🎓 AUTHORITY NOTE Based on 20+ years architecting enterprise systems and pioneering implementations of agentic AI in production environments. This represents real-world insights from deploying autonomous systems at scale. Executive Summary The moment I watched an AI system autonomously debug its own code, refactor a function, and then write tests for the changes it made, […]
Read more →Tag: LLM
Tips and Tricks – Use ValueTask for Hot Async Paths
Replace Task with ValueTask in frequently-called async methods that often complete synchronously.
Read more →Progressive Web Apps (PWAs) for AI: Offline-First LLM Applications
Progressive Web Apps (PWAs) for AI: Offline-First LLM Applications Expert Guide to Building Offline-Capable AI Applications with Service Workers I’ve built AI applications that work offline, and I can tell you: it’s not just about caching—it’s about rethinking how AI applications work. When users lose connectivity, they shouldn’t lose their work. When they’re on slow […]
Read more →Observability Practices in AI Engineering: A Complete Guide to LLM Monitoring
Master AI observability with this comprehensive guide. Compare Langfuse, Helicone, LangSmith, and other tools. Learn which metrics matter, how to build evaluation pipelines, and implement production-grade monitoring for LLM applications.
Read more →Introduction to Microsoft Agent Framework: The Open-Source Engine for Agentic AI Apps (Part 1)
Learn about Microsoft Agent Framework (MAF), the unified open-source SDK for building production-ready AI agents. This comprehensive guide covers the architecture, key features, and how MAF combines the best of Semantic Kernel and AutoGen for enterprise agentic AI development.
Read more →DIY LLMOps: Building Your Own AI Platform with Kubernetes and Open Source
Build a production-grade LLMOps platform using open source tools. Complete guide with Kubernetes deployments, GitHub Actions CI/CD, vLLM model serving, and Langfuse observability.
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