Practices & Patterns#community#jevbest#catalog-import
23GitHub stars
Live race rig that labels 1,000 real app reviews with Jev typed questions against Gemini JSON output on identical batches, then replays recordings and re-ranks rows locally without extra model calls.
Jev Practices & Patterns#community#jevbest#catalog-import
16GitHub stars
Vendor-neutral harness that tests whether Jev reranking improves a small RAG stack, using frozen hybrid BM25 plus BGE-M3 candidates and isolating retrieval, rerank, and generation stages.
Jev Practices & Patterns#community#jevbest#catalog-import

From the Jev guides

Original write-ups on what this entry is for.

Most Jev accuracy problems are state problems. What belongs in a state, what is a pre-made judgment to leave out, and why naming the slice you ask about matters more than the wording.
Jev Practices & Patterns#state#design#primitives
Read article
A Noul returns one number and no separate confidence. How to read the value, pin its boundary with criteria, split compound questions, and set a per-action threshold that matches the cost of being wrong.
Jev Practices & Patterns#noul#primitives#confidence
Read article
A Score is a weighted position, not a ruler. Why the number can land between levels, why levels are judged separately, and how to combine several atomic scores in code instead of one crowded rubric.
Jev Practices & Patterns#score#primitives#scoring
Read article
Back to all resources

From the Jev community

Posts from builders shipping with Jev right now.

Follow @typesafeai