DevOps, AI, and the Future of Engineering with Patrick Debois

"I feel this is a non-solution to a non-problem that is being given so much credence for very dubious reasons, and in general, it will be harmful.” On HangarDX, Ankit Jain talks with Ray Myers, a software developer and coding-agent expert, about why the push toward dark software factories, where agents write the code and no one reads it, is the wrong goal. They talk about: - Why dark factories are a reboot of vibe coding that still breaks down under real scale, risk, and change over time, and - Why you can get most of the value of dark factories from continuous delivery practices teams have taught for a decade, and - Why people who are telling you not to read code are actually reading the code Chapters 00:00 Introduction to Ray Myers and the concept of dark factories 04:09 What is a dark factory and its hypothetical nature 07:12 Scale, risk, and change in enterprise software 11:48 Why Ray Myers is anti-dark factory 13:27 The importance of reading code and guardrails 17:23 Leveraging guardrails and automated checks 22:25 The role of verification systems and revealing code 33:21 The limits of automation and the analogy of self-driving cars 36:51 The future of AI, automation, and human oversight in software development Replace code reviews with verified intent AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/ 📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast ✏️ Subscribe for more videos: @Aviator-Co 🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/

"We just pretend to do code reviews. I'm wading through this large PR, and I try to understand what's going on and be smart, but the fact is that I don't really know what's going on." In this episode of the HangarDX podcast, Ankit Jain, co-founder and CEO of Aviator, talks to Dr. Michaela Greiler, software engineering researcher and consultant, about: What code review surrender looks like on teams that never actually decided to stop reviewing How her SCOPE model moves oversight earlier, into planning, and scales it by risk, alignment, and understanding, and Why an engineer saying "I'm shipping code I don't understand" is a green flag for a team? Replace code reviews with verified intent AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/ 📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast ✏️ Subscribe for more videos: @Aviator-Co 🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/

"My working hypothesis is that human beings are too slow when dealing with code. If we're going to move at the speed of the agents, we're all going to have to back off a little bit and maintain high-level supervision." On this episode of HangarDX, Ankit Jain sits down with Robert C. Martin (Uncle Bob), co-author of the Agile Manifesto and author of Clean Code, to talk about: Why he stopped reading code, and the constraints he uses instead: unit tests, CRAP scores, mutation testing, and Gherkin, Why code review is slowing teams down, and why only humans can be responsible for what agents produce, and How a single engineer may now do the work of a team, and why Clean Code principles still apply. Note: At 17:05 Uncle Bob starts sharing his screen, so we recommend watching a video version of this episode to tget the full experience: https://youtu.be/5kCISBJwoZo Chapters 00:00 Introduction 01:48 How Uncle Bob codes today 03:12 What changed his mind about AI coding 04:52 Three constraints: unit tests, CRAP, and mutation testing 08:06 Aligning tests with intent 09:38 Gherkin and human-authored specs 11:00 Human judgment as the final arbiter 12:52 Small functions, readability, and complexity 13:20 Relaxing constraints as agents improve 13:54 Code written for agents, not humans 15:10 The future of code review and software teams 17:55 A dynamic UML tool for reviewing agent output 21:09 Architecture diagrams as a review surface 23:05 Disciplines every team needs 25:25 Why harness engineering is overrated 26:37 From harness to a single trusted agent 27:59 Are software factories doomed? 28:30 The AI exponential curve and its physical limits 29:30 Future optimizations and quantum computing 29:58 Responsibility and ownership of AI-generated code 32:26 Rewriting Clean Code for the AI era 33:30 Why Clean Code principles still apply 📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast ✏️ Subscribe for more videos: @Aviator-Co 🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/ Replace code reviews with verified intent AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/

David Curlewis, Senior Engineering Manager at Canva, joins Ankit Jain on HangarDX to talk about what happens to platform teams when AI gives every developer a bulldozer. Why platform teams should go down a layer and offer primitives instead of full highways, and How inner sourcing and a graduation model help platform teams scale without fighting their own developers, about What happens when vibe-coded dashboards go mission-critical overnight, and why outcome metrics beat volume? Chapters 00:00 Welcome to Hangar DX 01:03 David’s platform engineering background 02:13 Why AI is changing platform teams 04:04 The golden path versus AI-built roads 07:40 Platform primitives: observability, security, and MCPs 11:46 Why platform engineering is becoming a core company function 14:56 Centralization, autonomy, and maturity 20:51 Inner source as a model for shared ownership 23:17 Designing contribution guidelines for AI agents 26:50 Intent debt, cognitive debt, and keeping specifications aligned 31:00 Why companies are building their own AI platforms 32:25 Moving from code review to reliable validation 34:50 Measuring AI productivity beyond lines of code 40:06 The return of generalist engineers 📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast ✏️ Subscribe for more videos: @Aviator-Co 🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/ Replace code reviews with verified intent AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/

"The tools are so powerful that people actually want to manage them themselves. Everything is just moving up a tier of thinking.'" Chris Hodges and Kevin Altschuler from Dropbox's Agentic Experience team join Ankit Jain on the HangarDX podcast to talk about why they built Nova, their internal platform for AI coding agents, instead of buying off-the-shelf. They also discuss: Why owning the harness gives you leverage over cost, model selection, and the total developer experience, What makes Nova a platform rather than a point solution? What it takes to get security and privacy teams on board when agents touch your most sensitive data Where is AI engineering headed next? Agents that watch deployments, monitor logs, and message you when something's wrong 00:00 Introduction to Developer Experience and Nova 01:57 Overview of Nova: A New Development Platform 03:57 Challenges Faced by Developers Before Nova 08:34 Metrics for Success: Tracking Nova's Adoption 10:00 Capabilities of Nova: Tackling Toil and Flaky Tests 13:25 Comparing Nova with Existing Cloud-Based Tools 16:28 Nova as a Platform vs. Point Solutions 18:36 Experimentation with Goose and Future Directions 26:35 Building Reliable AI Systems 31:32 The Role of Orchestrators in AI Development 35:07 Future Challenges in AI Development 📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast ✏️ Subscribe for more videos: @Aviator-Co 🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/ Replace code reviews with verified intent AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/