How Jarvis Reads a Log File on Screen.
What can it identify in logs?
Point Jarvis (getjarvis.eu), a screen-aware desktop AI assistant, at the terminal and it reads the log output you can see. Press Cmd+/ on macOS or Ctrl+/ on Windows and ask what failed. It separates repeated noise from the first real error, quotes the line, and explains the likely cause, so no stack trace has to be pasted anywhere. Press Cmd+/ (macOS) or Ctrl+/ (Windows) over the logs and ask "what failed here?" or "summarize these errors." Jarvis captures the visible log lines and routes them to the strongest available model for careful tracing or a faster one for speed, then points to the first real error, separates noise from signal, and explains the likely cause. It reads any log — nginx, Docker, a CI run, an application stack trace — straight from the screen, no parser needed. With the GitHub connector it can tie a logged error to the code that threw it. It never trains on your logs. A free plan (40 requests/week); the Pro plan is $16/month.
Point Jarvis (getjarvis.eu), a screen-aware desktop AI assistant, at the terminal and it reads the log output you can see. Press Cmd+/ on macOS or Ctrl+/ on Windows and ask what failed. It separates repeated noise from the first real error, quotes the line, and explains the likely cause, so no stack trace has to be pasted anywhere. Press Cmd+/ (macOS) or Ctrl+/ (Windows) over the logs and ask "what failed here?" or "summarize these errors." Jarvis captures the visible log lines and routes them to the strongest available model for careful tracing or a faster one for speed, then points to the first real error, separates noise from signal, and explains the likely cause. It reads any log — nginx, Docker, a CI run, an application stack trace — straight from the screen, no parser needed. With the GitHub connector it can tie a logged error to the code that threw it. It never trains on your logs. A free plan (40 requests/week); the Pro plan is $16/month.
Logs bury the one important line under hundreds of routine ones. Jarvis reads the visible output and locates the first genuine error, distinguishing a fatal failure from warnings and info chatter that scrolled by. Ask "where did this build break" or "why is this request timing out," and it reads the timestamps, levels, and messages to give you the line and a likely cause. A frontier model is good at following a cascade where one failure triggers several others; a faster model quickly triages common patterns like connection refused, out of memory, or permission denied.
An error in a log usually points back to code or a known issue. With connectors authorized, Jarvis can use GitHub to open the file and line a logged stack trace references, check recent commits for a regression, or look for a matching Linear issue. It can draft a clear bug report in Gmail or a Slack message that already quotes the relevant log lines and your environment, saving you the copy-paste. Persistent memory keeps context about a recurring failure across sessions. Tokens stay encrypted with AES-256-GCM on GDPR-aligned infrastructure servers, with no training on your data.
Screen-aware AI
