Code, Cloud & AI

In this episode, I sat down with Heena Refai, a Cloud Solution Architect at Microsoft, to talk about how enterprise AI has evolved from the big-data and traditional machine learning era into today’s generative AI and agentic systems landscape. Heena reflected on her background in data engineering and machine learning, why strong data foundations still matter even in the age of large pretrained models, and why so many organizations are racing into AI without fully addressing the quality, governance, and structure of the data underneath it.We then dug deep into Microsoft Foundry: what it is, how the model catalog works, how models become agents through prompts, tools, and enterprise data, and how teams can build, host, evaluate, and govern AI systems at scale. Hina explained Foundry Agent Service, project-based organization, cost controls, Azure API Management as an AI gateway, and the growing need for centralized governance with Microsoft Agent 365. We also touched on developer productivity with GitHub Copilot and why, despite all the advances in AI, fundamentals still matter more than ever.Heena Refai  on LinkedIn:  Heena Refai | LinkedIn Microsoft Foundry: https://learn.microsoft.com/en-us/azure/foundry/Microsoft Foundry Agent Service: https://learn.microsoft.com/en-us/azure/foundry/agents/overviewMicrosoft Agent Framework: https://learn.microsoft.com/en-us/agent-framework/Microsoft Agent 365: https://learn.microsoft.com/en-us/office365/servicedescriptions/microsoft-agent-365/microsoft-agent-365Azure API Management: https://azure.microsoft.com/en-us/products/api-managementAzure Pricing Calculator: https://azure.microsoft.com/en-us/pricing/calculator/Azure Container Apps: https://learn.microsoft.com/en-us/azure/container-apps/overviewAzure AI Search: https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-searchAzure Cosmos DB: https://learn.microsoft.com/en-us/cosmos-db/Azure OpenAI Service: https://azure.microsoft.com/en-us/pricing/details/azure-openai/Anthropic: https://www.anthropic.com/companyClaude models in Microsoft Foundry: https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/claude-modelsClaude Fable 5 in Microsoft Foundry Models: https://ai.azure.com/catalog/models/claude-fable-5Hugging Face: https://huggingface.co/IBM: https://www.ibm.com/us-enMongoDB: https://www.mongodb.com/productsDatabricks: https://www.databricks.com/SAP: https://www.sap.com/ServiceNow: https://www.servicenow.com/Microsoft Teams: https://www.microsoft.com/en-us/microsoft-teams/teams-productsSlack: https://slack.com/GitHub Copilot: https://docs.github.com/en/copilot/get-startedVisual Studio Code: https://code.visualstudio.com/AWS: https://aws.amazon.com/what-is-aws/Kubernetes: https://kubernetes.io/

In this episode, I sat down with Rob Bos to talk about what’s happening across GitHub, Azure DevOps, GitHub Advanced Security, and the fast-moving world of GitHub Copilot. Rob shared how his work evolved from DevOps consulting and CI/CD into GitHub-focused training, governance, supply-chain security, and Copilot enablement. We talked about why so many teams are moving source control to GitHub first, how GitHub is pulling ahead on new features, and why products like Advanced Security, Dependabot, code scanning, and Copilot are becoming central to modern software delivery.We also went deep on what it actually takes to use AI coding tools well. Rob made the case that handing someone a Copilot license is nowhere near enough: teams need training, realistic expectations, strong specs, good test harnesses, and better feedback loops. We discussed usage-based billing, token costs, environmental impact, AI Engineering Fluency, and why the future of engineering may belong to teams that combine business context, solid controls, and AI-native workflows instead of treating LLMs like magic.Rob Bos on GitHub: https://github.com/rajbosDevOps Journal (Rob Bos blog): https://devopsjournal.io/Xebia: https://xebia.com/Xebia Tech Hub: https://tech.xebia.ms/GitHub: https://github.com/GitHub Copilot: https://github.com/features/copilotGitHub Actions: https://github.com/features/actionsGitHub Advanced Security: https://github.com/security/advanced-securityDependabot: https://github.com/dependabotAzure DevOps: https://azure.microsoft.com/en-us/products/devopsVisual Studio Code: https://code.visualstudio.com/Visual Studio: https://visualstudio.microsoft.com/JetBrains: https://www.jetbrains.com/AI Engineering Fluency for VS Code (Visual Studio Marketplace): https://marketplace.visualstudio.com/items?itemName=RobBos.copilot-token-trackerAI Engineering Fluency for Visual Studio (Visual Studio Marketplace): https://marketplace.visualstudio.com/items?itemName=RobBos.AIEngineeringFluencyGitHub Advanced Security (GHAS) course by Rob Bos on LinkedIn Learning: https://www.linkedin.com/learning/github-advanced-security-ghasLearning GitHub Advanced Security for Azure DevOps by Rob Bos on LinkedIn Learning: https://www.linkedin.com/learning/learning-github-advanced-security-for-azure-devopsResponsible GitHub Copilot: Creating Reliable Code Ethically by Rob Bos on LinkedIn Learning: https://www.linkedin.com/learning/responsible-github-copilot-creating-reliable-code-ethically-24981582GitHub Actions in Action: https://www.manning.com/books/github-actions-in-actionThe GitHub Copilot Handbook: https://www.packtpub.com/en-us/product/the-github-copilot-handbook-9781806116638

In this episode, I sat down with Anton Sizikov from GitHub to talk about how AI coding tools have evolved from “smart autocomplete” into full-blown agentic workflows. We unpacked the early days of GitHub Copilot, when the biggest customer concerns were privacy, legal implications, and whether the tool could be trusted at all, and compared that with today’s reality of agents, context engineering, cloud-based workflows, and outcome-driven development. Anton shared how his own usage has changed over time -from manually reviewing every suggestion to orchestrating multiple agents in parallel - and why experienced users often get dramatically more value from these tools because they understand how to shape context, control cost, and build the right guardrails around them.We also dug into the harder questions engineering teams are wrestling with now: how to measure ROI, how usage-based billing changes developer behavior, and why faster code generation doesn’t automatically mean better business outcomes. Anton explained why there is no universal playbook yet for rolling AI out at scale, why teams need to think more like product owners now that building is cheaper and faster, and why engineers who are still on the fence should start experimenting sooner rather than later. It was a thoughtful conversation about where AI-assisted software development is already delivering value, where it still creates friction, and what engineers need to learn to stay effective as these tools keep changing.Anton Sizikov on LinkedIn: https://nl.linkedin.com/in/sizikovAnton's Blog: https://blog.cloud-eng.nl/ GitHub Copilot: https://github.com/features/copilotGitHub Models: https://docs.github.com/en/github-models/about-github-modelsVisual Studio Code: https://code.visualstudio.com/

In this episode, I sat down with Jon Galloway to talk about what agentic development actually looks like in practice inside Microsoft and out in the real world. We explored how AI-assisted software development is changing fast, why prompt-only workflows are maturing into more disciplined engineering practices, and why the best results come from balancing LLMs with deterministic code, scripts, tests, and automation. A big theme throughout our conversation was that AI is pushing teams to get better at the fundamentals many of us have always known we should do more consistently: clearer specs, better documentation, stronger unit tests, better reviews, and more intentional architecture.We also got into practical tooling, including GitHub Copilot, Copilot CLI, VS Code, Visual Studio, Squad-style multi-agent workflows, MCP servers, and where these tools fit for developers versus non-technical users. We zoomed out to compare today’s AI shift with earlier platform waves like cloud and mobile, and closed with grounded advice for developers, enterprises, and everyday users: start small, make it fun, work iteratively, and treat AI like a collaborator that still needs direction, guardrails, and quality checks.Jon Galloway: Jon Galloway | LinkedIn.NET: https://learn.microsoft.com/en-us/dotnet/.NET Rocks!: https://www.dotnetrocks.com/Microsoft: https://www.microsoft.com/James Montemagno: https://montemagno.com/GitHub Copilot: https://github.com/features/copilotGitHub Copilot CLI: https://github.com/features/copilot/cliVisual Studio Code: https://code.visualstudio.com/Visual Studio: https://visualstudio.microsoft.com/Squad: https://github.com/bradygaster/squadBrady Gaster: https://developer.microsoft.com/en-us/blog/author/bradyg/GitHub Actions: https://github.com/features/actionsModel Context Protocol (MCP): https://modelcontextprotocol.io/Microsoft Copilot: https://copilot.microsoft.com/Microsoft 365 Copilot: https://www.microsoft.com/en-us/microsoft-365-copilotMicrosoft Foundry: https://azure.microsoft.com/en-us/products/ai-foundry/Microsoft Agent Framework: https://learn.microsoft.com/en-us/agent-framework/overview/SharePoint: https://support.microsoft.com/en-us/sharepoint/platform/what-is-sharepointChatGPT: https://chatgpt.com/PowerToys: https://github.com/microsoft/PowerToysGodot Engine: https://godotengine.org/Meta Quest 3: https://www.meta.com/quest/quest-3/Raspberry Pi: https://www.raspberrypi.com/Google Gemini: https://gemini.google.com/

In this episode, I sat down with Pascal van der Heiden to talk about how fast AI-assisted development is changing the day-to-day reality of building software. We got into his current workflow across Azure, GitHub Copilot, Visual Studio Code, the terminal, MCP servers, and Microsoft Foundry, and explored why he has shifted so much of his work into a CLI-first, agent-assisted setup. We also unpacked spec-driven development, including where it helps teams move from one-shot prompting toward something more structured, reviewable, and production-ready.We also talked through the practical side of enterprise AI adoption: how customers outside the “AI bubble” are perceiving these tools, where Foundry fits versus personal productivity tools like Microsoft 365 Copilot, how to think about guardrails and monitoring for agents, and what developers should do as usage-based billing becomes more common. Along the way, we covered model choice, testing with Playwright, token efficiency, and why human review still matters. And yes, we also opened with an unexpectedly memorable wellness tip: apparently, cuddling a chicken can lower your stress level.Pascal van der Heiden:  Pascal van der Heiden | LinkedIn Microsoft: https://www.microsoft.com/en-us/Azure Functions: https://azure.microsoft.com/en-us/products/functions/Azure API Management: https://azure.microsoft.com/en-us/products/api-managementAzure Kubernetes Service (AKS): https://azure.microsoft.com/en-us/products/kubernetes-service/Microsoft Foundry: https://ai.azure.com/?view=foundryMicrosoft 365 Copilot: https://www.microsoft.com/en-us/microsoft-365-copilotGitHub: https://github.com/GitHub Copilot: https://github.com/features/copilotGitHub Copilot CLI: https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-getting-startedGitHub Copilot SDK: https://docs.github.com/en/copilot/how-tos/copilot-sdk/getting-startedModel Context Protocol (MCP) for GitHub Copilot: https://docs.github.com/en/copilot/concepts/context/mcpVisual Studio Code: https://code.visualstudio.com/Spec Kit: https://github.github.io/spec-kit/OpenSpec: https://github.com/Fission-AI/OpenSpecSuperpowers: https://github.com/obra/superpowersPlaywright: https://playwright.dev/AI Engineering Fluency (Rob Bos): https://open-vsx.org/extension/RobBos/copilot-token-trackerRob Bos: https://devopsjournal.io/OpenAI: https://openai.com/Gemini: https://deepmind.google/models/gemini/