Engineering blog
13 deep-dives on Azure, Intune, AI Automation & Cloud Engineering.
Microsoft Copilot is everywhere in the Microsoft 365 ecosystem now. But as an IT admin, you need to know what it genuinely helps with, where it falls short, and how to start using it practically — without the marketing hype.
Every Monday morning our team needed a full compliance report from Intune. I got tired of doing it manually, so I automated the whole thing with PowerShell and Microsoft Graph API. Here is exactly how I did it.
Microsoft Intune is the backbone of modern endpoint management. This guide explains how it works, how devices get enrolled, how compliance is enforced, and how it connects to Azure AD — with real-world examples from production environments.
Stop writing AI prompts from scratch every time. These 10 ready-to-use prompts cover the most common IT admin tasks — from writing PowerShell scripts and troubleshooting Azure errors to drafting incident reports and explaining policies to non-technical stakeholders.
Three major AI tools, one IT admin, and months of real testing. This is an honest comparison of ChatGPT, Google Gemini, and Microsoft Copilot for the tasks IT professionals actually do — scripting, troubleshooting, documentation, and more.
Most people ask ChatGPT to write PowerShell the wrong way and get generic, untested code. This guide shows the exact prompt structure I use to get production-quality scripts first time — including the four elements that make the difference.
A system prompt is the single biggest upgrade you can make to your AI setup. It tells the AI who it is, how to behave, and what to focus on — before you even ask a question. Here is how to write one that transforms your results.
AI makes systems smart, LLM understands language, and Generative AI creates content. Together, they are transforming how we work and build smarter solutions.
A practical guide to configuring S3 lifecycle rules that automatically transition objects between storage classes, with real cost analysis and Terraform examples.
Move beyond toy demos to production-ready LLM integration: structured outputs, few-shot prompting, prompt chaining, cost control, and evaluation frameworks that actually work.
Deploy a production-ready Azure Kubernetes Service cluster with autoscaling, managed identity, private endpoints, and proper observability — lessons from running AKS at scale.
Six TypeScript patterns that solve real production problems: branded types, discriminated unions, builder patterns, and more — with the reasoning behind each one.
Build a reusable GitHub Actions workflow system with matrix builds, caching strategies, environment approvals, and security scanning — without copy-pasting YAML into every repo.
