The debate between monolithic and microservices architectures has evolved significantly over the past decade. What was once a straightforward “microservices are better” narrative has matured into a nuanced understanding that the right architecture depends entirely on context. After leading architecture decisions across dozens of enterprise systems, I’ve learned that the most expensive mistakes come not […]
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LLM Application Monitoring: Metrics, Tracing, and Alerting for Production AI Systems
Introduction: LLM applications fail in ways traditional software doesn’t. A model might return syntactically correct but factually wrong responses. Latency can spike unpredictably. Costs can explode without warning. Token usage varies wildly based on input. Traditional APM tools miss these LLM-specific failure modes. This guide covers comprehensive monitoring for LLM applications: tracking latency, tokens, and […]
Read more →Mastering Google Cloud Dataflow: Building Unified Batch and Streaming Pipelines at Scale
Introduction: Google Cloud Dataflow provides a fully managed, serverless data processing service built on Apache Beam that unifies batch and streaming pipelines. This comprehensive guide explores Dataflow’s enterprise capabilities, from pipeline design patterns and windowing strategies to autoscaling, cost optimization, and production monitoring. After building data pipelines processing terabytes daily across multiple cloud providers, I’ve […]
Read more →The Frontend Renaissance: Why 2025 Marks a Turning Point for Web Development
Something remarkable is happening in frontend development. After years of framework fatigue and build tool complexity, we’re witnessing a genuine renaissance—a convergence of mature tooling, refined patterns, and developer experience improvements that’s fundamentally changing how we build web applications. Having spent over two decades watching frontend evolution from table-based layouts to the current ecosystem, I […]
Read more →Streaming Response Patterns: Building Responsive LLM Applications
Introduction: Waiting for complete LLM responses creates poor user experiences. Users stare at loading spinners while models generate hundreds of tokens. Streaming delivers tokens as they’re generated, showing users immediate progress and reducing perceived latency dramatically. But streaming introduces complexity: you need to handle partial responses, buffer tokens for processing, manage connection failures mid-stream, and […]
Read more →LLM Monitoring and Observability: Metrics, Traces, and Alerts
Introduction: LLM applications are notoriously difficult to debug. Unlike traditional software where errors are obvious, LLM issues manifest as subtle quality degradation, unexpected costs, or slow responses. Proper observability is essential for production LLM systems. This guide covers monitoring strategies: tracking latency, tokens, and costs; implementing distributed tracing for complex chains; structured logging for debugging; […]
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