Podcasts about developer productivity

In this episode of Elixir Wizards, hosts Charles Suggs and Emma Whamond sit down with Marek Šuppa, creator of the Missing GitHub Status page, a project that reconstructs GitHub's historical uptime data and reveals discrepancies between official status reporting and the platform's actual reliability. Marek tells us about his dev journey from open source contributor at DuckDuckGo to machine learning engineer at Cisco-acquired Slido. Then, we discuss GitHub's evolution from a hosted Git service into a critical developer tool. We cover reliability, transparency, AI-driven platform growth, developer workflows, and the challenges of balancing convenience with resilience. Along the way, we cover alternative platforms, self-hosted solutions, and whether recent outages are changing how developers think about ownership, dependency, and the future of software collaboration. Topics Discussed in this Episode: Why did Mr. Shu create the Missing GitHub Status Page? GitHub's reported uptime versus developer experiences How open source contributions shaped Marek's career The evolution of GitHub from tool to critical infrastructure Centralization risks in modern software development Git's distributed roots and today's platform-centric workflows Developer reactions to GitHub outages Transparency and accountability in status reporting AI's impact on developer platforms and infrastructure demands Microsoft's stewardship of GitHub Forgejo, Codeberg, and alternative Git hosting platforms Self-hosted Git solutions and tradeoffs Network effects and platform lock-in The social side of software collaboration Building resilience into developer workflows What GitHub outages teach us about infrastructure dependency Links Mentioned: The Missing GitHub Status Page https://mrshu.github.io/github-statuses/ Slido https://www.slido.com/ https://duckduckgo.com/ The official GitHub Status Page https://www.githubstatus.com/ Statuspage.iohttps://www.atlassian.com/software/statuspage Zig Leaves GitHub https://ziglang.org/news/migrating-from-github-to-codeberg/ Ghostty Leaves GitHub https://mitchellh.com/writing/ghostty-leaving-github GitLab https://about.gitlab.com/ Codeberg https://codeberg.org/ https://git.kernel.org/ Forgejo Lightweight Self-Hosting https://forgejo.org/ Former GitHub CEO Thomas Dohmke launches Entire https://entire.io/news/former-github-ceo-thomas-dohmke-raises-60-million-seed-round Update on Spain and LALIGA blocks of the internet https://vercel.com/blog/update-on-spain-and-laliga-blocks-of-the-internet

Modern work can be frustrating and chaotic—if you don't have the right tools. From context engineering to multimodal search, go behind the scenes and hear how Dropbox engineers are building AI that actually understands you, so you can focus on the work that matters most. If you're new to Working Smarter, we've travelled from the F1 track to the bottom of a lake, and heard real stories from chefs, doctors, lawyers, and founders about how AI is helping them do more of what they love about their jobs. But in our third season, we're talking to the people behind the tools—the engineers and product leaders building helpful, time-saving AI features into the Dropbox experience you already know and trust. You'll hear all about their work on agents, inference, security, and, of course, how the people building AI use AI themselves. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

In this episode of Elixir Wizards, hosts Charles Suggs and Emma Whamond sit down with Saša Jurić, Elixir mentor and author of Elixir in Action, to discuss software craftsmanship in the age of AI. As AI coding tools become increasingly capable, Saša argues that the real challenge isn't generating code, it's maintaining quality, clarity, and shared understanding within a codebase. We explore the difference between correct code and good code, and why code is more than a set of instructions for a machine to execute. Code is also documentation, communication, and a long-term investment that future developers must be able to understand and maintain. Saša shares his concerns about the growing "theater of pull requests," where teams go through the motions of code review without creating meaningful opportunities for learning, feedback, or knowledge sharing. The hosts and Saša talk about practical ways to work effectively with AI, including taking smaller steps, carefully reviewing AI-generated code, and using AI as a collaborative tool rather than an autonomous developer. Throughout the discussion, Saša challenges the industry's obsession with speed and makes the case that the principles of good software development (incremental progress, clear communication, and human judgment) remain important in the age of AI. Key Topics Discussed The difference between correct code and good code Code as communication, documentation, and shared understanding The "theater of pull requests" and ineffective review practices How AI is changing software development workflows Using AI as a collaborator rather than a replacement Why smaller, incremental changes lead to better outcomes Human oversight in AI-assisted development Balancing development speed with maintainability Pull request size and review effectiveness Commit history as a tool for storytelling and context The risks of accumulating technical debt faster with AI Testing and validating AI-generated code Refactoring AI-generated solutions for clarity Applying agile principles to AI-assisted workflows The role of experience and judgment in software design Why software craftsmanship still matters in the age of AI Links mentioned Code Complete by Steve McConnell https://khmerbamboo.wordpress.com/wp-content/uploads/2014/09/code-complete-2nd-edition-v413hav.pdf Harness AI for DevOps, Testing, and AppSec https://www.harness.io/ Claude Code https://claude.com/product/claude-code Claude Code GitHub https://github.com/anthropics/claude-code Pull Request for Oban https://github.com/oban-bg/oban/pull/331 SMPP https://en.wikipedia.org/wiki/Short_Message_Peer-to-Peer OpenAI Codex https://chatgpt.com/codex/ Opus AI https://opus.ai/ Tidewave https://tidewave.ai/ Credo Static Code Analysis https://github.com/rrrene/credo https://smartlogic.io/podcast/elixir-wizards/s11-e09-static-code-analyzer-elixir-credo-ruby-rubocop/ Link to Sasa's X post https://x.com/sasajuric/status/2029522378196238503 Saša Jurić “Tell Me A Story” at Goatmire https://www.youtube.com/watch?v=GOrKfCs-mr0 https://meks.quest/blogs/the-theatre-of-pull-requests-and-code-review Looks Good to Me: Constructive Code Reviews by Adrienne Braganza https://www.manning.com/books/looks-good-to-me Towards Maintainable Elixir: Testing https://medium.com/very-big-things/towards-maintainable-elixir-testing-b32ac0604b99 TDD, Where Did It All Go Wrong (Ian Cooper) https://youtu.be/EZ05e7EMOLMSpecial Guest: Saša Jurić.

Right now, the questions we have about our careers feel existential. We keep coming back to the same theme: how do you prepare for an industry that's changing this fast, and what mindset actually works in this new reality? One skill keeps surfacing as the answer — your ability to update your own mental models. In today's episode, I want to push on that further and put some of software engineering's most beloved thinking models under scrutiny. Some of these models served you well for years. Some of them now deserve to be challenged, replaced, or thrown out entirely — and learning how to tell the difference is itself the skill that will determine whether you hit a ceiling. Move Past "So What" Questions: The typical engineering objection to agentic coding is that it produces quality issues. But the people deciding to adopt these tools already accept that. Our job is to stop arguing the surface-level point and start asking the real one: so what do we actually do about this new economic reality? The Economics of Acceptable Loss: Abstraction always leaves something to be desired. An agent's code may not match what a staff engineer produces by hand over months — but that gap is usually an acceptable trade against shipping something two, three, or four times faster. Understand the cost-benefit picture instead of pretending the cost doesn't exist. Abstraction Has Always Done This: This isn't new. The calculator dissolved the specialization once required for complex math. Spreadsheets commoditized ledgering and accounting. Agentic coding is the same pattern arriving for our work — making something that required deep specialization suddenly far more accessible. Roles Are Blurring: As these generic tools raise everyone's ability to abstract, the boundaries soften. You're already seeing product managers open pull requests and engineers making product decisions. The neat lines around "what an engineer is" are not as fixed as they used to feel. Why Your Hard-Won Wisdom Is the Target: If you've spent years in this industry, your models were bought with blood, sweat, and failed projects. That experience is real wisdom — and it's exactly what I'm asking you to be willing to challenge, because the thing that always worked for you is the thing most likely to become a ceiling. This Skill Survives Either Way: Even if you think AI is mostly hype and I've been infected by it — fine. The ability to challenge your pre-existing models is a critical skill regardless. It's how you keep growing as you get more senior instead of repeating what used to work. Models Are Approximations: The whole point of a model is to approximate the reality around us. That's their value and their limitation. When the underlying reality shifts this dramatically, holding tightly to an old approximation stops being wisdom and starts being a liability.

If you're a software engineer right now, you likely feel like your world is changing overnight. We are writing half or less the amount of code that we wrote even a year ago, which represents a seismic, groundbreaking shift in our industry. For many of us, this career has always been engaging for deeply creative and intellectual reasons—and that excitement is still here. But our mental models of what it means to be a good engineer, and what it means to keep improving, have gone a little stale. In today's episode, I want to talk about a distinction that I believe will become the cornerstone mistake for seasoned engineers: confusing _practice_ with _adaptation_, and leaning on the wrong one at the worst possible moment. Two Surfaces Coming Into Contact: Picture your knowledge, skills, and toolset as one surface, and the actual state of the art as another. We've always known the surface area we could learn far exceeds what we can learn, which forces us to place bets on a learning strategy. What's changing is how fast that second surface is moving underneath us. Improvement by Practice vs. Improvement by Change: Practice is wielding what you've already adopted—smoothing out errors, building muscle memory, refining what you already know. Adaptation is fundamentally folding something new into your repertoire. Both are real forms of improvement, but they are not interchangeable. The Cornerstone Mistake for Senior Engineers: Later in your career, the time you spend adapting naturally goes down as you settle into practice. The biggest error I'm already watching engineers make is moving too quickly toward practice when the industry is loudly calling for adaptation instead. Inspect and Adapt—at the Right Altitude: Sprint retros were never really about getting marginally better at the thing you already do. The intent of "inspect and adapt" is to step up one level and examine the system. The trap is treating adaptation like a minor refinement—getting a little better at prompting—when it should mean asking whether you're thinking about prompting in the wrong way entirely. Question the Ratio, Not Just the Output: Real adaptation looks like asking whether you have the right mix of human and agent on a problem. Are you leaning on the agent for things you shouldn't, or failing to lean on it for the things you should? Have you genuinely thought about how sub-agents or an agent team are working the problem you're producing? A Spectrum, Not a Binary: On one end, you make micro-adjustments to your refinement process. On the other end of experimentation, you ask whether refinement—or even having engineers plan the work—is the right thing at all. The point isn't that practice is dead; it's that the industry is changing fast enough that the adaptive end of that spectrum deserves far more of your attention than it used to. Episode Homework: Take something you currently treat as a practice problem—"how do I refine tickets faster?"—and step up a level. Ask the adaptive version of the question instead: "Is refinement even the right thing anymore?"

In Season 15 episode 3, Charles Suggs sits down with Greg Medland, aka “The Elixir Fixer,” to talk about the current state of hiring and the software jobs market in 2026.   Greg shares what he's seeing from both sides of the hiring process as an Elixir-focused recruiter, from shifting company expectations to the growing importance of specialization, communication skills, and real-world product thinking. We discuss how the market has changed since the 2021–2022 hiring boom, why things feel more uncertain today, and how developers are adapting to a slower, more competitive landscape.   The conversation also explores how AI is affecting hiring workflows, résumé quality, technical interviews, and even the rise of fraudulent candidates. Greg explains why human relationships and reputation still matter more than ever, especially in smaller ecosystems like Elixir where community connections carry real weight.   Along the way, we talk about what junior developers are up against, why senior engineers with domain expertise continue to stand out, and what developers can do to position themselves more effectively in today's market. Greg shares practical advice for building a sustainable career, developing a clear professional identity, and navigating a rapidly changing industry.   Topics discussed in this episode: The current state of the Elixir job market Hiring trends and market shifts since 2021–2022 How AI is changing hiring and recruiting workflows Fraudulent candidates and AI-generated résumés Domain expertise vs. generalist engineering skills Product thinking and customer-focused development What companies are looking for in 2026 Junior developer challenges in the current market Why senior specialists remain in demand Networking and relationship-building in tech Open source contributions and visibility in the Elixir community Standing out in a crowded hiring environment Résumé quality and application strategies The role of personal branding for developers Remote work trends and geographic hiring patterns Technical interview expectations and evaluation changes Startup vs. enterprise hiring differences Human connection in an increasingly automated industry Career resilience and long-term positioning Building a sustainable software engineering career   Links mentioned: Socially Responsible Recruitment https://sr2rec.com/en/ Greg's LinkedIn https://www.linkedin.com/in/elixirfixer/ Greg's email address: greg@sr2rec.com

Neal Ford: software architect, author, speaker, and independent consultant (formerly 20+ years at ThoughtWorks), joins Tobias to explore what happens to software architecture when AI agents write the code. We unpack the critical distinction between behavior and capabilities: why everyone focuses on what code does, but too few think about scalability, security, and responsiveness. Neal introduces architectural fitness functions as the essential guardrail for agentic systems, and explains why non-deterministic code generation demands deterministic tests. Finally, we dig into legacy modernization, the Dreyfus scale applied to LLMs, ephemerality as the new architectural dimension, and why AI is a multiplier, not a replacement, for experienced engineers.

Send us Fan MailCraig McLuckie is the Founder and CEO of Stacklok and one of the original inventors of Kubernetes — the open-source container orchestration system that became the backbone of modern cloud infrastructure. After leading Kubernetes into the CNCF and navigating VMware's pivot under Broadcom, Craig made a sharp turn and founded Stacklok: an Enterprise MCP Platform designed to make developers and AI agents dramatically more productive in secure, enterprise environments.In this episode, Craig and Al Martin explore what it means to build again after building something that changed the industry — and what the next wave of enterprise AI infrastructure actually requires.In this episode:01:14 Meet Craig McLuckie — background, Kubernetes origins10:08 Tanzu and Broadcom — what that chapter looked like from the inside11:28 Stacklok and the Big Pivot — why MCP, why now19:57 I Don't Know! — a rare founder moment of intellectual honesty21:32 Success is a Poor Teacher — what winning can hide from you23:36 The Future Developer — how AI changes what developers do26:38 Hiring Developers — what Craig looks for nowConnect with Craig: LinkedIn: https://www.linkedin.com/in/craigmcluckie/ Website: http://stacklok.comWant to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Send us Fan MailCraig McLuckie is the Founder and CEO of Stacklok and one of the original inventors of Kubernetes — the open-source container orchestration system that became the backbone of modern cloud infrastructure. After leading Kubernetes into the CNCF and navigating VMware's pivot under Broadcom, Craig made a sharp turn and founded Stacklok: an Enterprise MCP Platform designed to make developers and AI agents dramatically more productive in secure, enterprise environments.In this episode, Craig and Al Martin explore what it means to build again after building something that changed the industry — and what the next wave of enterprise AI infrastructure actually requires.In this episode:01:14 Meet Craig McLuckie — background, Kubernetes origins10:08 Tanzu and Broadcom — what that chapter looked like from the inside11:28 Stacklok and the Big Pivot — why MCP, why now19:57 I Don't Know! — a rare founder moment of intellectual honesty21:32 Success is a Poor Teacher — what winning can hide from you23:36 The Future Developer — how AI changes what developers do26:38 Hiring Developers — what Craig looks for nowConnect with Craig: LinkedIn: https://www.linkedin.com/in/craigmcluckie/ Website: http://stacklok.comWant to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Most conversations about AI focus on what it can do in a controlled setting. This one doesn't. Callum Sharrock spends his days deploying AI systems in real environments, watching them succeed and fail in ways no simulation predicted, and reporting what he finds. His conclusion? The trend line is steeper than most people realize, and snapshot thinking is getting a lot of organizations into trouble.Peter Maddison and Dave Sharrock dig into why reliability, not capability, is the real adoption bottleneck right now. They talk through what happens when non-deterministic models get applied to problems that need deterministic answers, why validation and testing are becoming more important than writing the code itself, and how the calculus around decision making is changing fast. If you can build and test something in the time it takes to debate whether to do it, the meeting starts to look like the problem.They also get into what this means for developers, for leaders, and for anyone trying to figure out where to actually invest their energy right now. The barriers to building have never been lower. That makes the question of what to build more important than ever.This isn't a conversation about AI hype. It's about what's actually happening at the frontier, and what it means for the way organizations make decisions.This Week's Takeaways:The barriers to building have never been lower - figuring out what's worth building is now the real workLeadership is shifting toward agency and rapid decision-making, away from top-down strategy settingIf you can run the experiment in the time it takes to schedule the meeting about it, run the experimentIf this episode resonated, follow Definitely Maybe Agile wherever you listen to podcasts so you never miss a conversation. And if you know someone spending two hours debating whether to test an idea they could just build, send this one their way. There are plenty more episodes worth your time at definitelymaybeagile.com.

This is a free preview of a paid episode. To hear more, visit www.latent.spaceAIE Europe CFP and AIE World's Fair paper submissions for CAIS peer review are due TODAY - do not delay! Last call ever.We're excited to welcome METR for their first LS Pod, hopefully the first of many:METR are keepers of currently the single most infamous chart in AI:But every Latent Space reader should be sophisticated enough to know that the details matter and that hype and hyperbole go hand in hand in AI social media, because the millions of impressions that got, by people who don't understand or care about the nuances, disclaimers, and error bars, far outreaches the 69k views on the corrections by the people who actually made the chart:There's a lot of nuance both in making benchmarks (as we discovered with OpenAI on our SWE-Bench Verified podcast) and in extrapolating results from them, especially where exponentials and sigmoids are concerned. METR's Long Horizons work itself has known biases that the authors have responsibly disclosed, but go far too underappreciated in the pursuit of doomer chart porn.If you're interested in a short, sharable TED talk version of this pod, over at AIE CODE we were blessed to feature Joel twice, as a stage talk and with a longer form small workshop with Q&A:We also make sure cover some of METR's lesser known work on Threat Evaluation but also Developer Productivity, where 2x friend of the pod and now Zyphra founder Quentin Anthony was the ONLY productive participant!Finally, if you're the sort to read these show notes to the end, then you definitely deserve some pictures of Joel shredding the guitar at Love Band Karaoke which we mention at the end: Full Video PodTimestamps00:00 What METR Means00:39 Podcast Intro With Joel01:39 ME vs TR03:33 Time Horizon Origin Story04:56 Picking Tasks And Biases09:13 Time Horizon Misconceptions11:37 Opus 4.5 And Trendlines14:27 Productivity Studies And Explosions29:50 Compute Slows Progress30:47 Algorithms Need Compute32:45 Industry Spend and Data34:57 Clusters and Shipping Timelines36:44 Prediction Markets for Models38:10 Manifold Alpha Story43:04 Beyond Benchmarks Evals51:39 METR Roadmap and FarewellTranscript

Developer productivity is often framed as a tooling initiative or a morale issue. At scale, it's a more complex socio-technical systems challenge that spans engineering foundations, leadership alignment, organizational structure, and culture.In this episode, Laura Tacho sits down with Uma Namasivayam, Senior Director, Engineering Productivity at Dropbox, to discuss how the company approaches developer experience across an organization of nearly 1,000 engineers. Uma explains why productivity must be treated as a business problem, how executive alignment enables sustained progress, and what it means to run developer experience like a product.The conversation also explores the intersection of AI and developer experience. Uma shares how Dropbox prepared its engineering systems to support AI adoption, why daily AI use depends more on habits than access, and how the company evaluates build-versus-buy decisions as AI tools struggle to scale in large environments.The episode concludes with a candid discussion of the open questions facing engineering leaders today: how to understand where AI-driven capacity actually goes, and how to connect improvements in developer experience to meaningful business outcomes in 2026.Where to find Uma Namasivayam:• LinkedIn: https://www.linkedin.com/in/unamasivayamWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(00:45) Dropbox's engineering org(01:59) Why developer productivity is a business problem(04:08) The role of executive sponsorship in developer productivity(06:02) How DX's Core Four framework created a shared language(08:13) Treating developer experience as a product(11:30) How Dropbox prioritizes developer experience work(14:20) The challenge of tying developer experience to business outcomes(16:38) How AI and developer experience intersect at Dropbox(18:35) The prerequisites for AI adoption to accelerate work(20:26) How Dropbox encourages daily AI use(23:12) AI use beyond code completion(25:00) Managing AI tool demand at scale(27:56) Early results from Dropbox's AI efforts(30:05) Progress on developer experience at Dropbox(32:55) Advice for organizations investing in developer experience(34:25) Capacity tradeoffs for developer experience(35:59) The unanswered questions around AI and capacity in 2026Referenced:• DX Core 4 Productivity Framework• Dropbox.com

Lee Zen, CTO of Yahoo, joins Tobias to unpack what it takes to modernize one of the internet's most iconic consumer portfolios—Mail, Finance, Sports, News, and Search—while operating with real legacy constraints at massive scale. We talk about Yahoo's evolution from its public days to private equity ownership, how modernization actually happens (cloud, platform bets, experimentation), and why shipping velocity becomes the most honest forcing function when you're rebuilding the engine mid-flight. Finally, we go deep on AI: where it meaningfully improves consumer experiences (mail catch-up, news takeaways, fantasy insights), how teams should avoid “AI labels” without user value, and what it means when AI becomes a tool—and increasingly a coworker.

In this episode of Semaphore Uncut, Jamie Dobson, co-founder and former CEO of Container Solutions, shares a thoughtful perspective on generative AI, developer productivity, and system stability.The conversation explores why recent research shows engineers feeling happier and more productive with AI tools—while system reliability quietly declines. Jamie explains how AI-generated code can appear correct while introducing subtle long-term risk, and why speed without deep understanding can be dangerous for growing systems.They also discuss why looking back at the history of computing—from time-sharing to early neural networks—helps demystify today's AI hype. The episode closes with a reflection on the stories we tell about technology, how humans and machines can work together, and what engineering leaders should be paying attention to next.

“I don't worry about being replaced by AI. I worry about being replaced by someone who's really good at using AI.”Atlassian has 10,000+ engineers currently split-testing the world's top AI coding tools, from GitHub Copilot and Cursor to Claude Code. In this episode, Co-Founder & CEO Mike Cannon-Brookes joins Lukas Biewald to share what their data reveals about the world's best AI tools today.Hear how 24 years of building a tech giant and a massive internal study on AI productivity have shaped Mike's vision for the future of dev jobs.Connect with us here:Mike Cannon-Brookes: https://www.linkedin.com/in/mcannonbrookes/?originalSubdomain=auAtlassian: https://www.linkedin.com/company/atlassian/?viewAsMember=trueLukas Biewald: https://www.linkedin.com/in/lbiewald/ Weights & Biases: https://www.linkedin.com/company/wandb/00:00 Trailer01:08 Introduction03:11 Connecting Technology and Business Teams07:22 The Impact of AI on Business Workflows13:26 Developer Productivity and AI21:03 Measuring Developer Efficiency25:41 Future of AI in Development34:59 Legacy Technology and Code Changes39:29 AI's Role in Developer Productivity47:40 AI and Junior Developers52:30 Product-Led Growth and Business Strategy01:00:29 Core Metrics for Sustainable Growth01:06:56 Staying Creative in the Tech Industry

As AI adoption accelerates across the software industry, engineering leaders are increasingly focused on a harder question: how to understand whether these tools are actually improving developer experience and organizational outcomes.In this year-end episode of the Engineering Enablement podcast, host Laura Tacho is joined by Brian Houck from Microsoft, Collin Green and Ciera Jaspan from Google, and Eirini Kalliamvakou from GitHub to examine what 2025 research reveals about AI impact in engineering teams. The panel discusses why measuring AI's effectiveness is inherently complex, why familiar metrics like lines of code continue to resurface despite their limitations, and how multidimensional frameworks such as SPACE and DORA provide a more accurate view of developer productivity.The conversation also looks ahead to 2026, exploring how AI is beginning to reshape the role of the developer, how junior engineers' skill sets may evolve, where agentic workflows are emerging, and why some widely shared AI studies were misunderstood. Together, the panel offers a grounded perspective on moving beyond hype toward more thoughtful, evidence-based AI adoption.Where to find Brian Houck:• LinkedIn: https://www.linkedin.com/in/brianhouck/ • Website: https://www.microsoft.com/en-us/research/people/bhouck/ Where to find Collin Green: • LinkedIn: https://www.linkedin.com/in/collin-green-97720378 • Website: https://research.google/people/107023Where to find Ciera Jaspan: • LinkedIn: https://www.linkedin.com/in/ciera • Website: https://research.google/people/cierajaspan/Where to find Eirini Kalliamvakou: • LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865/• X: https://x.com/irina_kAl • Website: https://www.microsoft.com/en-us/research/people/eikalliWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(02:35) Introducing the panel and the focus of the discussion(04:43) Why measuring AI's impact is such a hard problem(05:30) How Microsoft approaches AI impact measurement(06:40) How Google thinks about measuring AI impact(07:28) GitHub's perspective on measurement and insights from the DORA report(10:35) Why lines of code is a misleading metric(14:27) The limitations of measuring the percentage of code generated by AI(18:24) GitHub's research on how AI is shaping the identity of the developer(21:39) How AI may change junior engineers' skill sets(24:42) Google's research on using AI and creativity (26:24) High-leverage AI use cases that improve developer experience(32:38) Open research questions for AI and developer productivity in 2026(35:33) How leading organizations approach change and agentic workflows(38:02) Why the METR paper resonated and how it was misunderstoodReferenced:• Measuring AI code assistants and agents• Kiro• Claude Code - AI coding agent for terminal & IDE• SPACE framework: a quick primer• DORA | State of AI-assisted Software Development 2025• Martin Fowler - by Gergely Orosz - The Pragmatic Engineer• Seamful AI for Creative Software Engineering: Use in Software Development Workflows | IEEE Journals & Magazine | IEEE Xplore• AI Where It Matters: Where, Why, and How Developers Want AI Support in Daily Work - Microsoft Research• Unpacking METR's findings: Does AI slow developers down?• DX Annual 2026

Ned Bellavance and Kyler Middleton are joined by Rachel Stephens, Research Director at RedMonk, to discuss the state of DevOps and the impact of AI. They explore the distinction between developer productivity and development productivity, underlined by a DORA report finding that while AI dramatically boosts individual developer productivity, it often fails to improve overall... Read more »

Ned Bellavance and Kyler Middleton are joined by Rachel Stephens, Research Director at RedMonk, to discuss the state of DevOps and the impact of AI. They explore the distinction between developer productivity and development productivity, underlined by a DORA report finding that while AI dramatically boosts individual developer productivity, it often fails to improve overall... Read more »

Ned Bellavance and Kyler Middleton are joined by Rachel Stephens, Research Director at RedMonk, to discuss the state of DevOps and the impact of AI. They explore the distinction between developer productivity and development productivity, underlined by a DORA report finding that while AI dramatically boosts individual developer productivity, it often fails to improve overall... Read more »

Eric Bowman (CTO @ King.com, previously CTO at TomTom and VP Engineering at Zalando) returns to the alphalist podcast to unpack what “agentic engineering” really means in practice—and how to introduce it to teams without turning it into a mandate. We talk about the uncomfortable trade-offs behind “YOLO mode” tooling, why adoption should feel voluntary even when you set explicit goals (like “five AI-assisted commits” as a company-level key result), and why the real opportunity isn't just faster coding—it's building a learning system that relentlessly reduces time-to-learning and time-to-value. The conversation spans practical rollout patterns, DORA/value-stream thinking, Toyota's Andon-cord mindset applied to software, multi-agent decision support with MCP, and why the CTO role may keep converging with product as AI pushes organizations to optimize for iteration speed over output volume.

AI engineering tools are evolving fast. New coding assistants, debugging agents, and automation platforms emerge every month. Engineering leaders want to take advantage of these innovations while avoiding costly experiments that create more distraction than impact.In this episode of the Engineering Enablement podcast, host Laura Tacho and Abi Noda outline a practical model for evaluating AI tools with data. They explain how to shortlist tools by use case, run trials that mirror real development work, select representative cohorts, and ensure consistent support and enablement. They also highlight why baselines and frameworks like DX's Core 4 and the AI Measurement Framework are essential for measuring impact.Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseWhere to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  In this episode, we cover:(00:00) Intro: Running a data-driven evaluation of AI tools(02:36) Challenges in evaluating AI tools(06:11) How often to reevaluate AI tools(07:02) Incumbent tools vs challenger tools(07:40) Why organizations need disciplined evaluations before rolling out tools(09:28) How to size your tool shortlist based on developer population(12:44) Why tools must be grouped by use case and interaction mode(13:30) How to structure trials around a clear research question(16:45) Best practices for selecting trial participants(19:22) Why support and enablement are essential for success(21:10) How to choose the right duration for evaluations(22:52) How to measure impact using baselines and the AI Measurement Framework(25:28) Key considerations for an AI tool evaluation(28:52) Q&A: How reliable is self-reported time savings from AI tools?(32:22) Q&A: Why not adopt multiple tools instead of choosing just one?(33:27) Q&A: Tool performance differences and avoiding vendor lock-inReferenced:Measuring AI code assistants and agentsQCon conferencesDX Core 4 engineering metricsDORA's 2025 research on the impact of AIUnpacking METR's findings: Does AI slow developers down?METR's study on how AI affects developer productivityClaude CodeCursorWindsurfDo newer AI-native IDEs outperform other AI coding assistants?

This interview was recorded for GOTO Unscripted.https://gotopia.techLaura Tacho - CTO at DX & Executive Coach at Laura Tacho ConsultingCharles Humble - Freelance Techie, Podcaster, Editor, Author & ConsultantRESOURCESLaurahttps://x.com/rhein_weinhttps://bsky.app/profile/lauratacho.comhttps://www.linkedin.com/in/lauratachohttps://lauratacho.comCharleshttps://bsky.app/profile/charleshumble.bsky.socialhttps://linkedin.com/in/charleshumblehttps://mastodon.social/@charleshumblehttps://conissaunce.comLinkshttps://getdx.com/research/measuring-ai-code-assistants-and-agentshttps://www.conissaunce.com/professional-skills-shortcut.htmlhttps://getdx.com/research/measuring-developer-productivity-with-the-dx-core-4https://dora.devhttps://getdx.com/blog/understanding-dora-metricshttps://queue.acm.org/detail.cfm?id=3454124https://getdx.com/blog/space-metricshttps://getdx.com/research/devex-what-actually-drives-productivityhttps://getdx.com/news/introducing-genai-impact-reportDESCRIPTIONLaura Tacho, CTO at DX and executive coach, shares her take on the challenging transition from technical contributor to business leader. She discusses the most common leadership skill gaps she sees in CTOs, particularly around setting clear expectations without falling into the "micromanagement spiral of doom".Laura explains the development of the DX Core 4 framework for measuring developer productivity through four balanced dimensions:• Speed• Effectiveness• Quality• ImpactShe emphasizes the critical importance of connecting technical work to business outcomes, arguing that the era of engineering having a "blank check" is over and that today's leaders must think like business leaders who speak in terms of ROI and impact. The conversation with Charles Humble also covers emerging trends in AI-assisted development and unconventional approaches to performance management.RECOMMENDED BOOKSWill Larson • An Elegant Puzzle • https://amzn.to/4gb9VyCWill Larson • The Engineering Executive's Primer • https://amzn.to/3UURQuTMeri Williams • The Principles of Project Management • https://amzn.to/4lj5B1GDaniel H. Pink • Drive: The Surprising Truth About What Motivates Us • https://amzn.to/3UHx535Kathy Sierra • Badass • https://amzn.to/4b9fb2VJames Stanier • Become an Effective Software Engineering Manager • https://amzn.to/3vHrx1EBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

This episode was originally broadcast in April 2024, and includes discussions that are still relevant to developers today, so we are publishing it again. ---In this episode, SD Times Editor-in-Chief David Rubinstein discusses how cognitive fatigue is compounding the software productivity problem. His guest is Hans Dockter, CEO at Gradle Inc. and lead at Gradle Open Source Project. 

Nathen Harvey leads research at DORA, focused on how teams measure and improve software delivery. In today's episode of Engineering Enablement, Nathen sits down with host Laura Tacho to explore how AI is changing the way teams think about productivity, quality, and performance.Together, they examine findings from the 2025 DORA research on AI-assisted software development and DX's Q4 AI Impact report, comparing where the data aligns and where important gaps emerge. They discuss why relying on traditional delivery metrics can give leaders a false sense of confidence and why AI acts as an amplifier, accelerating healthy systems while intensifying existing friction and failure.The conversation focuses on how AI is reshaping engineering systems themselves. Rather than treating AI as a standalone tool, they explore how it changes workflows, feedback loops, team dynamics, and organizational decision-making, and why leaders need better system-level visibility to understand its real impact.Where to find Nathen Harvey:• LinkedIn: https://www.linkedin.com/in/nathenWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(00:55) Why the four key DORA metrics aren't enough to measure AI impact(03:44) The shift from four to five DORA metrics and why leaders need more than dashboards(06:20) The one-sentence takeaway from the 2025 DORA report(07:38) How AI amplifies both strengths and bottlenecks inside engineering systems(08:58) What DX data reveals about how junior and senior engineers use AI differently(10:33) The DORA AI Capabilities Model and why AI success depends on how it's used(18:24) How a clear and communicated AI stance improves adoption and reduces friction(23:02) Why talking to your teams still matters Referenced:• DORA | State of AI-assisted Software Development 2025• Steve Fenton - Octonaut | LinkedIn• AI-assisted engineering: Q4 impact report

Is the current level of AI funding and investment rational or irrational? Is it possible that it's both at the same time? Let's look at some numbers and the thought process behind them.SHOW: 976SHOW TRANSCRIPT: The Cloudcast #976 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:A whole bunch of AI-related statsSam Altman on BG2 podcastDO WE HAVE ANY IDEA HOW TO MEASURE THE IMPACT OF AI?How much is one model better than another (e.g. Gemini vs. CoPilot)?How much improvement should a software developer get?How much improvement should a knowledge worker get?How much cost savings should a chatbot provide?How long should it take to make a model understand a company's data?How many workers can a company displace with AI?OpenAI in 2030 - 26 gigawatts could power between 3.7 million to 17.3 million modern GPU serversOpenAI in 2035 - 50 gigawatts could power between 37 million to 173 million modern GPU serversFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

This week, we discuss cloud earnings, Siri teaming up with Gemini, and AI bottlenecks. Plus, is cloning your dog weird? Watch the YouTube Live Recording of Episode (https://www.youtube.com/live/1FjknxuDc9Y?si=JH6rSQHErGMQQp9w) 545 (https://www.youtube.com/live/1FjknxuDc9Y?si=JH6rSQHErGMQQp9w) Runner-up Titles Stack the deck Pets and Chickens Blame it on Android They're fungible Are they going to have to introduce a new principle? Managers of rocks The world we live in Marketing wins We're the healthy skeptics Rundown Ex-NFL star QB Brady claims his dog is a clone (https://www.espn.com/nfl/story/_/id/46848973/tom-brady-says-dog-clone-family-previous-pet) Cloud Earnings AI & Cloud Trends for October 2025 (https://www.thecloudcast.net/2025/11/ai-cloud-trends-for-october-2025.html) Alphabet tops $100 billion quarterly revenue for first time, cloud grows 34% (https://www.cnbc.com/amp/2025/10/29/alphabet-google-q3-earnings.html) Google Cloud Q3 revenue surges 34% as backlog hits $155 billion (https://www.constellationr.com/blog-news/insights/google-cloud-q3-revenue-surges-34-backlog-hits-155-billion) Microsoft Azure sees 40% revenue growth in Q1 (https://www.constellationr.com/blog-news/insights/microsoft-azure-sees-40-revenue-growth-q1) Meta stock drops 10% as heightened AI spending overshadows strong results (https://www.cnbc.com/2025/10/30/meta-stock-earnings-ai-spend.html) Amazon revenues rise 13% on strength in cloud computing unit (https://giftarticle.ft.com/giftarticle/actions/redeem/b798e937-c39d-4e40-84a6-aa9210774e49) Clouded Judgement 10.31.25 - Cloud Giants Report Q3 (https://cloudedjudgement.substack.com/p/clouded-judgement-103125-cloud-giants?utm_source=post-email-title&publication_id=56878&post_id=177617088&utm_campaign=email-post-title&isFreemail=true&r=2l9&triedRedirect=true&utm_medium=email) 7m OpenAI work users (https://openai.com/index/1-million-businesses-putting-ai-to-work/) Amazon's culture went the wrong way (https://cote.io/2025/11/01/amazons-culture-went-the-wrong.html) Octoverse: A new developer joins GitHub every second as AI leads TypeScript to #1 (https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/) What do we think of GitHub saying there are 180m developers in the world? (https://cote.io/2025/10/31/what-do-we-think-of.html) AWS and OpenAI announce multi-year strategic partnership (https://www.aboutamazon.com/news/aws/aws-open-ai-workloads-compute-infrastructure) Amazon stock jumps on $38 billion deal with OpenAI to use hundreds of thousands of Nvidia chips (https://finance.yahoo.com/news/amazon-stock-jumps-on-38-billion-deal-with-openai-to-use-hundreds-of-thousands-of-nvidia-chips-145357373.html) Relevant to your Interests Azure outage: Microsoft still working on fix, says recovery expected in several hours (https://www.cnbc.com/2025/10/29/microsoft-hit-with-azure-365-outage-ahead-of-quarterly-earnings.html) Microsoft takes $3.1 billion hit from OpenAI investment (https://www.cnbc.com/amp/2025/10/29/microsoft-open-ai-investment-earnings.html) Meta Stock Slides After Earnings. (https://www.investors.com/news/technology/meta-stock-q3-2025-earnings-ai-meta-news-zuckerberg/) AWS to Bare Metal Two Years Later: Answering Your Toughest Questions (https://oneuptime.com/blog/post/2025-10-29-aws-to-bare-metal-two-years-later/view) Meta denies torrenting porn to train AI, says downloads were for “personal use” (https://arstechnica.com/tech-policy/2025/10/meta-says-porn-downloads-on-its-ips-were-for-personal-use-not-ai-training/) Shocker! Reversal in AI ROI slide-wisdom: AI does works well (https://cote.io/2025/11/01/shocker-reversal-in-ai-roi.html) SaaS Monopoly | Khushi Lunkad (https://www.linkedin.com/posts/khushilunkad_saas-monopoly-activity-7390752595469914112-UWVw?utm_medium=ios_app&rcm=ACoAAADVjQ8Btsl3lKfl-gEYa6_6hmjCdJyRJyw&utm_source=social_share_send&utm_campaign=copy_link) The State of Developer Experience and Developer Productivity (https://lp.jetbrains.com/devex-productivity-report-full-2025-dataviz/?tab-OneOfTabWrapperBlock-1756889760421-44980=their-top-pain-points-) Why the “Free” Chef Version Could Be Your Most Expensive Mistake | Chef (https://www.chef.io/blog/chef-open-source-software-advice) Nonsense Disney yanks channels from YouTube TV after media giants fail to resolve carriage dispute | CNN Business (https://www.cnn.com/2025/10/30/media/disney-youtube-deal-biz-hnk) Traffic hits record high as commuters rewrite the rush hour - Texas A&M Transportation Institute (https://tti.tamu.edu/2025/10/traffic-hits-record-high-as-commuters-rewrite-the-rush-hour/) Denny's to be acquired and taken private in a deal valued at $620 million (https://apnews.com/article/dennys-investors-deal-private-company-f626f6b8c27f29f698a5c823ba855fc3) Conferences SREDay Amsterdam (https://sreday.com/2025-amsterdam-q4/), November 7th, Coté speaking. Wiz Wizdom Conferences (https://www.wiz.io/wizdom), November 17-19, London DevOpsDayLA at SCALE23x (https://www.socallinuxexpo.org/scale/23x), March 6th, Pasadena, CA Use code: DEVOP for 50% off. CFP open until Dec. 1st. SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: Liquid Glass Transparency Toggle (https://www.macrumors.com/guide/ios-26-1-features/) Matt: The Other Two (https://www.imdb.com/title/tt8310612) Coté: NØLSON shirts (https://nolson.nl) Photo Credits Header (https://unsplash.com/photos/a-dog-sniffing-a-box-full-of-chickens-wyCOBbCztVw)

In this episode of Engineering Enablement, host Laura Tacho talks with Fabien Deshayes, who leads multiple platform engineering teams at Monzo Bank. Fabien explains how Monzo is adopting AI responsibly within a highly regulated industry, balancing innovation with structure, control, and data-driven decision-making.They discuss how Monzo runs structured AI trials, measures adoption and satisfaction, and uses metrics to guide investment and training. Fabien shares why the company moved from broad rollouts to small, focused cohorts, how they are addressing existing PR review bottlenecks that AI has intensified, and what they have learned from empowering product managers and designers to use AI tools directly.He also offers insights into budgeting and experimentation, the results Monzo is seeing from AI-assisted engineering, and his outlook on what comes next, from agent orchestration to more seamless collaboration across roles.Where to find Fabien Deshayes: • LinkedIn: https://www.linkedin.com/in/fabiendeshayesWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro  (01:01) An overview of Monzo bank and Fabien's role  (02:05) Monzo's careful, structured approach to AI experimentation  (05:30) How Monzo's AI journey began  (06:26) Why Monzo chose a structured approach to experimentation and what criteria they used  (09:21) How Monzo selected AI tools for experimentation  (11:51) Why individual tool stipends don't work for large, regulated organizations  (15:32) How Monzo measures the impact of AI tools and uses the data  (18:10) Why Monzo limits AI tool trials to small, focused cohorts  (20:54) The phases of Monzo's AI rollout and how learnings are shared across the organization  (22:43) What Monzo's data reveals about AI usage and spending  (24:30) How Monzo balances AI budgeting with innovation  (26:45) Results from DX's spending poll and general advice on AI budgeting  (28:03) What Monzo's data shows about AI's impact on engineering performance  (29:50) The growing bottleneck in PR reviews and how Monzo is solving it with tenancies  (33:54) How product managers and designers are using AI at Monzo  (36:36) Fabien's advice for moving the needle with AI adoption  (38:42) The biggest changes coming next in AI engineering Referenced:Monzo The Go Programming LanguageSwift.orgKotlinGitHub Copilot in VS Code CursorWindsurfClaude CodePlanning your 2026 AI tooling budget: guidance for engineering leaders

This interview was recorded for GOTO Unscripted.http://gotopia.techRead the full transcription of this interview here:https://gotopia.tech/articles/384Liz Fong-Jones - Field CTO at Honeycomb.ioLesley Cordero - Staff Software Engineer, Tech Lead at The New York TimesRESOURCESLizhttps://bsky.app/profile/lizthegrey.comhttps://github.com/lizthegreyhttps://linkedin.com/in/efonghttps://www.lizthegrey.comLesleyhttps://www.lesleycordero.comhttps://twitter.com/clesleycodehttps://github.com/clesleycodehttps://www.linkedin.com/in/lesleycorderoVideoshttps://www.alex-hidalgo.comhttps://www.honeycomb.io/blog/most-important-developer-productivity-metric-build-timesDESCRIPTIONLiz and Lesley explore the evolution of platform engineering from its DevOps and SRE roots. They discuss the challenges of building effective developer platforms, the importance of psychological safety and evidence-based prioritization, the complexities of open source sustainability, and the delicate balance between centralized platform teams and developer autonomy.The conversation covers practical insights on documentation automation, onboarding strategies, the manager-engineer career pendulum, and why treating platform work as a service rather than a mandate is crucial for organizational success.RECOMMENDED BOOKSAdkins, Beyer, Blankinship, Lewandowski, Oprea & Stubblefield • Building Secure and Reliable Systems • https://amzn.to/4n0bjaeCharity Majors, Liz Fong-Jones & George Miranda • Observability Engineering • https://amzn.to/38scbmaBeyer, Murphy, Rensin, Kawahara & Thorne • The Site Reliability Workbook • https://amzn.to/3IwsiOlKelly Shortridge & Aaron Rinehart • Security Chaos Engineering • https://www.verica.io/sce-bookNoInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

In this episode, Jenna interviews Pia Nilsson, senior director of engineering at Spotify, about the company's decision to make its own AI tool to improve developer productivity.They discuss:Why they built their own tool instead of using something that already existsHow developers are using it day-to-dayHow AI is shaping developer culture at SpotifyWatch Spotify's webinar on October 22 to learn about the latest updates to Portal: https://info.backstage.spotify.com/webinar?utm_campaign=21639556-2025oct-webinar&utm_source=com-banner

Nicole Forsgren created the most widely used frameworks for measuring developer productivity—DORA and SPACE. She wrote the foundational book Accelerate and is about to release her newest book, Frictionless, a practical guide for helping teams move faster in the AI era. She's currently Senior Director of Developer Intelligence at Google.We discuss:1. Why most productivity metrics are a lie2. Signs that your engineering team could be moving much faster3. Why AI accelerates coding but developers aren't speeding up as much as you think4. AI's impact on engineers getting into “flow”5. Her framework for building and scaling a developer experience team6. The three components of developer experience: flow state, cognitive load, and feedback loops—Brought to you by:Mercury—The art of simplified finances: https://mercury.com/WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lennyCoda—The all-in-one collaborative workspace: https://coda.io/lenny—Where to find Nicole Forsgren:• Twitter: https://twitter.com/nicolefv• LinkedIn: https://www.linkedin.com/in/nicolefv/• Website: https://nicolefv.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Nicole Forsgren(05:09) The concept of developer experience (DevEx)(08:33) Flow state and cognitive load in the age of AI(12:02) Challenges in measuring productivity with AI(21:19) The importance of developer experience for business value(22:20) Common issues and solutions in developer experience(26:49) Signs your eng team is moving too slow(29:52) How AI is improving productivity(33:32) Real examples of productivity improvements(36:35) Introducing her new book, Frictionless(43:40) How to get started building a DevEx team(45:15) The impact of forming developer experience teams(46:15)  How to measure the impact of DevEx teams(48:53) Measuring the impact of AI tools on productivity(55:16) Survey design for developer experience(57:59) Popular AI tools for developers(59:08) Bringing a product mindset to DevEx improvements(01:00:40) AI corner(01:02:33) Lightning round and final thoughts—Referenced:• How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google): https://www.lennysnewsletter.com/p/how-to-measure-and-improve-developer• DORA: https://dora.dev/• The SPACE framework: A comprehensive guide to developer productivity: https://getdx.com/blog/space-metrics/• Measuring developer productivity with the DX Core 4: https://getdx.com/research/measuring-developer-productivity-with-the-dx-core-4/• Gloria Mark's website: https://gloriamark.com/• Taking Flight with Copilot: https://dl.acm.org/doi/10.1145/3589996• DevEx in Action: https://spawn-queue.acm.org/doi/10.1145/3639443• CodeX: https://openai.com/codex/• Devin: https://devin.ai/• Abi Noda on LinkedIn: https://www.linkedin.com/in/abinoda/• DX is joining Atlassian: https://getdx.com/blog/dx-is-joining-atlassian/• GitHub Copilot: https://github.com/features/copilot• Cursor: https://cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Gemini Code Assist: https://codeassist.google/• Claude Code: https://www.claude.com/product/claude-code• The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper• Love Is Blind on Netflix: https://www.netflix.com/title/80996601• Shrinking on AppleTV+: https://tv.apple.com/us/show/shrinking/umc.cmc.apzybj6eqf6pzccd97kev7bs• Ninja Creami: https://www.amazon.com/Ninja-NC301-CREAMi-Containers-Bundle/dp/B0BLGR5JPV/• Jura coffee maker: https://www.amazon.com/Jura-Nordic-Automatic-Coffee-Machine/dp/B0CF65BFZ1/—Recommended books:• Frictionless: https://developerexperiencebook.com/• DevEx Workbook: https://developerexperiencebook.com/#workbook• Outlive: The Science and Art of Longevity: https://www.amazon.com/Outlive-Longevity-Peter-Attia-MD/dp/0593236599• Back Mechanic: https://www.amazon.com/Back-Mechanic-Stuart-McGill-2015-09-30/dp/B01FKSGJYC• How Big Things Get Done: The Surprising Factors That Determine the Fate of Every Project, from Home Renovations to Space Exploration and Everything in Between: https://www.amazon.com/How-Big-Things-Get-Done/dp/0593239512/• The Undoing Project: A Friendship That Changed Our Minds: https://www.amazon.com/dp/B01KBM82M4/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

In this episode of Engineering Enablement, Laura Tacho and Abi Noda discuss how engineering leaders can plan their 2026 AI budgets effectively amid rapid change and rising costs. Drawing on data from DX's recent poll and industry benchmarks, they explore how much organizations should expect to spend per developer, how to allocate budgets across AI tools, and how to balance innovation with cost control.Laura and Abi also share practical insights on building a multi-vendor strategy, evaluating ROI through the right metrics, and ensuring continuous measurement before and after adoption. They discuss how to communicate AI's value to executives, avoid the trap of cost-cutting narratives, and invest in enablement and training to make adoption stick.Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro: Setting the stage for AI budgeting in 2026(01:45) Results from DX's AI spending poll and early trends(03:30) How companies are currently spending and what to watch in 2026(04:52) Why clear definitions for AI tools matter and how Laura and Abi think about them(07:12) The entry point for 2026 AI tooling budgets and emerging spending patterns(10:14) Why 2026 is the year to prove ROI on AI investments(11:10) How organizations should approach AI budgeting and allocation(15:08) Best practices for managing AI vendors and enterprise licensing(17:02) How to define and choose metrics before and after adopting AI tools(19:30) How to identify bottlenecks and AI use cases with the highest ROI(21:58) Key considerations for AI budgeting (25:10) Why AI investments are about competitiveness, not cost-cutting(27:19) How to use the right language to build trust and executive buy-in(28:18) Why training and enablement are essential parts of AI investment(31:40) How AI add-ons may increase your tool costs(32:47) Why custom and fine-tuned models aren't relevant for most companies today(34:00) The tradeoffs between stipend models and enterprise AI licensesReferenced:DX Core 4 Productivity FrameworkMeasuring AI code assistants and agents2025 State of AI Report: The Builder's PlaybookGitHub Copilot · Your AI pair programmerCursorGleanClaude CodeChatGPTWindsurfTrack Claude Code adoption, impact, and ROI, directly in DXMeasuring AI code assistants and agents with the AI Measurement FrameworkDriving enterprise-wide AI tool adoptionSentryPoolside

Kieron Allen speaks with Chris Pope, Chief Product Officer at AutomatePro, in an in-depth discussion that is part of a broader series of podcasts, articles, and reports on ServiceNow's evolving ecosystem. They explore how intelligent automation and agentic AI are reshaping DevOps and quality assurance. The conversation also highlights how AutomatePro's built-on approach enhances developer productivity, reduces risk, and ensures security, all within the ServiceNow environment.AutomatePro's AI EdgeThe Big ThemesAutomatePro's Core Mission: AutomatePro focuses on solving one of the most time-consuming parts of software delivery: testing and documentation. Pope explains that their goal isn't to replace humans but to augment their efforts through intelligent automation. By embedding deeply within the ServiceNow platform, AutomatePro allows developers and platform owners to automate repetitive tasks early in the development cycle, ensuring higher-quality releases and faster deployment.Human-AI Collaboration Wins: The myth of AI replacing people is outdated. Pope reframes the conversation: it's not about replacement, it's about enablement. The real winners will be those who know how to use AI effectively. Today's Copilots are context-aware, learning from human behavior and adapting to different personas — whether it's a developer, analyst, or HR owner. Prompt engineering is emerging as a vital skill, and the better the prompt, the better the AI-driven output.DevOps Innovation Without Compromise: AutomatePro and ServiceNow are reshaping DevOps by making speed and quality compatible. Historically, faster releases meant riskier ones. With AutomatePro's intelligent testing automation, that tradeoff no longer exists. Frequent, smaller releases — the “fixed forward” model — are now safer thanks to early automation, embedded security, and contextual AI. Pope argues that platform owners and developers are the new heroes in enterprise IT, and equipping them with Copilots, intelligent workflows, and instant feedback loops unlocks untapped value.The Big Quote: "You're not going to be replaced by AI per se, you're going to be replaced by someone that knows how to use AI effectively."More from ServiceNow and AutomatePro:Follow AutomatePro on LinkedIn or learn more about ServiceNow and intelligent automation. Visit Cloud Wars for more.

AI Assisted Coding: Pachinko Coding—What They Don't Tell You About Building Apps with Large Language Models, With Alan Cyment In this BONUS episode, we dive deep into the real-world experience of coding with AI. Our guest, Alan Cyment, brings honest perspectives from the trenches—sharing both the frustrations and breakthroughs of using AI tools for software development. From "Pachinko coding" addiction loops to "Mecha coding" breakthroughs, Alan explores what actually works when building software with large language models. From Thermomix Dreams to Pachinko Reality "I bought into the Thermomix coding promise—describe the whole website and it would spit out the finished product. It was a complete disaster." Alan started his AI coding journey with high expectations, believing he could simply describe a complete application and receive production-ready code. The reality was far different. What he discovered instead was an addictive cycle he calls "Pachinko coding" (Pachinko, aka Slot Machines in Japan)—repeatedly feeding error messages back to the AI, hoping each iteration would finally work, while burning through tokens and time. The AI's constant reassurances that "this time I fixed it" created a gambling-like feedback loop that left him frustrated and out of pocket, sometimes spending over $20 in API credits in a single day. The Drunken PhD with Amnesia "It felt like working with a drunken PhD with amnesia—so wise and so stupid at the same time." Alan describes the maddening experience of anthropomorphizing AI tools that seem brilliant one moment and completely lost the next. The key breakthrough came when he stopped treating the AI as a person and started seeing it as a function that performs extrapolations—sometimes accurate, sometimes wildly wrong. This mental shift helped him manage expectations and avoid the "rage coding" that came from believing the AI should understand context and maintain consistency like a human collaborator. Making AI Coding Actually Work "I learned to ask for options explicitly before any coding happens. Give me at least three options and tell me the pros and cons." Through trial and error, Alan developed practical strategies that transformed AI from a frustrating Pachinko machine into a useful tool: Ask for options first: Always request multiple approaches with pros and cons before any code is generated Use clover emoji convention: Implement a consistent marker at the start of all AI responses to track context Small steps and YAGNI principles: Request tiny, incremental changes rather than large refactoring Continuous integration: Demand the AI run tests and checks after every single change Explicit refactoring requests: Regularly ask for simplification and readability improvements Take two steps back: When stuck in a loop, explicitly tell the AI to simplify and start fresh Choose the right tech stack: Use technologies with abundant training data (like Svelte over React Native in Alan's experience) The Mecha Coding Breakthrough "When it worked, I felt like I was inside a Lego Mecha robot—the machine gave me superpowers, but I was still the one in control." Alan successfully developed a birthday reminder app in Swift in just one day, despite never having learned Swift. He made architectural decisions and guided the development without understanding the syntax details. This experience convinced him that AI represents a genuine new level of abstraction in programming—similar to the jump from assembly language to high-level languages, or from procedural to object-oriented programming. You can now think in English about what you want, while the AI handles the accidental complexity of syntax and boilerplate. The Cost Reality Check "People writing about vibe coding act like it's free. But many people are going to pay way more than they would have paid a developer and end up with empty hands." Alan provides a sobering cost analysis based on his experience. Using DeepSeek through Aider, he typically spends under $1 per day. But when experimenting with premium models like Claude Sonnet 3.5, he burned through $5 in just minutes. The benchmark comparisons are revealing: DeepSeek costs $4 for a test suite, DeepSeek R1 plus Sonnet costs $16, while Open AI's O1 costs $190. For non-developers trying to build complete applications through pure "vibe coding," the costs can quickly exceed what hiring a developer would cost—with far worse results. When Thermomix Actually Works "For small, single-purpose scripts that I'm not interested in learning about and won't expand later, the Thermomix experience was real." Despite the challenges, Alan found specific use cases where AI truly delivers on the "just describe it and it works" promise. Processing Zoom attendance logs, creating lookup tables for video effects, and other single-file scripts worked remarkably well. The pattern: clearly defined context, no need for ongoing maintenance, and simple enough to verify the output without deep code inspection. For these thermomix moments, AI proved genuinely transformative. The Pachinko Trap and Tech Stack Matters "It became way more stable when I switched to Svelte from React Native and Flutter, even following the same prompting practices. The AI is just more proficient in certain tech stacks." Alan discovered that some frameworks and languages work dramatically better with AI than others, likely due to the amount of training data available. His e-learning platform attempts with React Native and Flutter kept breaking, but switching to Svelte with web-based deployment became far more stable. This suggests a crucial strategy: choose mainstream, well-documented technologies when planning AI-assisted projects. From Coding to Living with AI Alan has completely stopped using traditional search engines, relying instead on LLMs for everything from finding technical documentation to getting recommendations for books based on his interests. While he acknowledges the risk of hallucinations, he finds the semantic understanding capabilities too valuable to ignore. He's even used image analysis to troubleshoot his father's cable TV problems and figure out hotel air conditioning controls. The Agile Validation "My only fear is confirmation bias—but the conclusion I see other experienced developers reaching is that the only way to make LLMs work is by making them use agility. So look at who's dead now." Alan notes the irony that the AI coding tools that actually work all require traditional software engineering best practices: small iterations, test-driven development, continuous integration, and explicit refactoring. The promise of "just describe what you want" falls apart without these disciplines. Rather than replacing software engineering principles, AI tools seem to validate their importance. About Alan Cyment Alan Cyment is a consultant, trainer, and facilitator based in Buenos Aires, specializing in organizational fluency, agile leadership, and software development culture change. A Certified Scrum Trainer with deep experience across Latin America and Europe, he blends agile coaching with theatre-based learning to help leaders and teams transform. You can link with Alan Cyment on LinkedIn.

CEO Abi Noda is joined by DX CTO Laura Tacho to discuss the evolving role of Platform and DevProd teams in the AI era. Together, they unpack how AI is reshaping platform responsibilities, from evaluation and rollout to measurement, tool standardization, and guardrails. They explore why fundamentals like documentation and feedback loops matter more than ever for both developers and AI agents. They also share insights on reducing tool sprawl, hardening systems for higher throughput, and leveraging AI to tackle tech debt, modernize legacy code, and improve workflows across the SDLC.Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro: Why platform teams need to evolve(02:34) The challenge of defining platform teams and how AI is changing expectations(04:44) Why evaluating and rolling out AI tools is becoming a core platform responsibility(07:14) Why platform teams need solid measurement frameworks to evaluate AI tools(08:56) Why platform leaders should champion education and advocacy on measurement(11:20) How AI code stresses pipelines and why platform teams must harden systems(12:24) Why platform teams must go beyond training to standardize tools and create workflows(14:31) How platform teams control tool sprawl(16:22) Why platform teams need strong guardrails and safety checks(18:41) The importance of standardizing tools and knowledge(19:44) The opportunity for platform teams to apply AI at scale across the organization(23:40) Quick recap of the key points so far(24:33) How AI helps modernize legacy code and handle migrations(25:45) Why focusing on fundamentals benefits both developers and AI agents(27:42) Identifying SDLC bottlenecks beyond AI code generation(30:08) Techniques for optimizing legacy code bases (32:47) How AI helps tackle tech debt and large-scale code migrations(35:40) Tools across the SDLCReferenced:DX Core 4 Productivity FrameworkMeasuring AI code assistants and agentsAbi Noda's LinkedIn postMeasuring AI code assistants and agents with the AI Measurement FrameworkThe SPACE framework: A comprehensive guide to developer productivityCommon workflows - AnthropicEnterprise Tech Leadership Summit Las Vegas 2025Driving enterprise-wide AI tool adoption with Bruno PassosAccelerating Large-Scale Test Migration with LLMs | by Charles Covey-Brandt | The Airbnb Tech Blog | MediumJustin Reock - DX | LinkedInA New Tool Saved Morgan Stanley More Than 280,000 Hours This Year - Business Insider

In this episode, host Laura Tacho speaks with Jesse Adametz, Senior Engineering Leader on the Developer Platform at Twilio. Jesse is leading Twilio's multi-year platform consolidation, unifying tech stacks across large acquisitions and driving migrations at enterprise scale. He discusses platform adoption, the limits of Kubernetes, and how Twilio balances modernization with pragmatism. The conversation also explores treating developer experience as a product, offering “change as a service,” and Twilio's evolving approach to AI adoption and platform support.Where to find Jesse Adametz: • LinkedIn: https://www.linkedin.com/in/jesseadametz/• X: https://x.com/jesseadametz• Website: https://www.jesseadametz.com/Where to find Laura Tacho:• LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(01:30) Jesse's background and how he ended up at Twilio(04:00) What SRE teaches leaders and ICs(06:06) Where Twilio started the post-acquisition integration(08:22) Why platform migrations can't follow a straight-line plan(10:05) How Twilio balances multiple strategies for migrations(12:30) The human side of change: advocacy, training, and alignment(17:46) Treating developer experience as a first-class product(21:40) What “change as a service” looks like in practice(24:57) A mandateless approach: creating voluntary adoption through value(28:50) How Twilio demonstrates value with metrics and reviews(30:41) Why Kubernetes wasn't the right fit for all Twilio workloads (36:12) How Twilio decides when to expose complexity(38:23) Lessons from Kubernetes hype and how AI demands more experimentation(44:48) Where AI fits into Twilio's platform strategy(49:45) How guilds fill needs the platform team hasn't yet met(51:17) The future of platform in centralizing knowledge and standards(54:32) How Twilio evaluates tools for fit, pricing, and reliability (57:53) Where Twilio applies AI in reliability, and where Jesse is skeptical(59:26) Laura's vibe-coded side project built on Twilio(1:01:11) How external lessons shape Twilio's approach to platform support and docsReferenced:The AI Measurement FrameworkExperianTransact-SQL - WikipediaTwilioKubernetesCopilotClaude CodeWindsurfCursorBedrock

In this episode of Engineering Enablement, host Laura Tacho talks with Bruno Passos, Product Lead for Developer Experience at Booking.com, about how the company is rolling out AI tools across a 3,000-person engineering team.Bruno shares how Booking.com set ambitious innovation goals, why cultural change mattered as much as technology, and the education practices that turned hesitant developers into daily users. He also reflects on the early barriers, from low adoption and knowledge gaps to procurement hurdles, and explains the interventions that worked, including learning paths, hackathon-style workshops, Slack communities, and centralized procurement. The result is that Booking.com now sits in the top 25 percent of companies for AI adoption.Where to find Bruno Passos:• LinkedIn: https://www.linkedin.com/in/brpassos/• X: https://x.com/brunopassosWhere to find Laura Tacho:• LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(01:09) Bruno's role at Booking.com and an overview of the business (02:19) Booking.com's goals when introducing AI tooling(03:26) Why Booking.com made such an ambitious innovation ratio goal (06:46) The beginning of Booking.com's journey with AI(08:54) Why the initial adoption of Cody was low(13:17) How education and enablement fueled adoption(15:48) The importance of a top-down cultural change for AI adoption(17:38) The ongoing journey of determining the right metrics(21:44) Measuring the longer-term impact of AI (27:04) How Booking.com solved internal bottlenecks to testing new tools(32:10) Booking.com's framework for evaluating new tools(35:50) The state of adoption at Booking.com and efforts to expand AI use(37:07) What's still undetermined about AI's impact on PR/MR quality(39:48) How Booking.com is addressing lagging adoption and monitoring churn(43:24) How Booking.com's Slack community lowers friction for questions and support(44:35) Closing thoughts on what's next for Booking.com's AI planReferenced:Measuring AI code assistants and agentsDX Core 4 FrameworkBooking.comSourcegraph SearchCody | AI coding assistant from SourcegraphGreyson Junggren - DX | LinkedIn

In this episode of Building Better Developers with AI, Rob Broadhead and Michael Meloche revisit an earlier conversation: “Building a Strong Developer Toolkit – Enhancing Skills and Productivity.” This time, they explore how AI and modern practices shape the discussion. The takeaway: enhancing developer productivity isn't just about tools—it's about habits, problem-solving, and continuous growth.

In this episode of Engineering Enablement, DX CTO Laura Tacho and CEO Abi Noda break down how to measure developer productivity in the age of AI using DX's AI Measurement Framework. Drawing on research with industry leaders, vendors, and hundreds of organizations, they explain how to move beyond vendor hype and headlines to make data-driven decisions about AI adoption.They cover why some fundamentals of productivity measurement remain constant, the pitfalls of over-relying on flawed metrics like acceptance rate, and how to track AI's real impact across utilization, quality, and cost. The conversation also explores measuring agentic workflows, expanding the definition of “developer” to include new AI-enabled contributors, and avoiding second-order effects like technical debt and slowed PR throughput.Whether you're rolling out AI coding tools, experimenting with autonomous agents, or just trying to separate signal from noise, this episode offers a practical roadmap for understanding AI's role in your organization—and ensuring it delivers sustainable, long-term gains.Where to find Laura Tacho:• X: https://x.com/rhein_wein• LinkedIn: https://www.linkedin.com/in/lauratacho/• Website: https://lauratacho.com/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda • Substack: ​​https://substack.com/@abinoda In this episode, we cover:(00:00) Intro(01:26) The challenge of measuring developer productivity in the AI age(04:17) Measuring productivity in the AI era — what stays the same and what changes(07:25) How to use DX's AI Measurement Framework (13:10) Measuring AI's true impact from adoption rates to long-term quality and maintainability(16:31) Why acceptance rate is flawed — and DX's approach to tracking AI-authored code(18:25) Three ways to gather measurement data(21:55) How Google measures time savings and why self-reported data is misleading(24:25) How to measure agentic workflows and a case for expanding the definition of developer(28:50) A case for not overemphasizing AI's role(30:31) Measuring second-order effects (32:26) Audience Q&A: applying metrics in practice(36:45) Wrap up: best practices for rollout and communication Referenced:DX Core 4 Productivity FrameworkMeasuring AI code assistants and agentsAI is making Google engineers 10% more productive, says Sundar Pichai - Business Insider

In this special episode of the Engineering Enablement podcast, recorded live at LeadDev London, DX CTO Laura Tacho explores the growing gap between AI headlines and the reality inside engineering teams—and what leaders can do to close it.Laura shares data from nearly 39,000 developers across 184 companies, highlights the Core 4 and introduces the AI Measurement Framework, and offers a practical playbook for using data to improve developer experience, measure AI's true impact, and build better software without compromising long-term performance.Where to find Laura Tacho:• X: https://x.com/rhein_wein• LinkedIn: https://www.linkedin.com/in/lauratacho/• Website: https://lauratacho.com/In this episode, we cover:(00:00) Intro: Laura's keynote from LDX3(01:44) The problem with asking how much faster can we go with AI?(03:02) How the disappointment gap creates barriers to AI adoption(06:20) What AI adoption looks like at top-performing organizations(07:53) What leaders must do to turn AI into meaningful impact(10:50) Why building better software with AI still depends on fundamentals(12:03) An overview of the DX Core 4 Framework(13:22) Why developer experience is the biggest performance lever(15:12) How Block used Core 4 and DXI to identify 500,000 hours in time savings(16:08) How to get started with Core 4(17:32) Measuring AI with the AI Measurement Framework(21:45) Final takeaways and how to get started with confidenceReferenced:LDX3 by LeadDev | The Festival of Software Engineering Leadership | LondonSoftware engineering with LLMs in 2025: reality checkSPACE framework, PRs per engineer, AI researchThe AI adoption playbook: Lessons from Microsoft's internal strategyDX Core 4 Productivity FrameworkNicole ForsgrenMargaret-Anne StoreyDropbox.comEtsyPfizerDrew Houston - Dropbox | LinkedInBlockCursorDora.devSourcegraphBooking.com

As the annual budgeting and planning season comes into full swing, we drill down into two unique pricing trends. We then take a detailed look at how to measure developer productivity.

Rob Zuber sits down with Tara Hernandez, VP of Developer Productivity at MongoDB and former Netscape engineer who helped create early continuous integration systems, to explore strategic frameworks for build vs. buy decisions in modern software delivery.Hernandez shares insights from scaling MongoDB's proprietary CI system—processing 10 engineer years of compute daily—and reveals how organizations can evaluate when custom infrastructure drives competitive advantage versus when strategic partnerships accelerate growth. Her perspective on navigating the evolving landscape of CI/CD tooling offers actionable guidance for engineering leaders balancing innovation with operational efficiency.Have someone in mind you'd like to hear on the show? Reach out to us on X at @CircleCI!

In this episode of the Engineering Enablement podcast, host Abi Noda is joined by Quentin Anthony, Head of Model Training at Zyphra and a contributor at EleutherAI. Quentin participated in METR's recent study on AI coding tools, which revealed that developers often slowed down when using AI—despite feeling more productive. He and Abi unpack the unexpected results of the study, which tasks AI tools actually help with, and how engineering teams can adopt them more effectively by focusing on task-level fit and developing better digital hygiene.Where to find Quentin Anthony: • LinkedIn: https://www.linkedin.com/in/quentin-anthony/• X: https://x.com/QuentinAnthon15Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro(01:32) A brief overview of Quentin's background and current work(02:05) An explanation of METR and the study Quentin participated in (11:02) Surprising results of the METR study (12:47) Quentin's takeaways from the study's results (16:30) How developers can avoid bloated code bases through self-reflection(19:31) Signs that you're not making progress with a model (21:25) What is “context rot”?(23:04) Advice for combating context rot(25:34) How to make the most of your idle time as a developer(28:13) Developer hygiene: the case for selectively using AI tools(33:28) How to interact effectively with new models(35:28) Why organizations should focus on tasks that AI handles well(38:01) Where AI fits in the software development lifecycle(39:40) How to approach testing with models(40:31) What makes models different (42:05) Quentin's thoughts on agents Referenced:DX Core 4 Productivity FrameworkZyphraEleutherAIMETRCursorClaudeLibreChatGoogle GeminiIntroducing OpenAI o3 and o4-miniMETR's study on how AI affects developer productivityQuentin Anthony on X: "I was one of the 16 devs in this study."Context rot from Hacker NewsTracing the thoughts of a large language modelKimiGrok 4 | xAI

What happens after AI helps you write code faster? You create a bottleneck in testing, security, and operations. In part two of their conversation, SADA's Simon Margolis and Google Cloud's Ameer Abbas tackle this exact problem. They explore how Google's AI strategy extends beyond the developer's keyboard with Gemini Code Assist and Cloud Assist, creating a balanced and efficient software lifecycle from start to finish. We address the burning questions about AI's impact on the software development ecosystem: Is AI replacing developers? What does the future hold for aspiring software engineers? Gain insights on embracing AI as an augmentation tool, the concept of "intentional prompting" versus "vibe coding," and why skilled professionals are more crucial than ever in the enterprise. This episode offers practical advice for enterprises on adopting AI tools, measuring success through quantitative and qualitative metrics, and finding internal champions to drive adoption. We also peek into the near future, discussing the evolution towards AI agents capable of multi-step inferencing and full automation for specific use cases. Key Takeaways: Gemini Code Assist: AI for developer inner-loop productivity, supporting various IDEs and SCMs. Gemini Cloud Assist: AI for cloud operations, cost optimization, and incident resolution within GCP. AI's Role in Development: Augmentation, not replacement; the importance of human agency and prompting skills. Enterprise Adoption: Strategies for integrating AI tools, measuring ROI, and fostering a culture of innovation. The Future: Agents with multi-step inferencing, automation for routine tasks, and background AI processes. Relevant Links: Blog: A framework for adopting Gemini Code Assist and measuring its impact Gemini Code Assist product page Gemini Cloud Assist product page Listen now to understand how AI is shaping the future of software delivery! Join us for more content by liking, sharing, and subscribing!

In this episode, Mike explores his growing dependence on AI tools like Cursor and ChatGPT to handle everyday coding tasks. From code generation and DevOps to security reviews and task planning, AI is integrated into nearly every part of his workflow. But as these tools take over more responsibilities, Mike asks the hard questions: Am I losing my edge? Should I still code manually even if AI can do it faster—or better? He shares how he uses AI day-to-day, when he steps in to take control, and whether it's time to focus on solving tougher problems that AI can't yet tackle. Show Notes: https://www.htmlallthethings.com/podcasts/boosting-developer-productivity-with-ai-without-losing-your-edge Powered by CodeRabbit - AI Code Reviews: https://coderabbit.link/htmlallthethings Use our Scrimba affiliate link (https://scrimba.com/?via=htmlallthethings) for a 20% discount!! Full details in show notes.

How do you drive meaningful AI transformation across 150 software engineers without mandates or force? Peter Gostev, Head of AI at Moonpig, reveals the technical strategies and organizational approaches behind scaling AI adoption from 130 to 400+ users while navigating the gap between industry hype and implementation reality. From managing complex integration challenges where 80% of AI projects involve traditional software engineering to implementing three-pillar strategies (tool adoption, automation workflows, experimental features), Peter shares hard-earned insights on building AI capabilities through process re-engineering rather than simple automation. Technical insights for CTOs and engineering leaders: •

Is generative AI just another tool in the belt, or is it a fundamental transformation of the developer profession? We kick off a two-part special to get to the bottom of how AI is impacting the enterprise. SADA's Associate CTO of AI & ML, Simon Margolis, sits down with Ameer Abbas, Senior Product Manager at Google Cloud, for an insider's look at the future of software development. They cut through the noise to discuss how tools like Gemini Code Assist are moving beyond simple code completion to augment the entire software delivery lifecycle, solving real-world challenges and changing the way we think about productivity, quality, and automation. In this episode, you'll learn: What Gemini Code Assist is and the broad range of developer personas it serves. The critical debate: Is AI augmenting developer skills or automating their jobs? How to leverage AI for practical enterprise challenges like application modernization, improving test coverage, and tackling technical debt. Why the focus is shifting from developer productivity to overall software delivery performance. Ameer's perspective on the future of development careers and why students should lean into AI, not fear it. The limitations of "vibe coding" and the need for intentional, high-quality AI prompting in a corporate environment.   Join us for more content by liking, sharing, and subscribing!

"How do you measure the impact you have with your platform engineering initiative?" is a question you should be able to answer. To show improvement you must first need to know what the status quo is. And this is where frameworks such as DX Core 4 come in. Never heard about it? Then tune into this episode where we have Dušan Katona, Sr Director of Platform Engineering at Ataccama, who is a big fan of the DX Core Four Metrics and who has just applied it in his current role to optimize developer experience.Dušan explains the details behind those 4 Core metrics: Speed, Effectiveness, Quality and Impact. He also shares how improving those metrics by a single point results in the equivalent of 10 hours saved per developer per year.And here the relevant links we discussed todayDusan's LinkedIn Profile: https://www.linkedin.com/in/dusankatona/DX Core 4 Blog: https://getdx.com/research/measuring-developer-productivity-with-the-dx-core-4/Marian's JIRA Analytics Open Source Project: https://github.com/marian-kamenistak/jira-lead-cycle-time-duration-extractor

Jack Herrington, podcaster, software engineer, writer and YouTuber, joins the pod to uncover the truth behind server functions and why they don't actually exist in the web platform. We dive into the magic behind frameworks like Next.js, TanStack Start, and Remix, breaking down how server functions work, what they simplify, what they hide, and what developers need to know to build smarter, faster, and more secure web apps. Links YouTube: https://www.youtube.com/@jherr Twitter: https://x.com/jherr Github: https://github.com/jherr ProNextJS: https://www.pronextjs.dev Discord: https://discord.com/invite/KRVwpJUG6p LinkedIn: https://www.linkedin.com/in/jherr Website: https://jackherrington.com Resources Server Functions Don't Exist (It Matters) (https://www.youtube.com/watch?v=FPJvlhee04E) We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Em, at emily.kochanek@logrocket.com (mailto:emily.kochanek@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Special Guest: Jack Herrington.

Thomas Dohmke, CEO of GitHub, joins Azeem to explore how AI is fundamentally transforming software development. In this episode you'll hear: (01:50) What's left for developers in the age of AI? (04:54) How GitHub Copilot unlocks flow state (07:09) Three big shifts in how engineers work today (10:47) Is software development art or assembly line? (15:26) Why developers are climbing the abstraction ladder (19:35) Have we already lost control of the code? (23:15) What it's actually like to work with AI coding agents (39:35) Welcome to the age of ultra-personalized software(45:37) Building the next-generation web Thomas's links:GitHub: https://github.com/LinkedIn: https://www.linkedin.com/in/ashtom/Twitter/X: https://x.com/ashtomAzeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeemOur new show This was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack. Produced by supermix.io and EPIIPLUS1 Ltd

Roy Derks, Developer Experience at IBM, talks about the integration of Large Language Models (LLMs) in web development. We explore practical applications such as building agents, automating QA testing, and the evolving role of AI frameworks in software development. Links https://www.linkedin.com/in/gethackteam https://www.youtube.com/@gethackteam https://x.com/gethackteam https://hackteam.io We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Roy Derks.