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In this episode, Scott Hanselman and Mark Russinovich dive into Mark’s new SemanticStart project, exploring how semantic search can make finding Windows apps, tools, and settings more intuitive. Along the way, they experiment with AI-assisted coding, discuss how agents could interact with local tools, and debate a bigger question: does prompting an AI actually count as programming?    Takeaways:     How semantic search combines keywords, embeddings, and enriched data  Why AI is changing the way developers build and prototype software  Whether prompting an AI should actually be considered programming.    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

In this episode, Scott Hanselman and Mark Russinovich dive into the security risks surrounding increasingly capable open-weight AI models and Mark’s latest research into a new defense called Fool’s Gold. Mark explains how safety guardrails can be removed from open models and explores an alternative approach: training models to provide convincing but deliberately flawed information when those protections are bypassed. They discuss how the technique works, whether it affects legitimate model behavior, and what it could mean for the future of AI safety as open models continue to advance.  Takeaways:     Why open-weight AI models can create new security risks  How Mark’s Fool’s Gold approach uses decoy responses as a defense decoy responses as a defense  Techniques that can protect against harmful use without degrading normal model performance  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

In this episode, Scott Hanselman and Mark Russinovich explore when to let AI solve a problem and when figuring it out is half the fun. They celebrate 30 years of Sysinternals by looking back at how the tools have evolved and how AI is changing software engineering and troubleshooting. Along the way, they debate efficiency versus learning by doing and consider a future where tools may increasingly be built for AI rather than humans.     Takeaways:     How Sysinternals has evolved over the past 30 years  How AI is changing software engineering and troubleshooting  Why future developer tools may be designed for AI, not humans    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich talk about gaming, from the countless hours they’ve spent playing over the years to why they gravitate toward very different types of games. Scott makes the case for immersive single-player experiences like Crimson Desert, while Mark explains why Battlefield is his go-to for switching his brain off. Along the way, the conversation turns to the future of AAA games, AI-generated content, and whether knowing something was created by AI changes how we value it.    Takeaways:     Different games serve different purposes for each player   Can AI-generated games deliver the same value as something created primarily by people  Does knowing the work and craftsmanship behind a game changes the experience  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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich explore how AI is rapidly changing the way software is built, from acting as a true research assistant to generating production-ready code and even helping write academic papers. They debate whether AI deserves credit for its work, discuss the future of developer tools like CLI vs. GUI and MCPs, and dive into creative AI projects like generating custom Doom levels from simple prompts. The conversation wraps with a thoughtful discussion about AI-generated art, creativity, and where human craftsmanship still matters most.     Takeaways:     Human judgment is still essential, even when AI does most of the implementation  AI is evolving from a coding assistant into a genuine research collaborator  The industry hasn't settled how AI contributions should be credited  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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich explore Mark's latest AI-assisted coding project: bringing the iconic Sysinternals Process Explorer to macOS. They discuss how AI dramatically accelerated development, where human expertise was still essential, and why building reliable software still requires careful iteration, debugging, and judgment. Along the way, they compare Windows and macOS internals and examine what these advances mean for the future of software development.    ZoomIt for Mac is now available here: Sysinternals ZoomIt for MacOS.    Takeaways:     What it took to bring Process Explorer to macOS  Key differences between Windows and macOS system tools.  Practical lessons for building with AI effectively    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich explore the realities of vibe coding by walking through a few of Mark's recent AI-assisted projects, including Polypost, a tool for writing and formatting social media posts and a nearly feature-complete Mac version of ZoomIt. They discuss where AI excels, where it still struggles, and why building software has become less about writing code and more about guiding, testing, and refining an AI collaborator. Along the way, they share lessons on prompt engineering, user experience, debugging, and why human judgment remains essential even as AI accelerates development.    Polypost — multi-platform post editor    Takeaways:     Successful AI development still relies on human decision-making  Mark shares the challenges and wins of building Polypost with AI  The iterative process of prompting, testing, and refining that turns AI-generated code into production-quality software    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich pull back the curtain on Microsoft's Build conference with a look at the cutting room floor of Mark's Inside Azure Innovations breakout session. They explore fascinating technologies that didn't make the final presentation, including AI-powered network repair agents, advanced data center cooling systems, supply chain security innovations, prompt injection defenses, and groundbreaking approaches to cloud infrastructure. Along the way, they discuss how major technology presentations are assembled, the challenge of choosing what to showcase, and the incredible engineering work happening behind the scenes at Microsoft.    See the innovations that did make the final cut in the recording of Mark’s Inside Azure Innovations talk at Microsoft Build 2026: Inside Azure Innovations with Mark Russinovich.    Takeaways:     How Mark decides what innovations make it into major conference presentations  New technologies and tools being used to manage cloud infrastructure at scale  Why some of the most interesting innovations never make it onto the conference stage    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich reflect on conversations from Microsoft Build before diving into a thoughtful debate about AI's impact on software engineering. They explore why coding is ultimately about clear specifications, testing, and evaluation, not just generating code and discuss whether AI will truly replace developers or simply change how software is built. Along the way, they compare AI-generated software to IKEA furniture versus custom craftsmanship, examine the future of junior engineers, and share their perspectives on quality, responsibility, and what it really means to ship software in an AI-driven world.     Takeaways:     How experienced engineers evaluate AI-generated code  The critical role of testing, evaluation, and human oversight in AI-assisted development  Why clear specifications are still the foundation of successful software projects.  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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich dive into a growing issue in AI-driven research: hallucinated references in academic papers. After scanning thousands of conference submissions, Mark uncovers widespread citation inaccuracies, sparking a broader conversation about accountability, cognitive surrender, and the risks of over-relying on AI tools. They explore where AI adds value versus where it erodes critical thinking, from academic writing to everyday coding and content creation. The conversation shifts into the realities of vibe coding, the tradeoffs of speed versus quality, and what it means to maintain taste and expertise in an AI-accelerated world, ultimately asking: if AI lowers the barrier to create, does it also lower the standard?     LLM Writing in Tech: Blogs and Articles    Takeaways:     AI hallucinations are already impacting academic research quality  The future challenge isn’t using AI; it’s maintaining standards while using it  There’s a growing tension between efficiency and craftsmanship    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich unpack the tension between subjective preference and objective usability, arguing that strong product instincts come from years of exposure, experience, and pattern recognition rather than innate talent. Through examples ranging from UI design to AI-assisted coding, they highlight how good decision-making requires both a holistic systems view and attention to detail. The conversation also examines the limits of accelerating expertise, the role of education in building foundational thinking, and why human judgment remains critical even as AI tools become more capable.    Takeaways:     Without clear intent and strong taste, outputs can drift or degrade  Strong design decisions come from balancing small details   Good product instincts come from repeated exposure to patterns, tools, and decisions over time    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich ​​dive into the evolution of ZoomIt, exploring new features like panoramic screen capture, webcam overlays, and lightweight video editing tools. They discuss the technical challenges behind building these capabilities, especially stitching images across any application and how AI-assisted coding is accelerating development while introducing new edge cases. Along the way, the conversation blends deep technical insight with candid, behind-the-scenes moments, highlighting both the complexity of modern software development and the value of experimentation and iteration.     Takeaways:     ZoomIt is evolving into a more advanced, all-in-one screen tool  Some of the most valuable moments in development and content come from failures  AI-assisted coding can dramatically speed up development, but still requires close human oversight    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich ​​explore how software development is evolving in the age of AI, challenging the idea that everything should start with a fully defined spec. They highlight a more iterative, sculpting approach to building, where continuous refinement, testing, and human judgment are essential and discuss the realities of AI-assisted coding, including edge cases, maintenance, and the limits of productivity gains.    Takeaways:     AI-assisted coding works best as an iterative process, not a one-shot, fully spec’d solution  Edge cases and real-world usage quickly expose gaps that initial builds  AI can accelerate development, but human review, testing, and bottlenecks still limit true productivity gains     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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich ​​unpack how AI systems actually behave beneath the surface, pushing past hype into the messy reality of how models are trained, aligned, and deployed.  They explore whether AI systems are inherently benevolent or simply shaped by incentives, training data, and reinforcement learning, and why behaviors like deception can emerge under certain conditions. The conversation moves from philosophical questions about human nature versus machine behavior into the practical mechanics of large language models, including how reinforcement learning with human feedback shapes outputs and why alignment is far from perfect.  Along the way, they ground the discussion in a real engineering challenge, stitching a scrolling panorama from screen captures, to show how complex systems come together through heuristics, edge cases, and iteration.    Takeaways:     AI behavior is shaped by training and incentives, not built-in intent or morality  AI can accelerate coding, but testing, edge cases, and reliability require human oversight  Reinforcement learning pushes models to be helpful and agreeable, sometimes at the cost of accuracy    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 Hangar Studios

In this episode, Scott Hanselman and Mark Russinovich dive into the realities of building complex software with AI coding agents. Mark shares his experience using modern models to implement a shared-memory transport for gRPC across Go and .NET, explaining how AI dramatically accelerated development while still requiring constant oversight. They discuss the surprising strengths and limitations of AI coding tools, to the massive productivity gains that make the frustration worthwhile. The conversation also explores the challenges of solving hard engineering problems, including an attempt to build a scrolling screenshot stitcher, and wraps with thoughts on the future of developer tooling and a potential live episode of the show.    Takeaways:     AI coding agents can speed up complex development but still require human oversight  Developers often need to guide and correct the model throughout the process  Even with challenges, AI can reduce months of work to days    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 Hangar Studios