Jennifer Reif talks with Dr. Robert Kerstein, a prosthodontist with 42 years of experience researching the T-Scan bite computer. T-Scan uses pressure-mapping sensors to measure bite force, timing, and friction. It's the same Tekscan sensor tech used in aerospace, automotive, and robotics. Software records bite dynamics like a movie, enabling data-driven analysis instead of subjective guesswork with traditional paper and ink. Recurring tracking lets clinicians compare recordings over time to catch changes early. Effective use requires hands-on, chair-side training, just like developers need real keyboard time with a new tool. Emerging research shows the bite communicates directly with the brain and cranial nerves, linking dental mechanics to neurology. The T-Scan incorporates AI to flag problem areas in bite data, but medical device regulations prohibit it from making diagnoses. This is a key constraint for developers building AI into medical tools. Resources: Website: Dr. Robert Kerstein Tekscan: Digital Occlusal Analysis Resources: DTR Providers | Digital Occlusion Seminars Thanks for listening and happy coding!
Jennifer shares updates on her upcoming AI First Java book, fall event prep, and spring 2027 conference abstracts. Key topics this episode: GraphAcademy makeover. Neo4j's learning platform now features browser-based code environments, auto-generated API keys, AI-assisted data modeling, and public datasets for self-paced learning. GraphRAG workshop. Jennifer recently delivered a virtual workshop on generative AI and GraphRAG using the updated GraphAcademy platform. Graph Summit NYC. Neo4j's flagship event featuring a brand-new hands-on workshop format where attendees build a working project. Upcoming events. Northeast JUG tour in late September (Raleigh, NC; Charlotte, NC; Richmond, VA) and a RAG benchmarking webinar in the works. "Building Software is Learning". Thorsten Ball's article on shortening the feedback loop between building and validating requirements, and why iterative development matters even more in the age of AI coding tools. Real-world example. Jennifer shares how writing her book's capstone project with Claude reinforced the need for upfront context, boundaries, and tight iteration when working with AI. Thanks for listening, and happy coding!
This week, I took a detour with a different kind of productivity and share highlights from two must-read articles on AI-era software development. My "brain sabbatical": a strategy day for deep focus, ideation, and conference abstract brainstorming. Upcoming events: Intro to AI Workshop (Sep 3) GraphSummit NYC (Sep 17) Northeast US meetup tour (Sep 21–23, Raleigh, Charlotte, Richmond) "Domain Knowledge Is the Leverage" by Beshr Kayali Reinholdsson: practices like TDD and DDD are more relevant than ever in the AI coding era Spec-driven development: specs as living decision documents, not page-count exercises Tests matter more than ever. AI-generated code still needs independent verification. "Coding Is Solved, Software Is Not" by Gao: writing code is no longer the bottleneck; context, specs, verification, and human checkpoints are Confirmation bias risk: AI-generated test suites may just validate the implementation the agent already chose Key takeaway: developers are spending more time on design decisions, domain clarity, and proving the right thing was built Thanks for listening, and happy coding!
I'm back after some time off and excited to catch you up on everything that's been happening. This week, hear updates on the Java book, some upcoming fall events, and dive into two articles that challenge how we think about AI in development and architecture. The Java book is in its final chapters. It now incorporates Claude, planning mode, and a capstone project so developers walk away with something personal and useful Feeling out of touch with the industry's pace? Me too! Balancing competing priorities is a real challenge Fall events ahead: Java User Group tours (East Coast in September, Southeast in November), plus new guest episodes and 2027 conference abstracts Article 1: "You Need AI That Reduces Maintenance Costs" by James Shore. AI boosts front-end productivity, but what about long-term maintenance costs? Maybe we should "shift right" just a bit — plan for your future self, not just immediate output Article 2: "AI versus Microservices" by Michael T. Nygard. AI tools speed up development but cause merge conflicts, governance gaps, and ownership sprawl The monolith vs. microservices debate extends beyond tech. Streaming services and cloud backups follow the same pendulum More data points lead to better architecture decisions, whether in AI processes or everyday life Thanks for listening, and happy coding!
In this episode, I sit down with John Willis, author of The DevOps Handbook and Rebels of Reason. We discuss how lessons from John's five decades of technology shifts apply to organizations the AI era. Highlights: The "fluorescence" of AI: why the hype is blinding and what we keep forgetting Patterns from Linux, cloud, and DevOps that repeat with every tech shift Ideation hackathons: a structured approach to organizational AI adoption Data provenance risks and why throwing tools at non-technical teams is dangerous Buy vs. build: open weight vs. public models and the hybrid approach "Code is the new assembler": will developers still write code? AI model drift: what happens when a model upgrade breaks your application Agentic AI, kill switches, and the efficiency-thoroughness trade-off Links and Resources: John's AI CIO newsletter John's Amazon Author Portal John on LinkedIn