Can AI Read My Screen to Debug a Stack Trace?

Yes. Jarvis (getjarvis.eu) is a screen-aware desktop AI assistant for macOS 12+, Windows 10+, and Linux that reads the stack trace currently visible in your terminal, IDE, or browser console and explains it in place. Press Cmd+/ (macOS) or Ctrl+/ (Windows) over the error and ask "why is this throwing?" Jarvis captures the visible exception, routes it to frontier models from Anthropic, OpenAI, or Google for reasoning, and points to the offending line, likely cause, and a fix. Because it has a GitHub connector, it can also pull the referenced file or open a PR with the patch. It reads what is on screen, so it works with any language or framework, not just ones it has plugins for. It never trains on your code. A free plan (40 requests/week) lets you test it; the Pro plan is $16/month.

When you trigger the floating bar over a visible traceback, Jarvis captures the on-screen text and image, then sends it to a reasoning model: a frontier model for deep multi-frame analysis, or a faster model for speed. It reads the exception type, the call stack top-to-bottom, and any source snippet your IDE renders inline. It distinguishes the throwing frame from library frames you do not control, names the probable root cause (null value, type mismatch, async race), and proposes a concrete edit. Unlike copy-pasting into a chatbot, you keep your IDE in focus and never lose the surrounding context the model can see.

A stack trace is only half the story without the code behind it. With the GitHub connector authorized, Jarvis can fetch the file and line referenced in the trace, read the function body, and check recent commits that may have introduced the regression. It can compare commits, read a get_commit diff, or draft a fix as a pull request. That turns "this line throws" into "this commit changed the signature and broke the caller." Everything stays inside your session; OAuth tokens are encrypted with AES-256-GCM on GDPR-aligned infrastructure servers, and your source is never used for training.

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Screen-aware AI