Token efficiency as the new FinOps metric for GitHub and Copilot agent workflows
Token efficiency as the new FinOps metric for GitHub and Copilot agent workflows
AI/ML
Artificial intelligence and machine learning
Cosmos DB Shell and the future of AI-assisted database operations
Cosmos DB Shell and the future of AI-assisted database operations
Why AI-era operating models require better data product ownership and lineage
Why AI-era operating models require better data product ownership and lineage
Three full-stack data platforms in a weekend: what Fabric and Azure AI Foundry enable for rapid delivery
Three full-stack data platforms in a weekend: what Fabric and Azure AI Foundry enable for rapid delivery
From experimentation to operations: what weekend-built AI data platforms teach us about production readiness
From experimentation to operations: what weekend-built AI data platforms teach us about production readiness
From Data Movement to Decision Velocity: Why Faster Python Reads Matter for Copilot and AI Analytics
From Data Movement to Decision Velocity: Why Faster Python Reads Matter for Copilot and AI Analytics
Building Copilot-style experiences in Python with the Microsoft Teams SDK
Building Copilot-style experiences in Python with the Microsoft Teams SDK
How Microsoft Agent 365 changes enterprise AI governance
How Microsoft Agent 365 changes enterprise AI governance
Why Most Enterprise AI Projects Stop at the Demo
Why Most Enterprise AI Projects Stop at the Demo
Build an Enterprise ready 2nd Brain on Azure Foundry + Cosmos DB
Build an Enterprise ready 2nd Brain on Azure Foundry + Cosmos DB
Azure
Automating Document Processing with Azure Document Intelligence
Processing hundreds of multi-page PDF forms manually is exactly the kind of repetitive work that AI should handle. I built an automated document processing pipeline on Azure that splits PDFs, extracts structured data using AI, and stores results — all triggered by a simple file upload. The Problem Imagine receiving a
AI/ML
Building a Local LLM Research Agent: Privacy-First AI with SQL, RAG, and MCP
What if you could query databases, search documents, and run multi-tool AI workflows — all without a single byte leaving your machine? That's exactly what I built with the Local LLM Universal Research Agent. The Problem Most AI agent frameworks assume cloud APIs. That's fine for many