Tech Lead Journal

• #239 - Taming Your Technical Debt: Mastering the Trade-Off Problem - Andrew Brown

  (06:06) Brought to you by JellyfishAI tools alone won’t transform your engineering org. Jellyfish provides insights into AI tool adoption, cost, and delivery impact – so you can make better investment decisions and build teams that use AI effectively. See for yourself at jellyfish.co/platform/ai-impact.Why do organizations constantly complain about having too much technical debt? Because they’re solving the wrong problem.In this episode, Dr. Andrew Brown, author of “Taming Your Dragon: Addressing Your Technical Debt,” reveals a profound insight: technical debt isn’t fundamentally a technical problem. It’s a trade-off problem rooted in human bias, organizational systems, and economic incentives. Through his innovative “Technical Debt Onion Model,” Andrew shows how decisions about code quality happen across five interconnected layers, from individual cognitive biases to wicked problem dynamics.Andrew explains why the financial debt analogy is dangerously misleading and, more importantly, how others can rack up debt you’ll eventually pay for. Drawing from behavioral economics, systems thinking, and organizational theory, he reveals why our emotions, not logic, drive most technical decisions, and how to work with this reality rather than against it.Key topics discussed:Why technical debt is a trade-off problem, not technicalHow emotions override logic in critical decisionsThe Technical Debt Onion Model framework explainedPrincipal-agent problems sabotaging your codebaseExternalities: who pays for shortcuts taken today?Why burning down debt is already too lateUlysses contracts for managing future obligationsSystems thinking applied to software developmentWicked problems: why different teams see different solutionsAI’s impact on technical debt creationTimestamps:(00:00:00) Trailer & Intro(00:02:24) Career Turning Points(00:06:06) The Importance of Skilling Up in Tech(00:06:49) The Definition of Technical Debt(00:09:08) The Broken Analogy of Technical Debt as a Financial Debt(00:09:58) The Role of Human Bias and Organization Issues in Technical Debt(00:12:41) Tech Debt is a Trade-off Problem(00:13:07) Building a Healthier Relationship with Technical Debt(00:15:15) The Technical Debt Onion Model(00:18:17) The Onion Model: Trade-Off Layer(00:25:10) The Ulysses Contract for Managing Technical Debt(00:33:03) The Onion Model: Systems Layer(00:36:32) The Onion Model: Economics/Game-Theory Layer(00:41:50) The Onion Model: Wicked Problem Layer(00:48:10) How Organizations Can Start Managing Technical Debt Better(00:52:03) The Al Impact on Technical Debt(00:56:16) 3 Tech Lead Wisdom_____Andrew Brown’s BioAndrew Richard Brown has worked in software since 1999, starting as an SAP programmer fixing Y2K bugs. He realized the biggest problems in software development were human, not technical, and has since helped teams improve performance by addressing these issues.Andrew coaches organizations on software development and quality engineering, focusing on technical debt, risk in complex systems, and project underestimation. He investigates how cognitive biases drive software problems and applies behavioral science techniques to solve them. His research has produced counterintuitive insights and fresh approaches. He regularly speaks at international conferences and runs a growing YouTube channel on these topics.Follow Andrew:LinkedIn – linkedin.com/in/andrew-brown-4b38062YouTube – @behaviouralsoftwareclub705Email – [email protected] Taming Your Dragon – https://www.amazon.com/Taming-Your-Dragon-Addressing-Technical/dp/B0CV4TTP32/Like this episode?Show notes & transcript: techleadjournal.dev/episodes/239.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

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• #238 - AI is Smart Until It's Dumb: Why LLM Will Fail When You Least Expect It - Emmanuel Maggiori

  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 repeatedlyHow LLMs work and why they brilliantly failWhy hallucinations can’t be fixed with better promptsWhy self-driving cars still need human operatorsAdopting AI without falling into hype trapsHow engineers stay relevant in the AI eraWhy AGI predictions are mostly marketingBuilding valuable products in boring industriesTimestamps:(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 BioEmmanuel 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:LinkedIn – linkedin.com/in/emaggioriWebsite – emaggiori.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/238.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

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• #237 - Tackling AI and Modern Complexity with Deming's System of Profound Knowledge - John Willis

  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 organizationsHow 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 pictureA 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 alertsTreating AI as “alien cognition” rather than human-like intelligenceThe missing piece in AI conversations: understanding the philosophy of AI, not just the technologyTimestamps:(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’ BioJohn 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:LinkedIn – linkedin.com/in/johnwillisatlantaTwitter – x.com/botchagalupe AI CIO – aicio.ai Attention Is All You Need – linkedin.com/newsletters/attention-is-all-you-need-7167889892029505536 Profound – linkedin.com/newsletters/profound-7161118352210288640 Rebels of Uncertainty – linkedin.com/newsletters/rebels-of-uncertainty-7359198621222719490Like this episode?Show notes & transcript: techleadjournal.dev/episodes/237.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

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• #236 - From Figma to Code: The Rise of Design Engineers (And Why It Matters Now) - Honey Mittal

  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 understandingHow proper design structure is the key to successful code generationThe emergence of “design engineers” who bridge design and developmentLessons from pivoting from consumer to enterprise SaaSBuilding global developer tools from Southeast AsiaThe real challenges of building deep tech startups in Southeast AsiaCareer advice for staying relevant in the AI eraWhether 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 BioHoney 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:LinkedIn – linkedin.com/in/honeymittalTwitter – x.com/HoneyMittal07Website – locofy.aiLike this episode?Show notes & transcript: techleadjournal.dev/episodes/236.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

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• #235 - From AI Chaos to Clarity: Building Situational Awareness with Wardley Mapping - Simon Wardley

  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 CEOsThe critical difference between graphs and maps in business strategyWhat Wardley mapping is and the three pattern types leaders must understandHow to identify where human decision-making adds value in your AI adoptionWhy vibe coding is powerful but dangerous without proper code reviewsWhy software development is still a craft, not engineeringHow Jevons Paradox means AI won’t eliminate jobs but expand codebasesThe hidden dangers of AI hallucinations and the need for critical thinkingTimestamps:(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 BioSimon 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:LinkedIn – linkedin.com/in/simonwardleyTwitter – x.com/swardleyWebsite – www.swardleymaps.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/235.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

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