Afleveringen
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Danila Shtan runs engineering at Nebius, one of the biggest AI clouds in the world, and he told me exactly which engineers he hires on the spot. There are only hundreds of people on the planet with the skill he wants most, and it is not the one you are grinding on. We get into which engineering skills are actually scarce and well paid today, and which ones are quietly on the way out.
In this episode we cover:
The engineering skills in highest demand right now and which ones are on the way outWhy an AI cloud CTO restricts Claude Code inside his own companyDan's rule for merging any AI-written code into productionWhy working with an agent is like managing a junior engineerThe interview question that surfaces top tier engineer qualitiesWhy he still runs algorithm interviews todayIf you are an engineer trying to work out where the value sits now that agents write the easy code, this is a straight answer from the person building the infrastructure underneath all of it.
Timestamps:
00:00:00 - AI Agents doing everything is a lie
00:00:44 - What Nebius Actually Does
00:04:31 - The Engineers In Highest Demand Right Now
00:06:58 - Inside the Hiring Process
00:08:12 - The Bootcamp: You Join the Company, Not a Team
00:10:51 - Why You Can't Use AI in Their Interviews
00:16:31 - Why He Banned the Word "Headcount"
00:22:25 - Why a CTO Is Not a Technical Role
00:24:49 - The One Skill Every Manager Needs
00:25:48 - Why Smart People Fail at This
00:28:17 - "The Promise of Agents Is Bullshit"
00:31:39 - How AI Multiplies Your Baseline Skill
00:35:32 - Why an AI Agent Is Just a Junior Engineer
00:36:57 - Why He Won't Let His Team Use Claude Code
00:37:46 - His Rule for Merging AI-Written Code
00:40:28 - The Interview That Predicts Great Engineers
00:42:32 - From T-Shaped to Round-Shaped Engineers
00:44:30 - Is There Still a Path for Juniors?
00:45:28 - Why Hard Skills No Longer Matter
00:47:11 - The Engineers Who Will Become Obsolete
00:50:04 - The Real Reason People Stay at Banks
00:52:26 - Where AI Agents Actually Help
00:54:40 - Why He Still Uses Algorithm Interviews
00:56:05 - Tech Enthusiasts vs. Real Engineers#AIEngineering #TechCareers #SoftwareEngineering
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120,000 tech workers have been laid off in 2026, yet there are 60,000 open roles. Engineers applying are sending out 100 applications for zero replies. Former Reddit, Uber and Disney Plus recruiter Keki Mwaba breaks down why the market broke, why every resume now looks identical, and what gets you hired when yours looks like everyone else's.
In this video, we cover:
Why 120,000 layoffs and 60,000 open roles don't add upWhy CVs have become too good and it's no longer enoughHow to treat LinkedIn as a platformGetting into companies like OpenAI and AnthropicHow to reach out to people without seeming fakeIf you're a software engineer trying to stand out in the most competitive tech market in years, this is the playbook.
Timestamps:
00:00:00 - Intro
00:00:35 - How bad is the tech job market in 2026?
00:02:38 - 120,000 laid off, 60,000 jobs open: the math is not mathing
00:04:05 - LinkedIn isn't a CV, it's a platform
00:08:30 - The underrated move: comment your way into a job
00:10:48 - Is AI ruining LinkedIn?
00:13:50 - Never feel safe: how to prepare before a layoff
00:15:34 - What layoffs do to the people who stay
00:17:03 - "Did I just automate myself out of a job?"
00:18:48 - Why every resume now looks the same
00:20:11 - Why referrals beat applications
00:22:13 - Do software engineers still have a future?
00:23:32 - The staff engineer who wants to quit for plumbing
00:26:16 - Patrick on his own job security
00:30:21 - 70% of job descriptions now demand AI skills
00:31:34 - Is middle management disappearing?
00:33:53 - The impossible ask: stay current, deliver, and not burn out
00:37:02 - How to get hired at OpenAI or Anthropic
00:39:18 - How to message someone without seeming fake
00:41:42 - Build a portfolio that shows your thinking
00:45:12 - Your personal branding plan for the next few weeksGuest: Keki Mwaba, career and recruitment expert:
https://www.linkedin.com/in/keki-mwaba#techjobs #softwareengineering #careeradvice
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Jeroen Gordijn and Jeroen Dee: two frontrunners who stopped writing code months ago and say software development is already solved. Typing code is no longer necessary, but what matters more now? If you're an engineer that loves coding, you're in a tougher spot than you might realize.
In this video, we cover:
- Why writing code is "solved" but engineering isn't
- Spec-driven development and how to get it started in your team
- The "Dark Factory" and why code review is a huge bottleneck
- Model vs harness: what matters more, and why
- The unhealthy side of agentic coding
If you write software for a living and you're trying to work out what your job becomes next, start here.
Timestamps:
00:00:00 - Coding Is No Longer Necessary
00:00:43 - Why "Software Development Is Already Solved"
00:02:57 - Should You Even Read the AI's Code?
00:05:05 - What Is a "Dark Factory"?
00:06:52 - If You Can Regenerate It, Why Care About Quality?
00:07:49 - Spec-Driven Development Explained
00:11:32 - Adopting Specs Without Starting From Scratch
00:13:23 - Model vs Harness: What Matters More?
00:17:27 - Is Your Harness the New IDE?
00:20:18 - Why Everyone Plateaus (and the Innovation Token)
00:22:50 - Where to Actually Spend Your Time
00:24:57 - The Unhealthy Side: "It's Free Cocaine"
00:28:00 - Is This Sustainable, or Just Subsidized?
00:30:33 - Should You Run Models Locally?
00:34:31 - Looping, Scale, and Automating Review
00:37:53 - What's Left for Engineers to Do?
00:39:13 - If You Love Writing Code, You're in Trouble
00:41:18 - Why Teams Are Getting Smaller
00:43:03 - What an "Agentic Company" Looks Like
00:46:25 - How to Start: Find Your Spark
00:50:13 - The One Habit That Keeps You AheadGuests:
Jeroen Gordijn: https://www.linkedin.com/in/jeroengordijn
Jeroen Dee: https://www.linkedin.com/in/jeroendee
#AgenticEngineering #SoftwareEngineering #Agents
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AI generates 10x more code, but your senior engineers still review it by hand and it's burning them out. Even Google admits code review is now the bottleneck nobody knows how to solve.
Florian Buetow, AI engineer at Xebia, has been running experiments to eliminate the human from the review loop entirely, and what he found changes where engineers should focus their effort.
In this episode, we cover:
Why "stop doing code reviews" is a serious answer (and what replaces them)The guardrails that gave the most value: Semgrep rules, architectural unit tests, and stop hooksWhy your harness matters more than the modelHow Amazon and Google police AI-generated code with policiesAI burnout, cognitive debt, and "cognitive surrender": what stays your responsibilityStep one for adopting agentic software engineering in your team this weekWhether you're an individual developer drowning in AI-generated PRs or driving AI adoption across a large engineering org, you'll leave with concrete experiments to run.
More from Florian:
https://cracking-ai-engineering.comTimestamps:
00:00:00 - Intro
00:00:40 - Code Review Is Software Engineering's Biggest Bottleneck
00:01:57 - How Amazon and Big Tech Police AI-Generated Code
00:02:55 - Horizontal vs Vertical Scaling of AI Engineering
00:04:37 - Why "No Code Reviews" Might Be the Answer
00:05:22 - Engineering Environments That Give Agents Feedback
00:06:46 - Why the Harness Matters More Than the Model
00:07:21 - When Spec-Driven Development Failed and TDD Worked
00:10:06 - Stop Hooks, Ralph Loops, and Automated Feedback
00:11:30 - The Guardrails That Gave the Most Value
00:14:00 - Architectural Constraints That Keep AI Code Sane
00:15:07 - What Remains a Human Responsibility
00:17:33 - Why All the Hard Work Moves Upfront Now
00:18:47 - The Incredible Skill Junior Engineers Should Learn
00:20:26 - AI Burnout: Why Engineers Are Exhausted
00:22:42 - Cognitive Surrender: Letting the Agent Take Over
00:23:25 - The Hand Grenade Problem with AI at Work
00:24:08 - Outsourcing Code Review to AI Itself
00:26:39 - Teams That Fully Adopted Spec-Driven Development
00:29:01 - Can You Rebuild Software From Tests Alone?
00:30:27 - How to Experiment and Stay Ahead
00:33:15 - Spying on What Subagents Tell Each Other
00:33:59 - Step One: How to Start with Guardrails
00:36:08 - Data Mining Your Session Logs for Patterns
00:37:00 - Stuck With One Harness? Here's What to Do
00:38:28 - The One Experiment to Run This Week#softwareengineering #aicoding #codereview
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Are you ready to adapt to the rapidly evolving rules of software development?
In this deep dive, Logan Kilpatrick, Director and Engineer at Google DeepMind, breaks down how AI agents, advanced model-product symbiosis, and tools like Gemini 3.5 Flash are fundamentally shifting the engineering bottleneck. Learn how to maintain your competitive advantage by moving beyond the keyboard to focus on problem-solving, architectural taste, and system understanding.
In this video, we cover:
The changing role of the IDE and the rise of agent managers in code generation.Overcoming team bottlenecks in code review and CI/CD test execution execution loops.Why "agent coverage" and context integration are the next big tech stack metrics.Building a bulletproof software portfolio through permissionless open-source contributions.The critical difference between outsourcing intelligence versus outsourcing understanding.This episode is for software engineers, tech leads, and computer science students looking to future-proof their careers and reset their ambitions in the era of autonomous engineering agents.
Timestamps:
00:00:00 - Intro
00:00:40 - Code Review Is Software Engineering's Biggest Bottleneck
00:01:57 - How Amazon and Big Tech Police AI-Generated Code
00:02:55 - Horizontal vs Vertical Scaling of AI Engineering
00:04:37 - Why "No Code Reviews" Might Be the Answer
00:05:22 - Engineering Environments That Give Agents Feedback
00:06:46 - Why the Harness Matters More Than the Model
00:07:21 - When Spec-Driven Development Failed and TDD Worked
00:10:06 - Stop Hooks, Ralph Loops, and Automated Feedback
00:11:30 - The Guardrails That Gave the Most Value
00:14:00 - Architectural Constraints That Keep AI Code Sane
00:15:07 - What Remains a Human Responsibility
00:17:33 - Why All the Hard Work Moves Upfront Now
00:18:47 - The Incredible Skill Junior Engineers Should Learn
00:20:26 - AI Burnout: Why Engineers Are Exhausted
00:22:42 - Cognitive Surrender: Letting the Agent Take Over
00:23:25 - The Hand Grenade Problem with AI at Work
00:24:08 - Outsourcing Code Review to AI Itself
00:26:39 - Teams That Fully Adopted Spec-Driven Development
00:29:01 - Can You Rebuild Software From Tests Alone?
00:30:27 - How to Experiment and Stay Ahead
00:33:15 - Spying on What Subagents Tell Each Other
00:33:59 - Step One: How to Start with Guardrails
00:36:08 - Data Mining Your Session Logs for Patterns
00:37:00 - Stuck With One Harness? Here's What to Do
00:38:28 - The One Experiment to Run This Week#SoftwareEngineering #AIAgents #GoogleDeepMind
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As AI agents transform software engineering, how do you leverage them without losing your coding skills or risking production disasters? In this episode, Google Cloud AI Director Addy Osmani breaks down the shift from babysitting basic models to mastering advanced agent harnesses.
Discover how to safely delegate complex technical tasks while maintaining your human engineering identity and setting up secure boundaries for your AI.In this episode, we cover:
Human Identity vs. Machine Identity: How to avoid the trap of "cognitive surrender" and keep your critical thinking sharp.Stopping the AI "Babysitting" Cycle: How to transition from constant manual oversight to secure agent governance.Rising Abstractions: Why agent harnesses (like Claude Code and Antigravity) are changing how software is built.The Verification Bottleneck: Why coding is easy, but verifying that your agent didn't ruin production is the real challenge.This episode is a must-watch for software engineers and tech leaders looking to integrate AI agents into their workflows safely and effectively. You’ll walk away with actionable frameworks to boost your development velocity without letting your own technical edge rot.
Guest:Addy Osmani is a Director at Google Cloud AI, famous for his work on Google Chrome and focused on AI agents in software engineering.
Timestamps:00:00:00 - Intro
00:00:45 - The Reality of "Babysitting" Your AI Agent Setup
00:01:16 - How to Stop Babysitting and Build Secure AI Agents
00:02:36 - The Dangerous Mistakes of Uncontrolled AI Experiments
00:03:39 - Rising Abstractions: From Code to Agent Harnesses
00:05:18 - Why You Should Delegate Technical Tasks to AI
00:07:05 - How to Choose the Best AI Agent Harness
00:08:31 - How to Manage Your Developer Innovation Budget
00:10:17 - Are We Losing Pair Programming to AI Agents?
00:12:14 - Cognitive Surrender: The Hidden Threat of Generated Code
00:13:40 - The Verification Bottleneck: How to Trust AI Code
00:15:59 - How to Safely Scale Your Personal AI Bandwidth
#AIAgents #SoftwareEngineering #DeveloperProductivity
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After 250 episodes of Beyond Coding, a pattern shows up again and again: the engineers who thrive aren't the ones chasing the newest tool or the cleanest code. They're the ones who learn fast, keep things simple, and understand the business they're building for.
This special pulls the sharpest moments from recent guests into one conversation about what actually makes a great software engineer in 2026.
We cover:
Why learning is the only skill that outlives every tool, language, and platformHow the best architects act more like scouts than cartographersWhy "simple is complicated enough" beats clean code dogma at scaleHow to design systems that evolve instead of trying to predict 10 years outWhat junior engineers should actually do in the age of AI agentsFor software engineers who want to think clearer, build better, and grow into the kind of engineer companies can't replace.
Timestamps:
00:00:00 - Intro
00:00:17 - Why You Should Increase Your Breadth, Not Just Focus
00:02:16 - The Only Skill That Survives Every Tech Cycle
00:04:14 - Buzzwords Are Just Old Ideas in New Clothes
00:05:26 - What Clients Say vs What They Actually Want
00:06:45 - The Bad Architects Are Easier to Spot
00:08:50 - Why Good Engineers Use Boring Technology
00:11:40 - Stop Building for 100x Scale on Day One
00:13:13 - The Dogma of Clean Code Is Hurting You
00:15:15 - Simple Is Complicated Enough at Scale
00:16:28 - Design Only for the Next Order of Magnitude
00:18:19 - How to Talk Tech with Non-Technical Stakeholders
00:19:30 - The $50,000-Per-Hour Container Terminal Lesson
00:22:11 - Architects Are No Longer Cartographers, They're Scouts
00:25:18 - Start with a Question, Not an Answer
00:26:49 - Junior to Senior in the Age of AI Agents
00:27:29 - Don't Be a Fool with a Tool
00:29:43 - From Explicit to Implicit Knowledge Economy
00:30:38 - Use AI to Validate, Not to Generate
#softwareengineering #engineeringcareer #softwarearchitecture
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Reddit Reacts is back. I'm taking the most controversial takes on software engineering from Reddit and giving you my unfiltered perspective on what's happening, from juniors leveraging AI tools, to the culling of engineers who refuse to adapt, to whether you should take a gap year after a layoff.
In this episode, we cover:
How to become technically "cracked" and what really separates great engineersWhy juniors learning with AI have an edge over 20-year veteransThe future of writing code by hand (and why fulfillment is shifting)Vibe coding, security holes, and what happens after 6 monthsThe brutal reality of layoffs, gap years, and AI-driven hiringIf you're an engineer trying to figure out where this industry is going and how to stay competitive, this one is for you.
Mentioned in the episode:ADP List - free mentorship from senior engineers
Timestamps:00:00:00 - Intro
00:00:54 - How to Become Technically Cracked in 2026
00:05:35 - Will Juniors Who Only Code with AI Get Stuck?
00:09:26 - Will Senior Engineers Stop Writing Code By Hand?
00:11:11 - I Vibe Coded for 6 Months and It's a Disaster
00:15:04 - Why Leaders Demand Screen Sharing on Incident Calls
00:17:34 - "I Don't Do Anything and Still Get Promoted"
00:20:33 - Have the Best Engineers Stopped Applying?
00:25:39 - The Future of Software Engineering in the AI Era
00:32:15 - Are Most Programmers Actually Bad?
00:34:58 - Should You Take a Gap Year After a Layoff?
#softwareengineering #aicoding #techcareers
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Most engineers are using AI coding tools without understanding what they actually are and it's costing them. Microsoft Certified Trainer Rob Bos has trained thousands of engineers on AI tooling, and he sees the same gaps in fundamentals show up again and again, regardless of seniority. This is what you need to know:
What an LLM actually is (and why understanding this changes how you use it)Why prompt engineering isn't optionalHow AI magnifies your existing technical debt instead of fixing itThe 6-month learning curve nobody warns you aboutWhy your role as an engineer was never about writing codeThe environmental cost behind every promptWhether you're skeptical of AI tools or already living in agent mode, these are the fundamentals that separate engineers who get real value from those who get burned by the hype.
Connect with Rob:
https://www.linkedin.com/in/bosrobReferences:Token tracker: https://marketplace.visualstudio.com/items?itemName=RobBos.copilot-token-tracker
Dev survey: https://www.activestate.com/wp-content/uploads/2019/05/ActiveState-Developer-Survey-2019-Open-Source-Runtime-Pains.pdfTimestamps:
00:00:00 - Intro
00:00:43 - The #1 Thing Engineers Get Wrong About AI
00:02:09 - How Much LLM Theory Do You Actually Need?
00:03:58 - Why Pair Programming Is Still the Best Way to Learn AI
00:05:26 - Why Rob Skips Tab Completion and Lives in Agent Mode
00:07:03 - The "AI Doesn't Increase Productivity" Debate
00:08:29 - Why Your Real Job Was Never Writing Code
00:09:14 - The 2-Hours-of-Coding Problem No One Talks About
00:11:02 - More Code = More Pressure on Your Review Process
00:12:21 - Why AI Magnifies Existing Technical Debt
00:13:39 - The Customer Who Couldn't Start AI With Developers Yet
00:15:11 - The Future Engineer: Reviewer, Not Writer
00:17:00 - Convincing the AI Skeptic Who Tried It Years Ago
00:19:17 - LLMs Explained Without Visuals (Attention & Semantics)
00:22:41 - Why Prompt Engineering Actually Matters
00:24:20 - From Zero to Hero: The 6-Month Learning Curve
00:26:18 - Is This Confrontational for 20-Year Veterans?
00:29:30 - Becoming a Better Engineer by Thinking in Systems
00:31:26 - Will AI Stop Working as Innovation Slows?
00:34:26 - The Lost Art of Pair Programming with AI
00:35:44 - Tribalism in AI Tools (And Why It's Pointless)
00:37:33 - Tool Agnostic: Start With the Foundations
00:39:40 - Is the IDE Still Relevant?
00:40:50 - The Bluescreen Story That Changed His Mind
00:41:47 - The Hidden Environmental Cost of AI Coding
00:44:15 - 36 Million Tokens in 30 Days: What Does It Mean?
00:45:47 - Running LLMs at the Edge to Cut the Footprint
00:46:48 - Why You Should Be Allowed to Wait Five Minutes Longer
00:47:05 - Outro
#githubcopilot #aicoding #softwareengineering
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Most engineers approach open source the wrong way. They write code, open a PR, and wonder why it never gets merged. Bruno Schaatsbergen, Terraform core contributor and ex-HashiCorp engineer, breaks down the real craft behind contributions that actually land, and why AI is quietly breaking the ecosystem we all depend on.
In this episode, we cover:
Why pull requests get ignored (and the counterintuitive fix)How AI slop is killing open source from the insideUsing AI agents without losing your identity as an engineerWhy open source beats a tailored resume in today's marketHow consistent contributions can reshape your entire careerIf you've ever wanted to contribute to open source but didn't know where to start, this episode gives you a clear perspective from someone who's been on both sides.
Connect with Bruno:
https://www.linkedin.com/in/bschaatsbergenOUTILNE
00:00:00 - Intro
00:01:04 - How Open Source Shaped My Entire Career
00:02:14 - Why I Take Pride in Every PR I Write
00:03:16 - Open Source vs Personal Projects: The Real Difference
00:04:18 - Why Your PRs Get Ignored (And How to Fix It)
00:05:41 - Know Your Audience: The Counterintuitive PR Hack
00:06:35 - Dealing With Imposter Syndrome as a Contributor
00:07:10 - Read Code Like a Writer Reads Books
00:09:31 - My First Contribution (And How It Changed My Career)
00:10:51 - Should You Contribute to Open Source Early in Your Career?
00:12:46 - The Dark Side: When Contributions Become Noise
00:13:44 - Killed With Kindness: The AI Slop Problem
00:16:17 - How Maintainers Are Fighting AI Slop
00:18:02 - How I Actually Use AI Agents in My Workflow
00:19:11 - Don't Outsource Your Thinking to AI
00:20:11 - Who's Liable for AI-Generated Code?
00:21:16 - Earned Rights: Why Trust Matters in Open Source
00:22:52 - How to Approach People at Tech Conferences
00:24:52 - Open Source Is Not a Democracy
00:26:04 - Why Open Source Beats a Tailored Resume
00:27:12 - Never Contribute With the Goal of Getting Hired
00:28:38 - The Real Reason Consistency Pays Off
00:29:30 - Admitting I'm a University Dropout
00:30:42 - Why I Haven't Contributed in Weeks (And That's Okay)
00:32:07 - The Trap of Chasing Contributor Rankings
00:34:32 - Open Source Lets You Work With Anyone in the World
00:35:52 - Final Advice: Don't Let AI Steal Your Identity
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What separates software that survives from software nobody wants to touch? Nico Krijnen has spent 30 years building systems, coaching teams, and learning why some projects thrive while others quietly become the legacy code everyone avoids. In this episode, he shares why the real work starts after you ship, what actually turns a system into legacy, and why the knowledge in your team's heads matters more than the code itself.
In this episode, we cover:
Why production is where the real learning beginsThe team composition that consistently delivers resultsPeter Naur's Theory Building and why documentation alone falls shortHow knowledge leaving your team turns working systems into legacyWhy assuming you're wrong leads to better architectureWhether you're a senior engineer rethinking how you build or earlier in your career trying to understand what really matters, this episode will change how you think about software that lasts.
Connect with Nico:
https://realworldarchitect.dev
TIMESTAMPS
00:00:00 - Intro
00:01:17 - Why He Keeps Choosing Engineering Over Management
00:04:01 - Three Seniors Solved in Three Weeks What Management Couldn't
00:05:14 - The Signals You Miss When You're Not in the Team
00:06:26 - The #1 Skill Behind Every Successful Project
00:08:04 - Why Production Is the Starting Line, Not the Finish
00:10:13 - The Habit Most Teams Skip After Deploying
00:11:28 - Why the Best Teams Mix Designers and Engineers
00:14:36 - Finding the Right People for the Job at Hand
00:17:01 - What Juniors Bring That Seniors Can't
00:20:57 - How to Handle Ideas You Disagree With as a Senior
00:24:21 - A Simple Technique to Surface Everyone's Best Ideas
00:27:09 - What Makes a System Survive Long-Term
00:30:53 - What Actually Makes a System "Legacy"
00:35:01 - The Knowledge That Keeps Software Alive
00:36:06 - Peter Naur's Theory Building: Why Documentation Isn't Enough
00:40:06 - How Knowledge Loss Is Killing Your Codebase
00:42:42 - The Hidden Risk of AI Tools for Team Knowledge
00:48:14 - Why You Should Assume Everything You Build Is Wrong
00:51:31 - Make Hard Things Easy to Change
#SoftwareEngineering #SystemDesign #TechPodcast
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Suzanne Daniels is a Top Microsoft Advisor who works with CTOs and engineering leaders across EMEA on developer productivity, GitHub, and AI adoption. Her take: the industry is obsessing over coding speed, but that was only ever level one. The real shift is in who defines the solution, not who writes the code.
In this episode, we cover:
Why the "55x faster coding" marketing misses the point entirelyThe counterintuitive research showing junior engineers adopt AI faster than seniors"Coding is cheap, software is expensive" and what that means for your careerHow the boundary between product and engineering is disappearingWhy most AI coding tools are 80% the same and what to focus on insteadWhether you're early in career and struggling to land a role, or a senior engineer rethinking where your value lies, Suzanne breaks down what actually matters when the coding part becomes cheap.
Timestamps:
00:00:00 - Intro
00:01:15 - Is AI Productivity the Whole Story?
00:03:26 - Why Outcomes Matter More Than Code Output
00:04:13 - The Real Value Was Never in the Coding
00:06:06 - The Product-Engineering Boundary Is Disappearing
00:07:37 - Why Junior Engineers Are Actually in High Demand
00:09:41 - Research Says Juniors Adopt AI Faster Than Seniors
00:11:31 - The Rise of Comb-Shaped Engineers
00:12:32 - The Energy Juniors Bring That Teams Need
00:14:06 - How Seniors Codify Knowledge for Agents and Humans
00:16:35 - Advice for Early Career Engineers Right Now
00:19:04 - Old Principles Getting a New Polish
00:21:13 - Coding Is Cheap, Software Is Expensive
00:22:52 - Will Agentic Development Change Your Programming Language?
00:24:53 - What Even Is an Application in the Agent Era?
00:28:34 - The Authenticity Paradox of AI-Written Content
00:30:12 - Why Your AI Output Needs a Human Value Add
00:32:12 - Is Open Source at Risk Because of AI?
00:35:09 - When Your Favorite Tool Doesn't Follow You to the Next Job
00:36:45 - Most AI Coding Tools Are 80% the Same
00:38:15 - What Engineering Leaders Should Enable Beyond Licensing
00:42:58 - Should You Leave If Your Company Won't Let You Experiment?
00:45:16 - Platform Engineering as the Foundation for AI Adoption
Guest: Suzanne Daniels
https://www.linkedin.com/in/suzannedaniels#SoftwareEngineering #AICoding #BeyondCoding
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The role of the software engineer is shifting from execution to orchestration, and it's happening faster than most of us realize. Dennis Vink, Principal Consultant at Xebia, breaks down how he approaches code modernization with AI, why fundamentals and system design matter more now than ever, and what the engineering role is actually becoming.
In this episode, we cover:
Why you need to mature your old codebase before you can migrate away from itHow to prove feature parity between legacy and modern systemsWhy vibe coding without architecture knowledge gives you zero controlThe shift from execution-focused engineering to orchestrationWhy Dennis worries about the next generation of engineersWhether you're sitting on legacy code at work or wondering how your role as an engineer is evolving, this conversation will make you think about where you need to invest your time next.
Timestamps:
00:00:00 - Intro
00:00:51 - Dennis's Early AI Engineering Assignments
00:02:23 - Side Projects: Reviving a 20-Year-Old Game in Rust
00:04:36 - Why Vibe Coding Without Fundamentals Fails
00:05:15 - The Fundamentals You Need for Code Migration
00:06:45 - Proving Feature Parity with Automated Testing
00:08:12 - Writing Tests First as Risk Mitigation
00:10:13 - How Much Should You Care About Code Structure?
00:11:18 - Migrating in Small Pieces of Value
00:12:26 - Will Engineers Still Find Fulfillment in Building?
00:14:01 - How to Actually Start Side Projects (ADHD Brain)
00:15:34 - Why Pivoting Is No Longer Painful
00:16:12 - Prompting as the New Bottleneck
00:17:23 - Parallelizing Work Across Projects
00:19:08 - Why System Design Is the #1 Audience Demand
00:20:19 - AI as a Differentiator for Strong Architects
00:21:11 - Why the New Generation Should Worry
00:23:01 - Are Bootcamps Still Worth It?
00:25:15 - The Shift from Collaboration to Business Understanding
00:27:56 - Infrastructure as a Core Competency Bet
00:30:15 - Deterministic vs Non-Deterministic Code Generation
00:32:16 - Can This Approach Scale to Million-Line Codebases?
00:34:20 - Why a Finger-Snap Migration Would Scare You
00:37:01 - Where to Start with Your Own Legacy Codebase
00:38:43 - Which Languages Do AI Models Struggle With?
00:40:24 - Building Around Hallucination with Scaffolding
00:42:30 - Spec-Driven Development as the Future Way of Working
00:43:30 - Turning a Non-Technical Colleague into a "Developer" in an Hour
00:46:21 - When the House Is on Fire, That's When You Need Real EngineersProjects we discussed:
Agent designer - hurozo.com
Game project - Zorlore.com (https://github.com/zorlore/)
Vibe coded solar system simulation - spacehaste.com#SoftwareEngineering #SystemDesign #AIEngineering
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Most senior engineers don't realize they're stuck until it's too late. The longer you stay, the more people around you have already decided who you are and what you're for. Ian Miell, CTO at Container Solutions, breaks down why this happens and how understanding the system around you is the first step to growing beyond it.
In this episode, we cover:
Why staying too long gets you put in a box (and how to escape it)How your software architecture is shaped by money flowsThe 30% rule: why you should feel uncomfortable at work and what it means if you don'tHow to pitch to senior leadership and actually get buy-inWhy AI makes distribution the real challenge, not buildingIf you're a senior engineer trying to grow beyond your current ceiling, this one is worth your time.
Timestamps:
00:00:00 - Intro
00:00:42 - How to Pitch to Senior Leadership and Get Buy-In
00:03:26 - Why You Should Feel Uncomfortable 30% of the Time
00:06:33 - How to Break Through a Seniority Ceiling
00:08:24 - The Burden of Context: Why Being the Go-To Person Traps You
00:10:16 - How Ian Became CTO Without Trying To
00:13:40 - Why a CTO's Job Is Mostly Coaching Now
00:18:20 - Understanding Incentives: The Key to Navigating Any Org
00:23:08 - Startups vs. Large Companies: Completely Different Rules
00:25:00 - Why AI Makes Distribution the Real Problem, Not Building
00:28:16 - The Hidden Maintenance Risk of Vibe-Coded Software
00:30:13 - Security and Compliance: More Nuanced Than Engineers Think
00:36:54 - Where "Architecture Follows the Money" Came From
00:42:36 - The Wrong Number of Customers: A Systems Thinking Story
00:47:23 - Why Engineers Think Individually Instead of Systemically
00:51:53 - How to Start Thinking in Systems
00:57:50 - How to Create Cross-Pollination in Consulting Teams
00:59:39 - What CTOs Actually Look for When Hiring
01:00:34 - Outro
#softwareengineering #systemsthinking #careergrowth
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Most architects stop coding... and that's exactly where they lose their edge. Dennis Doomen has been a hands-on coding architect for 30 years, and his take is blunt: if you're not in the code, you can't make good architectural decisions. Period.
In this episode, we get into the real causes of codebase rot, why dogmatic pattern-following destroys teams, how Dennis uses AI tools to build open source projects without compromising his standards, and why documentation and decision records might be the most underrated investment a software team can make.
This one is for software engineers and architects who want to stay sharp, stay relevant, and build systems that actually last.
00:00:00 - Intro
00:01:05 - Why Dennis Refuses to Stop Coding (After 30 Years)
00:02:54 - The Only Way to Be an Effective Software Architect
00:04:43 - What Happens When Teams Copy Patterns Without Understanding Them
00:06:23 - Software Engineering Is About Battling Complexity
00:08:20 - When to Break Consistency to Reduce Complexity
00:09:24 - The Problem with Overzealous SOLID Principles
00:11:06 - The Future Where We Don't Care About Code Anymore
00:12:07 - How Dennis Built an Open Source Library with GitHub Copilot
00:14:18 - Accepting AI-Generated Code That Doesn't Meet Your Standards
00:16:39 - How to Use AI Without Losing Code Quality
00:17:41 - The Execution Is Accelerating — What Actually Matters Now
00:20:19 - Why Tests Are Your Safety Net in an AI-First World
00:23:44 - Lessons Learned from Letting AI Run Unsupervised
00:26:46 - Should Teams Standardize Which AI Tool They Use?
00:27:32 - Junior Devs and AI: Learning Skills vs. Speed
00:29:21 - How to Stay Curious and Critical in an AI-Assisted Team
00:33:43 - How to Build a Software Engineer from Scratch Today
00:34:38 - Dennis's Emoji-Based Pull Request Review System
00:36:45 - What AI Still Can't Do: Holistic Architectural Thinking
00:38:38 - Why Your Git History Is More Valuable Than You Think
00:40:44 - Decision Records: The Architecture Investment That Pays Off
00:43:16 - When Documentation Saved Dennis from a Bad Management Decision
00:44:47 - The Tailwind Layoffs and the Open Source Business Model Crisis
00:46:27 - Guidelines for Consuming Open Source Responsibly
00:49:51 - Why You Should Open Source Your Own Projects
Guest: Dennis Doomen - Microsoft MVP, open source creator (FluentAssertions and more), and coding architect at Aviva Solutions.
#softwaredevelopment #softwarearchitecture #softwareengineering
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Sendil Nellaiyapen, Engineering Manager at Uber, has built systems that scale to millions of users. In this episode he shares what most engineers get wrong about both system design and the move into engineering management
In this episode, we cover:
Ingredients for designing systems that scale to millions of usersHow to know when to compromise on architectureThe trade-offs of going from IC to engineering manager and why the role is harder than it looksHow to handle opinionated engineers, set team guardrails, and build high-performing engineering cultureWhether you're a senior engineer weighing the move into management, or already leading teams and looking to sharpen your system design thinking, this one's for you.
OUTLINE:
00:00:00 - Intro
00:01:05 - The Ingredients for Building Systems at Scale
00:02:23 - When to Compromise on Your Foundation
00:03:42 - Scaling from 2,000 to 5 Million Users
00:06:37 - Why Clarity Beats Seniority Every Time
00:08:27 - The Danger of Muscle Memory in Engineering
00:10:25 - MVP Mindset: What You Can and Can't Compromise
00:13:22 - How High-Performing Teams Handle Growing Complexity
00:15:04 - Who Owns the Assumptions? Shared Team Responsibility
00:17:04 - Building Open Frameworks Instead of Closed Rules
00:19:53 - Latency Is Overrated (Here's Why)
00:22:52 - Recipes for Disaster: The Biggest System Design Pitfalls
00:24:17 - The Scala Horror Story: When Elegance Kills Velocity
00:26:52 - How to Handle Opinionated Engineers on Your Team
00:29:03 - Setting Guardrails: The Manager's Design Responsibility
00:32:01 - The Hardest Trade-Off Going from IC to Engineering Manager
00:34:35 - Should Great Engineers Stay IC or Go into Management?
00:37:11 - BFS vs DFS Engineers: Which Type Makes a Better Manager?
00:39:05 - The Real Cost of Becoming a Manager (And Why It's Worth It)
00:41:52 - Outro
#systemdesign #engineeringmanager #softwareengineering
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Are you over-engineering for a future that might never come? In this episode, we explore why "future-proofing" often leads to wasted time and sunk costs, and how shifting your mindset from opinions to hypotheses can drastically improve your Developer Experience (DevEx).
In this episode, we cover:
The trap of complex architecture decisions like Hexagonal Architecture too earlyHow to identify and remove friction points in the software development lifecycleThe reality of using AI agents in production and who is actually responsible for the codeIf you are a software engineer or tech lead tired of the "Sacred Cloud Committee" and slow processes, this deep dive into DevEx is for you.
Connect with Bas de Groot:
https://www.linkedin.com/in/bas-de-groot-635013100
Timestamps:
00:00:00 - Intro
00:01:00 - The Danger of "Future-Proofing" Your Architecture
00:03:18 - Why You Should Use Hypotheses Over Opinions
00:05:32 - "Shift Left Until There's Only Sh*t Left"
00:08:19 - At What Size Do You Need a DevEx Team?
00:11:02 - How to Measure Developer Friction Effectively
00:15:43 - Using Data to Fix Slow CI/CD Pipelines
00:17:26 - Why Surveys Beat DORA Metrics for Context
00:19:52 - The "Sacred Cloud Committee" Blocking Deployments
00:24:51 - How to Get Buy-In for DevEx Initiatives
00:28:56 - The Role of Hands-On Coding in DevEx
00:31:47 - Will AI Agents Fix Bad Processes?
00:34:44 - You Are Still Responsible for AI-Generated Code
#developerexperience #softwarearchitecture #techlead
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The difference between a junior and a senior engineer isn't coding speed, it's knowing when to say "no."
"The best code you can write is the code you don't write." In this episode, I sit down with Alessandro Mautone (Senior Software Engineer at Aquablu, ex-WeTransfer) to discuss the reality of engineering at a scale-up: how do you maintain technical excellence when the business demands speed?
We break down why delivering features "fast" pays your salary, but how to negotiate deadlines so you don't drown in technical debt later. If you want to move from writing code to owning product decisions, this conversation is for you.
In this episode, we cover:
- How to push back on features and negotiate deadlines without upsetting stakeholders
- Why chasing "perfect code" can hurt a company in growth mode
- The Generalist vs. Specialist career path: Which one is right for you?
- The potential pitfalls of using AI for unit tests without proper oversight
Timestamps:
00:00:00 - Intro
00:01:06 - Balancing Technical Excellence With Delivery Speed
00:04:11 - Why Delivering Features Pays Your Salary
00:06:51 - The Importance of Ownership and "Skin in the Game"
00:08:59 - Leaving WeTransfer: When Company Direction Shifts
00:11:49 - The Generalist vs. Specialist Career Path Debate
00:16:46 - How to Attract Top Engineering Talent to Your Team
00:18:50 - Is LeetCode the Right Way to Hire for Scale-Ups?
00:23:16 - Learning to "Say No" is a Sign of Seniority
00:25:17 - Negotiating Scope Without Burning Bridges
00:26:02 - When AI Generates Bad Unit Tests
00:28:14 - Never Compromise on Tests, Even in "Code Red"
00:33:59 - Communicating Technical Concepts to Non-Tech Stakeholders
00:35:35 - The Never-Ending Battle Against Complexity
00:37:26 - When to Build for the Future vs. Ship Now
00:42:30 - A Real-World Example of Refactoring for Simplicity
00:46:48 - The Skill That Will Be Make or Break for Engineers
#SoftwareEngineering #ScaleUp #TechnicalDebt
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We are at a unique point in history where there is finally an alternative to human coding. If AI can write the code effectively, what is left for the software engineer?
In this episode, Joris Conijn (AWS CTO at Xebia) argues that the era of "just coding" is over. We discuss why senior developers are safe (for now), why juniors are at risk of never learning the fundamentals, and how "Shadow AI" is forcing companies to change their security strategies.
Most importantly, we break down the difference between a "Programmer" and a "Software Engineer" with the introduction of agentic tools. If you want to future-proof your career and move from writing lines of code to designing systems, this conversation is for you.
In this episode, we cover:
Why banning AI at work actually increases your security riskHow to use AI to automate the boring parts of the SDLC (requirements & user stories)The critical difference between "Coding" and "System Architecture"Why you should check your AI Agents into your Git repositoryThe 20-year problem: what happens when engineers never learn the fundamentals?Connect with Joris Conijn:
https://www.linkedin.com/in/jorisconijn
TIMESTAMPS
00:00:00 - Intro
00:01:11 - What Keeps a CTO Excited About Tech?
00:02:58 - Stop Being the "Department of No" in Security
00:05:28 - The Real Risk of Banning AI at Work
00:06:32 - When Developers Hold the Organization Hostage
00:08:14 - The Hidden Dangers of Instant AI Code Fixes
00:09:50 - Will Future Devs Understand Object Oriented Programming?
00:11:36 - Using AI to Accelerate Learning vs Copy-Pasting
00:13:17 - Why Testing Matters More When AI Writes Code
00:16:42 - Automating the Boring Parts of the SDLC
00:19:06 - How to Turn Meeting Transcripts into User Stories
00:21:36 - The Critical Skill of Making Implicit Knowledge Explicit
00:23:10 - Why You Should Stop Obsessing Over Story Points
00:27:46 - The "A-Team" Approach to High-Trust Development
00:29:54 - Running Parallel Workflows with AI Agents
00:33:34 - Pro Tip: Check Your AI Agents into Git
00:35:52 - Balancing Autonomy and Governance in Large Teams
00:39:19 - There Is Finally an Alternative to Human Coders
00:41:07 - Programmer vs Software Engineer: What is the Difference?
00:44:45 - How to Teach Software Engineering in the AI Era
#SoftwareEngineering #SystemDesign #AIAgents
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Is your internal developer platform actually improving velocity, or is it a bottleneck? We discuss why platform teams building "cool" abstractions is a red flag, and you should aim to create the best platform for software engineers.
In this episode, we cover:
Why "Golden Paths" can turn into roadblocks for developers.The danger of Shadow IT and why it’s a symptom of a failed platform.How to measure if your platform is saving time.Connect with Adnan Alshar:
https://www.linkedin.com/in/adnanmalshar92
Connect with Jelmer de Jong:
https://www.linkedin.com/in/jelmerdejong-xebia
00:00:00 - Intro
00:00:54 - Is DevOps Dead? The Truth About Platform Engineering
00:03:07 - Why Developers Are Drowning in Complexity Today
00:04:37 - Why Having No Platform Is Better Than a Bad Platform
00:07:20 - Treating Software Engineers as Customers of the Platform
00:11:26 - The Exact Moment You Should Start Building a Platform
00:14:18 - Who Should Be on Your First Platform Team?
00:17:33 - Turning Your Angriest Developers Into Platform Evangelists
00:18:57 - Key Metrics: How to Measure Platform Engineering Success
00:21:01 - Why 60% of Companies Don't Measure Platform Success
00:23:35 - Why No Metrics Is the Biggest Red Flag
00:25:23 - The Disconnect Between Executives and AI Readiness
00:31:34 - Integrating AI Tools and Large Language Models Securely
00:34:22 - Shadow IT: The Symptom of a Broken Platform
00:38:03 - How to Scale Without Becoming a Bottleneck
00:41:45 - Don’t Forget the Business Side of Platform Engineering
#PlatformEngineering #DevOps #DeveloperProductivity
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