Tech Lead Journal

Why do the best developers often make the worst software architects? The skills that make someone a hero developer can become the habits that hold their team back once they move into architecture. In this episode, Lee Atchison, software architect and author of “The Software Conductor”, explains what changes when you move from writing code to leading through other people. He shares why he rewrote his book three times before settling on a story format, and how the orchestra conductor analogy captures the architect’s role. Lee walks through the four ways architects fail in hero mode, the five questions a conductor asks that architects can use to spot problems early, and why most system failures are really broken connections between people. He also explains why architects should act as mentors who remove obstacles and offer a test for whether you taught or just decided. Finally, he shares his view on AI, why judgment matters more than any particular role, and why he expects the dark factory trend to fade. Key…

Why can’t you “move fast and break things” in a factory? Because a small software bug doesn’t just crash an app, it can shut down a plant the size of a small city. David and Willem explain the hidden world of operational technology, where safety always comes before speed. In this episode, David Ariens and Willem van Lammeren, co-authors of “The IT/OT Handbook” and hosts of the IT/OT Insider podcast, break down the divide between IT and OT, information technology and operational technology, and why closing it matters more than ever as manufacturing digitalizes. They explain why OT prioritizes safety and integrity over confidentiality and share a war story where a single form change in China left hundreds of trucks stranded outside a factory. David and Willem walk through the seven cooperation patterns from their book, from “good shadow IT” to facilitation, project teams, and full product teams, and explain why breaking down silos too early can backfire. They also discuss why factories still run Windows XP…

What’s the one rule that matters most when AI writes most of your code? Emilie says it comes down to a single line: don’t outsource your thinking. In this episode, Emilie Schario, co-founder of Kilo Code, unpacks what it actually means to lead in a world where AI can write most of your code. She explains why Kilo went all-in on being model agnostic, supporting over 500 models instead of betting on a single lab. Emilie shares her core rule for using AI responsibly: never outsource your thinking, especially when it comes to auth, billing, or security. She introduces a metric more companies should be tracking, spend per merged pull request, as a better signal of value than raw AI cost. Emilie also talks about the “killing problem” that comes with software becoming nearly free to ship, and why teams now discover product-market fit after shipping rather than before. She closes with how engineers are shifting from producers to reviewers, and what that means for anyone now managing a team of AI agents instead of…

If you’re not using AI five times an hour, you’re already behind, says the man who built the first AI assistant back in 1998. Kevin explains why that pace is now the baseline, not the exception, for developers who want to stay employed. In this episode, Kevin Surace, the inventor of the first AI assistant and CEO of Appvance, joins to unpack six decades of AI evolution, from hidden Markov models to the LLM breakthroughs now powering coding agents. Kevin shares how his work at General Magic became the licensed technology behind Siri and Alexa, and how he ended up inventing post-training before anyone had a name for it. He explains why the only developers losing their jobs are the ones refusing to adopt AI, and how the role is shifting from writing code to specifying, coordinating, and testing it across a fleet of agents. Kevin also introduces AI Script Generation Bug Hunter, the testing technology uncovering thousands of bugs humans never catch. The conversation closes with his new book, Joy-Success Cycle,…

Does moving faster with AI mean you get to skip the laws that governed every software project before it? Milan Milanovic argues the opposite, the old laws of software engineering now apply twice as hard. In this episode, Milan Milanovic, CTO and author of Laws of Software Engineering, returns to unpack why the laws that have quietly governed software projects for decades are more relevant than ever in the AI era. He walks through Gall’s Law and why AI lets teams generate complex systems on unvalidated assumptions faster than ever, Conway’s Law and how it now runs twice, shaping both human organizations and agent topologies, and Goodhart’s Law and the trap of “tokenmaxxing” as a metric. Milan also covers Hofstadter’s Law and the 90-90 rule, explaining why the last 10% of a project still takes as long with AI in the loop, and the Dunning-Kruger effect, where vibe coders overestimate their skills while senior engineers underestimate theirs. The conversation closes with his advice for juniors entering the field,…

(04:48) Brought to you by SpeechifyAI Tired of text-to-speech that sounds robotic or costs too much at scale? SpeechifyAI's new Simba 3.2 model ranks #1 on Artificial Analysis for realness, priced under $10 per million characters. Building something conversational? Check out the Simba Voice Agents API for low-latency, natural back-and-forth interaction. Try it free at speechify.ai. Why do the same software delivery illusions keep fooling smart engineering teams? Elisabeth and Joel show how systems thinking, through signals and levers, helps you spot the illusions of progress, predictability, and control before they cost you. In this episode, Elisabeth Hendrickson and Joel Tosi, co-authors of Signals & Levers, share the story behind the book and trace the “software crisis” back to a 1968 NATO conference, arguing it never actually went away. They explain why software delivery is an adaptive sociotechnical system, and why treating it as a simple linear process leads leaders to pull the wrong levers. Elisabeth…

Is vibe coding quietly draining the thing that makes teams effective: their shared understanding? Margaret breaks down her new “triple debt” model, and why cognitive debt might be the one nobody’s tracking. In this episode, Margaret-Anne Storey, co-author of the SPACE framework and a leading developer experience researcher, returns three years after her first appearance to unpack her new “triple debt” model. She explains how technical debt is now joined by cognitive debt, the erosion of shared understanding across a team, and intent debt, the loss of the “why” behind decisions that agents also lack. Drawing on her Startup Studio course, where student teams built MVPs in minutes only to lose track of their own architecture weeks later, she walks through what pushed her to name these problems. The conversation covers cognitive surrender, developer fatigue from managing swarms of agents, and the warning signs leaders should watch for on their teams. Margaret also introduces Cognitive Tours, a tool inspired by…

If expertise is becoming almost free, why is judgment becoming the most expensive skill in tech? Ajey Gore, former Gojek CTO, explains why the age of AI agents rewards builders over typists. In this episode, Ajey Gore, former Group CTO of Gojek and Operating Partner at Peak XV Partners, explains why coding agents are forcing software teams to rethink what “correct” really means. He argues that as expertise becomes cheap, judgment becomes the scarcest and most valuable skill in engineering. Ajey walks through why the classic developer-to-reviewer workflow no longer makes sense when agents can generate thousands of lines a day, and why trunk-based development, feature flags, and rigorous testing matter more than ever. He also shares how he built ClawStation solo, coordinating specialized AI agents through story cards instead of writing code by hand. The conversation covers why most organizations still fail to see returns from AI adoption, and what happens when leaders cut headcount without redesigning how work…

What if optimizing for AI output is actually slowing your company down? When code becomes nearly free to produce, the organizations still measuring productivity by output are solving the wrong problem. In this episode, Mik Kersten, author of “Project to Product” and the forthcoming “Output to Outcome,” shares why the real challenge of the AI era isn’t generating more code — it’s building organizations that can turn that output into customer and business value. Drawing on Carlota Perez’s model of technological revolutions and the theory of constraints, Mik explains how AI has removed the output bottleneck that software organizations were built around, and where the new constraints now live. He introduces three core models from the book — the outcome loop, the product operating model, and the outcome tree — as a framework for adapting how organizations plan, fund, and deliver value. Mik also addresses one of the most pressing decisions leaders face today: whether to cut headcount based on AI productivity…

Why do 70% of change efforts fail — even when leadership is fully committed? The answer isn’t strategy or resources; it’s the hidden stories people unknowingly carry that silently block every initiative. In this episode, Ronica Roth, author of “Practice Makes Culture” and co-founder of The Welcome Elephant, shares a practical framework for creating lasting organizational change. Drawing on 25 years of experience helping teams and companies transform, she explains why culture declarations and vision speeches alone never work — and what leaders at any level can do instead. Ronica introduces the concept of “welcoming elephants” — the emotional, systems, and room elephants that surface whenever change is attempted — and why acknowledging them is the first step toward real progress. She also unpacks why most culture lives beneath the surface, in hidden stories that employees carry without realizing it, and how those stories quietly undermine even well-designed initiatives. The conversation covers how to build…

What if your AI coding agent is quietly cheating on your tests — and how do you stop it? Julien Verlaguet, who built the type system Meta used to migrate tens of millions of PHP lines, is now building Skipper: a closed-loop coding agent designed to make AI-generated code verifiably correct, without human intervention. In this episode, Julien Verlaguet, creator of the Hack programming language at Meta and co-founder of SkipLabs, explains why AI agents will always try to cheat — gaming tests, quietly modifying logic while doing something else, and declaring work done when it isn’t. He draws on his experience migrating Meta’s PHP codebase to a statically typed system, drawing sharp parallels between convincing engineers to trust a new type checker and building systems that can trust an LLM. Julien makes the case for spec-driven development with validation layers at every step, where separate AI instances verify correctness and the code-writing agent is locked out of touching tests. He shares the story of an LLM…

Why do companies with the best intentions end up betraying their customers, employees, and mission? Eric Ries calls it “financial gravity” — an invisible force that pulls even the most principled companies toward corruption, and understanding it is the first step to resisting it. In this episode, Eric Ries, entrepreneur and author of The Lean Startup and Incorruptible, shares why building a great company isn’t just about having a strong vision — it’s about building structures that protect that vision from external pressure. Eric revisits the core ideas behind the Lean Startup and MVP, explaining how the purpose of a minimum viable product is not to ship fast but to learn fast. He then introduces the central thesis of his new book: that the corruption we see in companies isn’t caused by bad people, but by a financial system that pulls organizations away from their values. Drawing on stories of Sol Price, FedMart, Costco, HEB, Novo Nordisk, and Anthropic, he shows that incorruptible companies are built through…

What if faster coding is actually slowing your software delivery down? Most teams are pouring AI into the coding phase, but the real bottleneck is everywhere else. In this episode, Andrew Haschka, Field CTO at GitLab for Asia Pacific and Japan, explains why most AI strategies in software engineering are failing and what it takes to fix them. He introduces the AI paradox: teams invest heavily in AI-assisted coding, yet coding accounts for less than 20% of the software delivery lifecycle, leaving the biggest bottlenecks untouched. Andrew makes the case for intelligent orchestration — moving from isolated AI interactions to governed, end-to-end agentic flows that span planning, coding, testing, security, compliance, and release. He shares how a unified system of record forms the foundation for high-quality AI outcomes, and why fragmented tools and siloed context actively limit what AI can deliver. Drawing on real customer examples — including Ericsson’s 50% faster deployments and 130,000 hours saved in six…

(07:22) Brought to you by Mailtrap Mailtrap is a modern email delivery for developers with native SDKs support along with security compliant API & SMTP. Plus, you get 4,000 emails a month completely on their free tier! It also provides 24/7 support where you actually talk to real people, not an AI chatbot. Try Mailtrap for free at ⁠mailtrap.io⁠. What does code review mean when AI writes most of the code? The answer isn’t to review more carefully. It’s a fundamentally different process, one built around rules, agents, and governance rather than diffs and comments. In this episode, Itamar Friedman, founder and CEO of Qodo.ai, shares how AI is forcing a complete rethink of code review — from inline comments on code diffs to multi-agent governance systems that verify intent, architecture, and business logic at scale. He traces the evolution of code review through successive generations, explains why traditional static analysis is no longer sufficient, and lays out what a modern quality and governance layer…

(05:00) Brought to you by Mailtrap Mailtrap is a modern email delivery for developers with native SDKs support along with security compliant API & SMTP. Plus, you get 4,000 emails a month completely on their free tier! It also provides 24/7 support where you actually talk to real people, not an AI chatbot. Try Mailtrap for free at mailtrap.io. What happens when AI ships code faster than your team can review it? As agentic development accelerates your SDLC, the guardrails matter more than ever — and most teams don’t have them. In this episode, Egil Osthus, CEO of Unleash, makes the case for FeatureOps as a strategic capability — not just a developer convenience. He explains the shift from a project mindset to a product mindset, where releases are decoupled from deployments and business outcomes matter more than shipping scope. Egil breaks down the four pillars of FeatureOps — gradual rollout, full stack experimentation, surgical rollback, and lifecycle management — and why each one becomes even more critical…

What if vibe coding is the worst thing you could do with AI agents? The developers seeing the biggest gains aren’t prompting harder. They’re planning smarter, spec-first, and treating AI as a facilitator rather than a code generation engine. In this episode, Brian Madison, creator of the BMad Method, shares how a year of late-night AI experiments led him to a structured, Agile-inspired approach to building software with AI agents. Brian explains why jumping straight into agent mode without upfront planning (what most people call vibe coding) reliably hits a wall, and how a disciplined spec-first workflow breaks through that ceiling. He walks through the BMad Method’s core workflow: brainstorming, PRD, architecture, UX design, and context-rich user stories, each feeding into the next so the agent always has exactly what it needs. Brian also recounts a transformative two-week sprint he ran with his team where engineers were given permission to fail, and how that single experiment changed the way his entire…

Why do 80-95% of AI initiatives fail — and why is your organization’s structure to blame? Most companies are treating AI like a software upgrade, when it actually demands a complete rewiring of how work gets done. In this episode, Melissa Reeve, author of Hyperadaptive and organizational change expert, shares a practical model for transforming legacy enterprises into AI-native organizations built to thrive — not just survive — in the age of AI. Drawing on her experience with the Toyota Production System, Scaled Agile, and deep research into leading AI adopters, Melissa argues that the real barriers to AI adoption are structural: Taylorist hierarchies, functional silos, and decision bottlenecks that organizations have never been forced to dismantle — until now. She introduces the Hyperadaptive model, a five-stage maturity path that gradually rewires how organizations operate, from establishing AI governance and identifying champions, to deploying agentic AI and organizing around customer value streams. Unlike…

What does it take to build a world-class engineering culture when you start with five engineers on minimum wage? Tommy Sullivan did exactly that at Vidio — and the team’s average tenure of seven years tells you everything about whether it worked. In this episode, Tommy Sullivan, CTO of Vidio (Indonesia’s largest streaming platform) shares how he built an engineering culture from almost nothing, growing a team of five to over two hundred using Extreme Programming principles and a relentless focus on hiring for attitude over aptitude. Tommy traces his journey from Pivotal Labs in San Francisco to the early days of Indonesia’s tech boom, explaining why Vidio survived when well-funded competitors like Hooq and iFlix all shut down. Along the way, he gets into where AI has worked and where it has failed at Vidio, how the team is rethinking pair programming in the age of AI agents, what it takes to stream four terabytes per second during live events, and why protecting code quality is ultimately a culture problem,…

Why do so many talented senior engineers struggle the moment they step into a tech lead role? Most of them are promoted based on their coding ability, but that same strength becomes a liability the moment they start leading a team. In this episode, Anemari Fiser, tech lead coach and author of “Leveling Up as a Tech Lead”, shares the three mindset shifts that define the transition from senior engineer to effective tech lead: moving from an “I” to a “We” mindset, shifting focus from code to value, and trading short-term thinking for long-term impact. She explains why so many engineers hold on to coding out of fear, how to delegate without losing accountability, and why most technical problems are really people problems in disguise. Anemari also addresses how AI is reshaping the tech lead role and why the fundamentals of leadership still apply regardless of the tools your team uses. Key topics discussed: The 3 mindset shifts required for the transition to tech lead Why your coding strength can hold back your…

In a world where AI can build your MVP overnight, what actually gives you a lasting competitive edge? Andrew Stevens argues it’s not the software — it’s the data, the trust, and the systems you build around them. In this episode, Andrew Stevens, CTO of Sakura Sky and a technology leader with 30+ years of experience building, scaling, and selling companies, shares hard-won lessons from his journey across startups, enterprises, and AI ventures. He explains why product-market fit matters more than shipping fast, why data outlasts software as a competitive moat, and how leaders must design systems that don’t depend on their own heroics. Andrew also shares how a near-fatal accident reshaped his thinking on resilience, delegation, and what it truly means to build something that scales. From hiring for attitude over technical skill to building AI governance that accelerates rather than blocks innovation, this conversation is packed with practical wisdom for anyone leading in the AI era. Key topics discussed: Why…

With AI generating code faster than ever, coding alone is no longer enough. The engineers who will stand out aren’t the ones who write the most code, but the ones who know what to build and why. In this episode, Drew Hoskins, author of “The Product-Minded Engineer”, shares how engineers can develop the product thinking skills that will define their careers in the AI era. Drew draws on his experience as a senior staff engineer at Microsoft, Meta, and Stripe to explain why the best engineers care as much about the what and why as the how. He introduces the Double Diamond Framework (Discover, Define, Develop, Deliver) and calls out why most engineers make the mistake of jumping straight to the Develop phase. He also explains the concept of the “great re-indexing”: the mental shift required to switch between thinking like an engineer and thinking like a user. As AI takes over more of the routine coding work, Drew argues that product skills, people skills, and ownership skills are what will separate good…

What if the MCP server you installed last week is silently leaking your emails to a stranger? The AI tools boosting your productivity could already be your biggest security liability. MCP (Model Context Protocol) has quickly become the standard for connecting AI agents to external tools and data sources. But as adoption accelerates, so do the risks – from malicious servers harvesting your credentials in the background, to local processes exposed to your entire network with no authentication. Most developers install MCP servers without fully understanding what code is running or who wrote it, creating serious supply chain and shadow IT problems inside organizations. In this episode, Ariel Shiftan, CTO of MCPTotal, explains how MCP actually works, why there is a wide gap between its original design and how it is used in practice, and what that gap means for security. He also walks through real zero-days his team has discovered and shares practical advice for developers and enterprise leaders trying to adopt…

Think you’re just “not a people person”? Most tech leaders quietly believe this about themselves, and it’s exactly what’s holding them back. In this episode, Martijn Versteeg, founder of peer leadership community Group Effort and former CPTO with a background in organizational psychology, makes the case that it’s not: human behavior follows predictable patterns you can understand and work with, just like any system. The conversation covers a six-variable model for understanding what drives behavior and disengagement on your team, why popular personality tools like MBTI and DiSC often do more harm than good, and a clear structure for delivering bad news without the usual stress buildup. We also get into what it really takes to let go of hands-on coding when you move into leadership, why developing a product mindset matters even if product isn’t in your title, and the psychological risks of heavy AI use that most teams still aren’t thinking about. Key topics discussed: The 6 human needs that predict human…

Is your platform engineering initiative struggling to deliver results? The problem might not be your tools or technology at all. In this episode, Sam Barlien, Community Organizer at Platform Engineering (the world’s largest platform engineering community), shares insights from speaking with nearly 400 engineering leaders last year about why their platform initiatives succeed or fail. The biggest revelation: it’s almost never about the tools. Sam explains why treating your internal platform like a product, complete with user research, documentation, and a product manager mindset, is the key differentiator between real platform engineering and just a rebranded operations team. He breaks down how to start small with a minimum viable platform, measure what actually matters, and build golden paths that developers want to follow. The conversation also covers how AI is both accelerating the need for platform engineering and transforming how platforms are built and operated. Key Topics Discussed: What platform…

(05:13) Brought to you by Sweep AI Sweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at ⁠sweep.dev⁠. What if Southeast Asia had its own ChatGPT that cost 20x less? Bruce Yang built Agnes AI to solve what global companies ignore: accessible AI for emerging markets. In this episode, Bruce Yang, CEO and founder of Agnes AI, explains how he’s built Southeast Asia’s fastest-growing AI platform with 4 million registered users and 300K daily active users. After working at Microsoft and LinkedIn in Silicon Valley, Bruce returned to Singapore and started his PhD at NUS right before COVID, positioning him perfectly to ride the AI wave. Agnes AI uses smaller, specialized models trained on Southeast Asian languages and local user data to deliver productivity features like deep research, PowerPoint generation, and AI-powered group chats at 1/20th the cost of major competitors. We discuss the challenges of building AI for emerging…

(05:22) Brought to you by Cyberhaven AI is exfiltrating your data in fragments. Not one big breach — a prompt here, a screenshot there, a quiet export into a shadow AI tool. Every week, AI makes your team faster and your data harder to see. Files are moved to new SaaS apps, models are trained on sensitive inputs, and legacy DLP is blind to the context that matters most. On February 3rd at 11 am Pacific, Cyberhaven is unveiling a unified DSPM and DLP platform, built on the original data lineage, so security teams get X-ray vision into how data actually moves — and can stop risky usage in real time. Watch the launch live at cyberhaven.com/techleadjournal. Did you know Singapore is one of the world’s top countries launching cyberattacks? Not as a victim, but as the source. Your routers, smart TVs, robot vacuums, or network-attached storage could be part of a massive botnet right now. In this eye-opening episode, Joseph Yap, founder of Otonata and cybersecurity expert, reveals the hidden cyber threat lurking in…

(06:23) Brought to you by Sweep AI Sweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at sweep.dev. Is the era of writing code by hand coming to an end? Gene Kim explains how vibe coding solved problems he abandoned for 13 years and why the best days of coding might be ahead of us. In this episode, Gene Kim shares his transformation from someone who hadn’t written production code in decades to building ambitious projects in minutes. He explains how meeting Steve Yegge and discovering vibe coding reignited his passion for programming. Gene breaks down the FAAFO framework (Fast, Ambitious, Autonomous, Fun, Optionality) of vibe coding benefits and addresses the real risks of vibe coding, from deleted databases to corrupted repos. He emphasizes that developers need to shift from line cook to head chef, mastering delegation, architecture, and faster feedback loops. The conversation also explores whether AI will eliminate…

Why are tech companies really laying off developers? The uncomfortable truth has nothing to do with AI efficiency and everything to do with running out of ideas. In this episode, Stephan Schmidt, CTO coach and author of “The Amazing CTO’s Missing Manual,” shares a perspective on AI adoption that most tech leaders aren’t talking about. Developer layoffs aren’t about AI replacing jobs; they reveal a deeper problem. Product management has become a bottleneck, creating shallow features just to keep developers busy rather than driving meaningful innovation. When AI accelerates development, this bottleneck becomes impossible to ignore. Stephan explains why architecture must be AI-ready before teams can benefit from AI tools, how CTOs can manage unrealistic business expectations, and why junior developers actually have a massive opportunity right now. He also challenges the common belief that vibe coding will democratize software development, explaining why you need to be a strong developer to prompt effectively.…

(04:11) Brought to you by Jellyfish AI tools alone won’t transform your engineering org. Jellyfish provides insights into AI tool adoption, cost, and delivery impact – so you can make better investment decisions and build teams that use AI effectively. See for yourself at jellyfish.co/platform/ai-impact. Are you managing your team the same way you did five years ago? With AI agents now part of the workforce, the old playbook no longer applies. In this episode, Jurgen Appelo, author of “Human Robot Agent” and creator of Management 3.0 and unFIX, challenges conventional thinking about management, organizational design, and the future of work in the AI era. He explains why rigid frameworks like Scrum are becoming bottlenecks to AI speed and why he believes we need to completely rethink how organizations operate. The conversation dives into the concept of creating “fast tracks” for AI agents while maintaining “slow tracks” for human collaboration. Jurgen also breaks down why team sizes are shrinking and why…

(04:00) Brought to you by Unleash Unleash is a private, flexible, and scalable feature flag system that lets teams decouple deployments from releases. It reduces the risk of shipping new features and gives organizations real-time control over what reaches production. And as AI accelerates development, Unleash helps engineering teams move fast and stay stable with safe rollouts and instant kill switches. Start a free trial of Unleash at ⁠getunleash.io/pricing⁠. Why do so many software projects still fail despite modern tools? The answer often lies in the psychology of the team, not the technology stack. Software development is often viewed purely as a technical challenge, yet many projects fail due to human factors and cognitive bottlenecks. In this episode, Adam Tornhill, CTO and Founder of CodeScene, shares his unique journey combining software engineering with psychology to solve these persistent industry problems. He explains the concept of “Your Code as a Crime Scene,” a method for using behavioral…

(06:03) Brought to you by Unleash Unleash is a private, flexible, and scalable feature flag system that lets teams decouple deployments from releases. It reduces the risk of shipping new features and gives organizations real-time control over what reaches production. And as AI accelerates development, Unleash helps engineering teams move fast and stay stable with safe rollouts and instant kill switches. Start a free trial of Unleash at getunleash.io/pricing. Are you making critical decisions without consulting AI? Greg argues it’s now irresponsible for any leader to make high-stakes decisions without talking to AI first. In this episode, Greg Shove, CEO of Section and a multi-time founder with 30 years of entrepreneurial experience, shares how AI is fundamentally different from any previous technology wave. Unlike traditional software that makes us more productive within our existing boundaries, AI allows us to jump capability boundaries – enabling individuals and organizations to do things they simply…

(06:06) Brought to you by Jellyfish AI tools alone won’t transform your engineering org. Jellyfish provides insights into AI tool adoption, cost, and delivery impact – so you can make better investment decisions and build teams that use AI effectively. See for yourself at jellyfish.co/platform/ai-impact. Why do organizations constantly complain about having too much technical debt? Because they’re solving the wrong problem. In this episode, Dr. Andrew Brown, author of “Taming Your Dragon: Addressing Your Technical Debt,” reveals a profound insight: technical debt isn’t fundamentally a technical problem. It’s a trade-off problem rooted in human bias, organizational systems, and economic incentives. Through his innovative “Technical Debt Onion Model,” Andrew shows how decisions about code quality happen across five interconnected layers, from individual cognitive biases to wicked problem dynamics. Andrew explains why the financial debt analogy is dangerously misleading and, more importantly, how others can…

Why does an AI that brilliantly generates code suddenly fail at basic math? The answer explains why your LLM will fail when you least expect it. In this episode, Emmanuel Maggiori, author of “Smart Until It’s Dumb” and “The AI Pocket Book,” cuts through the AI hype to reveal what LLMs actually do and, more importantly, what they can’t. Drawing from his experience building AI systems and witnessing multiple AI booms and busts, Emmanuel explains why machine learning works brilliantly until it makes mistakes no human would ever make. He shares why businesses repeatedly fail at AI adoption, how hallucinations are baked into the technology, and what developers need to know about building reliable AI products. Whether you’re implementing AI at work or concerned about your career, this conversation offers a grounded perspective on navigating the current AI wave without getting swept away by unrealistic promises. Key topics discussed: Why AI projects fail the same way repeatedly How LLMs work and why they…

Can decades-old management philosophy actually help us tackle AI’s biggest challenges? In this episode, John Willis, a foundational figure in the DevOps movement and co-author of the DevOps Handbook, takes us through Dr. W. Edwards Deming’s System of Profound Knowledge and its surprising relevance to today’s most pressing challenges. John reveals how Deming’s four-lens framework—theory of knowledge, understanding variation, psychology, and systems thinking—provides a practical approach to managing complexity. The conversation moves beyond theoretical management principles into real-world applications, including incident management mistakes that have killed people, the polymorphic nature of AI agents, and why most organizations are getting AI adoption dangerously wrong. Key topics discussed: Deming’s System of Profound Knowledge and 14 Points of Management—what they actually mean for modern organizations How Deming influenced Toyota, DevOps, Lean, and Agile (and why the story is more nuanced than most people…

In this episode, Honey Mittal, CEO and co-founder of Locofy.ai, explores one of the most exciting transformations in software development: the convergence of design and engineering through AI-powered automation. Honey shares the fascinating journey of building Locofy, a tool that converts Figma designs into production-ready front-end code. But this isn’t just another AI hype story. It’s a deep dive into why Large Language Models (LLMs) fundamentally can’t solve design-to-code problems, and why his team spent four years building specialized “Large Design Models” from scratch. Key topics discussed: Why 60-70% of engineering time goes to front-end UI code (and how to automate it) The technical limitations of LLMs for visual design understanding How proper design structure is the key to successful code generation The emergence of “design engineers” who bridge design and development Lessons from pivoting from consumer to enterprise SaaS Building global developer tools from Southeast Asia The real challenges of…

Can you navigate AI disruption without understanding your landscape? Discover how to gain true situational awareness. The rise of AI has exposed a fundamental problem in how organizations make decisions. Most leaders operate using stories and graphs, not actual maps of their landscape. This leaves them vulnerable to disruption and unable to make informed choices about where to apply new technologies. The result is chaos, waste, and strategic mistakes that could have been avoided. In this episode, Simon Wardley, creator of Wardley Mapping, explains how to build true situational awareness in your organization. He shares why most business “maps” aren’t really maps at all, how to understand the landscape before making decisions, and what leaders need to know about AI adoption beyond the current hype. Key topics discussed: Why leading with stories instead of maps creates fake CEOs The critical difference between graphs and maps in business strategy What Wardley mapping is and the three pattern types leaders must…

How much of your code exists only to prevent failures? Discover a new paradigm for building reliable applications. In this episode, Preeti Somal, SVP at Temporal, explores a paradigm shift that can dramatically boost productivity and give developers peace of mind. Drawing on her experience leading massive infrastructure at Yahoo and HashiCorp, she explains Temporal’s concept of durable execution that helps developers focus on business logic and remove reliability concerns. Preeti also discusses key findings from Temporal’s first State of Development Report. In this episode, you will learn about: Lessons from operating large-scale systems at Yahoo and HashiCorp Why reliability ranks higher than cost for most engineering teams How durable execution removes reliability complexity from developer concerns Why unlearning old patterns proves harder than learning Temporal’s model Creating a strong incident response culture through blameless post-mortem Nurturing psychological safety in infrastructure teams and…

“Engineering leaders are stuck between the expectations put out by sensational headlines and the reality of what they’re seeing in their organization. There’s a big disappointment gap.” Is your AI investment paying off? Many leaders struggle to see real ROI beyond the hype. In this episode, Laura Tacho, CTO of DX, shares DX’s new research on measuring AI adoption success across 38,000+ engineers. Our conversation reveals why acceptance rates are misleading metrics and introduces DX’s new AI Measurement Framework™ with its three critical dimensions: utilization, impact, and cost. Learn why treating AI as an organizational problem closes the “disappointment gap” between hype and reality. Note: This episode was recorded in July 2025. The AI adoption rate mentioned has since risen to nearly 80%. In this episode, you will learn about: The “Disappointment Gap” between AI hype and reality Why the popular “acceptance rate” metric is misleading The DX AI Measurement Framework™ and its three dimensions The top…

“Architecture is something that has to emerge naturally from the code. If it doesn’t make the code better, more elegant, and more flexible, then you should not be doing it.” Why do so many developers have a love-hate relationship with ORM? The creator of Hibernate reveals the real reasons behind the controversy and what’s being done to fix the fundamental issues. In this episode, Gavin King, the creator of Hibernate, shares the story behind its creation, from a debate with his boss to its rise as a popular open-source. He dives deep into why developers often dislike ORM, pinpointing the “magic” of the stateful persistence context as a major pain point. Gavin explains how modern specifications are fixing these historical issues with an emphasis on type safety and more explicit, stateless operations, giving developers greater control. Key topics discussed: The origin story of Hibernate and the early frustrations with Java EE The single biggest mistake that led some developers to hate ORM Why type safety…

Are long code review cycles killing your engineering team’s velocity? Learn how top engineering teams are shipping code faster without sacrificing quality. In this episode, Greg Foster, CTO and co-founder of Graphite, discusses the evolution of code review practices, from the fundamentals of pull requests to the future of AI in code review workflows. He shares the secrets behind how the Graphite team became one of the most productive engineering teams by leveraging techniques like small code changes and stacked PRs (pull requests). Key topics discussed: The evolution of code review from bug-hunting to knowledge sharing Best practices for PRs and why small PRs get better feedback How stacked PRs eliminate waiting time in development workflows The rise of AI in the code review process Why AI code review works best as an automated CI check How Graphite achieves P99 engineering productivity Hiring engineers in the age of AI-assisted coding Timestamps: (00:00) Trailer & Intro (02:21) Career Turning Points (05:11)…

Struggling with technical debt and code quality? Learn how a technical coach can help your team level up. In this episode, Emily Bache, a Samman technical coach, shares her proven method for building better engineering teams through structured learning and collaborative coding. We explore ensemble programming, learning hours, and why AI makes fundamental engineering practices more important than ever. Key topics discussed: The role of a Technical Coach and the Samman Method explained How AI amplifies good engineering practices instead of replacing them How to use ensemble programming to achieve single-piece flow Running effective ensemble sessions and avoiding common failure modes Why learning is part of the work, not only a side activity Why pull requests should not be the primary tool for mentoring junior developers The dangerous trend of “vibe coding” with AI tools Timestamps: (00:00) Trailer & Intro (02:22) Career Turning Points (03:23) Being Part of Modern Engineering YouTube Channel (04:27) The Role of…

Why do engineering teams slow down as they scale? It’s not the technology—it’s the management systems. In this episode, Michi Kono, CTO at Garner Health and former engineering leader at Meta, Capital One, and Stripe, shares his battle-tested approach to building scalable engineering organizations. We explore why most teams slow down as they scale and how to build systems that accelerate growth. Our conversation covers everything from designing effective org charts to creating accountability without killing psychological safety. You’ll learn practical strategies for nurturing engineering culture while maintaining high-performance standards. Key topics discussed: The challenges of hypergrowth and the need to constantly reinvent yourself How to avoid slowdowns by holding teams accountable for outcomes, not just shipping code The art of designing org charts that maximize team autonomy Building a culture of accountability and learning from mistakes without blame When managers should stop writing code (and why…

How do you build a high-performing engineering team in the AI era? And will AI make fundamental engineering skills obsolete? In this episode, Mohan Krishnan, Head of Engineering at Grab, shares lessons from leading multiple transformational engineering teams. Drawing from his experience at Grab, Bukalapak, BBM Emtek, and Pivotal Labs, Mohan explains why core engineering fundamentals still matter, even in the age of AI, and will become even more valuable than ever. He discusses building disciplined, high-performing engineering teams and the importance of hands-on leadership. We also explore the unique challenges and vast potential of the tech landscape in Southeast Asia. Key topics discussed: Why foundational skills like TDD and system design are becoming more critical in the age of generative AI How to effectively use AI as a pair programmer for upskilling and idea generation, while avoiding the pitfalls of “vibe coding” Mohan’s “sports team” analogy for building successful engineering teams with discipline,…

How has Infrastructure as Code changed in the last five years? Explore the key shifts and how to align your infrastructure to real business value. In this episode, Kief Morris, a Distinguished Infrastructure Engineer at Thoughtworks, returns to discuss the third edition of his book “Infrastructure as Code.” He shares fresh insights on designing and delivering dynamic systems for today’s cloud-driven world. Kief explores the evolution of IaC, practical methods for modern teams, the next generation of tools, and lessons learned from the recent years. Learn how to align infrastructure with business needs and manage today’s growing infrastructure complexities. Key topics discussed: How “Infrastructure as Code” book has evolved across three editions Why infrastructure decisions must align with business value How IaC and the toolchain have evolved over the last few years Handling the growing complexity of modern infrastructure The rise of platform engineering and internal developer platforms Terraform vs.…

Ever wondered how AI is being applied in the world of clinical trials where human lives are at stake? In this episode, Patrick Leung, CTO of Faro Health and former Google Duplex Engineering Lead, reveals how AI is transforming the clinical trial process — a process that can cost up to $2 billion per drug and take over 10 years to complete. Patrick reveals how Faro Health’s AI systems generate complex clinical documentation in minutes instead of months in which hallucinations aren’t acceptable, while navigating the strict regulatory requirements of the healthcare industry. Patrick also reflects on the evolution of AI technologies, the realities of large language models, and offers practical advice on how to thrive in the rapidly changing AI-driven era. Key topics discussed: The evolution of AI from image recognition and Google Duplex to LLMs How Faro Health uses AI to transform clinical trial process The challenges of applying AI in highly regulated industries AI’s potential to save time and millions in…

Is your engineering team running like the wild, wild west? What does engineering excellence look like in practice? In this episode, Ganesh Datta, co-founder and CTO of Cortex, explores what it takes to achieve engineering excellence. Ganesh shares lessons from his own journey, from early bug-fixing to building a company focused on engineering excellence. We discuss how platform engineering and internal developer platforms (IDPs) can help teams scale, improve reliability, and align with business outcomes. Ganesh also explains why culture, leadership, and clear metrics matter more than any single tool. If you’re looking to make your engineering team a true business driver, this conversation is for you. Key topics discussed: How to define engineering excellence and why it’s tied to business outcomes. The critical role of leadership in connecting engineering initiatives to business values. When to invest in platform engineering and internal developer platforms as your team grows. Common misconceptions about…

Is your software development process stuck on a conveyor belt? Discover how to break free from outdated manufacturing mindsets and build truly high-performing, agile teams that “Move Fast and Break Silos.” In this episode, experienced CPTO, Klaus Breyer, introduces a revolutionary approach to software development. He explains why treating software engineering like a factory assembly line leads to inefficiency, micromanagement, and disempowered teams. Learn how to slice work effectively—from objectives down to delivery—and align small, empowered teams to solve real customer problems and ship value faster. Key topics discussed: Why software development is a design process instead of a manufacturing process How Agile and Scrum has become micromanagement tools Why ticketing systems can create communication silos How to slice work into objectives, problems, solutions, and delivery Giving teams problems to solve, not just solutions to build The concept of empowered teams that own their outcomes Why small, dynamic…

Is AI taking over the craft of coding? Many engineers now face an identity crisis. In the episode, Distinguished Engineer Annie Vella discusses her research on AI’s impact on software development. She explores the “software engineering identity crisis” as the craft of coding becomes automated. Annie warns that the seductive speed of AI tools can lead to lower quality and delivery instability, a trend supported by reports from DORA and GitClear. She also cautions that over-reliance on AI prevents engineers from gaining the hands-on experience needed for deep skill acquisition. Key topics discussed: How AI is reshaping the software development lifecycle The software engineer’s professional identity crisis The real danger of over-relying on AI tools How to balance the seduction of speed with long-term quality Crucial advice for junior engineers entering the industry Why leaders must shift focus from speed to quality The idea of treating AI as a team member instead of just a tool Timestamps: (00:00:00) Trailer &…

What if your most critical systems run on code that no one fully understands? In this episode, Omer Rosenbaum, CTO and co-founder of Swimm, explains how to use AI to close the knowledge gap in your legacy codebase. Discover the limitations of AI in understanding legacy code and learn novel approaches to automatically document complex systems, ensuring their critical business logic is preserved and understood within the organization. Beyond legacy systems, Omer also shares practical advice for how junior developers can thrive in the AI era and how teams and organizations can conduct more effective research. Key topics discussed: How junior developers can thrive in the age of AI The danger of shipping code you don’t fully understand Why AI can’t deduce everything from your code alone How writing documentation becomes more critical now with AI How to analyze code that even LLMs struggle to read, like COBOL How to keep your organization’s knowledge base trustworthy and up to date The real danger of letting AI…

Feeling like you have valuable technical insights to share but struggle to put them into words? You’re not alone. In this episode, Piotr Sarna, author of “Writing for Developers” and an experienced open-source maintainer, shares the common hurdles developers face in writing and provides practical tips to get started. Discover how cultivating a writing habit can not only boost your personal brand but also improve your technical skills and create new career opportunities. Key topics discussed: The Writing Challenge: Why many developers who have interesting things to say don’t write and the importance of writing culture in a company. Finding Your First Topic: How to identify valuable topics from your daily work, even if you think they’re not interesting enough or have already been written about. Overcoming Writer’s Block: Practical tips to overcome the fear of writing, including dealing with imposter syndrome and language concerns. Leveraging AI for Writing: How to effectively use AI as a reviewer to find…