Beyond Coding

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 up
• Why CVs have become too good and it's no longer enough
• How to treat LinkedIn as a platform
• Getting into companies like OpenAI and Anthropic
• How to reach out to people without seeming fake

If 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 weeks

Guest: Keki Mwaba, career and recruitment expert:
https://www.linkedin.com/in/keki-mwaba

#techjobs #softwareengineering #careeradvice

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 Ahead

Guests:

Jeroen Gordijn: https://www.linkedin.com/in/jeroengordijn

Jeroen Dee: https://www.linkedin.com/in/jeroendee

#AgenticEngineering #SoftwareEngineering #Agents

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 hooks
• Why your harness matters more than the model
• How Amazon and Google police AI-generated code with policies
• AI burnout, cognitive debt, and "cognitive surrender": what stays your responsibility
• Step one for adopting agentic software engineering in your team this week

Whether 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.com

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 #aicoding #codereview

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

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

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 platform
• How the best architects act more like scouts than cartographers
• Why "simple is complicated enough" beats clean code dogma at scale
• How to design systems that evolve instead of trying to predict 10 years out
• What junior engineers should actually do in the age of AI agents

For 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

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 engineers
• Why juniors learning with AI have an edge over 20-year veterans
• The future of writing code by hand (and why fulfillment is shifting)
• Vibe coding, security holes, and what happens after 6 months
• The brutal reality of layoffs, gap years, and AI-driven hiring

If 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

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 optional
• How AI magnifies your existing technical debt instead of fixing it
• The 6-month learning curve nobody warns you about
• Why your role as an engineer was never about writing code
• The environmental cost behind every prompt

Whether 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/bosrob

References: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.pdf

Timestamps:

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

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 inside
• Using AI agents without losing your identity as an engineer
• Why open source beats a tailored resume in today's market
• How consistent contributions can reshape your entire career

If 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/bschaatsbergen

OUTILNE

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

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 begins
• The team composition that consistently delivers results
• Peter Naur's Theory Building and why documentation alone falls short
• How knowledge leaving your team turns working systems into legacy
• Why assuming you're wrong leads to better architecture

Whether 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