Tech Lead Journal

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
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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:
LinkedIn – linkedin.com/in/tommybsullivan

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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
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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:
LinkedIn – linkedin.com/in/anemari-fiser
Website – anemarifiser.com
 Leveling Up as a Tech Lead – oreilly.com/library/view/leveling-up-as/9781098177508

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Show notes & transcript: techleadjournal.dev/episodes/252.
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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
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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:
LinkedIn – linkedin.com/in/andrewjstevens
Sakura Sky – sakurasky.com
 The Executive AI Playbook – https://www.sakurasky.com/white-papers/ai-playbook/
 Executive White Papers & Frameworks – https://whitepaper.download/

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Show notes & transcript: techleadjournal.dev/episodes/251.
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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
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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:
LinkedIn – linkedin.com/in/drewhoskins2
Newsletter – drewhoskins.substack.com
 Product-Minded Engineer - https://www.oreilly.com/library/view/the-product-minded-engineer/9781098173722/
One-Page Bio – drewhoskins.carrd.co

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Show notes & transcript: techleadjournal.dev/episodes/250.
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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
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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:
LinkedIn – linkedin.com/in/shiftan
MCPTotal’s Website – mcptotal.io

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Show notes & transcript: techleadjournal.dev/episodes/249.
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