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jev-chat-jarvis (macOS)

A macOS floating panel that reads visible WeChat messages through OCR, has a local decider model or TypeSafe Jev classify intent and risk from 0 to 9, then generates and reranks reply candidates without injecting anything into WeChat.
Stars
498
Language
Python
License
MIT
Reading time
3 min
Published by
Community
Author
jev-chat
Last verified
Sep 2026

Highlights

  • Eight intents are recognized zero-shot at 86.4% on a 22-message Chinese regression set, with a 0-9 risk level and an action suggestion.
  • Ten built-in reply tones generate two candidates each in parallel, then a local model reranks them in place; sending stays manual in WeChat.
  • On an M1 Pro it reports about 1.5 s for intent and 1.5-2 s for candidates; OCR boxes can be toggled with JEV_BOXES=1.
  • The judgment layer can use TypeSafe Jev or a local decider-2b (about 7 GB); generation accepts OpenAI- or Anthropic-compatible endpoints.
  • Read-only by design: it grabs the WeChat window in-process for Vision OCR of the chat area, never hooks, injects, decrypts, or sends messages.

Quickstart

bash
mkdir -p ~/.config/jev-jarvis
cat > ~/.config/jev-jarvis/env <<'ENV'
export TYPESAFE_API_KEY="YOUR_API_KEY"
export OPENAI_API_KEY="YOUR_API_KEY"
export OPENAI_BASE_URL="https://api.deepseek.com"
export OPENAI_MODEL="deepseek-chat"
ENV
chmod 600 ~/.config/jev-jarvis/env

Watch out

MIT-licensed, macOS only, and prebuilt releases are not notarized. Needs screen recording and accessibility permissions, plus a judgment or generation key unless you use the local decider and bundled shared key.

Questions about this entry

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Reactions & coverage

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X: Copy and paste, with a decision in between

X: A keystroke oracle that reads intent in ~100 ms

X: Voice-controlled computer use on a Mac

X: A Downloads folder that sorts itself

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