How should AI assist during incidents without taking control?
We’re building UniDeploy to assist DevOps teams during production incidents using AI, but we’re intentionally avoiding fully autonomous actions. For those managing production systems: - Where does AI help the most during an incident? - What actions should always require human approval? - What signals (metrics, changes, logs) matter most when deciding a fix? We’re especially interested in...
We’re experimenting with AI-assisted DevOps incident recovery — would you trust this in production?
We’re building Unideploy, a DevOps automation platform that integrates directly with Claude / ChatGPT via MCP — no separate UI, no new dashboards. The idea we’re exploring now is AI-assisted incident recovery: Instead of jumping between CloudWatch, kubectl, CI/CD logs, and Slack during an incident, you ask the AI: “Production API latency is high. What changed and what’s the safest way to fix...
Questions & Insights About Using Atlas – Share Your Experience
This thread is for open discussion around Atlas. If you’ve tried the app, share your thoughts, feedback, or questions here. Some ideas to get started: What feature of Atlas do you find the most useful? Did you face any challenges while uploading or processing files? What improvements or new features would make Atlas more valuable for you? Any insights on performance, ease of use, or areas where...