Deep research tool for local knowledge base.
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Updated
Sep 26, 2026 - Python
Deep research tool for local knowledge base.
Meet JevEmbed — turn embeddings into decisions. Choose, score, and judge with your choice of embedding model.
Open-source platform for creating, distributing and running sovereign EU-compliant LLMs. Verticalize any model for your domain, language and brand. AI Act ready.
When Jev meets LLM -- Pair a small local LLM (System 2) with Jev model (System 1) to make small model more faster and accurate — OpenAI-compatible, private, and almost free to run in Kilo code and Cline.
Evidence-first long-term experiment memory skill for AI coding agents, with optional Jev decision-model layers
The Awesome List of Awesome Jev Lists
Jebadiah, an open System One decision model: trainer, data builders, evals and every run record
Lev: Thinking Fast and Deep with Looped Decision Models. One checkpoint, multiple depths: System 1.5 decision models that think in loops, not tokens.
Find out whether a smaller model could handle some of your AI agent’s routine choices.
Decision-driven AI agent optimization using OpenJEV to route requests to deterministic tools or LLMs, reducing unnecessary LLM calls, token usage, latency, and cost.
Decision model on a Qwen3-0.6B backbone with LoRA: fused decision endpoint (/v1/systemone) + OpenAI-compatible chat completions with thinking, SSE streaming and vLLM-style sampling, running on Apple Silicon MPS.
decision-first data cleaning system powered by Jev
Windows port of Laya typed-decision AI (ONNX Runtime + DirectML) — Core ML/Apple Neural Engine alternative with first-class Arabic support. No text generation, no hallucination, runs on any DX12 GPU.
When Jev meets LLM — Transparent proxy that makes AI coding agents 3× faster and 80% cheaper. Cut response times from 2.4s to 890ms with Line J architecture.
AI-powered Git hook that generates concise, high-quality commit messages from staged diffs.
A production-shaped Python example of using Jev as a typed semantic decision layer inside a deterministic expense-policy workflow.
A Proxy that enables zero-shot system one typed-decisions endpoint for decoder only models served with vLLM or Sglang or Openrouter (Experimental)
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