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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

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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 – [email protected]
•  Taming Your Dragon – https://www.amazon.com/Taming-Your-Dragon-Addressing-Technical/dp/B0CV4TTP32/

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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

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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

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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

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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:

• LinkedIn – linkedin.com/in/johnwillisatlanta
• Twitter – 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-7359198621222719490

Like this episode?
Show notes & transcript: techleadjournal.dev/episodes/237.
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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

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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

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Show notes & transcript: techleadjournal.dev/episodes/236.
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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

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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:

• LinkedIn – linkedin.com/in/simonwardley
• Twitter – x.com/swardley
• Website – www.swardleymaps.com

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Show notes & transcript: techleadjournal.dev/episodes/235.
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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

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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:

• LinkedIn – linkedin.com/in/preeti-somal-131890
• Twitter – x.com/psomal
• 📖 Temporal’s State of Development Report 2025 – temporal.io/pages/state-of-development-2025

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“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

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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:

• LinkedIn – linkedin.com/in/lauratacho
• Twitter – x.com/rhein_wein
• Website – lauratacho.com
•  AI Measurement Framework – getdx.com/whitepaper/ai-measurement-framework/?utm_source=techleadjournal
•  Guide to AI-Assisted Engineering – getdx.com/guide/ai-assisted-engineering/?utm_source=techleadjournal
• AI code metrics – getdx.com/ai-code-metrics

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Show notes & transcript: techleadjournal.dev/episodes/233.
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“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

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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:

• LinkedIn – linkedin.com/in/gavinking
• Twitter – x.com/1ovthafew
• Website – hibernate.org

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Show notes & transcript: techleadjournal.dev/episodes/232.
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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

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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:

• LinkedIn – linkedin.com/in/gregmfoster
• X – x.com/gregmfoster
• Email – [email protected]
• Graphite – graphite.dev
• Graphite X – x.com/withgraphite

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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:

• LinkedIn – linkedin.com/in/emilybache
• X – x.com/emilybache
• Mastodon – sw-development-is.social/web/@emilybache
• GitHub – github.com/emilybache
• Website – emilybache.com
• Samman Coaching – sammancoaching.org
• YouTube – youtube.com/@EmilyBache-tech-coach
• Modern Software Engineering – youtube.com/@ModernSoftwareEngineeringYT

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