The MonkCast

Why does ChatGPT recommend one DevTool over another, and what can founders do about it? In this RedMonk Conversation, Kate Holterhoff sits down with Adam DuVander, Principal Consultant at EveryDeveloper and author of Developer Marketing Does Not Exist, to explore how AI assistants are transforming software discovery. They discuss Adam’s LLM Rank research, which tracks how models like ChatGPT, Claude, and Gemini recommend developer tools, and why those recommendations often differ. The conversation covers the practical steps DevTool companies can take to improve AI discoverability, from documentation and developer experience to use-case positioning and contextual content. Along the way, Kate and Adam examine the expanding definition of “developer,” the rise of AI-assisted builders, and why developer marketing is evolving rather than disappearing. Whether you’re building a developer product or rethinking your go-to-market strategy, this episode offers a practical framework for succeeding in an LLM-first world.Show notes: https://redmonk.com/videos/adam-duvander/Chapters00:00 Defining the Modern Developer04:25 The Role of Every Developer09:38 Marketing to the New Breed of Developers12:37 Understanding LLM Rank and AI in Development19:57 The Evolution of Documentation and Developer Experience22:06 Understanding Developer Context and Use Cases25:31 Navigating AI Recommendations for Developer Tools28:55 The Importance of Language and Phrasing in Queries31:50 The Long Game of SEO and LLM Rankings36:44 Addressing Outdated Documentation and Bad Advice42:20 The Distinction Between Discoverability and Developer Success47:51 The Future of Developer Marketing in an AI World

James Governor sits down with Jason Willeford, Global Lead for Secure Global Support at Red Hat, to dig into digital sovereignty and why it's become such a big deal for European enterprises. Jason explains Red Hat's Confirmed Sovereign Support, launched last year, which keeps EU customer data and the engineers handling it inside the EU. They cover what sovereignty actually means, why banks and telcos now care about it as much as public sector agencies do, and how AI complicates the picture when you don't know where a model was trained or what data it consumed on the way there. The conversation also touches on IBM's Sovereign Core, Project Lightwell, and RHEL for SAP.This RedMonk conversation is sponsored by Red Hat.Show notes: https://redmonk.com/videos/jason-willeford/Chapters00:00 Introduction to Digital Sovereignty02:26 Understanding Sovereignty: Definitions and Spectrum05:31 The Complexity of Sovereignty in Europe08:21 Data Sovereignty and Support Models11:28 AI Sovereignty and Its Implications14:15 Operational Sovereignty and Customer Engagement17:20 The Role of Red Hat in Sovereignty20:40 Sovereign Core and Global Expansion23:36 Mindset Shift Towards Sovereignty26:22 Engaging Developers and Enterprises on Sovereignty29:36 The Future of Sovereignty and Open Source

What does it mean to trust software? For this RedMonk Conversation, Kate Holterhoff sits down with John Ellis, President of Codethink and the contributor to the Eclipse Trustable Software Framework, to pull that question apart. Ellis leans on an old image: the bridge builders who once slept under their own bridges to prove the work was sound. Modern software rarely faces that kind of test, even when it steers a car or flies a plane. He explains where trust tends to break, especially the integration step where hidden dependencies finally show themselves, and points out that a safety certificate almost never uses the word "safe." Rather than pass-or-fail box-ticking, the framework asks teams to state their confidence and back it with evidence that others can inspect and challenge. They also dig into AI-written code, the EU Cyber Resilience Act, and why rising software recalls suggest current habits fall short.This RedMonk video is sponsored by the Eclipse Foundation.Show notes: https://redmonk.com/videos/john-ellis/Chapters00:04 Introduction to the Eclipse Trustable Software Framework02:57 Understanding Trust in Software05:59 The Integration Moment of Truth08:54 Challenges in Software Development Lifecycle11:25 The Role of the Eclipse Trustable Software Framework14:39 Managing Change in Software Development17:20 Versioning and Continuous Improvement20:01 Risk Analysis and Trustworthiness in Software24:39 Automating Testing and Confidence in Software25:23 Bridging the Gap with Regulators27:15 The Complexity of Software and Safety Standards29:18 Understanding Certification and Safety Claims31:12 The Need for a Shift in Mindset32:16 The Role of the Eclipse Trustable Software Framework34:18 Navigating the EU Cyber Resilience Act37:41 The Journey of Compliance and Awareness39:16 AI's Impact on Software Provenance43:34 Future Directions for the Eclipse Trustable Software Framework

AI agents are writing code faster than anyone can build, test, and ship it. James Governor talks with Helen Altshuler, CEO and co-founder of EngFlow, about why the bottlenecks that once happened at Google scale are now showing up everywhere. Helen traces the story back to Google's 2008 push for five-minute builds, which produced Blaze and eventually Bazel, and explains why that system is finally having its moment a decade after it was open sourced. The numbers are hard to ignore: EngFlow's customers went from 250 million build actions a week to nearly 2 billion in a single year. Companies like Snowflake, Databricks, and Tinder are using Bazel, managed by EngFlow to keep up. James and Helen also dig into agent-ready documentation, why verification is increasingly automated or even ignored, and what happens once people stop using pull request workflows. We're about to hit a wall of AI created scale, and companies are going to fundamentally rethink architectures and approaches if the want to make it.This RedMonk video is sponsored by EngFlow.Show notes: https://redmonk.com/videos/helen-altshuler/Chapters00:04 Introduction and Context Setting01:25 Challenges of Build Management at Scale04:29 Google's Historical Perspective on Build Management07:15 The Impact of AI on Software Development10:19 Bazel's Role in Modern Development13:23 Community Growth and Open Source Collaboration16:36 Enhancing Developer Experience with AI19:22 Customer Insights and Scaling Challenges22:34 Bazel's Unique Advantages and Market Position25:21 Multi-Cloud Strategies and Infrastructure Flexibility29:36 Scaling Software Development with Data33:06 Investing in Developer Infrastructure36:37 Embracing GitHub and CI Challenges42:13 The Evolution of Code Review Practices47:11 The Future of Build Management at Scale

James Governor sits down with Marek Poliks, Head of AI at LaunchDarkly, for a spicy conversation about what it actually takes to manage agents in production. Marek makes the case that feature flags were almost designed for this moment: pulling agents out of application code lets teams gate, roll back, and iterate on whole agent harnesses as testable variations rather than hard-coded entities. Along the way he argues that everyone is now experimenting in production whether they admit it or not, and that the popular gateway approach introduces brittle third-party dependencies at the most security-sensitive point in the stack. According to Marek, observability "will not save us." Passive logging is a lagging indicator; the real value lies in runtime control and adaptive triggers that intervene before bad things happen. The discussion ranges across progressive delivery, software factories, healthcare and finance adoption, and closes with Stafford Beer and outcomes-driven engineering.The RedMonk video is sponsored by LaunchDarkly.Show notes: https://redmonk.com/videos/marek-poliks/Chapters00:00 Introduction to AI and Agent Management03:11 The Role of Feature Flags in AI Agents06:01 Experimentation in Production09:05 Runtime Control and Progressive Delivery11:49 The Future of Software Development and AI15:04 Challenges in AI Adoption Across Industries18:08 The Importance of Context in AI Systems20:57 Navigating Regulations and Compliance in AI24:02 Conclusion and Future Perspectives26:34 The Evolution of Automation and AI27:51 Shifting Focus: From Inputs to Outcomes29:07 The Role of Gateways in AI Security33:02 The Limitations of Observability38:58 Continuous Feedback Loops in Software Development

James Governor talks with Charity Majors, Co-founder and CTO of Honeycomb, about what AI is doing to engineering practices. Majors argues that non-determinism takes more discipline, not less, and every CEO who wants their AI cookies has to eat their broccoli first. Agents in production are finally forcing teams to do the instrumentation work they should have done years ago. They get into Chad Fowler's Phoenix architectures and the idea that the system, not the code, holds the truth about how software behaves. Also: which problems AI has genuinely solved and which it hasn't, why bragging about token spend is like driving a Hummer, and the last year of AI hype has been aimed almost entirely at individual productivity when the hard useful work is always about software development as a team sport. They discuss Honeycomb's Canvas, the company's AI-guided Observabiolity workspace. And finally they discuss the difference between performative outrage and actually artists in the AI era - and make some recommendations accordingly!Honeycomb is a RedMonk client, but this video is free an independent content.Show notes: https://redmonk.com/videos/charity-majors/Chapters00:00 Intro01:51 Nobody has any change budget left03:15 AI is forcing the discipline conversation05:37 Broccoli first, then ice cream06:01 Phoenix architectures, and why code isn't the source of truth08:47 The problems AI hasn't solved11:30 Where determinism meets non-determinism13:29 Token maxxing, Goodhart, and the Hummer problem15:34 Superpowers for one vs engineering as a team sport19:00 npmx and doing open source in public20:41 Honeycomb Canvas, and social debugging at 2am26:22 AI norms and values27:42 Writing is thinking32:25 Work you stand behind vs feedback you can't outsource35:06 The other bitter lessons37:33 If you support artists, support artists43:05 Performative anger and Bluesky44:49 Knowledge management is back46:27 Canvas, timeline analysis, and what's next