Tech Lead Journal

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 that silently swapped a union for an intersection while splitting a file — a subtle bug that passed all tests — and why no human would ever have made that mistake. He then walks through how Skipper works: you write a spec, hand over control, and a compiler-like agent produces correct, runnable TypeScript without back-and-forth, backed by a sound incremental type system, reachability analysis, and a reactive runtime that applies diffs in milliseconds.

He closes with a grounded take on how the developer role is shifting — not disappearing — toward the kind of design, integration, and oversight work that always mattered most.

Key topics discussed:

• Why AI agents will always try to cheat on your tests
• The union-vs-intersection bug an LLM introduced silently
• Spec-driven development to keep LLMs on track
• How to separate the AI that verifies from the one that fixes
• Skipper: a compiler-like closed-loop coding agent
• Sound, incremental TypeScript built for AI-speed iteration
• Hot-reloading state without restarting — in milliseconds
• Why developers are all becoming tech leads

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:34) How Did Julien Create the Hack Programming Language at Facebook?
• (00:05:53) Does Static Typing Make Your Code More Secure?
• (00:09:54) How Did You Convince Facebook Engineers to Adopt Hack at Scale?
• (00:17:15) How Can Engineers Overcome Skepticism Toward AI Coding Tools?
• (00:22:44) Should Junior Engineers Trust AI-Generated Code?
• (00:29:44) How Do You Build Reliable Guardrails for LLM-Generated Code?
• (00:42:15) What Validation Strategies Prevent AI Agents From Cheating on Tests?
• (00:45:54) What Is Skipper and How Does a Closed-Loop Coding Agent Work?
• (00:54:59) How Does Skipper Compare to Claude Code in Terms of Correctness?
• (00:58:27) How Do You Get Started With Skipper and What Does the Output Look Like?
• (01:04:50) How Will the Software Developer Role Change in an AI-First World?
• (01:09:06) 3 Tech Lead Wisdom

_____

Julien Verlaguet’s Bio
Julien Verlaguet is a programming language designer and the Founder and CEO of SkipLabs. He is best known as the creator of Hack—the gradually typed language he built at Facebook that currently powers over 100 million lines of the company’s production code. After creating the open-source reactive framework Skip, Julien founded SkipLabs in 2022. His company recently launched Skipper, a closed-loop coding agent that takes a single prompt from a developer and returns a running, validated service.

Follow Julien:

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 a combination of ethos — a deep operational commitment to doing right — and structural governance that resists outside pressure. He also unpacks how false metrics like OKRs can hollow out a company’s integrity over time, and how Mary Parker Follett’s concept of the “invisible leader” helps culture survive beyond any single founder or CEO.

Key topics discussed:

• What “financial gravity” is and why even good companies fall to it
• The true purpose of an MVP (hint: it’s not about shipping fast)
• Why OKRs become dangerous false proxies over time
• Blueprint for building a truly incorruptible company
• Why Costco and Novo Nordisk resisted forces that killed FedMart
• Mary Parker Follett’s invisible leader explained
• Why Anthropic’s structure gives it a lasting competitive edge
• How everyday decisions become acts of systemic change

Timestamps:

• (00:00) Trailer & Intro
• (02:31) What Two Mega-Trends Make Lean Startup More Relevant Than Ever?
• (04:03) What Is the True Purpose of a Minimum Viable Product?
• (11:04) Has AI Actually Made Building Software Cheaper and Better?
• (13:41) What Two Stories Inspired the Book Incorruptible?
• (20:38) What Is Financial Gravity and Why Does It Corrupt Even Good Companies?
• (26:29) What Is Surrogation and Why Do OKRs Become Dangerous False Proxies?
• (29:55) What Is the Blueprint for Building an Incorruptible Company?
• (33:53) What Is the Invisible Leader and How Does It Keep Company Culture Alive?
• (39:56) What Governance Structures Can Shield a Company’s Mission from Financial Gravity?
• (48:27) Why Does Anthropic’s Unique Structure Give It a Competitive Advantage in AI?
• (51:43) 3 Tech Lead Wisdom

_____

Eric Ries’s Bio
Over the last two decades, Eric Ries’s ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader’s Guide; and The Startup Way.

As a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; Virgil, a legal services startup; and IMVU. On The Eric Ries Show, he talks with world-class technologists, thought leaders, and executives building for the long-term. He lives in the San Francisco Bay Area with his wife and three children.

Follow Eric:

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 months — he shows what a holistic approach actually looks like in practice.

The conversation also covers how tech leads, developers, and junior engineers need to evolve their skills in a world where AI handles routine implementation. Andrew closes with a compelling argument: in the agentic era, governance isn’t just a compliance burden, it’s the primary source of competitive advantage.

Timestamps:

• (02:30) What Are the Key Responsibilities of a Field CTO at GitLab?
• (03:26) Why Should Organizations Govern AI Strategy Rather Than Chase the Latest Features?
• (06:41) Why Is an End-to-End Agentic Flow More Valuable Than Individual AI Tools?
• (09:39) What Is the AI Paradox and How Does Intelligent Orchestration Solve It?
• (14:47) How Does Shifting Focus to Requirements Quality Transform Software Delivery Outcomes?
• (18:19) How Has GitLab Evolved Beyond CI/CD Into a Full End-to-End Delivery Platform?
• (20:20) What Should Software Teams Prioritize Beyond Coding in the AI Era?
• (24:14) How Do Organizational Silos Create a Capability Threshold for AI Adoption?
• (27:49) What Practical Strategies Can Organizations Use to Break Down Internal Silos?
• (30:58) How Did Ericsson Achieve 50% Faster Deployments and Save 130,000 Hours With GitLab?
• (33:07) How Should Software Developers Evolve in the Age of AI Agents?
• (36:26) How Is the Tech Lead Role Evolving in a Hybrid Human-AI Team?
• (39:22) How Can Junior Developers Keep Up With the Rapid Shift in Industry Expectations?
• (42:40) Why Do 79% of Singapore DevSecOps Practitioners Believe AI Will Create More Jobs?
• (45:27) Why Are Companies Reducing Staff Despite the Growing Demand for Software?
• (48:34) What Are the Most Common Pitfalls When Implementing Agentic Workflows?
• (52:29) What Practical Steps Should Engineering Leaders Take to Govern AI Responsibly?
• (55:13) Why Should Engineering Leaders Build an AI Strategy Before Choosing Technology?
• (57:15) 3 Tech Lead Wisdom

_____

Andrew Haschka’s Bio
Andrew Haschka serves as Field CTO for Asia Pacific & Japan at GitLab, where he acts as a trusted strategic advisor to enterprise customers and partners navigating complex technology transformation. With over 20 years of experience spanning software delivery, cybersecurity, cloud infrastructure, and organisational transformation, Andrew brings a rare combination of technical depth and executive-level counsel to the organisations he works with.

Prior to GitLab, Andrew held senior leadership roles across APAC at Google and VMware, and has led large-scale digital transformation programmes for organisations including Downer, IBM, Jones Lang LaSalle, Thomson Reuters, Optus, and across the Fiji and Pacific Islands.

Follow Andrew:

(07:22) Brought to you by Mailtrap

(05:00) Brought to you by Mailtrap

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 organisation works with AI.

Finally, he reflects on what this shift means for the future of software engineering — where the unit of work is moving from tasks and stories to full features and epics, and every engineer can operate more like a tech lead.

Key topics discussed:

• Why vibe coding hits a wall and how spec-driven dev fixes it
• Using AI as a facilitator, not just a code generator
• The BMad Method: PRD → architecture → context-rich stories
• How a 2-week “no typing” sprint transformed his engineering team
• Giving teams permission to fail as a leadership tool
• The shift from user stories to epics as the unit of work
• Why problem decomposition is engineers’ biggest AI superpower

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:44) How Did the US Army Shape Brian’s Journey into Software Engineering?
• (00:06:35) How Can Engineers Overcome Imposter Syndrome and Build Self-Confidence?
• (00:10:23) What Does BMad Actually Stand For?
• (00:13:49) What Is the BMad Method?
• (00:22:11) How Does BMad Approach Context and Spec Engineering?
• (00:29:02) What Sparked the Creation of the BMad Method?
• (00:44:55) What Productivity Gains Has the BMad Method Produced?
• (00:48:36) How Will AI Change the Unit of Work for Software Engineers?
• (00:55:51) How Does BMad Keep Specs and Code in Sync Over Time?
• (01:01:01) What Is the Best Way to Get Started with the BMad Workflow?
• (01:05:00) Which AI Models and Tools Does the BMad Method Support?
• (01:08:21) 4 Tech Lead Wisdom

_____

Brian Madison’s Bio
Brian Madison is the creator of the BMad Method, an open-source framework that treats AI as a facilitator for workflows across any domain—software development, product management, operations, and beyond. Used globally, the BMad Method helps people work through complex processes using AI personas, from engineers driving spec-driven development to product managers crafting better PRDs and requirements.

Currently a Senior Engineering Manager at Extend, Brian led product engineering teams toward becoming an AI-native organization and now leads the entire AI SDLC transformation for the company, using the BMad Method as a framework, reimagining how AI flows through the full software development lifecycle.

Brian’s approach to leadership was forged during his service in the U.S. Army, where he learned the values of servant leadership, discipline, and mission-first execution.

Follow Brian:

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 past transformations, AI will compress both the strategy-to-execution and concept-to-delivery dimensions simultaneously — and the organizations that fail to adapt will be displaced by AI-native competitors rising far faster than Uber or Airbnb ever did.

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:50) How Did Melissa’s Background in Lean and Agile Lead to the Hyperadaptive Model?
• (00:05:57) How Is the AI Revolution Different From Past Digital Transformations?
• (00:07:39) Will AI-Native Companies Disrupt Incumbents the Way Airbnb and Uber Did?
• (00:09:08) How Did the DevOps Model Inspire the Concept of Automated Execution Pipelines?
• (00:12:41) What Is a Hyperadaptive Organization?
• (00:14:10) Why Has AI Adoption Failed to Deliver Results in Most Organizations?
• (00:17:05) What Are the Three Structural Barriers to AI Adoption?
• (00:19:39) Why Is Taylorism Considered a Major Barrier to Becoming Hyperadaptive?
• (00:22:48) What Are the Five Capabilities Required to Become Hyperadaptive?
• (00:26:45) Why Does AI Make Age-Old Principles Like Lean and Agile More Relevant Than Ever?
• (00:28:49) How Will the Human-in-the-Loop Role Evolve as Agentic AI Takes Over?
• (00:32:52) How Should Organizations Start Transitioning from Functional Silos to Value Streams?
• (00:35:07) How Is AI Enabling Adjacent Competencies and Expanding Professional Roles?
• (00:38:43) Will AI Replace Workers or Unlock More of What Organizations Can Achieve?
• (00:41:52) What Are the Five Stages of Maturity for Becoming Hyperadaptive?
• (00:48:21) Why Do Most AI Implementations Fail When Organizations Skip the Foundation?
• (00:50:55) What Does Dynamic AI Governance Look Like in Practice?
• (00:55:20) How Does Kahneman’s Thinking Fast and Slow Explain the Human-AI Partnership?
• (00:58:07) How Can AI Help Organizations Optimize for People, Profit, and Planet?
• (01:00:24) 3 Tech Lead Wisdom

_____

Melissa Reeve’s Bio
Melissa Reeve creator of the Hyperadaptive Model and author of Hyperdaptive: Re-wiring the Enterprise to Become AI-Native. Hyperadaptive brings together process excellence, systems thinking, and the human side of AI integration to help leaders reimagine how their organizations learn and adapt.

Prior to leaning into AI, Melissa spent 25 years as an executive and Agile thought leader, which led to pioneering work in Agile marketing and her role as the first VP of Marketing at Scaled Agile and co-founding the Agile Marketing Alliance. She lives in Boulder, CO, with her husband, dogs, and chickens, where she enjoys hiking and gardening.

Follow Melissa:

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, not a tooling one. Tommy also shares a hard-earned view on the agentic AI trend and why understanding the underlying mechanics matters more than chasing the hype.

Key topics discussed:

• How Extreme Programming built Vidio’s 7-year average tenure
• Hiring for attitude: why aptitude alone isn’t enough
• Pair programming reimagined for the AI-agent era
• Why code quality is a culture problem, not a tool problem
• AI failures and wins at Vidio
• How Vidio streams 4TB/s to 2.2M concurrent users
• AVOD vs. SVOD: the model that saved Vidio
• Vendor independence for CDN and AI — why it matters
• What engineers need to understand about agentic AI

Timestamps:

• (00:00:00) Trailer & Intro
• (00:03:07) How Did Tommy Go From Silicon Valley to Jakarta?
• (00:07:22) How Has Indonesia’s Tech Scene Evolved Over the Past Decade?
• (00:13:12) What Happened to Indonesia’s Engineering Talent After the VC Bubble Burst?
• (00:15:03) Why Is Indonesia One of the World’s Most Exciting Tech Markets?
• (00:17:26) How Do You Build a World-Class Engineering Team When Starting From Scratch?
• (00:22:01) What Are the Hidden Benefits of Pair Programming Beyond Code Quality?
• (00:25:28) How Is AI Blurring the Lines Between Engineers and Product Managers?
• (00:28:48) How Do You Justify XP Practices to a Results-Driven Business?
• (00:36:11) What Has Worked and What Has Failed When Integrating AI at Vidio?
• (00:44:19) Is AI an Amplifier or a Threat to Software Engineers?
• (00:46:59) How Does Vidio Use Team Rotation and Shared Ownership to Retain Engineers?
• (00:51:16) How Do You Protect Code Quality Culture in the Age of AI?
• (00:54:16) What Metrics Actually Matter for Engineering Quality?
• (00:58:07) How Will AI-Generated Content Reshape the Streaming Industry?
• (01:06:51) What Does It Take to Stream at 4 Terabytes per Second?
• (01:09:26) How Do You Keep a Streaming Platform Stable During Massive Live Events?
• (01:14:12) How Did Vidio Survive When Other OTT Platforms Failed?
• (01:18:15) Why Does Vendor Independence Matter for Both CDNs and AI?
• (01:21:44) What Should Engineers Understand About the Agentic AI Trend?
• (01:26:17) Tech Lead Wisdom

_____

Tommy Sullivan’s Bio
Tommy Sullivan leads the software engineering behind Vidio — Indonesia’s leading video-streaming platform. Before joining the Vidio / Emtek group, he helped startups and global enterprises implement agile engineering and lean product development practices in Silicon Valley and Southeast Asia. As a founding member of Vidio, Tommy shaped its early development and steered its evolution from a user-generated content platform to a premium streaming service supporting millions of subscribers. He leads with a focus on data-driven decisions and a humble, collaborative developer culture.

Follow Tommy:

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 team
• How to let go of coding without losing your technical edge
• Delegation secrets: setting expectations that actually stick
• Influencing without authority — and when it’s not enough
• How to measure your impact when results are hard to see
• Leading your team through AI adoption without creating chaos

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:41) What Motivated Anemari to Write Her Book, Leveling Up as a Tech Lead?
• (00:05:41) How Is the Tech Lead Role Defined?
• (00:06:45) How Does the Engineering Manager Role Differ From a Tech Lead?
• (00:09:37) Why Is the Transition to Tech Lead One of the Most Challenging Career Moves?
• (00:14:21) How Can Tech Leads Shift From Short-Term to Long-Term Thinking?
• (00:18:34) How Can Tech Leads Learn to Let Go of Writing Code?
• (00:26:30) Why Is Every Tech Problem Actually a People Problem?
• (00:30:52) How Can Tech Leads Delegate Effectively?
• (00:37:18) How Can Tech Leads Influence Without Authority?
• (00:40:37) Why Is Accountability Without Authority Unfair to Tech Leads?
• (00:43:42) How Can Tech Leads Measure Their Impact?
• (00:46:52) How Does AI Change the Role of a Tech Lead?
• (00:52:26) Should Tech Leads Use AI to Get Back to Hands-On Development?
• (00:55:33) How Can Tech Leads Stay Accountable for AI-Generated Code?
• (01:00:26) With AI in the Mix, Is a Tech Problem Still Just a People Problem?
• (01:01:10) 3 Tech Lead Wisdom

_____

Anemari Fiser’s Bio
Anemari Fiser is a tech leadership trainer, coach and O’Reilly author of Leveling Up as a Tech Lead. With over a decade in tech, she has coached 500+ engineers and trained 400+ tech leads worldwide, and shares practical leadership insights on LinkedIn with a community of 30,000+ tech professionals.

Follow Anemari:

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 data — not software — is your real moat in the AI era
• What breaks when a startup scales past 10–100 people
• How to make decision rights explicit to move faster
• Design the system, not the hero: building beyond you
• Hiring for resilience and attitude over technical skill
• How governance can speed up AI adoption, not slow it down
• What trustworthy AI agents actually require

Timestamps:

• (00:00) Trailer & Intro
• (02:45) What Breaks When You Scale a Startup From Zero to 100 People?
• (08:44) Why Is Product-Market Fit More Important Than Building an MVP?
• (17:20) How Do You Build a Lasting Moat in the AI Era?
• (21:29) Why Must Leaders Learn to Let Go to Scale?
• (23:27) What Can Leaders Learn From a Near-Fatal Motorcycle Accident?
• (26:29) How Do Technical Leaders Stay Hands-On Without Becoming a Bottleneck?
• (31:32) Why Should You Hire for Resilience Over Technical Skill?
• (34:56) How Do You Build a Team That Innovates Safely in the AI Era?
• (41:12) How Do You Build AI Governance That Speeds Up Innovation?
• (47:37) Are AI-Driven Layoffs Justified or Just an Excuse?
• (52:06) How Do You Build Trustworthy AI Agents?
• (59:34) 3 Tech Lead Wisdom

_____

Andrew Stevens’s Bio
Andrew Stevens, CTO of Sakura Sky, is an executive leader and hands-on technologist who has scaled AI and cloud ventures from idea to acquisition. Based between Europe and the US, he blends deep expertise in cloud architecture, machine learning, and security with a track record in fintech, media, gaming, and AI.

Known for making complex tech relatable - often with pop-culture twists - Andrew brings sharp insights on AI guardrails, infrastructure resilience, and the creative edge humans hold in an AI-driven world. Whether advising founders, investing in early-stage startups, or speaking on global stages, Andrew helps audiences cut through the hype and focus on what matters most.

Follow Andrew:

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 engineers from truly impactful ones.

Key topics discussed:

• What makes an engineer “product-minded”
• Why engineers skip Discovery and what it costs them
• The Double Diamond: a framework for building the right thing
• How to think in user scenarios, not just system diagrams
• The “great re-indexing” between engineer and user thinking
• Why discoverability can 10x your feature’s impact for little cost
• How AI is making product skills more valuable, not less
• What junior engineers should focus on to stay relevant

Timestamps:

• (00:00) Trailer & Intro
• (02:35) What Is a Product-Minded Engineer?
• (05:37) What Did Drew Learn Working at Microsoft, Meta, and Stripe?
• (14:13) What Are the Biggest Challenges When Switching from Engineering to Product Management?
• (16:33) What Skill Gaps Hold Engineers Back from Product Thinking?
• (20:56) How Do You Bridge the Communication Gap Between Engineers and PMs?
• (26:07) What Are The Four Pillars (Double Diamond Framework)?
• (29:43) Why Should Engineers Care About the Deliver Phase?
• (32:40) How Should Engineers Apply the Double Diamond Framework Day-to-Day?
• (36:15) How Is AI Reshaping the Role of Product Engineers?
• (40:06) Should Product Managers Learn to Code in the AI Era?
• (43:56) What Is the Right PM-to-Engineer Ratio in the AI Era?
• (45:48) How Should Engineering Leaders Respond to AI Productivity Pressure?
• (51:04) What Advice Would You Give Junior Engineers Entering the Industry Today?
• (55:17) What Other Topics Does the Product-Minded Engineer Book Cover?
• (57:03) 3 Tech Lead Wisdom

_____

Drew Hoskins’s Bio
Drew Hoskins blends product, engineering, and storytelling in his work and writing. He is the author of The Product-Minded Engineer. As an engineer, Drew has helped design and build a wide range of innovative products and platforms for Microsoft, Meta, and Stripe.

Throughout his career, he has carried a passion for empowering developers. He’s founded and led several teams to major successes with developer platforms that have withstood the test of time. He’s currently a Staff Product Manager at Temporal Technologies, bringing durable execution to the masses.

He is an expert bridge player, having won a North American Championship in 2025, and lives in the beautiful and nerdy San Francisco Bay Area.

Follow Drew:

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 MCP without compromising their security posture.

Key topics discussed:

• What MCP is and why it won the “USB for AI” race
• Why most MCP servers are just API wrappers done wrong
• Real zero-days found in popular, widely used MCPs
• How malicious MCPs can silently leak your credentials
• The supply chain risks hiding inside your dev toolchain
• Why banning MCP in your org is the wrong move
• Best practices for writing well-designed MCP servers
• Why agent permission prompts need better security defaults

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:49) What Is MCP and Why Is It Called the USB for AI?
• (00:07:22) How Does MCP Differ from Standard REST APIs?
• (00:13:40) What Can AI Agents Do with MCP Beyond Reading Data?
• (00:16:56) What Is RAG and How Did AI Evolve to Tool Calling?
• (00:19:54) Why Is MCP Misused as an API Catalog and What Does That Cost?
• (00:25:04) What Are AI Skills and How Do They Compare to MCP?
• (00:30:29) How Does MCP Server Architecture Work Under the Hood?
• (00:37:01) How Do Malicious and Vulnerable MCP Servers Put Organizations at Risk?
• (00:45:30) What Real-World MCP Vulnerabilities and Zero-Days Have Been Found?
• (00:50:30) How Should Enterprises Enable MCP Adoption Without Compromising Security?
• (00:53:16) What Are Best Practices for Writing a Well-Designed MCP Server?
• (00:59:14) How Should AI Agents Handle Permissions Without Overwhelming Users?
• (01:05:26) 3 Tech Lead Wisdom

_____

Ariel Shiftan’s Bio
Ariel is a software engineer and security expert with more than 20 years of hands-on and executive leadership experience across cybersecurity, distributed systems, and AI infrastructure. He holds a PhD in Computer Science, specializing in advanced algorithms and systems. Earlier in his career, Ariel founded NorthBit, a deep-tech cybersecurity firm that was acquired by Magic Leap in 2016, where he led product security globally, overseeing the security lifecycle across more than 700 engineers. He has also led applied AI breakthroughs, including heading an XPRIZE-winning team that used deep learning to fight malaria in Africa.

Follow Ariel:

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 behavior
• Why MBTI and DiSC often do more harm than good
• How to stop avoiding difficult conversations
• Deliver bad news clearly using a 10-second rule
• Why becoming a bottleneck is a slow career killer
• Building a product mindset when you’re in tech
• The mental health risks of heavy AI use
• What peer groups give you that books can’t

Timestamps:

• (00:00:00) Trailer & Intro
• (00:03:06) Why Small Steps Matter More Than Career Turning Points
• (00:05:11) About Martijn Versteeg
• (00:07:01) How Can I Learn People Skills Systematically?
• (00:13:19) Six Human Needs That Predict Behavior
• (00:17:28) How Does It Compare to Maslow’s Hierarchy of Needs?
• (00:19:49) Why Are Personality Tests Like MBTI Unreliable?
• (00:23:20) How Do I Use Pain and Pleasure to Drive Growth?
• (00:28:30) How Do I Handle Conflict and Difficult Conversations?
• (00:32:47) A Model for Delivering Bad News in 10 Seconds
• (00:36:12) How Do I Transition from Tech Lead to Engineering Leader?
• (00:41:12) How Do I Let Go of Coding as a Leader?
• (00:42:49) The Vanilla Orchid Story: Why Leaders Must Let Go
• (00:46:55) How Can Engineers Develop a Product Mindset?
• (00:53:17) What Are the Hidden Risks of AI for Mental Health?
• (01:02:19) What Is the Value of Learning Through Podcast Conversations?
• (01:07:19) Why Consuming Knowledge Is Not the Same as Producing
• (01:09:06) 3 Tech Lead Wisdom

_____

Martijn Versteeg’s Bio
Martijn Versteeg is the founder of Group Effort, a Netherlands-based collective that empowers tech and product leaders across Europe through peer groups, offsites, and specialized training. As a key figure in the global product community, he is also an organizer of the Product Mastery Conference, where he helps curate insights for the next generation of product leaders.

Before founding Group Effort, Martijn built and successfully sold an EdTech IT platform and spent over five years as an Agile coach and Scrum Master. His unique perspective on leadership is rooted in high-performance athletics; at just 22 years old, he served as the National Rowing Coach for Singapore.

Today, Martijn is a vocal advocate for community-led learning. He frequently challenges leaders to move past the search for “golden nuggets” of wisdom and instead focus on the consistent, incremental iterations that solve the “hard people stuff” in scaling organizations.

Follow Martijn:

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 engineering really means (hint: it’s product management)
• Why DevOps and SRE often fail without product thinking
• The “Golden Path” vs “Golden Cage” approach to developer experience
• How to measure ROI and pitch platform engineering to executives
• The symbiotic relationship between AI and platform engineering
• Why starting with a Minimum Viable Platform beats big-bang transformations
• PlatformCon 2025 key takeaways and emerging trends

Timestamps:

• (00:00:00) Trailer & Intro
• (00:03:16) What Background Do You Need for Platform Engineering?
• (00:06:32) How Does Storytelling Help in Platform Engineering?
• (00:08:53) What Is Platform Engineering?
• (00:12:27) Why Are Organizations Adopting Platform Engineering?
• (00:19:51) What’s the Difference Between DevOps, SRE, and Platform Engineering?
• (00:23:25) Why Is the “Plug and Play” Approach to Tools a Trap?
• (00:28:45) How Do You Pitch Platform as a Product Instead of a Project?
• (00:34:01) How Do You Measure the ROI of Platform Engineering?
• (00:40:42) What Is the Golden Path in Platform Engineering?
• (00:47:12) What Were the Key Takeaways from PlatformCon 2025?
• (00:53:41) How Does Platform Engineering Leverage AI?
• (00:58:41) What Are the Hidden Costs of AI-Generated Code?
• (01:04:01) Why Is Platform Engineering Actually Product Management?
• (01:07:12) 1 Tech Lead Wisdom

_____

Sam Barlien’s Bio
Sam Barlien is a community organiser for the Platform Engineering Community. He is a tech nerd, and has been involved in tech communities for more than 10 years. He helps manage Platform Weekly, co-hosts PlatformCon, and drives the community Ambassador program, blog and Youtube channel.

Follow Sam:

(05:13) Brought to you by Sweep AI

(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 our homes. He reveals how everyday devices from routers to smart TVs become attack weapons. He explains why Singapore’s excellent infrastructure ironically makes it attractive for hackers and shares practical steps to protect your network. From residential proxies renting out your internet connection to teenagers running ransomware gangs, this conversation exposes the gap between our connected lives and our digital security practices.

Key topics discussed:

• Why Singapore, Indonesia, and Vietnam are top cyberattack source countries
• Why Singapore’s infrastructure makes it attractive for hackers
• How 700,000+ compromised devices launch 30 terabits per second DDoS attacks
• The rise of residential proxies and dark web rental of home networks
• How hackers exploit publicly disclosed vulnerabilities in outdated firmware
• Why AI is lowering the barrier to entry for hackers
• What makes executives and high-net-worth individuals attractive targets
• Practical steps to audit and protect your home network

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:40) How Can I Apply Journalism Skills to Tech
• (00:06:14) Why is Curiosity Essential for Tech Leaders?
• (00:08:48) Why is Singapore a Top Source for Cyber Attacks?
• (00:12:11) What Makes Singapore Attractive for Cyber Attacks?
• (00:16:39) How Many Devices in Singapore are Already Compromised?
• (00:20:40) How Can I Tell if My Home Network is Compromised?
• (00:30:13) Which Devices are Hackers’ Favorite Entry Points?
• (00:33:18) What is a Residential Proxy and Why Should I Care?
• (00:36:27) How do Hackers Actually Break into My Network?
• (00:47:47) Why are Executives and High-Net-Worth Individuals Prime Target?
• (00:55:12) Why isn’t Singapore’s Cyber Attack Problem in the News?
• (00:59:26) Can Internet Providers Stop These Attacks?
• (01:02:16) What Can I Do to Protect My Home Network?
• (01:05:19) How Do I Protect My Network-Attached Storage (NAS)?
• (01:10:41) How is AI Changing the Cyber Attack Landscape?
• (01:17:35) How Can Otonata Help Protect My Home Network?
• (01:23:39) What are Real-World Examples of Home Network Compromises?
• (01:28:20) 3 Tech Lead Wisdom

_____

Joseph Yap’s Bio
With 20+ years in Operations and Supply Chain, Joseph Yap founded Otonata (https://otonata.com) after realizing how vulnerable home networks are to security breaches. Otonata brings corporate-grade cybersecurity to homes using digital hygiene and lean management principles, protecting dozens of households from growing threats posed by AI, smart devices, and expanding attack surfaces.

Follow Joseph:

(06:23) Brought to you by Sweep AI

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.

Key topics discussed:

• Why AI layoffs reveal companies ran out of good ideas
• Architecture must be AI-ready for real productivity gains
• Vibe coding only works if you’re already a strong developer
• Product engineering roles will replace traditional developers
• MCP connections unlock AI value beyond code generation
• Juniors have huge advantage as AI-native engineers
• Iterate on plans, not prompts, when using AI tools
• CTOs can finally “rise and shine” using AI strategically

Timestamps:

• (00:00) Trailer & Intro
• (03:19) How do companies become truly AI-first?
• (04:13) How should CTOs manage unrealistic AI velocity expectations?
• (08:35) AI Use Cases Beyond Code Generation
• (12:04) What is MCP and how does it unlock AI value?
• (15:04) Why Developers Resist AI Adoption
• (18:35) Are AI layoffs caused by a lack of product innovation?
• (21:22) What is the future for junior developers in the age of AI?
• (24:36) Critical Thinking and Moving Up the Abstraction Layer
• (27:24) Vibe Coding: Benefits and Pitfalls
• (31:59) What is the difference between a Developer and a Product Engineer?
• (35:59) Building an Effective AI Adoption Strategy
• (38:06) AI Adoption Strategy for Development Teams
• (40:44) Avoiding the AI Tech Zoo
• (44:48) How do tech leaders handle AI data privacy and security?
• (50:31) How is the CTO role changing in 2026?
• (57:23) 3 Tech Lead Wisdom

(04:11) Brought to you by Jellyfish

(04:00) Brought to you by Unleash

(06:03) Brought to you by Unleash

(06:06) Brought to you by Jellyfish

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 brilliantly fail
• Why hallucinations can’t be fixed with better prompts
• Why self-driving cars still need human operators
• Adopting AI without falling into hype traps
• How engineers stay relevant in the AI era
• Why AGI predictions are mostly marketing
• Building valuable products in boring industries

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:32) Career Turning Points
• (00:06:41) Writing “Smart Until It’s Dumb” and “The AI Pocket Book”
• (00:08:14) The History of AI Booms & Winters
• (00:11:34) Why Generative AI Hype is Different Than the Past AI Waves
• (00:13:26) AI is Smart Until It’s Dumb
• (00:16:45) How LLM and Generative AI Actually Work
• (00:22:53) What Makes LLMs Smart
• (00:27:25) Foundational Model
• (00:30:01) RAG and Agentic AI
• (00:34:09) Tips on How to Adopt AI Within Companies
• (00:37:56) How to Reduce & Avoid AI Hallucination Problem
• (00:45:49) The Important Role of Benchmarks When Building AI Products
• (00:50:57) Advice for Software Engineers to Deal With AI Concerns
• (00:56:49) Advice for Junior Developers
• (00:59:34) Vibe Coders and Prompt Engineers: New Jobs or Just Hype?
• (01:01:55) The AGI Possibility
• (01:07:23) Three Tech Lead Wisdom

_____

Emmanuel Maggiori’s Bio
Emmanuel Maggiori, PhD, is a software engineer and 10-year AI industry insider. He has developed AI for a variety of applications, from processing satellite images to packaging deals for holiday travelers. He is the author of the books Smart Until It’s Dumb, Siliconned, and The AI Pocket Book.

Follow Emmanuel:

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 think)
• The dangers of polymorphic agentic AI and what happens when quantum computing enters the picture
• A practical framework for managing Shadow AI in your organization (learning from the cloud computing era)
• Why incidents are “unplanned investments” and the fatal cost of dismissing P3 alerts
• Treating AI as “alien cognition” rather than human-like intelligence
• The missing piece in AI conversations: understanding the philosophy of AI, not just the technology

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:27) Career Turning Points
• (00:05:31) Why Writing a Book About Deming
• (00:12:53) Deming’s Influence on Toyota Production System
• (00:19:31) Deming’s System of Profound Knowledge
• (00:28:12) The Importance of Systems Thinking in Complex Tech Organizations
• (00:31:43) Deming’s 14 Points of Management
• (00:44:17) The Impact of AI Through the Lens of Deming’s Profound Knowledge
• (00:49:56) The Danger of Polymorphic Agentic AI Processes
• (00:53:12) The Challenges of Getting to Understand AI Decisions
• (00:55:43) A Leader’s Guide to Practical AI Implementation
• (01:05:03) 3 Tech Lead Wisdom

_____

John Willis’ Bio
John Willis is a prolific author and a foundational figure in the DevOps movement, co-authoring the seminal The DevOps Handbook. With over 45 years of experience in IT, his work has been central to shaping modern IT operations and strategy. He is also the author of Deming’s Journey to Profound Knowledge and Rebels of Reason, which explores the history leading to modern AI.

John is a passionate mentor, a self-described “maniacal learner”, and a deep researcher into systems thinking, management theory, and the philosophical implications of new technologies like AI and quantum computing. He actively shares his insights through his “Dear CIO” newsletter (aicio.ai) and newsletters on LinkedIn covering Deming, AI, and Quantum.

Follow John:

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 building deep tech startups in Southeast Asia
• Career advice for staying relevant in the AI era

Whether you’re a front-end engineer tired of translating design pixel-by-pixel, a designer curious about coding, or a technical leader evaluating AI development tools, this episode offers practical insights into the future of software development.

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:13) Career Turning Points
• (00:05:28) Transition from Developers to Product Management
• (00:09:53) The Key Product Lessons from Working at Major Startups
• (00:14:12) Learnings from Locofy Product Pivot Journey
• (00:19:36) An Introduction to Locofy
• (00:22:40) The Story Behind The “Locofy” Name
• (00:23:27) How Locofy Generates Pixel Perfect & Accurate Codex
• (00:28:01) Why Locofy Pivoted to Focus on Enterprises
• (00:29:39) The Locofy’s Code Generation Process
• (00:32:13) Why Locofy Built Its Own Large Design Model
• (00:39:25) Locofy Integration with Existing Development Tools
• (00:42:44) LLM Strengths and Weaknesses
• (00:48:47) Other Challenges Building Locofy
• (00:50:59) The Future of Design & Engineering
• (00:58:35) The Future of AI-Assisted Development Tools
• (01:02:53) There is No AI Moat
• (01:04:37) The Potential of SEA Talents Solving Global Problems
• (01:08:14) The Challenges of Building Dev Tools in SEA
• (01:10:39) The Challenges of Being a Fully Remote Company in SEA
• (01:14:36) Locofy Traction and ARR
• (01:18:09) 3 Tech Lead Wisdom

_____

Honey Mittal’s Bio
Honey Mittal is the CEO and co-founder of Locofy.ai, a platform that automates front-end development by converting designs into production-ready code. Originally an engineer who built some of the first mobile apps in Singapore, Honey transitioned into product leadership after realizing his natural strength lay in identifying high-impact problems. He set a goal to become a CPO by 30 and achieved it, leading product transformations at major Southeast Asian scale-ups like Wego, FinAccel, and Homage.

Driven by a decade of experience and the “grunt work” he and his co-founder faced, he started Locofy to solve the costly friction between design and engineering. Honey is passionate about the future of AI in development, the rise of the “Design Engineer”, and proving that globally competitive, deep-tech companies can be built from Southeast Asia.

Follow Honey:

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 understand
• How to identify where human decision-making adds value in your AI adoption
• Why vibe coding is powerful but dangerous without proper code reviews
• Why software development is still a craft, not engineering
• How Jevons Paradox means AI won’t eliminate jobs but expand codebases
• The hidden dangers of AI hallucinations and the need for critical thinking

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:59) Career Turning Points
• (00:06:45) Importance of Understanding Landscape for Leaders
• (00:10:42) The Problem of Leading with Stories
• (00:12:49) Wardley Maps vs Other Types of Business Maps/Analysis
• (00:17:32) Wardley Map Overview
• (00:23:54) Why Mapping is Not a Common Industry Practice
• (00:26:23) Climatic Patterns, Doctrines, and Gameplay
• (00:30:51) Understanding Disruption by Using a Map
• (00:33:17) Navigating the Recent AI Disruption
• (00:39:37) A Leader’s Guide to Adopting AI
• (00:42:49) Turning Coding From a Craft Into Engineering
• (00:48:05) Simon’s AI & Vibe Coding Experiments
• (00:55:28) The Importance of Critical Thinking for Software Engineers
• (01:03:49) Navigating Career Anxiety Due to AI Fear
• (01:08:56) Tech Lead Wisdom

_____

Simon Wardley’s Bio
Simon Wardley is a researcher, former CEO, and the creator of Wardley Mapping, a powerful method for visualizing and developing business strategy. His journey began accidentally after a bookseller recommended Sun Tzu’s The Art of War, which sparked a fascination with understanding the competitive “landscape.”

As the former CEO of an online photo service acquired by Canon, he felt like a “fake CEO,” leading with stories while lacking true situational awareness. This led him to discover that almost all business “maps” were merely graphs, prompting him to develop his own mapping technique. Today, his work is used by organizations like NASA and taught at multiple MBA programs, helping leaders to “look before they leap” and navigate complex technological and market shifts, including the current disruption caused by AI.

Follow Simon:

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 on-call engineers
• Building security and compliance from day one versus retrofitting later

Timestamps:

• (00:00) Trailer & Intro
• (02:20) Career Turning Points
• (04:43) Key Learnings from Operating Large Scale Infrastructure
• (07:56) Key Learnings on Platform Engineering
• (09:59) Key Learnings on Maintaining High Reliability
• (12:02) Key Highlights Working at HashiCorp
• (13:52) Running Infra as Code using Temporal
• (15:28) Key Principles for Managing a Strong Incident Response
• (18:37) The Importance of Nurturing Psychological Safety within Infra Team
• (21:13) The Temporal’s State of Development Report
• (22:39) The State of AI Usage & Adoption
• (23:54) Using Temporal for Building AI Applications
• (26:06) The Complexities Involved in Building AI Applications
• (28:51) Key Learnings from Temporal’s State of Development Report
• (31:03) The Choice of Developer Tooling Misalignment
• (33:12) Integrating Security, Compliance, and Cost into Your Engineering Mindset
• (33:39) Building with Security and Compliance-First Mindset
• (36:57) Temporal Paradigm Shift
• (39:14) How Temporal Hides Away The Complexities of Building Reliable Applications
• (42:47) Unlearning Required for Using Temporal Programming Model
• (46:33) Getting Started Building with Temporal
• (48:34) Temporal’s Durable Execution Guarantee
• (51:23) The Concern About Temporal Lock-In
• (54:09) Temporal’s Strong Developer Focus
• (56:16) The Compliance and Security Aspect of Temporal Cloud
• (58:41) 3 Tech Lead Wisdom

_____

Preeti Somal’s Bio
Preeti is Senior Vice President of Engineering at Temporal. Preeti is passionate about building great products, growing world class organizations and solving complex problems. Prior to Temporal, Preeti led the Platform, Security and IT engineering organizations at HashiCorp. Her extensive career includes engineering leadership roles at Yahoo!, VMware and Oracle. While at Yahoo! Preeti was VP of Cloud Services in the Platform organization delivering highly scalable services used by engineers across Yahoo to build and operate applications with improved agility, reliability and security. These services power Yahoo!’s consumer and advertising business.

Follow Preeti:

“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 time-saving AI use case (it’s not code generation!)
• How AI impacts long-term software quality and maintainability
• Why organizational readiness matters for successful AI adoption
• The bigger bottlenecks beyond coding that AI has not yet solved
• Treating AI agents as team extensions, not digital employees

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:32) Latest DX Research on AI Adoption
• (00:03:54) AI Role on Developer Experience
• (00:05:43) The Current AI Adoption Rate in the Industry
• (00:09:27) The Leader’s Challenges Against Al Hype
• (00:13:22) Measuring AI Adoption ROI Using Acceptance Rate
• (00:17:39) The DX AI Measurement Framework™
• (00:23:05) AI Measurement Framework: Utility Dimension
• (00:27:51) DX AI Code Metrics
• (00:30:31) AI Measurement Framework: Impact Dimension
• (00:32:57) The Importance of Measuring Productivity Holistically
• (00:35:54) AI Measurement Framework: Cost Dimension
• (00:38:34) AI Second Order Impact on Software Quality and Maintainability
• (00:42:38) The Danger of Vibe Coding
• (00:46:31) Treating AI as Extensions of Teams
• (00:52:31) The Bigger Bottlenecks to Solve Outside of AI Adoption
• (00:55:47) DX Guide to AI-Assisted Engineering
• (01:00:38) Being Deliberate for a Successful AI Rollout
• (01:02:32) 3 Tech Lead Wisdom

_____

Laura Tacho’s Bio
Laura Tacho is CTO at DX, a developer intelligence platform, co-author of the Core 4 developer productivity metrics framework, and an executive coach. She’s an experienced technology leader and engineering leadership coach with a strong background in developer tools and distributed systems.

Her career includes leadership roles at organizations such as CloudBees, Aula Education, and Nova Credit, where she specialized in building high-performing engineering teams and delivering impactful products. Laura has worked with thousands of engineering leaders as they work to improve their engineering practices with data.

Follow Laura:

“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 matters and how the new Jakarta specifications enable type-safe queries
• Why architecture should emerge from code, not from whiteboard diagrams
• A critique on industry dogmas and architecture best practices, including DDD aggregates
• Why disagreement is essential for healthy engineering teams

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:24) Career Turning Points
• (00:16:11) The Problems That Led to Hibernate Creation
• (00:24:22) Key Things That Make Hibernate Successful
• (00:31:57) Behind the Scene of Java EE Specifications
• (00:37:42) The Renaming of Java EE to Jakarta EE
• (00:40:15) Jakarta Persistence, Jakarta Data, Jakarta Query Language
• (00:47:20) The Importance of Type Safety
• (00:54:08) Why Some People Dislike ORM
• (01:00:47) The Fundamental of Data Fetching and Association
• (01:08:52) The Upcoming Jakarta Data and QL Updates
• (01:16:06) Gavin’s View on Software Architecture
• (01:26:08) The DDD from Gavin’s Perspective
• (01:30:55) Tech Lead Wisdom

_____

Gavin King’s Bio
Gavin King is the creator of Hibernate, the revolutionary framework that redefined data persistence for millions of Java developers. A key figure in the evolution of enterprise Java, he has led the development of major industry standards like the Java Persistence API (JPA) and CDI. After a decade designing the Ceylon programming language, he has returned to his roots to advance the next generation of data persistence with Jakarta EE.

Follow Gavin:

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) Now is The Golden Time to Be in Software Engineering
• (09:08) The Evolution of Code Review in Software Development
• (14:59) The Popularity of Pull Request Workflow
• (21:01) Pull Request Best Practices
• (26:17) The Stacked PR and Its Benefits
• (34:07) How Graphite Ships Code Remarkably Fast
• (40:03) The Cool Things About AI Code Review
• (45:23) Graphite’s Unique Recipes for Engineering Productivity
• (50:55) Hiring Engineers in the Age of AI
• (55:31) 2 Tech Lead Wisdom

_____

Greg Foster’s Bio
Greg Foster is the CTO and co-founder of Graphite, an a16z and Anthropic-backed company helping teams like Snowflake, Figma, and Perplexity ship faster and scale AI-generated code with confidence. Prior to Graphite, Greg was a dev tools engineer at Airbnb. There, he experienced the impact of robust internal tooling on developer velocity and co-founded Graphite to bring powerful, AI-powered code review to every team. Greg holds a BS in Computer Science from Harvard University.

Follow Greg:

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 a Technical Coach
• (05:42) The Impact of AI on Technical Coaching
• (08:20) Sofware Engineering is a Learning Process
• (09:55) Optimizing Learning With Samman Method
• (11:40) The Samman Method: Ensemble (Mob Programming)
• (14:59) The Main Benefit of Ensemble: Single Piece Flow
• (17:26) How to Do Ensemble and Avoid Common Failure Modes
• (20:27) The Types of Coding to Ensemble On
• (22:12) The Importance of Trust, Communication, and Kindness
• (23:52) Common Things Development Teams Are Struggling With
• (25:37) Prompt Engineering
• (27:16) The Samman Method: Learning Hours
• (29:08) Learning is Part of the Work
• (31:32) The Practice of Learning as a Team
• (34:39) The Constraint When Learning from Pull Requests
• (36:30) Putting Aside Time for Learning Hours
• (39:14) Becoming a Technical Coach
• (41:23) How to Measure the Effectiveness of Technical Coaching
• (43:52) Danger of AI Assisted Coding
• (46:59) The (Still) Important Skills in the AI Era
• (49:56) Why We Should Not Refactor Through AI
• (52:41) The Samman Method & Technical Coaching Resources
• (53:29) 3 Tech Lead Wisdom
• (54:56) Finding Mentors for Career Progression

_____

Emily Bache’s Bio
Emily Bache is an independent consultant, YouTuber and Technical Coach. She works with developers, training and coaching effective agile practices like Refactoring and Test-Driven Development.

Emily has worked with software development for 25 years, written two books and teaches courses on platforms including Pluralsight and O’Reilly. A frequent conference speaker, Emily has been invited to keynote at prestigious developer events including EuroPython, Craft and ACCU. Emily founded the Samman Technical Coaching Society in order to promote technical excellence and support coaches everywhere.

Follow Emily:

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 this decision matters)
• The difference between being a people manager and an executive
• Why communication becomes the most critical skill at senior levels

Timestamps:

• (00:00) Trailer & Intro
• (02:10) Career Turning Points
• (03:55) Skills Advice for Engineers
• (06:46) The Challenges of a Hypergrowth Company
• (09:09) Learning and Growing in a Hypergrowth Company
• (12:07) The Slowdown in Engineering as You Scale
• (15:55) Designing Organization Structure Well
• (18:11) Effective Organization Chart Tips
• (21:05) Nurturing a Good Engineering Culture
• (25:37) Nurturing Psychological Safety
• (28:14) Learning from Mistakes & Performance Review
• (30:27) Being a Mission-Driven Company
• (32:11) Aligning Mission and Values in the Day-to-Day Work
• (34:45) The Importance of Management System in Organization
• (41:53) The Importance of Having Good Managers
• (45:30) For Strong ICs: Writing Code or Being a Manager?
• (50:55) The Difference Between a Manager Role and Executive Role
• (56:01) A Unique Thing Learned from Doing Payment Systems
• (58:43) 3 Tech Lead Wisdom

_____

Michi Kono’s Bio
Michi Kono is the Chief Technology Officer (CTO) at Garner Health, a company on a mission to help people get better healthcare. With a unique and extensive career spanning multiple industries, Michi has navigated the entire spectrum of the tech world. He began his journey in startups, one of which was acquired, leading him to a role at Capital One. From there, he gained invaluable experience at tech giants like Meta and financial-tech leader Stripe before taking the helm at Garner Health. Michi is passionate about the art and science of scaling engineering teams, building resilient cultures, and designing effective management systems to drive success in high-growth environments. He believes deeply in empowering engineers, fostering accountability, and the critical importance of clear communication for any leader.

Follow Michi:

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, a mix of seniority, and a culture of deep learning
• The importance of hands-on technical leadership, and why even CTOs should “dive deep” to set the right engineering bar
• The state of engineering talent in Southeast Asia and what’s needed to bridge the gap in deep tech and AI development
• Actionable career advice for junior and mid-career professionals navigating the AI-infused software industry

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:08) Career Turning Points
• (00:06:03) Things We Should Learn in the AI Era
• (00:09:53) AI as a Pair Programmer
• (00:13:58) The Danger of Outsourcing Our Thinking to AI
• (00:17:29) The Dopamine Hit of Using AI
• (00:20:36) Building a Successful Transformational Engineering Team
• (00:25:33) The Discipline Rigor in An Engineering Team
• (00:29:14) Understanding & Delivering Outcomes for the Business
• (00:32:21) Having a Tough Approach as an Engineering Leader
• (00:39:07) Going Back as an IC at Google
• (00:45:40) The Importance of Being Hands-On with Recent Technologies for Leaders
• (00:52:40) Hands-on vs Micromanagement
• (00:55:11) Engineering Talents in Southeast Asia
• (00:58:06) Building Tech Talents in Southeast Asia
• (01:01:17) Bridging the AI Gap in Southeast Asia
• (01:04:03) Should We Still Pursue a Tech Career in the AI Era?
• (01:07:24) 2 Tech Lead Wisdom

_____

Mohan Krishnan’s Bio
Mohan Krishnan, based in Singapore, is currently a Head of Engineering at Grab. Mohan Krishnan brings experience from previous roles at Google, Bukalapak, BBM and Pt. Kreatif Media Karya. Mohan Krishnan holds a 1998 - 2002 Bachelor of Engineering in Multimedia, Electronics at Multimedia University. With a robust skill set that includes Ruby on Rails, Multithreading, Web Services, HTML, Services and more.

Follow Mohan:

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. OpenTofu: which one should you use?
• Balancing governance, speed, and innovation in the cloud era
• The current limitations and role of AI in managing infrastructure

Timestamps:

• (00:00) Trailer & Intro
• (02:39) Updates in the Last Five Years
• (04:13) Infrastructure as Code Definition
• (05:58) The Practice of Infrastructure as Code
• (06:32) The Differences Between the Book Editions
• (10:21) Aligning Infrastructure to the Business Value
• (15:03) Handling the Growing Infrastructure Complexities
• (19:10) The Tools and New Inventions in IAC
• (24:11) Terraform vs OpenTofu
• (27:38) Orchestrating Infrastructure Changes Using IAC
• (30:35) Platform Engineering
• (33:06) Internal Developer Platform Key Success Factor
• (37:15) Key Considerations of Building Teams with Infrastructure Skills
• (41:56) Infrastructure Compliance and Governance
• (45:53) Using AI for Infrastructure as Code
• (50:31) Using AI for Troubleshooting and Root Cause Analysis
• (51:50) 3 Tech Lead Wisdom

_____

Kief Morris’s Bio
Kief Morris is the author of the O’Reilly book Infrastructure as Code, and is a Distinguished Infrastructure Engineer at Thoughtworks, based in London. He works with clients and project teams around the world to explore, shape, and share better ways of working with cloud and infrastructure architecture.

Kief started out as a developer and systems administrator in the dot-com boom days, then worked with a series of digital scaleups applying infrastructure automation before DevOps was a thing. He joined Thoughtworks in 2010 as the wider industry was discovering Infrastructure as Code, DevOps, and Cloud, which gave him the opportunity to bring what he had learned in the previous fifteen years to enterprise clients in many industries and many countries.

He wrote the book Infrastructure as Code (now on the third edition) to share these ideas with a wider audience, which has given him a platform to meet and learn from an ever-growing variety of people and organizations.

Follow Kief:

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 clinical trials
• How to tackle AI hallucinations and ensure high-quality outputs
• Patrick’s thoughts on AGI and the future of AI beyond current capabilities
• The viability and limitations of vibe coding
• Strategies and advice for individuals to thrive in the AI era

Timestamps:

• (00:00) Trailer & Intro
• (02:09) Career Turning Points
• (02:46) The Advancements of AI in the Past 10 Years
• (04:13) Non-LLM Types of AI
• (05:42) The Google Duplex
• (07:28) The Use of AI in Faro Health
• (09:44) Tackling AI Hallucination for Clinical Documents
• (12:25) Building the Evaluation Process on AI Results
• (14:28) AI as a Research Assistant
• (16:40) The Need of Building Custom AI Model
• (18:50) The Huge Impact of AI in Clinical Trials
• (21:15) The Regulations on Applying AI Technology
• (23:28) AI Success Stories in the Life Science Industry
• (25:16) The Possibility of AGI
• (28:36) The Path to AGI Using LLM
• (30:43) Actions People Should Take in the AI Era
• (35:48) AI Engineers and AI-Enabled Engineers
• (38:37) The Viability of Vibe Coding
• (41:03) Hiring AI Engineers
• (42:26) Important Engineer Attributes in the AI Era
• (44:23) Important Leader Attributes in the AI Era
• (46:59) The Room for Juniors in the AI Era
• (49:04) Inspirational Story of a Successful Junior
• (51:33) 3 Tech Lead Wisdom

_____

Patrick Leung’s Bio
Patrick Leung is a Chief Technology Officer at Faro Health, a company at the forefront of optimizing clinical trial development through the use of artificial intelligence.

In his role, he is instrumental in applying large language models and other AI technologies to enhance protocol design and outcomes for clinical trials. A native of New Zealand, Mr. Leung holds degrees in Computer Science and Finance.

His career includes being a foundational member of an early e-commerce software company, where he played a key role in guiding the company from its initial stages to a successful initial public offering.

Follow Patrick:

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 platform engineering.
• The importance of clear metrics, shared language, and transparency for continuous improvement.
• Building a culture that supports operational excellence through rituals and repeated messaging.
• Real-world examples of using generative AI to accelerate platform adoption and incident analysis.

Timestamps:

• (00:00) Trailer & Intro
• (01:56) Career Turning Points
• (07:50) The Practice of Finding the Patterns in Issues
• (11:39) The Definition of Engineering Excellence
• (17:10) The Leader’s Role in Engineering Excellence
• (22:31) Aligning Engineering Excellence with the Business Outcomes
• (26:30) The Importance of Metrics in Engineering Excellence
• (33:35) The Culture that Drives Engineering Excellence
• (39:05) Platform Engineering and Internal Developer Platform
• (45:02) The Biggest Misconception of Platform Engineering or IDP
• (50:36) Cortex as an Engineering Excellence Platform
• (52:39) Generative AI Use Case in Platform Engineering
• (55:26) 3 Tech Lead Wisdom

_____

Ganesh Datta’s Bio
Ganesh Datta is a Co-Founder & CTO of Cortex. Before co-founding Cortex, he was a Principal Software Engineer at Mission Lane where he was responsible for driving the development of real-time underwriting infrastructure. At LendUp, Ganesh was a Senior Software Engineer leading the development and optimization of the company’s decisioning infrastructure and financial account management system. Ganesh holds a bachelor of science in computer science from the University of California San Diego.

Follow Ganesh:

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 groups of 2-3 people work best
• Aligning your teams’ work with company goals and business objectives

Timestamps:

• (00:00) Trailer & Intro
• (02:10) Career Turning Points
• (05:26) Critical Key Skills as CPTO
• (07:40) Juggling Between Being Optimistic vs Pessimistic
• (09:15) Move Fast and Break Silos
• (13:08) The Difference Between Manufacturing and Software Development
• (16:51) The Problems with the Status Quo of Software Development Practices
• (23:50) Key Practice 1: Slicing Work
• (25:51) Slicing Objectives
• (28:30) Slicing Problems
• (33:25) Slicing Solutions
• (38:03) Slicing Delivery
• (41:09) Key Practice 2: Aligning Teams
• (43:21) The Effective Teams Alignment Practices
• (48:10) Working in Small Teams at a Time
• (51:07) Alignment with the Value Streams
• (53:15) Mapping the Sliced Work to the Organization
• (56:41) The Importance of Reporting Structure in the Large Organization
• (58:52) 3 Tech Lead Wisdom

_____

Klaus Breyer’s Bio
Klaus Breyer is an experienced B2B SaaS CPTO who specializes in bridging the gap between technical delivery and agile product strategy, driven by a passion for breaking down silos. His career includes founding and leading the startups Buddybrand (a digital agency) and BuzzBird (a B2B marketplace), as well as building corporate startups and business units for major companies like Voith and edding in the IoT and B2B SaaS sectors.

Based in Berlin, he has extensive experience working with diverse and primarily remote teams. In addition to his leadership roles, he sometimes invests in and advises leadership teams on building effective interdisciplinary teams themselves. He is also a speaker, blogger, and book author who champions the philosophy of “Move Fast And Break Silos!”

Follow Klaus:

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 & Intro
• (00:02:32) AI Impact on Career and Software Engineering
• (00:07:00) The Future of AI-Driven Software Engineering
• (00:14:29) The Shift in the Role of Software Engineer
• (00:22:13) When Writing Code is Not the Bottleneck Anymore
• (00:32:04) The Danger of Over-Reliance on AI
• (00:38:51) The Software Engineering Identity Crisis
• (00:48:09) Advice for Junior Engineers in This Challenging Time
• (00:53:34) The Shift in the Role of Engineering Management
• (00:59:46) You Are Not Alone
• (01:00:50) 3 Tech Lead Wisdom

_____

Annie Vella’s Bio
Annie Vella is a Distinguished Engineer at Westpac NZ with two decades of experience in software engineering and technical leadership across various industries and countries.

Vella has returned to an engineering role after a period in management and is also a part-time Master’s student at the University of Auckland, researching the impact of AI on software engineering. She believes that technologies like Generative AI, LLMs, and Agentic AI will revolutionize the field and problem-solving in general.

Follow Annie:

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 agents run unchecked
• A practical approach to conducting more effective research  

Timestamps:

• (00:00) Trailer & Intro
• (02:10) Career Turning Points
• (05:24) What Juniors Should Do in the Age of AI
• (11:05) Junior Developer’s Responsbility When Using AI
• (14:50) AI and Critical Thinking
• (16:20) Understanding & Preserving Domain Knowledge
• (18:11) The Importance of Written Knowledge for AI Usage
• (21:51) Limitations of AI in Understanding Knowledge Base
• (26:34) The Limitations of LLM in Navigating Legacy Codebases (e.g. COBOL)
• (32:38) Effective Knowledge Sharing Culture in the Age of AI
• (34:54) Keeping Knowledge Base Up-to-Date
• (36:55) Keeping the Organization Knowledge Base Accurate
• (39:08) Fact Checking and Preventing AI Hallucination
• (41:24) The Potential of MCP
• (43:24) The Danger of AI Agents Hallucinating with Each Other
• (45:00) How to Get Better at Research
• (53:41) The Importance of Investing in Research
• (57:18) 3 Tech Lead Wisdom

_____

Omer Rosenbaum’s Bio
Omer Rosenbaum is the CTO and co-founder of Swimm, a platform reinventing the way engineering organizations manage internal knowledge about their code base. Omer founded the Check Point Security Academy and was the Cyber Security Lead at ITC, an educational organization that trains talented professionals to develop careers in technology. Omer has a MA in Linguistics from Tel Aviv University and is the creator behind the Brief YouTube Channel.

Follow Omer:

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 logical fallacies, get feedback, and improve your writing without sacrificing authenticity.
• Proven Blog Post Patterns: Learn about effective patterns like the “Bug Hunt” to create engaging and educational content.
• Promoting Your Writing: Strategies to get your work in front of a larger audience, from company blogs to social media and content aggregators.
• Beyond the Blog Post: Discover how writing can open doors to speaking at conferences and even writing a book.  

Timestamps:

• (00:00) Trailer & Intro
• (02:06) Career Turning Points
• (04:30) The Challenge of Writing for Developers
• (06:08) The Importance of Writing Culture
• (08:36) Piotr’s Journey to Writing Books
• (11:19) The Impact of Writing on Engineering Culture
• (13:39) How to Overcome Common Excuses for Not Writing
• (16:32) Finding The First Blog Post Topic
• (20:32) Tips on How to Start Writing
• (22:19) The Importance of Goal and Perspective in Writing a Draft
• (24:55) The Use of AI in Writing
• (29:01) AI Prompts to Improve Your Writing
• (30:14) The Best LLM Model for Writing
• (31:53) The Best Workflow Working with AI
• (33:41) Blog Post Pattern: Bug Hunt
• (37:16) Blog Post Pattern: Thoughts on Trends
• (40:13) The Importance of Promoting Our Writing
• (42:47) How to Promote Your Writing Independently
• (45:00) Future Opportunities of Writing
• (47:55) Writing as a Developer
• (49:02) 3 Tech Lead Wisdom

_____

Piotr Sarna’s Bio
Piotr Sarna is a software engineer who is keen on open source projects and the Rust and C++ languages. He previously developed an open source distributed file system and had a brief adventure with the Linux kernel. He’s also a long-time contributor and maintainer of ScyllaDB, as well as libSQL and Turso. Piotr graduated from University of Warsaw with a Master’s degree in computer science.

Follow Piotr:

In this special in-person episode, Sanne Grinovero shares the story of Java’s evolution from his unique perspective as a long-time open-source contributor. He shares his 16-year career journey at Red Hat, highlighting his amazing work on key projects like Hibernate, Infinispan, and especially the creation of Quarkus. His career trajectory, from a student who initially disliked Java’s complexity to a leading figure in its modernization, shows the transformative power of open source.

A key part of the conversation focuses on how technical challenges spark innovation. Sanne explains how the task of making the popular Hibernate framework compatible with GraalVM’s limitations led directly to the birth of Quarkus. This journey tells the bigger story of how Java adapted for cloud-native development, ensuring it continues to be a top choice for developers seeking high performance and a great developer experience.  

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:16) Career Turning Points
• (00:04:52) Winning an Innovation Award
• (00:06:35) Java Heroes
• (00:08:04) Working as a Consultant
• (00:09:56) Taking a Massive Pay Cut to Work on Open Source
• (00:10:59) Contributing to Big Open Source as a Youngster
• (00:12:53) State of Hibernate Project
• (00:15:15) Spring Boot
• (00:16:54) Making Hibernate Work on GraalVM
• (00:21:05) GraalVM Limitations for Running Hibernate
• (00:26:09) Java for Cloud Native Application
• (00:28:04) Quarkus vs Spring Boot
• (00:33:21) JRebel & Quarkus
• (00:34:35) Java vs New Programming Languages
• (00:39:22) The ORM Dilemma
• (00:42:38) Some Hibernate Design Pattern Tips
• (00:46:40) Getting Paid Working on Open Source
• (00:48:41) Hibernate License Change
• (00:51:05) Intellectual Property & Meaningful Contributions
• (00:52:52) AI Usage & Copyright in Open Source
• (00:55:21) Biggest Challenge Working in a Big Open Source
• (00:56:08) Politics in Open Source
• (00:58:32) Security Risks in Open Source
• (01:02:25) Donating Hibernate to Commonhaus Foundation
• (01:04:49) The Future of Red Hat
• (01:06:39) 3 Tech Lead Wisdom

_____

Sanne Grinovero’s Bio
Sanne Grinovero has been a member of the Hibernate team for 10 years; today he leads this project in his role of Sr. Principal Software Engineer at Red Hat, while also working on Quarkus as a founding R&D engineer.

Deeply interested in solving performance and concurrency challenges around data access, scalability, and exploring integration with new storage technologies, distributed systems and search engines.

Working on Hibernate features led him to contribute to related open source technologies; most notably to Apache Lucene and Elasticsearch, Infinispan and JGroups, ANTLR, WildFly, various JDBC drivers, the OpenJDK and more recently getting interested in GraalVM.

After being challenged to reduce memory consumption and improve bootstrap times of Hibernate, Sanne worked as part of a small R&D team at Red Hat on some ideas which have evolved into what is known today as Quarkus.

Follow Sanne:

Tired of feeling like your team is stuck in a cycle of frustration and miscommunication? What if the biggest blocker in your tech career isn’t your code, but your thinking?

That’s the core premise of Systems Thinking, and in this episode, Diana Montalion (author of “Learning Systems Thinking”) shares the practical insights and mental models to help you make that essential shift.

Key topics discussed:

• What systems thinking is and its core principles
• The difference between linear thinking (which we need) and systems thinking (which we’re missing)
• Why building a metaphorical “car boat” is a failure of “conceptual integrity” and how to avoid it
• How to break free from a “change-my-mind” culture and improve our collaboration
• The critical skill of metacognition: why you must understand your own thinking before you can influence others
• Practical ways to foster collective systems thinking and bridge the gap between Product and Tech
• Using modeling and visual tools to create alignment and solve the right problems
• How AI’s inability to handle true inference makes human systems thinking more valuable than ever

Whether you’re a software engineer, architect, team leader, or anyone tackling complex problems, learn why your technical skills alone are not enough and how a shift in your thinking can revolutionize your work and career.  

Timestamps:

• (00:00:00) Trailer & Intro
• (00:02:23) Career Turning Points
• (00:04:35) Writing Learning Systems Thinking
• (00:08:53) Definition of Systems Thinking
• (00:13:39) Systems Thinking vs Linear Thinking
• (00:19:31) Definition of System
• (O0:24:13) Conceptual Integrity
• (00:30:02) Practices to Improve Our Systems Thinking
• (00:36:21) Metacognition and Self-Awareness
• (00:44:42) Practices to Improve Our Collective Systems Thinking
• (00:53:04) Collaboration with Consent
• (00:55:29) The Importance of Modeling
• (01:02:20) AI Usage and System Thinking
• (01:11:04) 3 Tech Lead Wisdom

_____

Diana Montalion’s Bio
Diana Montalion is a systems architect, learning facilitator, and founder of Mentrix Group, with over 20 years of experience delivering transformative software initiatives for organizations like Stanford, The Gates Foundation, The Economist, and The Wikimedia Foundation. As the author of Learning Systems Thinking: Essential Nonlinear Skills & Practices for Software Professionals (O’Reilly), she empowers tech professionals to navigate complex systems through practices like systemic reasoning, metacognition, and collaborative modeling.

Follow Diana:

Understand the secret behind one of Amazon’s most powerful innovation tools and learn how you can use it to drive clarity, alignment, and better decision-making.

In this episode, Marcelo Calbucci, author of “The PRFAQ Framework,” dives deep into the PRFAQ (Press Release & Frequently Asked Questions) framework, a unique approach that combines narrative storytelling and strategic FAQs to crystallize initiative vision and strategy.

Key topics discussed:

• What the PRFAQ framework is — and why it’s more than just a product management tool
• How PRFAQ brings Amazon’s “working backwards” philosophy to life
• The structure of a PRFAQ: press release, customer FAQs, and internal FAQs
• Why storytelling and precise writing are essential for strategic vision and alignment
• Overcoming resistance: making writing and reading strategic documents part of your culture
• Practical tips for adopting PRFAQ in any organization, large or small
• Common mistakes to avoid when implementing PRFAQ
• The importance of collaborative feedback in the PRFAQ process

Whether you’re launching a startup, building a new product, or transforming internal processes, this episode breaks down how this method can help you avoid common pitfalls and deliver results that matter.

Timestamps:

• (00:00) Trailer & Intro
• (02:10) Career Turning Points
• (03:56) The PRFAQ Framework
• (05:19) PRFAQ is Forward-Looking
• (07:09) Working Backwards & PRFAQ
• (07:58) Why Writing a Book About PRFAQ
• (11:37) PRFAQ: Why Less Adoption Than Other Frameworks?
• (14:40) Writing PRFAQ vs Speed of Execution
• (16:28) The PRFAQ Template
• (19:05) The Six Page of PRFAQ
• (21:24) Precise Writing
• (25:09) The Strict Guidelines of PRFAQ
• (26:40) PRFAQ: Press Release
• (29:56) The Power of Narratives / Storytelling
• (32:03) PRFAQ: Customer FAQ
• (34:15) Jobs-to-Be-Done vs. Personas
• (36:46) PRFAQ: Internal FAQ
• (39:34) How to Come Up with the Internal FAQs
• (40:49) The Level of Details in the FAQs
• (43:20) PRFAQ: Appendix
• (45:27) Advice on Starting PRFAQ
• (46:11) Adapting from the Amazon’s PRFAQ
• (48:55) Common Mistakes when Adopting PRFAQ
• (50:05) Providing Good Feedback for PRFAQ
• (51:18) 3 Tech Lead Wisdom

_____

Marcelo Calbucci’s Bio
Marcelo Calbucci is an entrepreneur, innovator, and technologist. He’s been building software products for over thirty years, having sold his first software at age fourteen. He has worked at Microsoft (Exchange Server & Bing) and Amazon (People eXperience & Technology), leading software engineering, product, data science, and UX. He is an author of The PRFAQ Framework.

Follow Marcelo Calbucci:

(08:54) Brought to you by Swimm.io.

(05:46) Brought to you by Swimm.io.

(04:07) Brought to you by Swimm.io.

(03:59) Brought to you by Swimm.io.

(05:57) Brought to you by Swimm.io.

(07:40) Brought to you by Swimm.io.

(03:43) Brought to you by Swimm.io.