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@@ -117,7 +117,7 @@ Open weights and code that rebuild the System One shape from encoders, small dec
 
 Open, licensed software that puts Jev inside something that runs: routers, agents, games and integrations.
 
-- [Jev Chat Assistant](https://github.com/jev-chat/jev-chat-jarvis), Android chat overlay where Jev judges intent and ranks replies while a separate LLM drafts them; the author reports roughly one-second judgments and device-tested WeChat, QQ, X and Lark adapters. [![Code](https://img.shields.io/github/stars/jev-chat/jev-chat-jarvis?style=flat-square&logo=github&label=Code&color=181717)](https://github.com/jev-chat/jev-chat-jarvis)
+- [Jev Chat Assistant](https://github.com/jev-chat/jev-chat-jarvis), Android chat overlay where Jev judges intent and ranks replies while a separate LLM drafts them; the author reports device-tested QQ, X and Feishu/Lark adapters, and the app also reads WhatsApp. [![Code](https://img.shields.io/github/stars/jev-chat/jev-chat-jarvis?style=flat-square&logo=github&label=Code&color=181717)](https://github.com/jev-chat/jev-chat-jarvis)
 - [RoboJEV](https://github.com/lykycy123/RoboJEV), Two-stage Franka Panda control from structured simulator state; 43 of 50 Jev trials succeed versus 48 of 50 rule-baseline trials across five MuJoCo tasks, with success and failure recordings. [![Code](https://img.shields.io/github/stars/lykycy123/RoboJEV?style=flat-square&logo=github&label=Code&color=181717)](https://github.com/lykycy123/RoboJEV) [![Website](https://img.shields.io/badge/Website-2EA44F?style=flat-square&logo=googlechrome&logoColor=white)](https://lykycy123.github.io/RoboJEV/)
 - [jevals](https://github.com/openlayer-ai/jevals), Agent trace evaluations and tool-call gates batched into Jev requests; the authors' 20-row RAG comparison reports 244 ms request p50, while local Kev and Laya timings are estimates. [![Code](https://img.shields.io/github/stars/openlayer-ai/jevals?style=flat-square&logo=github&label=Code&color=181717)](https://github.com/openlayer-ai/jevals)
 - [jev-calibrate](https://github.com/smkrv/jev-calibrate), CLI for tuning criteria and thresholds with a ledger of holdout reuse; the authors' small support-ticket example improves frustration accuracy from 0.69 to 0.92 on tuning data and scores 0.97 on holdout. [![Code](https://img.shields.io/github/stars/smkrv/jev-calibrate?style=flat-square&logo=github&label=Code&color=181717)](https://github.com/smkrv/jev-calibrate)