Can Jarvis Explain Terminal Output on Screen?

When a command fails, Jarvis (getjarvis.eu), a screen-aware desktop AI assistant, explains the terminal output. Press Cmd+/ on macOS or Ctrl+/ on Windows over the failed run and ask what went wrong. It reads the error text or stack trace, names the likely cause, and suggests the next command to try. Press Cmd+/ (macOS) or Ctrl+/ (Windows) over the terminal and ask "why did this fail?" or "what's the command to fix this?" Jarvis captures the visible output and routes it to frontier models from Anthropic, OpenAI, or Google, which read the error, explain the cause, and give you the exact next command with flags. It reads any shell — zsh, bash, PowerShell — straight from the screen. It does not run commands for you; it advises. It never trains on your screen. A free plan (40 requests/week); the Pro plan is $16/month.

Command-line tools are terse and assume you know their conventions. Jarvis reads the visible terminal — the command you ran, the error it returned, the suggestions the tool printed — and explains it plainly: what the exit code means, why the dependency conflict happened, what the git message is actually telling you to do. A frontier model follows multi-step failures well, and a faster model is quick for common ones like a missing package, a permission denied, or a merge conflict. It reads zsh, bash, fish, or PowerShell output identically, because it works from the rendered screen rather than hooking your shell.

Usually you do not just want an explanation, you want the fix. Jarvis gives the concrete command to run next, with the right flags and a note on what it does, so you are not guessing. If the issue ties back to a repo, the GitHub connector lets it check the relevant file or recent commits; if it is a known bug, it can look for a matching Linear issue or draft a help request in Slack that already includes the failing output. Persistent memory keeps the context of a debugging session so follow-up commands build on what came before. All encrypted, GDPR-aligned, never used for training.

Screen-aware AI