(05:13) Brought to you by Sweep AI Sweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at ⁠sweep.dev⁠. What if Southeast Asia had its own ChatGPT that cost 20x less? Bruce Yang built Agnes AI to solve what global companies ignore: accessible AI for emerging markets. In this episode, Bruce Yang, CEO and founder of Agnes AI, explains how he’s built Southeast Asia’s fastest-growing AI platform with 4 million registered users and 300K daily active users. After working at Microsoft and LinkedIn in Silicon Valley, Bruce returned to Singapore and started his PhD at NUS right before COVID, positioning him perfectly to ride the AI wave. Agnes AI uses smaller, specialized models trained on Southeast Asian languages and local user data to deliver productivity features like deep research, PowerPoint generation, and AI-powered group chats at 1/20th the cost of major competitors. We discuss the challenges of building AI for emerging markets, the importance of keeping humans in the loop for critical thinking, and why Bruce believes the future of AI belongs to applications, not just models. Key topics discussed: Making AI 20x cheaper than ChatGPT Why Southeast Asia needs its own AI models Using multi-agent systems to reduce hallucinations AI group chats and social features Critical thinking in an AI-assisted world Why Agnes avoids the AI coding space AI bubble debate: hype vs. real value Getting emerging markets to adopt AI Subscription vs. pay-per-use business models Timestamps: (00:00:00) Trailer & Intro (00:02:49) Why Did Bruce Start a PhD During COVID to Build an AI Company? (00:06:16) Why Build Another AI Model When Thousands Already Exist? (00:09:48) How Is Agnes AI Cheaper and Faster Than ChatGPT? (00:14:00) Does Agnes AI Support Southeast Asian Languages and Cultures? (00:15:34) How Does Agnes AI Handle Local Languages Better Than Global Models? (00:17:57) How Does Agnes AI Reduce Hallucinations? (00:20:03) What Can Agnes AI Do That ChatGPT Cannot? (00:25:31) Why Is AI in Group Chats the Next Big Thing? (00:29:18) How Does Agnes AI Keep Your Private Group Conversations Secure? (00:31:41) Will AI Make Us Lose Our Critical Thinking Skills? (00:37:43) Should Children Use AI for Schoolwork? (00:40:27) Can Agnes AI Help With Coding Like Cursor? (00:43:07) Will Everyone Host Their Own AI Model in the Future? (00:47:39) Is AI a Bubble or Real Economic Transformation? (00:51:01) How Can Southeast Asians Start Using AI Today? (00:53:56) What Are Real-World Examples of People Using Agnes AI? (00:57:30) How Does Agnes AI Make Money While Offering Free Features? (01:01:19) 3 Tech Lead Wisdom _____ Bruce Yang’s Bio Bruce Yang is the founder and CEO of Agnes AI, a consumer AI platform making intelligence more collaborative, creative, and accessible. A Raffles Institution graduate, he studied Math and Computer Science at UC Berkeley, earned a Master’s from HEC Paris, and is pursuing a PhD at NUS. He previously worked at Microsoft and LinkedIn in Silicon Valley. Agnes AI redefines how people interact with AI through group chats, AI-assisted games, real-time content creation, slides generation, and research tools. Bruce envisions AI as a shared experience that amplifies human creativity and collaboration, enhancing rather than replacing human thinking and imagination. Follow Bruce: LinkedIn – linkedin.com/in/tongbruceyang Agnes AI - https://agnes-ai.com/ Email – bruce@sapiens-ai.io Like this episode? Show notes & transcript: techleadjournal.dev/episodes/246. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(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: LinkedIn – linkedin.com/in/-joseph-yap Otonata – https://otonata.com/ Free Hack Check – https://otonata.com/hack-check Like this episode? Show notes & transcript: techleadjournal.dev/episodes/245. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(06:23) Brought to you by Sweep AI Sweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at sweep.dev. Is the era of writing code by hand coming to an end? Gene Kim explains how vibe coding solved problems he abandoned for 13 years and why the best days of coding might be ahead of us. In this episode, Gene Kim shares his transformation from someone who hadn’t written production code in decades to building ambitious projects in minutes. He explains how meeting Steve Yegge and discovering vibe coding reignited his passion for programming. Gene breaks down the FAAFO framework (Fast, Ambitious, Autonomous, Fun, Optionality) of vibe coding benefits and addresses the real risks of vibe coding, from deleted databases to corrupted repos. He emphasizes that developers need to shift from line cook to head chef, mastering delegation, architecture, and faster feedback loops. The conversation also explores whether AI will eliminate or expand developer roles, what skills matter most when hiring, and how organizations can build a vibe coding culture. Key topics discussed: Gene’s jaw-dropping a-ha moment solving his 13-year problem The FAAFO framework for measuring vibe coding benefits From line cook to head chef: the new developer skillset Real risks and downsides of vibe coding Will we need fewer developers or 10x more software? Why feedback loops must be 100x faster than before Building vibe coding culture across enterprise teams Timestamps: (00:00) Trailer & Intro (03:13) What shaped Gene Kim’s career in DevOps and technology? (07:26) How did Gene Kim’s books like Phoenix Project come about? (09:55) What’s the story behind the Phoenix Project graphic novel? (12:21) What was Gene Kim’s a-ha moment with vibe coding? (14:41) How did Steve Yegge and Gene Kim collaborate on the book? (21:06) What is vibe coding and how is it different from regular coding? (25:57) What is the FAAFO framework for vibe coding benefits? (32:08) Will AI replace software developers? (36:10) What are the risks and downsides of vibe coding? (41:51) What skills do developers need in the age of vibe coding? (46:56) Why are feedback loops critical when using AI for coding? (51:59) How can organizations adopt vibe coding as a culture? (57:37) What should you look for when hiring developers in the AI era? (59:45) 2 Tech Lead Wisdom _____ Gene Kim’s Bio Gene Kim is a WSJ bestselling author and researcher who has studied high-performing technology organizations since 1999. The founder and former CTO of Tripwire, he has authored several industry-defining books, including The Phoenix Project and The DevOps Handbook, with over 1 million copies sold. He also organizes the Enterprise Technology Leadership Summit. Follow Gene: LinkedIn – linkedin.com/in/realgenekim Twitter – @RealGeneKim IT Revolution – itrevolution.com  Vibe Coding - https://itrevolution.com/product/vibe-coding-book/ Like this episode? Show notes & transcript: techleadjournal.dev/episodes/244. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

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 Like this episode? Show notes & transcript: techleadjournal.dev/episodes/243. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(04:11) Brought to you by Jellyfish AI tools alone won’t transform your engineering org. Jellyfish provides insights into AI tool adoption, cost, and delivery impact – so you can make better investment decisions and build teams that use AI effectively. See for yourself at jellyfish.co/platform/ai-impact. Are you managing your team the same way you did five years ago? With AI agents now part of the workforce, the old playbook no longer applies. In this episode, Jurgen Appelo, author of “Human Robot Agent” and creator of Management 3.0 and unFIX, challenges conventional thinking about management, organizational design, and the future of work in the AI era. He explains why rigid frameworks like Scrum are becoming bottlenecks to AI speed and why he believes we need to completely rethink how organizations operate. The conversation dives into the concept of creating “fast tracks” for AI agents while maintaining “slow tracks” for human collaboration. Jurgen also breaks down why team sizes are shrinking and why professionals must move beyond T-shaped skills to become M-shaped, multidisciplinary workers to remain relevant. He also shares his controversial take on why Scrum is “done” and why he trusts AI more than the average human when solving complex problems. Key topics discussed: Managing systems vs people in hybrid human-AI teams Why patterns beat frameworks for organization design Why Scrum is done: adapting Agile for the AI era M-shaped workers: the new multidisciplinary skill Fast and slow tracks: redesigning work for AI Why AI outperforms average humans at complex problems Critical thinking as the essential leadership skill The new optimal team size and dynamic reteaming Timestamps: (00:00:00) Trailer & Intro (00:02:20) Career Turning Points: Seven-Year Career Pivots (00:05:29) Origins of Management 3.0 (00:08:31) Managing Systems, Not People (00:12:35) Everlasting Management Principles (00:17:21) unFIX: Patterns Over Frameworks (00:24:27) Core unFIX Patterns (00:31:39) Pipedrive Case Study: unFIX in Action (00:38:16) M3K: Merging Management 3.0 and unFIX (00:41:33) Skeptical Enthusiast: Balanced AI Perspective (00:47:18) Co-Creating with Humans and Machines (00:51:51) From T-Shaped to M-Shaped Workers (00:56:38) Why I Trust AI More Than Humans (01:00:19) Scrum is Done (Not Dead) (01:05:50) Redesigning Organizations for AI: Fast and Slow Tracks (01:09:25) 3 Tech Lead Wisdom _____ Jurgen Appelo’s Bio Jurgen Appelo is an author, speaker, and entrepreneur who helps leaders rewire their organizations for AI-driven leadership and autonomous digital agents. Recognized by Inc.com as a Top 50 Leadership Expert and Top 100 Leadership Speaker, he bridges opposing worldviews: human ingenuity and AI, leadership versus governance, stability with innovation, and individual growth fueling collective success. As founder of The unFIX Company (and previously founder of Management 3.0 and co-founder of Agile Lean Europe), Jurgen pioneers the future of work through stories, games, tools, and practices that challenge conventional thinking. Follow Jurgen: LinkedIn – linkedin.com/in/jurgenappelo Website – jurgenappelo.com Substack – substack.jurgenappelo.com  Human Robot Agent – https://jurgenappelo.com/pages/human-robot-agent Like this episode? Show notes & transcript: techleadjournal.dev/episodes/242. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(04:00) Brought to you by Unleash Unleash is a private, flexible, and scalable feature flag system that lets teams decouple deployments from releases. It reduces the risk of shipping new features and gives organizations real-time control over what reaches production. And as AI accelerates development, Unleash helps engineering teams move fast and stay stable with safe rollouts and instant kill switches. Start a free trial of Unleash at ⁠getunleash.io/pricing⁠. Why do so many software projects still fail despite modern tools? The answer often lies in the psychology of the team, not the technology stack. Software development is often viewed purely as a technical challenge, yet many projects fail due to human factors and cognitive bottlenecks. In this episode, Adam Tornhill, CTO and Founder of CodeScene, shares his unique journey combining software engineering with psychology to solve these persistent industry problems. He explains the concept of “Your Code as a Crime Scene,” a method for using behavioral analysis to identify high-risk areas in a codebase that static analysis tools often miss. Adam covers the tangible business impact of code health, specifically how it drives predictability and development speed. He explains why 1-2% of our codebase accounts for up to 70% of our development work, and how focusing on these hotspots can make our team 2x faster and 10x more predictable. Adam also provides a critical reality check on the rise of AI in coding, exploring whether it will help reduce technical debt or accelerate it, and offers strategies for maintaining quality in an AI-assisted future. Key topics discussed: Combining psychology and software engineering Why predictability matters more than speed Treating your codebase as a crime scene Behavioral analysis vs. static analysis The hidden danger of the “Bus Factor” Will AI help or hurt code quality? Why healthy code helps both humans and AI Essential guardrails for AI-generated code Timestamps: (00:00) Trailer & Intro (02:36) Career Turning Point: From Developer to Psychologist (07:43) Why Engineering Leaders Need Psychology Knowledge (09:29) The Root Cause of Failing Software Projects (11:37) Why Code Abstractness Makes Quality Hard to Measure (12:58) Aligning Code Quality with Business Outcomes (14:15) Code Health: 2x Speed, 10x Predictability (17:06) Why Predictability is Undervalued in Software (19:53) TDD and Practices That Drive Code Quality (21:57) Benchmarking Code Health Across the Industry (24:06) Introducing “Your Code as a Crime Scene” (26:30) Behavioral Code Analysis: Hotspot Analysis vs Static Code Analysis (29:40) Behavioral Code Analysis: Understanding Change Coupling (31:33) Dealing with God Classes (33:14) Behavioral Code Analysis: The Social Side of Code (36:48) Why Developers Aren’t Interchangeable (39:14) Introduction to CodeScene (42:06) Will AI Help or Hurt Code Quality? (43:06) Essential Guardrails for AI-Generated Code (45:54) Using CodeScene to Maintain Quality in the AI Era (48:32) How AI Accelerates Technical Debt at Scale (50:42) Why AI-Friendly Code is Human-Friendly Code (54:31) The Reality Check: Future of Software Development with AI (58:27) 3 Tech Lead Wisdom _____ Adam Tornhill’s Bio Adam Tornhill is the founder and CTO of CodeScene and the best-selling author of Your Code as a Crime Scene. Combining degrees in engineering and psychology, Adam helps companies optimize software quality using AI-driven methodologies. He is an international keynote speaker and researcher who enjoys retro computing and martial arts in his spare time. Follow Adam: LinkedIn – linkedin.com/in/adam-tornhill-71759b48 CodeScene – codescene.com  Your Code as a Crime Scene – pragprog.com/titles/atcrime2/your-code-as-a-crime-scene-second-edition Like this episode? Show notes & transcript: techleadjournal.dev/episodes/241. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(06:03) Brought to you by Unleash Unleash is a private, flexible, and scalable feature flag system that lets teams decouple deployments from releases. It reduces the risk of shipping new features and gives organizations real-time control over what reaches production. And as AI accelerates development, Unleash helps engineering teams move fast and stay stable with safe rollouts and instant kill switches. Start a free trial of Unleash at getunleash.io/pricing. Are you making critical decisions without consulting AI? Greg argues it’s now irresponsible for any leader to make high-stakes decisions without talking to AI first. In this episode, Greg Shove, CEO of Section and a multi-time founder with 30 years of entrepreneurial experience, shares how AI is fundamentally different from any previous technology wave. Unlike traditional software that makes us more productive within our existing boundaries, AI allows us to jump capability boundaries – enabling individuals and organizations to do things they simply couldn’t do before. Greg explains why most enterprise AI rollouts are failing (hint: they’re treating AI like software when it’s actually co-intelligence), how to cultivate resilience through multiple startup failures, and the practical strategies for getting teams to adopt AI (from simple hacks like putting a post-it note on your monitor to creating an entire AI-dedicated screen). This conversation goes beyond the hype to explore both the superpowers and limitations of AI, the real organizational outcomes you can expect (spoiler: it’s not just about layoffs), and why moving from efficiency to creation is the key to unlocking AI’s true potential in your organization. Key topics discussed: Why AI breaks capability boundaries unlike any other tech Treating AI as a thought partner, not just a productivity tool Why most large organizations fail at AI deployment Managing workforce anxiety during AI transformation The four possible team outcomes when rolling out AI Moving from efficiency (cut) to growth (create) with AI The Post-it note hack that changed how teams use AI daily Walking the walk: leading authentically in AI adoption Timestamps: (00:00:00) Trailer & Intro (00:02:44) Career Turning Points (00:06:03) Cultivating Entrepreneurial Resilience (00:07:49) Understanding the AI Wave: Scale and Transformation (00:12:29) Pivoting to AI: Section’s Transformation Journey (00:17:57) AI as a Thought Partner (00:22:57) Practical Tips for Leaders Using AI Daily (00:30:49) Rolling Out AI Organization-Wide: Managing Change and Anxiety (00:41:30) AI ROI: Beyond Efficiency to Creation (00:51:01) AI-Powered Education: The ProfAI Approach (00:57:53) 1 Tech Lead Wisdom _____ Greg Shove’s Bio Greg Shove is a seven-time CEO, all in on AI. After first using ChatGPT in February 2023, he pivoted his company Section to be AI-powered. Now he helps enterprise organizations move from AI-anxious to AI-proficient with a proven playbook, delivered through keynote speaking and executive workshops. Greg is also the founder of Machine & Partners, an AI lab building custom enterprise AI applications, and co-author of Personal Math, a weekly newsletter sharing business insights for early-career leaders and founders. Follow Greg: LinkedIn – linkedin.com/in/gregshove Newsletter – personalmath.substack.com Section AI – sectionai.com Prof AI – prof.ai Like this episode? Show notes & transcript: techleadjournal.dev/episodes/240. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(06:06) Brought to you by Jellyfish AI tools alone won’t transform your engineering org. Jellyfish provides insights into AI tool adoption, cost, and delivery impact – so you can make better investment decisions and build teams that use AI effectively. See for yourself at jellyfish.co/platform/ai-impact. Why do organizations constantly complain about having too much technical debt? Because they’re solving the wrong problem. In this episode, Dr. Andrew Brown, author of “Taming Your Dragon: Addressing Your Technical Debt,” reveals a profound insight: technical debt isn’t fundamentally a technical problem. It’s a trade-off problem rooted in human bias, organizational systems, and economic incentives. Through his innovative “Technical Debt Onion Model,” Andrew shows how decisions about code quality happen across five interconnected layers, from individual cognitive biases to wicked problem dynamics. Andrew explains why the financial debt analogy is dangerously misleading and, more importantly, how others can 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 technical How emotions override logic in critical decisions The Technical Debt Onion Model framework explained Principal-agent problems sabotaging your codebase Externalities: who pays for shortcuts taken today? Why burning down debt is already too late Ulysses contracts for managing future obligations Systems thinking applied to software development Wicked problems: why different teams see different solutions AI’s impact on technical debt creation Timestamps: (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 Bio Andrew 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-4b38062 YouTube – @behaviouralsoftwareclub705 Email – brownsensei@hotmail.com  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.

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: LinkedIn – linkedin.com/in/emaggiori Website – emaggiori.com Like this episode? Show notes & transcript: techleadjournal.dev/episodes/238. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

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: LinkedIn – linkedin.com/in/honeymittal Twitter – x.com/HoneyMittal07 Website – locofy.ai Like this episode? Show notes & transcript: techleadjournal.dev/episodes/236. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.