CarGurus’ journey building a developer portal and increasing AI adoption
In this episode, Abi Noda talks with Frank Fodera, Director of Engineering for Developer Experience at CarGurus. Frank shares the story behind CarGurus’ transition from a monolithic architecture to microservices, and how that journey led to the creation of their internal developer portal, Showroom. He outlines the five pillars of the IDP, how it integrates with infrastructure, and why they chose to build rather than buy. The conversation also explores how CarGurus is approaching AI tool adoption across the engineering team, from experiments and metrics to culture change and leadership buy-in.Where to find Frank Fodera : • LinkedIn: https://www.linkedin.com/in/frankfodera/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro: IDPs (Internal Developer Portals) and AI (02:07) The IDP journey at CarGurus(05:53) A breakdown of the people responsible for building the IDP(07:05) The five pillars of the Showroom IDP(09:12) How DevX worked with infrastructure(11:13) The business impact of Showroom(13:57) The transition from monolith to microservices and struggles along the way(15:54) The benefits of building a custom IDP(19:10) How CarGurus drives AI coding tool adoption (28:48) Getting started with an AI initiative(31:50) Metrics to track (34:06) Tips for driving AI adoptionReferenced:DX Core 4 Productivity Framework Internal Developer Portals: Use Cases and Key ComponentsStrangler Fig Pattern - Azure Architecture Center | Microsoft LearnSpotify for BackstageThe AI adoption playbook: Lessons from Microsoft's internal strategy
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Snowflake’s playbook for operational excellence
In this episode, Abi Noda speaks with Gilad Turbahn, Head of Developer Productivity, and Amy Yuan, Director of Engineering at Snowflake, about how their team builds and sustains operational excellence. They break down the practices and principles that guide their work—from creating two-way communication channels to treating engineers as customers. The conversation explores how Snowflake fosters trust, uses feedback loops to shape priorities, and maintains alignment through thoughtful planning. You’ll also hear how they engage with teams across the org, convert detractors, and use Customer Advisory Boards to bring voices from across the company into the decision-making process.Where to find Amy Yuan: • LinkedIn: https://www.linkedin.com/in/amy-yuan-a8ba783/Where to find Gilad Turbahn:• LinkedIn: https://www.linkedin.com/in/giladturbahn/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro: an overview of operational excellence(04:13) Obstacles to executing with operational excellence(05:51) An overview of the Snowflake playbook for operational excellence(08:25) Who does the work of reaching out to customers(09:06) The importance of customer engagement(10:19) How Snowflake does customer engagement (14:13) The types of feedback received and the two camps (supporters and detractors)(16:55) How to influence detractors and how detractors actually help (18:27) Using insiders as messengers(22:48) An overview of Snowflake’s customer advisory board(26:10) The importance of meeting in person (learnings from Warsaw and Berlin office visits)(28:08) Managing up(30:07) How planning is done at Snowflake(36:25) Setting targets for OKRs, and Snowflake’s philosophy on metrics (39:22) The annual plan and how it’s shared Referenced:CTO buy-in, measuring sentiment, and customer focusSnowflakeBenoit Dageville - Snowflake Computing | LinkedInThierry Cruanes - Snowflake Computing | LinkedIn
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The biggest obstacles preventing GenAI adoption — and how to overcome them
In this episode, Abi Noda speaks with DX CTO Laura Tacho about the real obstacles holding back AI adoption in engineering teams. They discuss why technical challenges are rarely the blocker, and how fear, unclear expectations, and inflated hype can stall progress. Laura shares practical strategies for driving adoption, including how to model usage from the top down, build momentum through champions and training programs, and measure impact effectively—starting with establishing a baseline before introducing AI tools.Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• Website: https://lauratacho.com/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro: The full spectrum of AI adoption(03:02) The hype of AI(04:46) Some statistics around the current state of AI coding tool adoption(07:27) The real barriers to AI adoption(09:31) How to drive AI adoption (15:47) Measuring AI’s impact (19:49) More strategies for driving AI adoption (23:54) The Methods companies are actually using to drive impact(29:15) Questions from the chat (39:48) Wrapping upReferenced:DX Core 4 Productivity FrameworkThe AI adoption playbook: Lessons from Microsoft's internal strategyMicrosoft CEO says up to 30% of the company's code was written by AI | TechCrunchViral Shopify CEO Manifesto Says AI Now Mandatory For All EmployeesDORA | Impact of Generative AI in Software DevelopmentGuide to AI assisted engineeringJustin Reock - DX | LinkedIn
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DORA’s latest research on AI impact
In this episode, Abi Noda speaks with Derek DeBellis, lead researcher at Google’s DORA team, about their latest report on generative AI’s impact on software productivity.They dive into how the survey was built, what it reveals about developer time and “flow,” and the surprising gap between individual and team outcomes. Derek also shares practical advice for leaders on measuring AI impact and aligning metrics with organizational goals.Where to find Derek DeBellis: • LinkedIn: https://www.linkedin.com/in/derekdebellis/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro: DORA’s new Impact of Gen AI report(03:24) The methodology used to put together the surveys DORA used for the report (06:44) An example of how a single word can throw off a question (07:59) How DORA measures flow (10:38) The two ways time was measured in the recent survey(14:30) An overview of experiential surveying (16:14) Why DORA asks about time (19:50) Why Derek calls survey results ‘observational data’ (21:49) Interesting findings from the report (24:17) DORA’s definition of productivity (26:22) Why a 2.1% increase in individual productivity is significant (30:00) The report’s findings on decreased team delivery throughput and stability (32:40) Tips for measuring AI’s impact on productivity (38:20) Wrap up: understanding the data Referenced:DORA | Impact of Generative AI in Software DevelopmentThe science behind DORAYale Professor Divulges Strategies for a Happy Life Incredible! Listening to ‘When I’m 64’ makes you forget your ageSlow Productivity: The Lost Art of Accomplishment without BurnoutDORA, SPACE, and DevEx: Which framework should you use?SPACE framework, PRs per engineer, AI research
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Setting targets for developer productivity metrics
In this episode, Abi Noda is joined by Laura Tacho, CTO at DX, engineering leadership coach, and creator of the Core 4 framework. They explore how engineering organizations can avoid common pitfalls when adopting metrics frameworks like SPACE, DORA, and Core 4.Laura shares a practical guide to getting started with Core 4—beginning with controllable input metrics that teams can actually influence. The conversation touches on Goodhart’s Law, why focusing too much on output metrics can lead to data distortion, and how leaders can build a culture of continuous improvement rooted in meaningful measurement.Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• Website: https://lauratacho.com/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro: Improving systems, not distorting data(02:20) Goal setting with the new Core 4 framework(08:01) A quick primer on Goodhart’s law(10:02) Input vs. output metrics—and why targeting outputs is problematic(13:38) A health analogy demonstrating input vs. output(17:03) A look at how the key input metrics in Core 4 drive output metrics (24:08) How to counteract gamification (28:24) How to get developer buy-in(30:48) The number of metrics to focus on (32:44) Helping leadership and teams connect the dots to how input goals drive output(35:20) Demonstrating business impact (38:10) Best practices for goal settingReferenced:DX Core 4 Productivity FrameworkEngineering Enablement PodcastDORA’s software delivery metrics: the four keysThe SPACE of Developer Productivity: There’s more to it than you thinkDevEx: What Actually Drives ProductivityDORA, SPACE, and DevEx: Which framework should you use?Goodhart's law Nicole Forsgren - Microsoft | LinkedInCampbell's law Introducing Core 4: The best way to measure and improve your product velocityDX Core 4: Framework overview, key design principles, and practical applicationsDX Core 4: 2024 benchmarks - by Abi Noda
This is a weekly podcast focused on developer productivity and the teams and leaders dedicated to improving it. Topics include in-depth interviews with Platform and DevEx teams, as well as the latest research and approaches on measuring developer productivity. The EE podcast is hosted by Abi Noda, the founder and CEO of DX (getdx.com) and published researcher focused on developing measurement methods to help organizations improve developer experience and productivity.
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