Building
GTOps
AI-powered operations assistant for faster incident resolution
Desktop application that integrates with operational tools to help engineers troubleshoot production issues using AI-powered analysis and automated tool calling.
Role
Solo Engineer & Product Lead
Technology
RustNode.jsClaude SonnetVS Code lm-bridgeDataDog APIAWS CLITerraform EnterpriseHarness CLI
Key Metrics
Development Time
4 weeks from concept to demo
Memory Usage
80% reduction vs embedded model
Response Time
50% faster than embedded gguf
GTOps emerged from observing how Claude's CLI integration capabilities could dramatically accelerate operational troubleshooting. The core insight was simple: if an AI model could read deployment status and logs faster than humans, why not give it direct access to all operational tools to reduce Mean Time To Resolution (MTTR)?
The desktop application serves as an intelligent operations assistant, automatically ingesting alerts from monitoring systems and using AI to systematically troubleshoot issues through integrated CLI tools. Rather than engineers manually correlating alerts across multiple systems, GTOps orchestrates this process automatically, providing both immediate analysis and step-by-step resolution guidance.
What makes GTOps unique is its **tool-calling architecture** that translates AI requests into actual bash commands, giving the model real operational capabilities rather than just providing advice. The system bridges the knowledge gap highlighted during major outages like the recent us-east-1 incident, where teams struggled to troubleshoot infrastructure they didn't originally build.