
Hangar DX Podcast
The Hangar DX podcast focuses on developer experience and learning how different companies solve developer productivity challenges at scale.
Ankit Jain
Show overview
Hangar DX Podcast has been publishing since 2024, and across the 2 years since has built a catalogue of 63 episodes. That works out to roughly 40 hours of audio in total. Releases follow a fortnightly cadence.
Episodes typically run thirty-five to sixty minutes — most land between 35 min and 41 min — and the run-time is fairly consistent across the catalogue. None of the episodes are flagged explicit by the publisher. It is catalogued as a EN-language Technology show.
The show is actively publishing — the most recent episode landed 4 days ago, with 22 episodes already out so far this year. Published by Ankit Jain.
From the publisher
The Hangar DX podcast focuses on developer experience and learning how different companies solve developer productivity challenges at scale. <br/><br/><a href="https://www.aviator.co/podcast?utm_medium=podcast">www.aviator.co/podcast</a>
Latest Episodes
View all 63 episodesAI Is Giving Engineering Teams False Confidence with Jay Paulson
Nobody Needs a 50,000-Line PR with Ray Myers
Why Teams Only Pretend to Review AI Code with Dr. Michaela Greiler
Uncle Bob: Humans Are Too Slow for AI-Generated Code
Your Developers Are Building Their Own Roads. Now What?
Why Dropbox Built Their Own AI Coding Platform
Code Review Is a Taste Problem | David Poll @GitHub
How AI Broke Developer Productivity Measurement with Brian Houck(DX)
What Happens to Code Review When Agents Write the Code with Vanitha Kumar, Thoughtworks
Multiplayer Development, AI Enablement, and the PR Review Crisis — Frances Coronel, Slack
Dark Factories, Cargo Cult AI, and Drunk Agents with Geoffrey Huntley
"We Don't Use AI to Produce Magic", Wayne Duso, 1Password
Ship to Production Without Code Review? with Jade Rubick
The Hidden Cost of AI-Generated Code: Cognitive Debt and Intent Debt
All In on Claude Code at 400-Engineer Scale with Brian Scanlan, Intercom
Ep 48Are You Using AI to Go Faster in the Wrong Direction? | Steve Pereira on Flow and Engineering
"We can be in a flow state running in the wrong direction. Unless you can tie all your actions back to your strategic imperative, you might look back in five years and think: That was fun, but could I have gotten further?"In this episode of the HangarDX podcast, Ankit Jain, co-founder and CEO of Aviator, talks to Steve Pereira, lead consultant at Visible Value Stream Consulting and co-founder of the Flow Collective, to discuss where AI is genuinely moving the needle versus just generating more code to review, how to think about context switching and flow state when AI makes task-switching cheaper than ever, and how teams can use value stream mapping as a framework for getting AI adoption right.00:00 Introduction to Developer Experience and Value Stream Mapping05:20 Understanding Value Stream Mapping in Practice08:09 The Impact of AI on Value Stream Flow17:06 Context Switching and Flow State in Software Development27:48 Intentionality in Context Switching and Flow State35:07 Value Stream Mapping as a Superpower for AI Success📫 Sign up to 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/
Ep 47How Honeycomb Is 2Xing Its Engineers with AI
“Our internal target is to 2X our impact with AI over one year. Unlike some more outlandish mandates, that one is both aspirational and achievable,” says Emily Nakashima, SVP of Engineering at Honeycomb.In this episode of The Hangar DX podcast, Emily shares how Honeycomb approached AI adoption at scale and why they try not to focus on metrics that can be gamed but rely more on self-reporting by developers. Emily also discusses:- Why flattening org charts is a short-term optimization that will cost companies later- How Honeycomb issued a company-wide 2X mandate and what actually happened when they did- Why the "buffet phase" of AI tool adoption is over and what a structured rollout looks like- Why self-reporting beats hard metrics when measuring AI's impact on your team- Why observability is more critical than ever in a world of non-deterministic AI-generated code- Why AI SRE tools demo well but often fall short, and what they need to actually work00:00 Introduction to Developer Experience and AI02:19 Emily's Journey in Engineering and Leadership05:15 Navigating Career Growth in Engineering06:28 Cultural Shifts in Engineering Management09:50 The Evolving Role of Engineering Managers12:59 Upskilling in the Age of AI17:50 AI Strategy and Product Development at Honeycomb21:38 Measuring AI Impact and Productivity28:09 The Future of Observability and AI in EngineeringAbout Emily NakashimaEmily serves as SVP of Engineering at Honeycomb. A former manager and engineering leader at multiple developer tools companies, including Bugsnag and GitHub, Emily is passionate about building best-in-class, consumer-quality tools for engineers. She has a background in product engineering, performance optimization, client-side monitoring, and design.About Hangar DX (dx.community)The Hangar is a community of senior DevOps and senior software engineers focused on developer experience. This is a space where vetted, experienced professionals can exchange ideas, share hard-earned wisdom, troubleshoot issues, and ultimately help each other in their projects and careers.
Ep 46Scaling AI Adoption Across Engineering Teams with Ryan J. Salva
"The future is already here, it's just unevenly distributed.", says Ryan J. Salva, Google and GitHub dev tools veteran. Ryan also shares:How OpenAI nearly took down GitHub's servers and how that incident seeded the creation of Copilot The five stages of autonomous engineering and why most teams are stuck between stage three and fourWhy DevOps and SRE are the next untouched frontier of AIThe three-step playbook for rolling out AI tools across large engineering teams without regressing on qualityWhy context, not the model, is the most important investment in AI-assisted development.About Ryan J. SalvaRyan is an experienced developer, product manager, and founder with 25 years of experience building developer tools at startups, Microsoft. GitHub, and now Google. Ryan leads product teams responsible for developer onboarding, code authoring, build and deployment systems, logging, observability, and end-to-end developer experiences. About Hangar DX (https://dx.community/)The Hangar is a community of senior DevOps and senior software engineers focused on developer experience. This is a space where vetted, experienced professionals can exchange ideas, share hard-earned wisdom, troubleshoot issues, and ultimately help each other in their projects and careers.We invite developers who work in DX and platform teams at their respective companies or who are interested in developer productivity.
Ep 45Build, Deploy, and Merge Queues at Scale with Jon Block
“Engineers don’t always like merge queue because they have to wait longer for their PRs to merge. But the trade-off is that the quality of those merges will be higher, and the company will have less downtime and outages,” says Jon Block, founder of LowRouchAdvisor. Using a merge queue is like wearing a seatbelt, he adds, the only responsible thing to do for large engineering organizations that ship products that matter.Jon also shares best practices and lessons learned about scaling build and deploy from his 26 years of experience. Chapters00:00 Introduction to Developer Experience and Scaling Repositories01:37 Managing Repositories in Large Organizations05:24 Monorepo vs. Multirepo: Pros and Cons07:53 Challenges of Merge Queues and Deployment at Scale13:02 GitHub Merge Queue Limitations and Solutions15:36 Batching, Stability, and Deployment Strategies19:16 Train Method of Deployment and Rollbacks22:37 Build Systems, Bazel, and Build Avoidance23:20 Impact of Flaky Tests and Automation28:46 Adopting Merge Queues and Cultural Challenges34:11 AI in Development: Opportunities and Risks37:41 Closing Remarks and ResourcesAbout Jon BlockJon Block has spent 26 years in software engineering, nearly all of it at high-growth startups. He has served as VP of Engineering and CTO multiple times and today advises engineering organizations through his firm, Low Touch Advisors.About Hangar DX (https://dx.community/)The Hangar is a community of senior DevOps and senior software engineers focused on developer experience. This is a space where vetted, experienced professionals can exchange ideas, share hard-earned wisdom, troubleshoot issues, and ultimately help each other in their projects and careers.We invite developers who work in DX and platform teams at their respective companies or who are interested in developer productivity.Verify AI CodeAI 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/
Ep 44Engineering Discipline in the AI Era with Dave Farley
The way that AI is changing software engineering is a bigger shift than object-oriented programming, the internet, and Agile together.", says Dave Farley, author of Continuous Delivery and Modern Software Engineering.Dave also shares why programming languages were designed to help engineers decompose problems into smaller chunks, the three fundamental problems of AI coding, why verification becomes the bottleneck in AI-assisted coding, and why engineering discipline, test-driven development, and behavior-driven development matter even more in this new era.00:00 Introduction to Developer Productivity and Experience02:13 Dave Farley's Journey in Software Engineering08:23 The Impact of AI on Software Development11:00 AI Tools and Their Role in Coding16:39 The Importance of TDD and BDD in AI Development20:37 Testing and Feedback Loops in AI Programming25:30 Navigating Ambiguity in Specifications29:29 Future of Software Architecture with AI34:55 Adapting to AI in Software Engineering Practices37:28 Conclusion and Future PerspectivesAbout Dave Farley Dave is a pioneer of continuous delivery, a thought leader and expert practitioner in CD, DevOps, TDD, and software design, and shares his expertise through his consultancy, YouTube channel @ModernSoftwareEngineeringYT , books, and training courses. Dave co-authored the definitive book on Continuous Delivery and has published Modern Software Engineering. About Hangar DX (https://dx.community/)The Hangar is a community of senior DevOps and senior software engineers focused on developer experience. This is a space where vetted, experienced professionals can exchange ideas, share hard-earned wisdom, troubleshoot issues, and ultimately help each other in their projects and careers.We invite developers who work in DX and platform teams at their respective companies or who are interested in developer productivity.More: https://dx.community/