Code, Cloud & AI

In this episode, I sat down with Albert Tanure, a Senior Cloud Solution Architect at Microsoft, to talk about what AI is really changing in day-to-day engineering work—and what still matters just as much as ever. We got into the tension between speed and fundamentals: how AI can massively accelerate coding, dashboards, documentation, and learning, while also amplifying weak architecture, poor security, rising costs, and operational risk. Alberto shared how this shows up in the enterprise world, especially around Azure, resilience, disaster recovery, compliance, and the reality that AI still needs experienced humans to provide boundaries, context, and judgment.

We also explored the tradeoffs between senior and junior engineers in the AI era, why “vibe coding” can be both empowering and dangerous, and how teams are thinking about model choice, local models, hardware costs, gateways, and cloud versus on-device AI. Toward the end, the conversation turned philosophical: if AI keeps taking over more of the work, what happens to human purpose, attention, and our connection to the real world? It’s a practical conversation about architecture, tooling, and cost—but also a human one about where all of this is heading.

• Albert Tanure on LinkedIn: https://nl.linkedin.com/in/albert-tanure
• Code FC on YouTube: https://www.youtube.com/@CodeFC 
• WALL-E: https://www.pixar.com/wall-e

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/overview
• Microsoft 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-365
• Azure API Management: https://azure.microsoft.com/en-us/products/api-management
• Azure Pricing Calculator: https://azure.microsoft.com/en-us/pricing/calculator/
• Azure Container Apps: https://learn.microsoft.com/en-us/azure/container-apps/overview
• Azure AI Search: https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search
• Azure 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/company
• Claude models in Microsoft Foundry: https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/claude-models
• Claude Fable 5 in Microsoft Foundry Models: https://ai.azure.com/catalog/models/claude-fable-5
• Hugging Face: https://huggingface.co/
• IBM: https://www.ibm.com/us-en
• MongoDB: https://www.mongodb.com/products
• Databricks: 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-products
• Slack: https://slack.com/
• GitHub Copilot: https://docs.github.com/en/copilot/get-started
• Visual 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/rajbos
• DevOps 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/copilot
• GitHub Actions: https://github.com/features/actions
• GitHub Advanced Security: https://github.com/security/advanced-security
• Dependabot: https://github.com/dependabot
• Azure DevOps: https://azure.microsoft.com/en-us/products/devops
• Visual 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-tracker
• AI Engineering Fluency for Visual Studio (Visual Studio Marketplace): https://marketplace.visualstudio.com/items?itemName=RobBos.AIEngineeringFluency
• GitHub Advanced Security (GHAS) course by Rob Bos on LinkedIn Learning: https://www.linkedin.com/learning/github-advanced-security-ghas
• Learning GitHub Advanced Security for Azure DevOps by Rob Bos on LinkedIn Learning: https://www.linkedin.com/learning/learning-github-advanced-security-for-azure-devops
• Responsible GitHub Copilot: Creating Reliable Code Ethically by Rob Bos on LinkedIn Learning: https://www.linkedin.com/learning/responsible-github-copilot-creating-reliable-code-ethically-24981582
• GitHub Actions in Action: https://www.manning.com/books/github-actions-in-action
• The 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/sizikov
• Anton's Blog: https://blog.cloud-eng.nl/ 
• GitHub Copilot: https://github.com/features/copilot
• GitHub Models: https://docs.github.com/en/github-models/about-github-models
• Visual 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/copilot
• GitHub Copilot CLI: https://github.com/features/copilot/cli
• Visual Studio Code: https://code.visualstudio.com/
• Visual Studio: https://visualstudio.microsoft.com/
• Squad: https://github.com/bradygaster/squad
• Brady Gaster: https://developer.microsoft.com/en-us/blog/author/bradyg/
• GitHub Actions: https://github.com/features/actions
• Model Context Protocol (MCP): https://modelcontextprotocol.io/
• Microsoft Copilot: https://copilot.microsoft.com/
• Microsoft 365 Copilot: https://www.microsoft.com/en-us/microsoft-365-copilot
• Microsoft 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-sharepoint
• ChatGPT: https://chatgpt.com/
• PowerToys: https://github.com/microsoft/PowerToys
• Godot 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-management
• Azure Kubernetes Service (AKS): https://azure.microsoft.com/en-us/products/kubernetes-service/
• Microsoft Foundry: https://ai.azure.com/?view=foundry
• Microsoft 365 Copilot: https://www.microsoft.com/en-us/microsoft-365-copilot
• GitHub: https://github.com/
• GitHub Copilot: https://github.com/features/copilot
• GitHub Copilot CLI: https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-getting-started
• GitHub Copilot SDK: https://docs.github.com/en/copilot/how-tos/copilot-sdk/getting-started
• Model Context Protocol (MCP) for GitHub Copilot: https://docs.github.com/en/copilot/concepts/context/mcp
• Visual Studio Code: https://code.visualstudio.com/
• Spec Kit: https://github.github.io/spec-kit/
• OpenSpec: https://github.com/Fission-AI/OpenSpec
• Superpowers: https://github.com/obra/superpowers
• Playwright: https://playwright.dev/
• AI Engineering Fluency (Rob Bos): https://open-vsx.org/extension/RobBos/copilot-token-tracker
• Rob Bos: https://devopsjournal.io/
• OpenAI: https://openai.com/
• Gemini: https://deepmind.google/models/gemini/

In this episode, I sat down with Tim Warner to talk about how generative AI is reshaping the day-to-day reality of technical work. We compared this moment to earlier platform shifts like the web, mobile, cloud, and virtualization, and dug into what feels different this time: the speed, the accessibility, and the way AI has moved from being a novelty to becoming part of the operating system for how many of us think, learn, and build. Tim shared how he uses tools like Claude Code, ChatGPT, and GitHub Copilot in real development workflows, why he still insists on keeping a human in the loop, and how prompting, context, sub-agents, and memory all change the way he approaches building software and teaching others to do the same.

We also explored how AI is affecting infrastructure work, instructional design, and technical education, including how Tim uses it to improve Pluralsight course demos and certification content for Kubernetes. Toward the end, we zoomed out and talked about careers, kids, and the future of software development: if code generation becomes increasingly automated, what should people actually study, and what work will still matter? Tim’s answer was refreshingly human: focus on the intersection of passion and aptitude, and use AI as a force multiplier rather than a replacement for judgment, curiosity, and craft.

• Tim Warner: https://www.pluralsight.com/authors/tim-warner
• Barry Luijbregts: https://www.pluralsight.com/authors/barry-luijbregts
• Pluralsight: https://www.pluralsight.com/
• Cornell University: https://www.cornell.edu/
• Microsoft: https://www.microsoft.com/
• Claude Code: https://www.anthropic.com/product/claude-code
• Claude Code for VS Code: https://code.claude.com/docs/en/vs-code
• GitHub Copilot: https://github.com/features/copilot
• Visual Studio Code': https://code.visualstudio.com/
• Windows Terminal: https://learn.microsoft.com/en-us/windows/terminal/
• ChatGPT: https://chatgpt.com/
• OpenAI: https://openai.com/
• OpenAI Codex CLI' https://github.com/openai/codex
• Gemini CLI: https://github.com/google-gemini/gemini-cli
• Everything Claude Code: https://github.com/worldflowai/everything-claude-code
• Getting Things Done (GTD): https://gettingthingsdone.com/
• David Allen / GTD About: https://gettingthingsdone.com/about/
• Microsoft Bicep: https://learn.microsoft.com/en-us/azure/azure-resource-manager/bicep/
• Kubernetes: https://kubernetes.io/
• Certified Kubernetes Administrator (CKA): https://www.cncf.io/training/certification/cka/
• Camtasia: https://www.techsmith.com/camtasia/
• TechSmith: https://www.techsmith.com/
• ScreenFlow: https://www.telestream.net/screenflow/overview.htm
• Adobe Premiere Pro: https://www.adobe.com/products/premiere.html

In this episode, I sat down with Mads Kristensen, a principal product manager from the Visual Studio team to unpack what a PM actually does inside a large engineering organization and how that role blends user research, roadmap planning, technical decision-making, and product experimentation. We talked about his 15+ years working on Visual Studio, why he still builds extensions himself to test ideas quickly with real users, and how Visual Studio continues to grow even in the middle of all the noise around AI-powered coding tools. He also shared why Visual Studio remains especially strong for professional C# and C++ developers, enterprise teams, and anyone who treats code as a long-term asset rather than just a throwaway artifact.

We also got into what’s next for Visual Studio: deeper AI integration, especially around debugging, testing, code review, and project scaffolding, while still preserving the strengths that make the IDE unique. Along the way, we covered some lesser-known but powerful features like Dev Tunnels, saved window layouts, and floating tool windows, plus the move to yearly Visual Studio naming with evergreen in-place updates. If you’re a longtime Visual Studio user, curious about where the product is headed, or wondering how AI fits into serious software development, this conversation is packed with practical insight and a lot to be excited about.

 People / Social

 • Mads Kristensen on X/Twitter (@mkristensen): https://twitter.com/mkristensen
• Mads Kristensen's blog (Fancy Zones + Visual Studio post referenced): https://www.madskristensen.net
• Mads Kristensen on GitHub: https://github.com/madskristensen

Microsoft / Visual Studio

• Visual Studio: https://visualstudio.microsoft.com
• Visual Studio Insiders / Preview channel (recommended in the episode): https://visualstudio.microsoft.com/vs/preview/
• Visual Studio Marketplace (extensions): https://marketplace.visualstudio.com
• Visual Studio Code: https://code.visualstudio.com
• GitHub Copilot: https://github.com/features/copilot
• GitHub Copilot CLI: https://docs.github.com/copilot/github-copilot-in-the-cli
• Microsoft PowerToys: https://learn.microsoft.com/windows/powertoys/
• PowerToys FancyZones: https://learn.microsoft.com/windows/powertoys/fancyzones
• Dev Tunnels (mentioned as "DevTunnel"): https://learn.microsoft.com/azure/developer/dev-tunnels/overview
• .HTTP file support in Visual Studio: https://learn.microsoft.com/aspnet/core/test/http-files
• Hot Reload (.NET): https://learn.microsoft.com/visualstudio/debugger/hot-reload
• ASP.NET (web apps referenced): https://dotnet.microsoft.com/apps/aspnet
• WPF: https://learn.microsoft.com/dotnet/desktop/wpf/

 In this episode, I sat down with Gerald Versluis from Microsoft’s .NET MAUI team for a wide-ranging conversation that started with mental health, sunlight, and fitness, and quickly moved into the future of cross-platform app development. Gerald shared what it’s like building .NET MAUI today, why creating apps for iOS, Android, macOS, and Windows from one C#/.NET codebase is still a deeply complex challenge, and how the platform has matured from its Xamarin roots into something far more capable for production use. We also talked about Blazor Hybrid, native UI versus web-based approaches, and why MAUI continues to be an appealing option for .NET developers who want flexibility across platforms.

The biggest theme of our conversation was how AI and Copilot are transforming the way Gerald’s team works. He explained how Microsoft’s MAUI team is using AI not just to generate code, but to review pull requests, validate fixes, interact with running apps, and even expand MAUI into new territory like Linux support through the new MAUI Labs efforts. We explored practical patterns for agentic development, custom instructions, skills, MCP servers, and self-verifying workflows, along with the broader implications for software careers, education, and the future of developer productivity. If you want a grounded but exciting look at what AI-assisted software development already looks like inside a real product team, this episode delivers.

 People / Social

 • Gerald Versluis (guest) on YouTube: https://www.youtube.com/@jfversluis
• Gerald Versluis on X/Twitter (@jfversluis): https://twitter.com/jfversluis
• Gerald Versluis on GitHub: https://github.com/jfversluis
• Gerald Versluis website/blog: https://blog.verslu.is
• Jonathan Dick (creator of DevFlow, .NET MAUI team) on GitHub: https://github.com/Redth
• David Fowler (Microsoft, mentioned re: Aspire) on GitHub: https://github.com/davidfowl

Microsoft / .NET

• .NET MAUI: https://dotnet.microsoft.com/apps/maui
• .NET MAUI on GitHub: https://github.com/dotnet/maui
• MAUI Labs (mentioned explicitly): https://github.com/dotnet/maui-labs
• DevFlow (in MAUI Labs): https://github.com/dotnet/maui-labs/tree/main/DevFlow
• Blazor Hybrid: https://learn.microsoft.com/aspnet/core/blazor/hybrid/
• .NET for iOS (formerly Xamarin.iOS): https://github.com/dotnet/macios
• .NET for Android: https://github.com/dotnet/android
• .NET Aspire: https://learn.microsoft.com/dotnet/aspire/
• Microsoft Learn (docs): https://learn.microsoft.com
• Microsoft Loop: https://loop.cloud.microsoft

GitHub / Copilot tooling

• GitHub Copilot: https://github.com/features/copilot
• GitHub Copilot CLI: https://docs.github.com/copilot/github-copilot-in-the-cli
• GitHub Agentic Workflows: https://github.com/githubnext/gh-aw
• GitHub Actions: https://github.com/features/actions

Partner UI frameworks

• Avalonia UI: https://avaloniaui.net
• Uno Platform: https://platform.uno

Other tools / libraries

• Lottie animations: https://lottiefiles.com
• Rive (interactive animations): https://rive.app
• Appium (UI test framework): https://appium.io
• Anthropic Claude (Opus 4.6 referenced): https://www.anthropic.com/claude
• Duolingo (mentioned re: Rive animations): https://www.duolingo.com

Off-topic / lifestyle

• Hyrox (fitness race, Maastricht / Lisbon / Madrid mentioned): https://hyrox.com 

In this episode, I sat down with Brittany Ellich, Staff Engineer at GitHub, to talk about what software engineering looks like when AI is part of nearly every step of the workflow. We got into her path from a smaller medical education company to GitHub, what it means to work on enterprise billing and metered usage, and why billing systems are such a fascinating engineering problem: speed matters less than accuracy, and in distributed systems that’s a surprisingly hard thing to optimize for. We also explored how GitHub’s different cloud and enterprise offerings complicate that work, especially when data residency and product flexibility enter the picture.

From there, we went deep on AI-assisted development: async vs. sync coding, delegating work to cloud agents, using GitHub Copilot CLI and VS Code in agent mode, and how tools like Spec Kit and SQUADS are changing the way features get planned and built. Brittany shared how AI has shifted her role toward product thinking, architecture, and review, and we talked honestly about trust, pride, technical debt, junior engineers, and where the profession may be heading next. It’s a thoughtful, grounded conversation about adapting to a fast-moving moment in software without losing the joy of building.

• Brittany Ellich website: https://brittanyellich.com/
• GitHub: https://github.com/
• Microsoft: https://www.microsoft.com/en-us
• GitHub Actions: https://github.com/features/actions
• GitHub Enterprise: https://github.com/enterprise
• GitHub Enterprise Server docs: https://docs.github.com/en/[email protected]/admin/overview/about-github-enterprise-server
• GitHub Copilot: https://github.com/features/copilot
• GitHub Copilot CLI: https://github.com/features/copilot/cli
• GitHub Copilot CLI getting started docs: https://docs.github.com/copilot/how-tos/copilot-cli/cli-getting-started
• Visual Studio Code: https://code.visualstudio.com/
• OpenAI Whisper: https://openai.com/index/whisper/
• GitHub Spec Kit: https://github.com/github/spec-kit
• Spec Kit documentation: https://github.github.io/spec-kit/
• SQUADS CLI: https://squads.sh/en/docs/cli
• CascadiaJS: https://cascadiajs.com/
• Craig Dennis talk, “AIdentity Crisis: Can I be Proud of This?”: https://cascadiajs.com/2025/talks/aidentity-crisis-can-i-be-proud-of-this
• Hacker News: https://news.ycombinator.com/
• Rust programming language: https://rust-lang.org/
• React: https://react.dev/
• Stack Overflow: https://stackoverflow.com/questions
• LeetCode: https://leetcode.com/
• OpenAI: https://openai.com/
• Gemma: https://deepmind.google/models/gemma/
• Gemma 4: https://deepmind.google/models/gemma/gemma-4/
• Anthropic Claude Mythos: https://www.anthropic.com/claude/mythos