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Venkat Subramaniam joins Marco to talk about what AI is really changing for developers. They discuss why AI often impresses novices more than experts, why “AI first” is the wrong mindset, and why critical thinking, fundamentals, and judgment matter more than ever. 💡 In this episode: Why AI is better at finding problems than writing code Why AI needs more skilled developers, not fewer Why “AI is second” The danger of bad AI metrics and token maxing Why critical thinking still matters What excites Venkat about the future of Java and virtual threads Timestamps: (00:00) Teaser (00:40) Introduction (01:12) What is different about AI? (03:26) Why AI is better at finding problems than writing code (04:50) Should AI implement an entire feature? (09:15) How junior developers can become experts in the AI era (12:46) Speed vs. sustainable software development (14:11) Timeboxing and learning to verify solutions (18:18) Why AI should come second (21:19) Why developers and AI are better together (24:22) Token maxing and failed AI adoption strategies (26:43) Why powerful tools require more skill (29:28) Programmer productivity vs. business agility (31:03) What CTOs should do about AI (33:35) Threat-driven development and forced AI adoption (36:06) How Venkat learns new technologies (41:44) How Venkat prepares talks and keynotes (44:50) Why Venkat does not rehearse his talks (47:00) Stage fright after thousands of presentations (49:16) When Venkat’s laptop died before a live coding keynote (53:53) What keeps Venkat learning and creating? (54:21) From a failing student to earning a PhD (56:00) “I hate studying, but I love learning” (01:00:19) Why grades can destroy the joy of learning (01:04:54) How Java has changed over the last decade (01:05:29) Why Java 8 was the game changer (01:09:13) Java’s release cycle and true agility (01:11:29) Virtual threads and asynchronous programming (01:15:47) What makes Java’s innovation different (01:16:56) What is next for Java? (01:19:25) Generics with value types (01:20:30) Giveaway question (01:21:53) Rapid-fire questions (01:23:33) Outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/_590TxMwvWM

Sébastien Deleuze, a longtime Spring Framework committer and member of the Spring AI team, joins Marco to talk about how 13 years on the Spring team shaped his pragmatic approach to software development. The conversation covers how Spring AI fits into the Spring ecosystem, why enterprise AI still needs deterministic workflows and human accountability, and why long-term software quality matters even as GenAI changes how code gets written. 💡 In this episode: How Sébastien joined the Spring team through open source contributions Why he only introduces features he once wished Spring had as a developer How Spring AI brings AI services into the familiar Spring programming model Why AI helps with speed, but not with the most important decisions Why accountability and sustainable engineering still matter in the age of GenAI Timestamps: (00:00) Teaser (00:32) Introduction (00:54) Sébastien Deleuze’s 13 years on the Spring team (01:22) How an open-source contribution led to joining Spring (03:08) Working on a fully remote team (03:54) Building the Spring features he wished he had as a developer (05:22) Why Kotlin was the right fit for Spring (06:35) How the Spring team discovers what developers need (08:23) Learning from competing frameworks without copying them (11:06) What is Spring AI? (13:27) Bringing AI into enterprise applications  (14:53) AI-generated pull requests and the accountability problem (16:36) How open-source maintainers deal with low-quality AI contributions (18:27) Keeping up with the speed of change in AI (20:43) Using AI tools for most coding tasks (21:21) Writing better prompts by providing the right context (24:05) Running multiple AI agents and managing context switching (24:53) Can an AI agent fix a complex Spring issue? (25:38) Why detailed issues and commit messages matter more in the AI era (27:22) What’s next for Spring AI 2.0 and 2.1 (29:44) Spring AI’s relationship with Embabel and the wider ecosystem (30:56) Why Spring AI is reducing its scope (32:05) The most important engineering skill AI cannot replace (33:29) Using AI outside of work (38:10) Giveaway (39:04) Rapid-fire questions (40:13) Outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://www.youtube.com/watch?v=mCOGZGV48hU

Josh Long, Spring Developer Advocate at Broadcom, joins Marco to cover 25 years of Spring history and what the JVM's real shot at the AI era actually looks like. Josh has published a weekly blog since 2011 and a weekly podcast since 2018 without missing an episode. This conversation gets into why vibe coding concerns him as an engineer, what accountability for AI-generated code actually means, and why Python's dominance in AI is more a marketing gap than a technical one. 💡 In this episode: Spring's origin story: why the documentation came before the code How Spring Boot changed what a developer talk could look like The hidden cost of vibe coding: context, accountability, and integration Why 95% of generative AI projects fail — and what the 5% have in common Project Valhalla, Project Panama, and the Vector API Why Java could have built Kubernetes, and what that means for AI Timestamps: (00:00) Teaser (00:37) Introduction (01:02) Josh's journey before joining the Spring team (03:06) Becoming Spring's first Developer Advocate (05:47) What a Spring Developer Advocate actually did (08:41) How Spring Boot changed Java forever (12:32) Building a career around talks, blogs, and community (15:32) Writing a weekly blog for 15+ years without missing one (19:14) Stage fright and speaking at hundreds of conferences (20:07) Live coding, talk preparation, and presentation failures (26:33) How DevRel changed after COVID (29:08) Why live coding beats slides (31:46) Advice for aspiring Developer Advocates (33:12) AI coding agents, vibe coding, and why more code isn't better (39:27) How AI changed search and everyday work (40:17) The future of Java, Spring, and AI (46:48) Giveaway (47:32) Rapid-fire questions (49:36) Outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/_ftzJ33mCTI

Martin Lippert, longtime maintainer of Spring Tools and one of the people who has witnessed developer tooling evolve from Eclipse to VS Code, joins Marco to discuss how AI is transforming Java development, and what that means for the future of software engineering. ⏱️ Timestamps: (00:00) Teaser(00:38) Meet Martin Lippert, Spring Tools & the evolution of Java IDEs(03:28) What happened to Eclipse? Lessons from the rise and fall of a Java giant(12:20) Why Spring Tools moved to VS Code and embraced the Language Server Protocol(20:03) How AI is changing Java development and everyday coding workflows(21:59) Martin's AI workflow: Cursor, IDEs, code review & testing(26:16) Building developer tools for AI agents instead of humans(31:10) Why AI is changing release cycles and developer tooling(36:03) AI review fatigue, cognitive depth & trusting generated code(39:06) Will AI replace senior developers? What happens to junior engineers?(42:16) The future of Java, Spring & software architecture in an AI-first world(48:42) AI's hidden energy problem and the sustainability challenge(54:39) Practical tips for greener software engineering and AI usage(1:00:06) Giveaway & rapid-fire questions 💡 In this episode: How AI is changing Java development and developer workflows Why IDEs still matter alongside coding agents like Cursor Building developer tools for AI instead of humans AI review fatigue and the importance of writing code yourself Will AI replace senior developers? Why software architecture still matters in an AI-first world The evolution of Java tooling: Eclipse, VS Code, Language Server Protocol, and beyond The future of Java and Spring in the age of AI AI's hidden environmental cost and the rise of Green Software Engineering New episodes every other Wednesday. Subscribe for more conversations with the people shaping the future of software development. 🎥 Watch the full episode on YouTube: https://youtu.be/gUm6DOf1NHI

Martin Odersky, creator of Scala and co-designer of Java generics, joins Marco to trace the full arc from Pizza (the 1996 functional Java experiment) to Scala 3, and on to his vision for capabilities as a safety mechanism for AI-generated code. They discuss how Scala unified object-oriented and functional programming, the Scala 2 to 3 evolution (implicits, Tasty, the new compiler), higher-kinded types, and why Martin believes programming languages need to grow up fast to keep AI agents from doing catastrophic things in production. Topics in this episode: The origins of Scala and the Pizza language Java generics: design, type erasure, and the 20-year wait for pattern matching Scala 2 vs Scala 3: what changed and why Higher-kinded types explained accessibly Capabilities and effect polymorphism How capabilities can sandbox untrusted AI agents Scala in the real world: finance, Spark, media, education The future of programming languages in an AI-first world Timestamps: (00:00) Intro(00:31) Meet Martin Odersky, creator of Scala(03:11) Why Scala was created(04:49) How Scala took off(07:01) The story behind Scala’s name and logo(08:03) Java generics and Scala’s design principles(10:41) Haskell, functional programming, and Scala’s identity(12:18) Pizza, Java, and features that came later(14:28) Type erasure and higher-kinded types(16:05) Scala 2 vs Scala 3(18:49) TASTy and Scala 3 compiler changes(19:21) What Martin would change about Scala(20:25) Kotlin, Java, and JVM languages(23:09) Capabilities, concurrency, and function coloring(29:28) Where Scala is used today(32:07) Scala’s ecosystem and community(36:50) Scala, AI agents, and the future of programming(43:17) Using AI and teaching programming in the AI era(45:56) Scala’s future(49:18) Why code review may be doomed(50:24) Giveaway question(51:26) Rapid fire questions(54:41) Outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/Xn_YpUtXWT4

Mohamed Aboullaite, backend engineer at Spotify, Java Champion, Google Cloud Developer Expert, and Docker Captain, joins Marco to talk about building AI-powered integrations at scale, what software engineering looks like when you're running five AI agents in parallel, and why the foundations still matter in an AI-first world. They discuss the engineering behind Spotify's ChatGPT integration (built on MCP), the non-determinism challenges of tool-calling, agentic coding workflows, review fatigue, and a frank conversation about the junior developer pipeline and what it takes to become senior today. 💡 In this episode: How Spotify's ChatGPT integration works (MCP apps, the Spotify widget inside ChatGPT) Siri/Alexa/Google Home backends and Spotify's ubiquity strategy Non-determinism in MCP tool-calling and how Spotify works around it Running 5 AI agents in parallel: the plan mode, review loops, cognitive fatigue Java at Spotify: monorepo, microservices, Backstage AI's impact on junior hiring and how to become senior anyway Finding mentors and the power of the Java community The token economy: measuring productivity by tokens burned ⏱️Timestamps: (00:00) Teaser(00:50) Meet Mohamed: Spotify backend engineer and Java Champion(01:53) What Mohamed works on at Spotify(03:12) Spotify inside ChatGPT and MCP apps(06:23) Building for new AI platforms(08:53) Spotify tools, playback, and device switching(09:48) Tool calling challenges with AI models(11:24) Using AI in day-to-day development(13:29) Running multiple coding agents in parallel(14:39) Why planning matters more than prompting(16:56) Review fatigue and cognitive load(19:43) Spotify’s backend, microservices, and Backstage(21:34) Java’s evolution and the AI era(24:14) Scala, Kotlin, Haskell, and JVM languages(25:46) Advice for junior developers in the AI age(29:27) How to become senior when AI solves everything(34:09) Finding mentors and growing through community(37:41) Giveaway question(39:28) Rapid-fire questions: Morocco, Sweden, Spotify, AI New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/6WvoouJ9Mrk

James Ward (Developer Advocate at AWS, Agentic AI Foundation Technical Committee Member) joins Marco to map out the fast-moving landscape of AI agents on the JVM. From MCP and ACP to Spring AI, Embabel, and Ktor — James explains how the JVM ecosystem has not only caught up with Python for building agents, but may have surpassed it. He also introduces SkillsJars (putting agent skills on Maven Central), explains effect-oriented programming and why it supercharges AI coding, and shares how he's been shipping five projects in two months entirely from his phone. 💡 In This Episode • Why the JVM is no longer second-class for AI agents • MCP vs ACP vs A2A — when to use which • Spring AI, Embabel (Rod Johnson), Ktor, LangChain4J compared • GOAP planning and domain-integrated context engineering • Agent skills vs MCP servers — and why skills are winning • SkillsJars: versioned, composable skills on Maven Central • Testing non-deterministic agents with evals • Effect-oriented programming and why types matter more than ever Timestamps:  (00:00:00) Intro (00:00:49) Guest intro: James Ward, AWS, and the Agentic AI Foundation (00:01:37) Are developers now orchestrating AI agents? (00:02:51) Agent setup, context switching, and review fatigue (00:05:58) Why typed languages matter more in the AI era (00:07:14) Scala vs Kotlin vs Java (00:10:02) What agentic frameworks are and why they matter (00:14:08) MCP explained (00:19:42) ACP explained (00:21:56) How to get started with agent protocols and frameworks (00:23:43) JVM agent frameworks: Spring AI, Embabel, Koog, and LangChain4j (00:27:50) AIforJVM.com and building projects with AI (00:29:55) AI from your phone, dopamine, and productivity (00:33:14) Testing, evals, orchestration, and reliability in agent systems (00:41:03) What skills are and where they fit (00:43:46) SkillsJars and packaging skills for the JVM (00:49:34) Which AI standards will actually last? (00:55:35) Effect-oriented programming explained (01:06:37) Giveaway question (01:08:24) Rapid-fire round (01:10:58) Outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/ACP0Nx-sW10

Andres Almiray, Java Champion and creator of JReleaser, joins Marco to talk about the realities of open source, release automation, and the evolving Java ecosystem. They dive into what it really takes to maintain and grow open source projects, why code isn’t the most important part, and how communication, community, and sustainability determine whether projects thrive or die. The conversation also explores release engineering challenges, why most automation setups fail, and how tools like JReleaser simplify software delivery. 💡In this episode: Open source realities: burnout, maintenance, and sustainability Why code is NOT the most important part of OSS JReleaser and release automation in modern workflows Common CI/CD and release mistakes developers make Maven vs Gradle: trade-offs and real-world experience The future of the Java ecosystem and tooling AI in open source: PR spam, licensing, and quality concerns Advice for newcomers contributing to open source Timestamps:  (00:00:00) Intro (00:00:41) Guest intro + Java journey (00:01:45) JReleaser: origin, use cases, and adoption (00:07:49) Software releases and automation best practices (00:11:31) JReleaser roadmap and release cadence (00:14:39) Commonhaus, open source sustainability, and succession (00:20:22) What makes open source projects successful (00:25:17) Burnout, community management, and prioritization (00:31:24) Hackergarten and open source collaboration (00:34:40) Motivation, Java’s evolution, and favorite features (00:40:44) Maven vs Gradle (00:44:29) CI/CD, supply chain security, and the future of Java tooling (00:53:16) AI, licensing, and open source contributions (01:01:39) Giveaway question (01:03:25) Rapid-fire round (01:06:04) Advice for getting started in open source (01:08:34) Outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://www.youtube.com/watch?v=Jts62hWkRO8

Moritz Halbritter, Spring Boot engineer at Broadcom and team lead for start.spring.io (Spring Initializr), joins Marco to talk about the inner workings of the Spring ecosystem and the future of Java performance. 💡 In this episode: GraalVM Native Image vs Project Leyden and Java startup performanceObservability in Spring Boot (logs, metrics, tracing)Developer experience improvements: Testcontainers, Docker Compose, SSL hot reloadAI coding tools, JSpecify nullability, and the future of Spring ⏱️Timecodes: (00:00) Teaser (00:53) Meet Moritz Halbretter from the Spring Boot team (02:23) From school programming to consulting to Spring (08:14) How Moritz joined the Spring team (14:20) Spring Native, GraalVM, and Project Leyden (25:35) Observability, Micrometer, and customer-driven features (32:08) Developer experience: Docker Compose, Testcontainers, SSL hot reload (40:35) JSpecify and annotating Spring Boot for nullability (45:12) start.spring.io and generating Spring projects (50:45) Using AI in coding, reviews, and open source PRs (57:30) Where Spring is headed in the next few years (1:00:08) Favorite languages, Kotlin, and Linux (1:01:13) Personal projects: solar monitoring, Modbus, and heat pump predictions (1:08:46) Giveaway and rapid-fire questions (1:11:32) Outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/FUFsul26rgA

Ronald Dehuysser, creator of JobRunr, joins Marco to talk about distributed job scheduling in Java, building a high-throughput background processing framework, and turning an open-source side project into a profitable business. They dive into what really happens when microservices lack distributed tracing, why dead letter queues can silently lose invoices, how JobRunr scales to thousands of jobs per second, and what it takes to monetize open source in the Java ecosystem.  💡In this episode: Distributed job scheduling and background processing in Java JobRunr architecture and high-throughput performance Quartz vs modern scheduling approaches Retries, exponential backoff, and reliability patterns Dead letter queues and observability challenges Microservices vs monoliths in enterprise systems Monetizing open source and pro licensing models Enterprise sales and scaling a developer product Burnout, sustainability, and building a team AI, LLMs, and the future of junior developers ⏱️Timestamps (00:00) Teaser(00:48) Who's Ronald Dehuysser and what's JobRunr(01:37) From enterprise dev to freelancing (and switching to .NET)(11:19) Job scheduling pain and birth of JobRunr(16:21) Quitting, COVID, and building the first version(28:48) First customers and monetizing open source(40:13) Big enterprise deal and going full-time(47:16) Burnout, Vipassana, hiring, and building a team(53:20) Sustainability features and the future of JobRunr(56:08) AI, junior developers, the future of coding(01:07:28) Giveaway and Rapid-fire questions New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/9Zgw_0kVFk8

Episode description:Thomas Wuerthinger, Founder and Project Lead of GraalVM and Vice President at Oracle, joins Marco to unpack how modern Java runtimes actually work. They explore why duplicating compilers and garbage collectors across languages is a waste of engineering effort, how GraalVM grew from a research project into production technology, and why Native Image unexpectedly became its most impactful feature. 💡 In this episode: GraalVM, Native Image, and Java performance JIT vs AOT and predictable runtime behavior Polyglot runtimes and shared memory models Cloud-native Java, startup time, and memory footprint Performance lessons from the One Billion Row Challenge Branch misprediction and hardware-level bottlenecks Research vs product engineering AI in compilers and testing 🕐 Timestamps (00:00) Intro (01:09) Meet Thomas Wuerthinger & what GraalVM really is (03:31) Why duplicating language runtimes is a waste (06:08) How GraalVM started at Sun Microsystems Labs (10:26) Writing an interpreter and getting a JIT “for free” (14:26) Going open source and finding real users (16:48) Why Native Image took off (23:02) From research project to real product (26:56) Native Image, Spring, Quarkus, and Java in the cloud (35:38) Why JIT performance can be unpredictable (39:31) When JIT still makes sense (43:02) Python, JavaScript, and polyglot runtimes (46:16) Using AI in compilers and testing (01:04:06) The One Billion Row Challenge (01:09:50) Branch misprediction and performance surprises (01:13:26) How to think about performance optimization (01:25:33) Giveaway question (01:27:11) Rapid fire and wrap-up New episodes every other Wednesday. Subscribe for in-depth conversations on software engineering, performance, and developer tools. 🎥 Watch the full episode on YouTube: https://youtu.be/naO1Up63I7Q

Daniel Gräfe, founder of Timing and Cotypist, joins Marco to talk about the realities of indie software development after leaving Big Tech. They discuss turning side projects into a real business, risky rewrites, subscription pricing, and how AI fits into day-to-day coding and support. Daniel shares lessons from more than a decade of building and maintaining Mac apps, including what worked, what didn’t, and why sustainability matters more than quick wins. A grounded conversation for developers thinking about indie SaaS, side projects, or life beyond a full-time engineering role. 💡 Topics in this episode: Leaving Google to go indie Side projects vs running a business Rewriting a product from scratch Subscription pricing decisions B2B vs B2C tradeoffs Using AI in everyday development Long-term maintenance and sustainability 🕑 Timestamps: (00:00) Intro (04:08) Timing, CoTypist, and Daniel’s background (05:30) Early side projects and first Mac apps (13:16) Getting into Google (20:02) Promotion, performance, Big Tech reality (27:33) Quitting Google and going indie (28:27) Rewriting Timing from scratch (37:52) Launch day, sales, and burnout (46:37) Subscriptions and pricing decisions (54:13) Using AI for coding and support (01:03:34) Advice for aspiring indie developers (01:12:10) Agency vs intelligence (01:13:22) Giveaway + outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/t4DkaadHLnI

J. B. Rainsberger, TDD coach and software consultant, joins Marco to challenge common assumptions about software testing. They discuss why JB argues that “integrated tests are a scam,” how testing choices affect design and refactoring, and what teams get wrong when they rely too heavily on end-to-end tests. The conversation draws on decades of real-world experience working with development teams of all sizes. 💡 Topics in this episode: What “integrated tests” really are and why they cause problems Micro tests, fast feedback, and confidence-driven testing When deleting tests is the right decision How tests create pressure on system design Collaboration and contract tests explained Why there is no single testing best practice Practical advice for junior and senior developers New episodes every other Wednesday. Subscribe for more developer-focused conversations. ⏰ Timestamps: (00:00) Teaser (01:24) Intro (02:44) How J. B. got into testing (06:12) What “integrated tests” actually mean (10:42) Stop asking “what’s the right test size?” (13:22) Removing irrelevant details from tests (15:30) Refactoring, coupling, and deleting tests (18:08) “There’s no best practice.” (23:52) Old TDD books and timeless ideas (26:01) Collaboration and contract tests explained (33:53) “99.5% of your tests can be micro tests” (46:21) Do you want to be right or solve the problem? (01:00:48) Advice for junior developers (01:19:32) Testing strategy distilled (01:23:58) Manual QA testing (01:29:44) Giveaway (01:33:15) Rapid fire and outro 🎥 Watch the full episode on YouTube: https://youtu.be/j0NjFsb-at8

The first season of The Marco Show has officially come to an end! In this Christmas special episode, we’re announcing all of our giveaway winners and wrapping up the season with a fun Q&A with Marco. 🎁 Giveaway winners: If you’re one of the winners featured in this episode, please reach out to us at [email protected] and let us know what you’d prefer: a 1-year license to one of our JetBrains products, or a coupon for the JetBrains merch store. Timestamps: (00:00) Season Finale Intro & What to Expect (00:34) LLMs with Jodie Burchell: giveaway winner (01:08) Software performance with Casey: giveaway winner (01:39) Hibernate with Gavin King: giveaway winner (02:15) Flyway with Axel Fontaine: giveaway winner (02:45) SQL with Torben: giveaway winner (03:16) Quarkus with Kevin Dubois: giveaway winner (03:46) Jetty with Ludovic: giveaway winner (04:18) ByteBuddy with Rafael: giveaway winner (04:52) How to claim your prize (05:08) Q&A with Marco (09:35) Outro and meet the team Thank you to everyone who watched, listened, and supported the show this season. We truly appreciate it. See you next year! New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/hZemYWNKa6I

Rafael Winterhalter, creator of ByteBuddy and long-time Java consultant, joins Marco to break down the hidden world of JVM bytecode instrumentation. They discuss how ByteBuddy powers tools like Mockito and Hibernate, why legacy Java still dominates enterprise systems, and what it’s really like maintaining a massive open-source project as a single developer. 💡 Topics in this episode: How ByteBuddy works and why frameworks rely on it JVM bugs, JIT issues, and surprises from instrumentation Supporting legacy Java (5, 6, 8…) in modern environments Open-source sustainability and avoiding burnout Java modules, unsafe, and evolving with new JDK releases Rethinking Java build tools and software engineering complexity 🕑 Timestamps: (00:00) Intro (01:10) Rafael’s background & the origin of ByteBuddy (03:00) What ByteBuddy does and why it matters (04:32) Replacing CGLIB and early challenges (07:05) ByteBuddy’s design philosophy (09:15) Mockito, Hibernate and real-world adoption (13:14) Open source reality (15:48) Performance myths and JVM behavior (18:47) JVM bugs, JIT issues and instrumentation pitfalls (21:11) Supporting legacy Java in enterprise (23:56) Testing ByteBuddy across many JDKs (25:53) Why companies still run Java 5/6 (28:25) Engineering vs economics (30:39) Modules, unsafe and evolving with Java (36:12) Maintaining a one-person project (39:31) Conferences and developer evangelism (42:02) Consulting vs product engineering (49:51) Burnout and sustainability (52:02) Thoughts on AI in software development (57:13) Rethinking Java build tools (01:05:07) Build security and dependency risks (01:10:16) Giveaway (01:10:48) Rapid fire and outro New episodes every other Wednesday. Subscribe for more developer-focused conversations. 🎥 Watch the full episode on YouTube: https://youtu.be/AzfhxgkBL9s