Software engineer with experience building backend services, cloud infrastructure, and AI-powered applications. Over the past few years, I’ve worked across API design, distributed systems, DevOps, and AI workflows, helping take products from idea to production. I enjoy solving engineering problems end-to-end—whether that involves designing system architecture, building reliable backend services, automating infrastructure, or integrating AI where it creates real value. Interests: Backend Engineering, Platform Engineering, Cloud Infrastructure, Distributed Systems, and Applied AI.
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Excellent explanation of Metrics, Logs, and Traces, especially Understanding Traces and Spans.
Excellent breakdown of LLM observability. The distinction between metrics, logs, traces, and spans makes the topic easy to understand, while the OpenTelemetry pipeline shows how these concepts fit into a real production system. I especially liked the focus on tracing retrieval steps, tool calls, latency, and token costs rather than monitoring only infrastructure.
Ratish Jain
Sarah Pan
This was a really useful breakdown. The metrics / logs / traces distinction also made the topic much easier to understand. I’m curious whether you think LLM observability will eventually become a default layer in AI products, the same way application monitoring is now.