If you’ve been writing C# for more than a decade, you’ve witnessed something remarkable: the language you learned in the early 2000s bears only a superficial resemblance to what we write today. Modern C# development feels like a different language entirely. C# Syntax Evolution: 2002 vs 2025 The Transformation Journey When .NET Framework first appeared, […]
Read more →Month: June 2025
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; […]
Read more →DevSecOps: Integrating Security into DevOps – Part 7
Continuing from my previous blog, let’s explore some more advanced topics related to DevSecOps implementation. Automated Vulnerability Management Automated vulnerability management is a key practice in DevSecOps. It involves using automated tools to identify, prioritize, and remediate vulnerabilities in an organization’s systems and applications. Automated vulnerability management includes the following activities: Shift-Left Testing Shift-left testing […]
Read more →LLM Security Best Practices: Protecting AI Applications from Attacks
Introduction: LLM applications face unique security challenges. Prompt injection attacks can hijack model behavior, sensitive data can leak through responses, and malicious outputs can harm users. Traditional security measures don’t fully address these risks—you need LLM-specific defenses. This guide covers practical security strategies: validating and sanitizing inputs, detecting prompt injection attempts, filtering sensitive information from […]
Read more →Building the Modern Data Stack: How Spark, Kafka, and dbt Transformed Data Engineering
The data engineering landscape has undergone a fundamental transformation over the past decade. What once required massive Hadoop clusters has evolved into a sophisticated ecosystem of specialized tools: Kafka for ingestion, Spark for processing, and dbt for transformation. Modern Data Stack Architecture The Paradigm Shift: Monolithic → Modular The old approach centered around monolithic platforms […]
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