The EU Cross-Border Healthcare Challenge EU eHealth Digital Service Infrastructure (eHDSI) IPS FHIR Bundle Structure Generating IPS in .NET Ireland’s Participation in eHDSI EU Member State Participation Standards and References Related Articles in This Series Conclusion
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Cloud Spanner Deep Dive: Building Globally Distributed Databases That Never Go Down
Introduction: Cloud Spanner represents a breakthrough in database technology—the world’s first horizontally scalable, strongly consistent relational database that spans continents while maintaining ACID transactions. This comprehensive guide explores Spanner’s enterprise capabilities, from its TrueTime-based consistency model to multi-region configurations and automatic sharding. After architecting globally distributed systems across multiple database technologies, I’ve found Spanner uniquely […]
Read more →AKS Workload Identity
AKS workload identity is a feature of Azure Kubernetes Service (AKS) that enables you to use Azure Active Directory (AAD) to manage access to Azure resources from within a Kubernetes cluster. In this blog post, we’ll explore how AKS workload identity works and how to use it with an example code. How does AKS workload […]
Read more →LlamaIndex: The Data Framework for Building Production RAG Applications
Introduction: LlamaIndex (formerly GPT Index) is the leading data framework for building LLM applications over your private data. While LangChain focuses on chains and agents, LlamaIndex specializes in data ingestion, indexing, and retrieval—the core components of Retrieval Augmented Generation (RAG). With over 160 data connectors through LlamaHub, sophisticated indexing strategies, and production-ready query engines, LlamaIndex […]
Read more →Function Calling Deep Dive: Building LLM-Powered Tools and Agents
Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just describing what to do, the model can actually do it—query databases, call APIs, execute code, and interact with external systems. OpenAI’s function calling (now called “tools”) and similar features from Anthropic and others let you define available functions, and the model […]
Read more →Quantization Methods for LLMs: GPTQ, AWQ, and BitsAndBytes
Last year, I needed to run a 13B parameter model on a 16GB GPU. Full precision required 52GB. After testing GPTQ, AWQ, and BitsAndBytes, I reduced memory to 7GB with minimal accuracy loss. After quantizing 30+ models, I’ve learned which method works best for each scenario. Here’s the complete guide to LLM quantization. Figure 1: […]
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