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The Android assistant reads visible conversations through accessibility and local OCR, displaying analysis and reply suggestions in an overlay. Jev judges intent and response choices; a generative model drafts three candidates and Jev ranks them.
The pipeline develops a learning brief, mathematics, visual plan and Manim scene, then reviews the rendered result. Jev evaluates candidate artifacts and Astra's text audits through typed Score, Noul and Choice answers; it does not receive the images.
The bot replaces post-only limit quotes on Monad blocks and streams book, order and fill events to a dashboard. With MODEL=jev and TYPESAFE_AI_API_KEY, Jev chooses buy or sell; the default mock model is a momentum heuristic.
PyGPT is a multi-provider desktop assistant for Linux, Windows and macOS, with chat, agents, RAG and tool integrations. The Jev plugin exposes jev_evaluate(state, questions), returning Choice, Score or Noul results to the current model.
ollaya downloads named open decision models and serves typed questions through a local daemon. It implements TypeSafe-compatible endpoints for existing Jev clients, while remaining an independent project. Use its CLI, desktop app or MCP server; Modelfiles can package reusable question sets.
Shapeshift turns typed text into event cards, checklists, timers, bill splits and other task-specific interfaces. Optional Jev classifies intent and signals; deterministic code handles dates and arithmetic. Offline keyword classification is the default. Type into the Bun-powered app, choose a card and save it; a server-side TypeSafe key enables Jev.
JevChat-Windows reads WeChat and KakaoTalk conversations with local OCR and offers candidate replies beside the chat window. Jev judges intent and tension and ranks three candidates; a separate language model drafts them. Configure a decision-provider key and a drafting-provider key on Windows 10 1903+ or Windows 11.
Simple Jev uses compatible Hugging Face models to classify and score inputs, constructing JSON answers from next-token logits instead of generating a completion. Send shared context as either state or messages, plus typed questions, to POST /v1/classifier on the configured server.
OpenJev reads typed answers from open-model probabilities and serves choices, scores and yes/no judgments, including image questions for its main model. Point the TypeSafe SDK’s base URL at your server or the documented hosted endpoint, then call system_one with state and questions.
Jev Search turns plain-language requests into web links and snippets with editable filters and visible relevance scores. For local use, configure Search1API and a Jev provider in .dev.vars, install dependencies with pnpm, and run pnpm dev.
The library identifies IRS forms and page kinds from extracted text using a JSON registry of form descriptions. Use classifyPage with text lines, jevBackend and criteria.json. PDF helpers additionally require Poppler; the backend needs a TypeSafe key.
DocJev classifies PDF, DOCX and PPTX documents or splits packets into categorized page ranges, with optional PDF exports. Install the Python package with uv, export TYPESAFE_API_KEY, and run docjev classify or split with category rules. Office formats require LibreOffice.
The floating chat panel displays intent and risk judgments and candidate replies that users can copy or insert into the chat input. Use an Apple Silicon Mac with macOS 13 or newer. Grant Screen Recording for screenshot-based reading and Accessibility for text access or insertion, then choose judgment and generation providers in settings.
classifier.dev exposes HTTP and CLI text classification, including batches and multiple label dimensions. Pass labels and text in a URL, install the classify CLI, or POST items and dimensions to /v1/classify.
AI Stock combines market data, news and AI analysis for stock research, screening and simulated trading. Optional JEV Decisions only returns buy/sell/hold, probabilities and confidence; research chat and stock selection still need an LLM. Configure models and data sources, select a stock and review the saved report’s evidence, dates and risks.
The tool extracts and indexes document content in Qdrant, plans an outline and researches each section against the local collection. Jev judges and reranks retrieved chunks and scores missing coverage; a separate LLM plans, queries and writes while Python controls retries.
This experimental CLI develops binary, multiclass, multilabel and scoring functions through repeated evaluation, labeling and definition proposals. Jev evaluates the data through TypeSafe, Vercel or Cloudflare; GEPA uses a separately configured reflection model.
jeff serves a Jev-compatible System One API using GLiFormer weights for Choice, Score and Noul decisions. Use Python 3.12 and uv, download the checkpoint, set JEFF_API_KEYS and point the client at localhost:8000.
Scala client for OpenAI API and other major LLM providers
One CLI for all your robots. Connect them, command them, and let them work together, each with an LLM for a brain (Claude, OpenAI, Gemini, Grok, or local via Ollama or vLLM) and a decision LLM for multiple choice (Jev, Laya, Kev). Drives Microduck, Open Duck Mini, LeRobot, XLeRobot, AlohaMini, ToddlerBot, or any ROS 2 base from a laptop, offboard.
Use LLMs to find the needles in your haystack
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Structured outputs, vectorization and tool calling for your Ash application