재순위 및 관련성

Hermes Jev Skills는 모델 라우팅, 검색, 메모리 필터링, 메시지 분류, 브라우저 동작용 스킬을 제공합니다. Jev는 모델 선택이나 검색 문단 순위화처럼 범위가 정해진 판단을 맡고, 호스트 모델은 문장을 작성합니다. Hermes, Claude Code, Codex에서 사용할 수 있습니다. 섀도 모드는 모델을 바꾸지 않고 라우팅 판단만 기록합니다.

Jev Search는 자연어 요청을 웹 링크와 발췌문으로 바꾸고 수정 가능한 필터와 관련성 점수를 보여줍니다. 로컬에서는 .dev.vars에 Search1API와 Jev 제공자를 설정하고 pnpm으로 의존성을 설치한 뒤 pnpm dev를 실행합니다.

지연 시간에 초점을 둔 개별 데모 모음으로 Sift 메일 라벨링과 Turbo Rerank 검색 정렬을 포함합니다. 각 앱 디렉터리에서 의존성을 설치하고 TYPESAFE_API_KEY를 설정한 다음 개발 명령을 실행합니다.

Practical, tested recipes for TypeSafe's Jev decision model on OpenRouter: support triage, database indexing, file organizing, tagging, taxonomies, dedupe, PII detection, extraction, search re-ranking and a browser agent.

Jev-powered relevance filtering and reranking for RAG in Python.

Does a TypeSafe Jev rerank beat embedding search? Graded relevance eval (9,831 pairs, 164 zh/en queries) over the Agent Skills Hub catalog, with the judge-circularity bias measured.

Can a decision model beat dedicated rerankers? TypeSafe Jev vs Cohere Rerank 4 vs ZeroEntropy zerank-2 vs a chat-model baseline: 14 datasets, every raw API response, bootstrap ranges on every gap.

Cut Claude Code's skill manifest by ~75% with TypeSafe Jev. Scores every installed skill for relevance and hides the rest via skillOverrides — 12,750 → 3,185 tokens on a 217-skill install, for $0.0009 a session.

An automated research paper radar that fetches the latest papers from arXiv, evaluates their relevance to a configurable research profile using TypeSafe AI, and ranks them by relevance score. Designed for personalized, daily literature discovery across different research domains.

Agent Seek — precision web recall for agents. You.com discover + TypeSafe Jev ranking. MCP + REST. Live demo: https://agentseek.dev

Drop-in relevance filter for LangChain RAG retrievers powered by TypeSafe Jev.

Contextual, inline clipboard decomposition for macOS — Tab-to-paste with full history and time-decay ranking. Powered by TypeSafe JEF.

Wikipedia link races with direct Jev ranking and a live terminal display.

Agentic RAG pipeline for biomedical evidence discovery: query planning and multi-round search across twelve public databases plus citation-graph expansion, ranking by relevance and study design, and citation-grounded synthesis with disagreements reported explicitly.

An MCP server and CLI that rank postings against your profile, explain every score, and measure the ranking against your own judgment.

Find Amazon keyword ideas with Nexscope and evaluate their relevance to listing text with Jev.

Production reranker benchmark: TypeSafe Jev vs Qwen3-Reranker-0.6B on BEIR SciFact and NFCorpus — nDCG@10, cost/query and p50/p95 latency. Quality is indistinguishable; Jev is ~2x faster, Qwen ~4x cheaper. From the team at denser.ai

Agent Skill and Python workflow for Clay lead scoring, B2B prospect qualification, and people-search ranking with TypeSafe JEV.

Semantic code completion for Neovim using Jev (TypeSafe AI) as a ranking oracle. Fuzzy candidates appear instantly; Jev re-ranks them by code context.

RAG quality gate adapted for TypeSafe Jev - relevance filtering + document injection detection, fan-out in one call

Does ORDER BY over a Jev probability put rows in a defensible order? Independent ranking, calibration and invariant measurements of TypeSafe AI's Jev: passes six pre-registered gates on 360 labeled rows, fails four of six on graded product relevance.

The data pipeline behind LinklyAI/best-skills — a daily, cross-ecosystem ranking of AI agent skills

System-1 decision models (Jev/Laya/Kev-class) as the governance layer for an LLM agent context lifecycle: growing association graph over session segments, relevance-gated recall with Trace-as-State ordering, and plan pre-ranking — measured in solve rate, cache hit/miss tokens, cost and latency. DSH plugin + harness-agnostic proxy.

Use Jev instead of an LLM for RAG decisions: relevance grading, query routing, hallucination checks and LLM-as-a-judge evals. Faster, cheaper, measured first.