In this episode of Dynamics Corner, Kris and Brad welcome back Torben Leth, Microsoft MVP and creator of CentralGauge, for a follow-up conversation about how much has changed in just a few months. A lot has changed. Torben's benchmarks that used to separate the good models from the great ones? The models blew right past them. They're so good now that he has to completely redo his test sets just to find meaningful differences. The real gem in this conversation is Torben's Eureka moment for any AL developer getting started with AI. It's not fancy tooling. It's not the perfect model. It's the first time you get your setup to automatically compile, deploy, and run your tests. That's when it clicks. That's when you see how fast the iteration loop can be and realize you're not going back. From there, Torben walks through his full multi-model workflow: Opus handles the planning, Fable steps in as the "talking to an adult" for spec reviews, and Sonnet does the actual coding because the spec is solid enough that it no longer needs a heavyweight model. He's even pulling in GPT and Gemini through an MCP called PAL to get a third and fourth opinion before anything gets built. Then there's the story about his Linux laptop. He needed a fingerprint sensor driver that didn't exist. So, he pointed AI at Wireshark, had it capture the USB traffic from Windows, and let it work overnight. By morning, he had a working Linux driver. He didn't write a single line of it. The conversation wraps with where costs are headed. Torben's prediction? Premium models will handle the thinking, and local open-source models will handle the coding. Whether you've been deep in AI development for months or you're still figuring out where to start, Torben's Eureka moment might be the push you need.

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In this episode of Dynamics Corner, Kris and Brad talk with Matt Traxinger, Development Leader, Archer Point, who manages over 50 developers, and Tonya Bricco-Meske, also known as the “BC Cat Lady.” Their conversation doesn’t fit into any usual conference track, and that’s the point. They discuss what it’s really like to run a large development team when AI tools keep changing, credits cost real money, and there’s no playbook to follow. Matt shares a key idea: instead of trying to work faster, focus on getting more done in the same amount of time. Tanya explains how this works in practice. Her team is finally able to tackle tasks they never had time for before, such as tests, performance benchmarks, and edge cases. They’re also figuring out who should get access to the expensive models and who used up their credits too quickly. Matt describes his design workflow, which got some strong reactions. How do you estimate a project when AI might finish it in minutes, but the credits could cost more than the billable hours? How do you keep standards consistent across a hundred developers, especially when half aren’t even on your team? And the big question: are these models already learning from content made by earlier models?

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In this episode of Dynamics Corner, Kris and Brad talk with Steven Renders about two topics that every implementation faces but often struggles with: training and reporting. Steven shares why training should start before go-live and tells a story about how early training helped a customer avoid building something that already existed in the product. The discussion then moves to a common question for both partners and customers: with options like RDLC, Word, Excel, Power BI, analysis mode, and scorecards, which tool should you use, and when? Steven explains the differences clearly, shares a useful Power BI tip that many people miss, and points out a detail about analysis mode that got Brad excited. Since Microsoft is moving away from RDLC, one type of report could become the primary option. You'll find out which one it is when you listen. Check out this episode.

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