In this episode, Scott Hanselman and Mark Russinovich dive into a wide-ranging conversation about the future of software, debating whether apps are dead in an era of AI agents, chat interfaces, and automation. They explore the resurgence of text-based and terminal user interfaces, the limits of using large language models as stand-ins for deterministic workflows, and why reliability, security, and repeatability still demand traditional applications and SaaS platforms. Along the way, they unpack common misconceptions about AI replacing apps, argue for better UX and APIs instead of throwing AI at broken systems, and emphasize that AI is best used for reasoning and ambiguity, not as a replacement for well-designed software.

Takeaways:

• AI tools don’t eliminate the need for well-built apps
• Chat and terminal interfaces expand, not replace, software
• Dynamic interfaces blur boundaries, but durable apps still anchor workflows

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

Hosted on Acast. See acast.com/privacy for more information.

In this episode, Scott Hanselman and Mark Russinovich explore how AI-assisted vibe coding is changing the way complex software gets built, debugged, and refined. Mark walks through real-world experiments using AI to tackle difficult engineering problems, including gRPC shared memory, Win32 UI work, and major new features coming to ZoomIt. The conversation digs into where AI meaningfully accelerates development, where it still requires careful human oversight, and how tools like screenshots, debug output, MCP servers, and multimodal feedback loops are reshaping modern developer workflows. The result is an honest, technical discussion about productivity gains, limitations, and the evolving role of human judgment in AI-driven software engineering.

Takeaways:

• Debug output and screenshots are critical for helping AI understand
• Faster iteration often leads to more polished software, not just quicker results
• Concurrency and low-level systems problems remain difficult for AI to solve without supervision

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

Hosted on Acast. See acast.com/privacy for more information.

In this episode, Scott Hanselman and Mark Russinovich pull back the curtain on what really goes into a large-scale conference talk, using their recent Ignite session as a case study. They reflect on the balance between educational and soft talks, the importance of credibility and audience expectations, and why not every talk needs a rigid takeaway to be valuable. The conversation traces a playful but technically deep journey through computing history, from early machine code and Altair systems to modern Azure infrastructure, massive virtual machines, and experimental demos that intentionally blur the line between serious engineering and creative exploration. Along the way, they share behind-the-scenes lessons about live demos, pacing, risk, and why delight, curiosity, and a bit of silliness still matter in technical storytelling.

Takeaways:

• Setting expectations early helps the audience judge a talk on the right terms
• Credibility can come from perspective, not just authority
• Risky demos work when the fundamentals are solid

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

Produced by Microsoft & Hangar Studios

Hosted on Acast. See acast.com/privacy for more information.