jev-chat-jarvis puts judgment before drafting. Its README observes that most AI tools skip that step and go straight to writing a reply. Jev, per its README, first works out what the other person really means, how risky the moment is and the best way to respond, and only then drafts replies. The last step is also deliberate: Jev fills the box, and you press send. It never sends on its own.
The project, published as jev-chat, calls itself a chat decision assistant. It ships five apps across Android, Windows, macOS and iOS under the MIT license, and all of them follow the same three steps: read, judge, then draft, check and rank.
Judgment before drafting
The README's "Why jev-chat?" section frames the problem as reading the other person: are they upset, testing you, or just chatting? A judgment model answers that before any text is written, and every draft is then checked against the judgment and ranked so the best fit comes first. Tapping a draft fills it into the message box.
I like that ordering as a pattern. Keeping "what is going on here" apart from "what should I say" means the judgment is a separate step from drafting, checking and ranking. The README does not describe how the judgment is shown to the user in detail, but its screenshots are labeled "analysis" and "scored replies", which suggests both are visible.
How it reads your chats
Reading is where the platforms differ, and the README is specific:
- Android: through the accessibility service, as an overlay on chat apps. The global edition targets WhatsApp chats in English; the Chinese edition covers chats in Chinese on QQ, Feishu, X and WhatsApp.
- Windows: beside your chat window, reading it with screenshots and on-device OCR or system text APIs.
- macOS: an overlay that reads the chat on screen and judges with a local model, on Apple Silicon.
- iOS: a custom keyboard. You copy a message and see intent, risk and replies on the keyboard. There is no App Store build yet, so you build it with Xcode.
The README says Jev does not modify the chat app or log in to it. On Android, setup means turning on the accessibility service and the overlay permission.
The privacy model and the account risk
On Android, the README states that chat text, plus any background you turn on, goes only to the model provider you configure, with your own key. The project says it runs no server and never receives your chats, and that screenshots used for OCR never leave the phone. For the other platforms it points to each project's own README.
So on Android, your configured model provider receives your chat text. My rule would be simple: whatever you would not send to that provider's API, do not let Jev read. Setup requires enabling the accessibility service, and I would read the README's linked privacy policies before granting it.
The README also includes a plain warning: using Jev inside apps such as QQ, Feishu, X or WhatsApp may not comply with their terms of service, and your account could be restricted or banned. It leaves the decision to you. If I were considering Jev for a work account, I would show that warning to my compliance team.
Licensing terms worth knowing
The code is MIT licensed and the README says commercial use is allowed without payment or permission. It adds two conditions in its own words: keep LICENSE and NOTICE and credit the source in your product, and do not use the names "Jev Chat Assistant" or "jev-chat", or the chatjevs.com domain, to imply endorsement.
Who it fits
The status column shows a mix of preview, released and source-only apps. The global WhatsApp edition is a preview at v0.1.0, the Chinese Android edition is at v1.7, the Windows and macOS apps are released, and iOS is source only, built with Xcode. If I were trying it, I would configure a provider and key I already trust, use it on low-stakes conversations, and check whether the intent and risk read matches mine before relying on the ranking.
GitHub: https://github.com/jev-chat/jev-chat-jarvis
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