Beyond Coding
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- 11. Nov. 2026
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For software engineers ready to level up. Learn from CTOs, principal engineers, and tech leaders about the skills beyond coding: from technical mastery to product thinking and career growth. Created by Patrick Akil. Powered by Xebia
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What Coding Agents Need From Engineers to Be Trusted
What do coding agents need from engineers to be trusted? AI consultant Fausto Albers builds production software with agents and doesn't believe anyone still reads the code line by line. Here is what he relies …
Shownotes
What do coding agents need from engineers to be trusted? AI consultant Fausto Albers builds production software with agents and doesn't believe anyone still reads the code line by line. Here is what he relies on instead.
We cover:
- Why newer models are "eating the harness", and the shift from context engineering to loop engineering
- The state a coding agent should be in before it runs for hours, and the gates its output has to pass
- Observability with open-source MLflow when you can't read every trace
- A shared ontology and a three-layer memory stack: a Karpathy-style Markdown wiki in Obsidian, graph memory and a local vector database in Docker
- Disposable vs durable software, and why software built by intent threatens traditional SaaS
- Why AI makes attacks cheaper than defense
This episode is for software engineers and tech leads who want to give coding agents real autonomy without giving up responsibility for what gets built.
Timestamps:
00:00:00 - Intro
00:00:49 - Why Newer Models Keep Eating the Harness
00:02:26 - Will SaaS Survive the Next Ten Years?
00:03:39 - From Sociology to Cocktail Bartender to Restaurant Owner
00:07:19 - Why Human Preference Can't Be Reduced to Data
00:10:10 - What I Learned From a Failed Restaurant Chatbot
00:14:16 - Learning Faster With AI and Who Gets Left Behind
00:18:38 - What State Should Your Coding Agent Be In?
00:22:09 - Building a Shared Understanding Before Kicking Off a Loop
00:26:58 - Why Nobody Really Reads AI Code Line by Line
00:31:34 - The Real Reason Engineers Push Back on AI
00:33:48 - Sloppy, Disposable or Durable: How to Judge Code
00:35:18 - A Three-Layer Memory System for Coding Agents
00:39:44 - Why Domain Experts Matter When Execution Gets Cheap
00:42:23 - How AI Makes Attacks Cheaper Than DefenseGuest: Fausto Albers, AI consultant at Wonder Why
https://www.linkedin.com/in/stepintoliquidThe Most Future-Proof Engineering Skill Nobody Wants to Learn
What is the most future-proof engineering skill to learn now with AI and Coding agents? Time is up for traditional development cycles as Agents shrink vulnerability exploit windows from 200 days to just 18 …
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What is the most future-proof engineering skill to learn now with AI and Coding agents? Time is up for traditional development cycles as Agents shrink vulnerability exploit windows from 200 days to just 18 hours.
In this video, IBM HashiCorp Field CTO Anna McDougall shares why brilliant engineers have been avoiding the single most resilient career path in tech and how it's time to change that. It's now part of what software engineering is becoming:
- Why 20% of software engineers are grieving hands-on IDE coding (and why manual code is becoming like carpentry)
- The #1 future-proof engineering specialty that protects your career as AI agents write more code
- How to stand out in interviews using the "Pumpkin" CV strategy, high-signal personal achievements, and human networking
- Why non-deterministic coding agents ignore skills.md files and why 88% of breaches come down to credentials
- How engineering leaders can implement deterministic runtime guardrails, workload identity, and scoped credentials at scale
Whether you are looking to future-proof your career or a software engineer navigating agentic workflows, you will walk away with a clear roadmap for what comes next.
This episode was recorded at TechLead Conference. Join for the next edition of the event in Amsterdam on May 20: https://techleadconf.com
Guest - Anna Mcdougall:
https://www.linkedin.com/in/annajmcdougallTimestamps:
00:00:00 - From 200 Days to 18 Hours: Why Time Is Up
00:00:47 - What a Field CTO Actually Does Every Day
00:03:53 - Why 20% of Engineers Are Grieving Hands-On Coding
00:06:55 - Why Hand-Crafted Code Is Becoming Like Carpentry
00:08:26 - The Most Future-Proof Engineering Skill Nobody Wants
00:10:53 - How to Network by Becoming the Connective Tissue
00:13:55 - How to Stand Out When AI Writes Every Resume
00:18:31 - The "Pumpkin" Strategy to Be Remembered in Interviews
00:22:25 - Why Brilliant Engineers Avoid This Career Path
00:26:42 - Why Skills Files Are Not Enough for Coding Agents
00:31:10 - Building Deterministic Guardrails Without Slowing Teams
00:36:32 - Finding Your People in Engineering LeadershipMeta Staff Engineer: What the Best Engineers Do Differently
Meta Staff Engineer Sajal Rustagi reveals what Staff Engineers do that most don't and what the best engineers do differently to reach the top. While foundational behaviors like proactivity and ownership still …
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Meta Staff Engineer Sajal Rustagi reveals what Staff Engineers do that most don't and what the best engineers do differently to reach the top. While foundational behaviors like proactivity and ownership still separate growing engineers from stuck ones, leveling up today requires a completely new set of staff skills built around agents.
In this video, we cover:
- The behavioral shift from IC4 and IC5 (Senior) to IC6 (Staff Engineer) at Meta
- How top engineers treat on-call firefighting as a system-wide ownership opportunity
- Why rotating between Engineering Manager (EM) and Staff IC sharpens your business and technical influence
- Transitioning from Developer Experience (DX) to Agent Experience (AX) using MCP, Claude, and markdown context
This conversation is for software engineers, senior ICs, and tech leads who want to learn the new staff skills required to multiply their impact and get promoted now.
This episode was recorded at TechLead Conference, a conference for engineering leaders on adopting AI.
Timestamps:
00:00:00 - The New Staff Engineer Skills
00:00:44 - 2 Behaviors That Separate Mediocre and Staff Engineers
00:02:16 - How the Best Engineers Build an Ownership Mindset
00:04:26 - What Staff Engineers Do Differently During On-Call
00:06:26 - Senior vs. Staff Engineer: Expanding Your Sphere of Influence
00:09:56 - Why Switching Between Manager and Staff IC Builds Better Engineers
00:14:04 - Why Unblocking Engineers Now Means Unblocking Agents
00:16:41 - Managing Multi-Agent Handoffs Without Losing Control
00:19:02 - Shifting from Developer Experience to Agent Experience
00:21:47 - Why the Empty Engineering Backlog Is Still a Myth
00:25:03 - How to Build Custom Agent Workflows and Reusable Skills
00:28:31 - The #1 New Skill Software Engineers Must Build Today
00:31:10 - How Meta Engineering Culture Compares to StartupsGuest - Sajal Rustagi, Staff Engineer at Meta: https://www.linkedin.com/in/sajalrustagi
How Senior Engineers Use Coding Agents On Prod Infrastructure
How do senior engineers use coding agents on prod infrastructure when “make no mistakes” is just a polite request? Kerim Satirli (HashiCorp, an IBM company) explains why prompt rules don’t hold in production …
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How do senior engineers use coding agents on prod infrastructure when “make no mistakes” is just a polite request? Kerim Satirli (HashiCorp, an IBM company) explains why prompt rules don’t hold in production, and what he runs instead.
In this video, we cover:
- How Infrastructure as Code (Terraform, Contentful, Datadog, Okta, Cloudflare providers) evolved from open-source PRs to automated agent harnesses
- Why AI skills files and “make no mistakes” prompts are only requests, and what replaces them in production pipelines
- How AI-generated pull requests are overwhelming open-source maintainers (and why projects like cURL are pushing back)
- Personalized “software factories”, spec-driven development (GitHub Spec Kit), and cutting AI documentation noise with BLUF (Bottom Line Up Front)
- What open-source Rust clones of Adobe mean for the future of SaaS
For infrastructure engineers, DevOps practitioners, and software architects who want to move past one-shot prompting and run coding agents on production infrastructure without losing control.
Timestamps:
00:00:00 - Why “Make No Mistakes” Is a Polite Request
00:00:38 - How Open Source Terraform Changed Provisioning
00:01:59 - Why API-First Services Matter Beyond AWS and Azure
00:04:19 - How AI Agents Changed Terraform Provider Updates
00:07:03 - Why “Make No Mistakes” Won’t Stop Your Agent
00:10:31 - Bringing Determinism Back to Agentic Workflows
00:13:07 - How AI Pull Requests Are Overwhelming Open Source
00:15:41 - Why Every Engineer Is Building a Software Factory
00:18:52 - Why AI-Generated Documentation Slows Down Humans
00:23:12 - Using Agents to Filter Noise and Summarize Specs
00:27:02 - Escape Hatches for Automated Terraform Applies
00:29:03 - Where the Joy in Engineering Lives Now
00:31:24 - Spec-Driven Development and the Return of Architecture
00:34:51 - Open-Source Rust Clones, Reverse Engineering, and SaaS
00:38:19 - How to Build Software Products People Actually WantGuest: Kerim Satirli, Senior Developer Advocate at HashiCorp, an IBM company.
https://www.linkedin.com/in/ksatirli
#Terraform #CodingAgents #DevOps
Lead Architect Reveals His Rules for Coding Agents
Lead Architect Carlos Kelkboom shares his rules for coding agents and how great engineers architect software with AI. One of the great ones is: he throws away generated code the moment he doesn't understand it …
Shownotes
Lead Architect Carlos Kelkboom shares his rules for coding agents and how great engineers architect software with AI. One of the great ones is: he throws away generated code the moment he doesn't understand it. He breaks down why coding agents fail at iterative changes, why his main agent never touches files, and how he scaffolds projects for one-shot execution.
In this video, we cover:
- Why AI coding agents struggle with iterative changes (SlopCodeBench) and excel at one-shot execution
- How to manually scaffold code, types, and folder boundaries before letting sub-agents run
- Designing a distributed Markdown and indexed-search graph harness for progressive context disclosure
- Using Git worktrees to run parallel coding agents and merge the best implementations
- Why dynamic memory and the heap need to disappear in favor of deterministic programming languages
- How AI bubbles and solo coding impact junior engineers and team collaboration
This episode is for software engineers, tech leads, and system architects who want to move beyond "vibe coding" and build maintainable, deterministic production software with AI agents.
Connect with Carlos:
https://www.linkedin.com/in/carlos-k-4016b8225
Timestamps:
00:00:00 - Why Carlos Throws Away Working AI Code
00:00:53 - Why Pull Requests and Team Collaboration Are Dying
00:03:50 - The Trap of Starting Projects You Never Finish
00:06:06 - Escaping the Solo AI Developer Bubble
00:11:54 - How to Scaffold Code Before Using AI Agents
00:15:54 - Learning New Programming Languages With AI Agents
00:21:14 - Why We Must Get Rid of the Heap
00:28:32 - Building a Shared Language Across Engineering Teams
00:31:31 - Vibe Coding vs Deep Software Engineering
00:40:12 - Code Is Cheap: Git Worktrees for Parallel Agents
00:41:45 - How Deep to Scaffold Before the Agent Runs
00:45:09 - Democratizing Software, Missing Juniors, and the AI Bubble
00:53:33 - Designing a Graph Harness for AI Agent Context
00:58:18 - Why His Main Agent Never Touches Files
01:03:07 - Throw Away Code You Don't Understand
01:06:25 - Rethinking Code Reviews and Mutation Testing for AIWhat the Best Engineers Built for Their Coding Agents
What happens when coding agents see your entire system: code, services, infrastructure and live traffic, all connected? Most agents are blind to everything outside the repo. Adyen Staff Engineer Mat Jones …
Shownotes
What happens when coding agents see your entire system: code, services, infrastructure and live traffic, all connected? Most agents are blind to everything outside the repo. Adyen Staff Engineer Mat Jones explains what changed once theirs weren't, and why he calls it somewhat scary.
In this video, we cover:
- Running five or six coding agents in parallel with git worktrees and Atrium, an internal tool that lets agents coordinate and share memory
- Architect: nine connected graphs in Neo4j covering services, code, DNS, data stores and infrastructure
- Ingesting 7 million trace spans a second with Kafka and Apache Flink, and what collapsed on the first try
- Sentinel: anti-pattern monitoring, with agents that raise the fix as a merge request
- MCP vs APIs vs CLI tools, and how to deal with tool bloat
This episode is for software engineers and platform teams who want coding agents to reason about real systems, not just the repo in front of them. You'll leave with a concrete place to start and an honest view of the risks.
00:00:00 - Intro
00:00:42 - Running Five Coding Agents in Parallel
00:01:35 - Atrium: How Agents Avoid Stepping on Each Other
00:06:57 - Why Graphs Save Coding Agents Tokens
00:12:25 - What Is Architect? Nine Graphs in One Database
00:16:54 - What Broke at 7 Million Spans a Second
00:20:37 - The Timeline From Proof of Concept to Production
00:27:59 - Tracing Code Changes to Critical Payment Flows
00:30:26 - How to Start: Build Scrappy, Then Earn Buy-In
00:35:07 - Making a Codebase Agent Friendly With Skills and Hooks
00:42:07 - Why You Should Start With a Service Graph
00:44:46 - The Downside of Everyone Seeing Everything
00:48:34 - Sentinel: Agents That Find and Fix Anti-Patterns
00:53:01 - Are We Going Too Fast With AI?
01:01:55 - Two Engineers, Four Months: Why Big Bets Got Cheaper
01:07:11 - MCP vs CLI Tools and the Tool Bloat Problem
Guest: Mat Jones, Staff Engineer at Adyen
https://www.linkedin.com/in/mathewwjonesWhy Coding Agents Keep Making Your Codebase Worse
HumanLayer CEO Dexter Horthy, who coined "context engineering", on why AI agents degrade codebases over time. He breaks down a benchmark that measures AI code quality across features, and how his team ships …
Shownotes
HumanLayer CEO Dexter Horthy, who coined "context engineering", on why AI agents degrade codebases over time. He breaks down a benchmark that measures AI code quality across features, and how his team ships with agents without drowning in slop.
In this video, we cover:
- SlopCodeBench, SWE-bench and which coding benchmarks to trust
- Token maxing, dark factories and the "Instagram phase" of AI engineering
- Why 2-4x productivity beats chasing 100x
- Pull requests, design docs and how engineers build taste now
- Dex's agentic engineering workflow: one-shots, overnight janitor bots and research-design-plan
For engineers and engineering leaders adopting AI coding agents who want to know what's real and what works in teams at scale.
Timestamps:
00:00:00 - The Man Who Coined the Term: Context Engineering
00:00:54 - SlopCodeBench: Proof AI Makes Codebases Worse Over Time
00:05:49 - Background Agents and the Enterprise Friction Problem
00:08:13 - Why You'll Never One-Shot a Big Feature
00:11:18 - Scaling Agentic Engineering to 1,000 Engineers
00:16:19 - Dark Factories, Fake Hype and Token Maxing
00:19:55 - Why 2-4x Faster Beats Chasing 100x
00:21:31 - Should Teams Stop Doing Pull Requests?
00:24:20 - Can Deterministic Tools Replace Code Review?
00:27:37 - How to Build Engineering Taste When AI Writes Code
00:33:05 - Dex's Agentic Workflow: One-Shots and Janitor Bots
00:35:18 - Research, Design, Plan: Shipping Big Features With Agents
00:38:35 - Why Overnight Agents and Heavy Parallelism Backfire
00:40:10 - Subscription Maxing vs Paying Per Token
00:42:40 - Parallelize Like Poker, Not a Slot MachineDex Horthy: https://x.com/dexhorthy
Dex's talks and deep dives: https://aithatworks.bold.videoHow Amp Ships 50 Times a Day With AI Agents
Amp ships to production around 50 times a day with AI agents, but the change that makes their team ship faster isn't AI at all. Dario Hamidi, AI engineer at Amp, makes the case that pull requests are holding …
Shownotes
Amp ships to production around 50 times a day with AI agents, but the change that makes their team ship faster isn't AI at all. Dario Hamidi, AI engineer at Amp, makes the case that pull requests are holding your team back, and explains what replaces them.
In this video, we cover:
- Why Amp works without pull requests or feature branches, and how to try it with your own team
- Agent permissions, approval fatigue, and building environments where you can trust agents
- Orbs: remote agent execution, parallelism, and multiplayer sessions
- Short-lived credentials and workload identity federation for cloud agents
- Triaging 60+ bug reports a day with agents without frying your brain
For engineers and engineering leads who want to ship faster with AI agents and are ready to question the workflows they've always used.
Timestamps:
00:00:00 - Is It Time to Kill the Pull Request?
00:00:45 - What Happens If Every PR Gets Auto-Closed Tomorrow
00:05:04 - Pull Requests Were Built for Strangers, Not Teammates
00:07:20 - Does Dropping PRs Hurt Quality at Scale?
00:08:53 - Agent Permissions: Why No Default Makes Everyone Happy
00:12:42 - Don't Juggle Knives: Building Environments You Can Trust
00:14:56 - The Hidden Hard Parts of Cloud Agents
00:16:39 - What's an Orb? Remote Agent Execution Explained
00:19:10 - How to Give Agents Credentials Without Leaking Secrets
00:22:51 - Why Orbs Live Forever (and Multiplayer Orbs)
00:25:49 - The State Management Problem Nobody Talks About
00:29:22 - When Will Everyone Have Remote Dev Environments?
00:32:23 - Orbs That Spawn Orbs: Orchestrating Agents at Scale
00:34:42 - How Dario Triages 60 Bug Reports a Day With Agents
00:39:23 - Avoiding Burnout When Agents Remove the CeilingHow Top Engineers Still Get Hired When Most Get Ghosted
How do top engineers still get hired in 2026 when most applicants get ghosted, 120,000 people have been laid off this year, and hiring managers say they can't find talent? A recruiter, a hiring lead, a career …
Shownotes
How do top engineers still get hired in 2026 when most applicants get ghosted, 120,000 people have been laid off this year, and hiring managers say they can't find talent? A recruiter, a hiring lead, a career coach and an open source engineer explain why your resume is dead on arrival when every CV looks the same, and what actually gets you in the room instead.
In this episode, we cover:
- Why 120,000 tech layoffs and 60,000 open engineering roles exist at the same time
- Why only 20% of LinkedIn messages get a reply, and how to write the ones that do
- Why one hiring team bans AI tools in interviews and is building an agentic coding session instead
- Adaptability and resilience: the two soft skills that keep you in the room
- Getting hired through GitHub, referrals and open source when your CV can't stand out
- Whether junior engineers still have a path, and which engineering cohort is at risk in 3 to 5 years
- Titles vs scope: how to grow when your title never changes
For software engineers, students about to graduate, and anyone in tech who wants to know what recruiters and hiring managers are actually filtering on right now.
Timestamps:
00:00:00 - Intro: 1,000 Layoffs a Day
00:00:47 - How Bad Is the Tech Job Market in 2026?
00:02:05 - Why 120K Layoffs and 60K Open Jobs Don't Add Up
00:03:40 - Why AI Tools Are Banned From Interviews
00:06:28 - Hard Skills Get You In, Soft Skills Keep You There
00:09:20 - "I'm a University Dropout": How GitHub Got Me Hired
00:10:18 - CVs Are Too Good Now: Why Referrals Win
00:13:04 - How to Get Your Open Source PR Merged
00:15:02 - Why 80% of Your LinkedIn Messages Get Ghosted
00:16:51 - How Open Source Led to a HashiCorp Job Offer
00:19:15 - Is There Still a Place for Junior Engineers?
00:22:00 - The Engineers Who'll Be Obsolete in 3 to 5 Years
00:24:20 - Should You Contribute to Open Source at All?
00:26:05 - AI Skills Required, Algorithms Still Tested
00:27:54 - Stop Chasing Titles: Scope, Impact and Owning Your CareerWhy an Ex-Googler Bet Everything on an Open-Source Database
Jordan Tigani helped create Google BigQuery, then got fired as Chief Product Officer on a Friday morning. He planned to hack on DuckDB to learn Rust. Investors offered to fund it before he'd decided to start a …
Shownotes
Jordan Tigani helped create Google BigQuery, then got fired as Chief Product Officer on a Friday morning. He planned to hack on DuckDB to learn Rust. Investors offered to fund it before he'd decided to start a company. That company became MotherDuck
In this episode, we cover:
- The DuckDB Labs partnership: why MotherDuck gave the open-source creators a co-founder share instead of going open-core
- From alpha to paid product: 11 founders, 3 to 4 months to alpha, two years to something people would pay for
- AI on top of the data warehouse: vibe-coded dashboards (Dives), pipelines (Flights), a context layer (Guides), and why business users catch mistakes analysts miss
- Are dashboards dead? Jordan wrote "Big Data Is Dead"; his answer on dashboards is different
- Career advice: why you shouldn't want to work on the query optimizer, and the skill Jordan says matters more
For engineers curious about database and infrastructure companies, open-source business models, and how AI is changing who gets to ask questions of data.
Timestamps:
00:00:00 - Intro
00:00:32 - Fired on a Friday: how MotherDuck accidentally started
00:04:50 - Giving DuckDB Labs a co-founder share of the company
00:07:43 - Why most open-source SaaS products are just "managed"
00:09:31 - Why VCs said yes: Snowflake, DuckDB, and BigQuery credibility
00:10:50 - The 11-person founding team that skipped the wrong designs
00:12:20 - Alpha in 4 months, beta in a year, paid in two
00:15:20 - Vibe-coded BI: Dives, Flights, Guides, and an agent harness
00:20:10 - The questions business users ask that analysts never do
00:24:14 - Are dashboards dead in the age of agents?
00:27:57 - Everybody wants to work on the optimizer (don't)
00:31:23 - The engineer superpower most engineers look down on
00:33:20 - Writing: the one skill Jordan would learn (and still hates)
00:36:05 - Does contributing to DuckDB get you hired at MotherDuck?
00:37:20 - Why a database company leaned into the duckHow the Best Engineers Build a World Model (Google Earth Creator & Niantic Spatial CTO)
Niantic Spatial CTO Brian McClendon on how the best engineers solve problems most give up on — from a 4D model of the world to shipping research in months. He built Google Earth and ran Google Maps for over a …
Shownotes
Niantic Spatial CTO Brian McClendon on how the best engineers solve problems most give up on — from a 4D model of the world to shipping research in months. He built Google Earth and ran Google Maps for over a decade, and he's been there, done that. What he's building now is harder, and it's already live for customers. Along the way: who makes it on his team and who doesn't, and the one piece of advice he'd give every engineer using AI.
In this video, we cover:
- The 4D model of the world: visual positioning, change detection, and treating a pile of photos like a database
- Gaussian splats and real-to-sim: capturing a room and loading it into Nvidia Isaac to train robots
- Turning a research idea into a production service in six months
- What Google Maps taught him about building for robots instead of humans
- Designing problems AI can self-check, and why token maxing is a waste
For engineers and engineering leaders who want to work on problems that don't have a known answer yet — and who want to know what a CTO who's built the definitive product in his field looks for in the people he hires.
Recorded at the AI4 conference 2026.
Timestamps:
00:00:00 - Google Earth? Been There, Done That
00:00:46 - Turning a Research Idea Into Production in 6 Months
00:02:37 - How Any Photo Gets Located Within Half a Meter
00:06:37 - The Long-Term Goal: A 4D Model of the World
00:08:37 - Treating a Pile of Photos Like a Database
00:11:58 - What Google Maps Taught Him About Training Robots
00:15:22 - Gaussian Splats Explained in Plain Terms
00:17:42 - The Unsolved Problem: Scale and Semantic Change
00:20:34 - Why Google Earth Is Good Enough
00:22:09 - Who Makes It on His Team and Who Doesn't
00:23:38 - Designing Problems AI Can Self-Check
00:26:03 - The Insights Hidden in the Physical World
00:28:18 - Digital Twins, Cities, and Ready Player One
00:31:35 - Visual Positioning When GPS Gets Spoofed
00:33:24 - Token Maxing Is Bullshit: Advice for EngineersGuest: Brian McClendon, CTO at Niantic Spatial. The engineer behind Google Earth; ran Google Maps for over a decade.
https://www.linkedin.com/in/brianmcclendon
#NianticSpatial #GoogleEarth #SoftwareEngineering
How New Staff Engineers Build Judgment Without Years of Experience
How do new staff engineers build judgment without the years of experience that used to come with the role? Mallika Rao, engineering leader in big tech, explains why the data-structures-and-algorithms foundation …
Shownotes
How do new staff engineers build judgment without the years of experience that used to come with the role? Mallika Rao, engineering leader in big tech, explains why the data-structures-and-algorithms foundation everyone was trained on is no longer enough on its own, and where the complexity has actually shifted now that AI writes the implementation.
In this video, we cover:
- Why "how does AI affect engineers" is the wrong question, and what to ask instead
- Rehearsing multiple futures: what judgment looks like in a staff engineer
- The case method: building judgment from incident reports and system design history instead of waiting years for it
- Cognitive coordination, code review load, and the surprise ask for more meetings at staff level
- Tiger teams vs scaled teams, trust as architecture, and building evals from a spreadsheet
- Splitting planning from execution so engineers stop falling behind with agents
- Taste vs judgment, and how to build both outside of software
If you've just made staff, or you're about to, this conversation gives you a frame for what the level actually demands now and how to grow into it faster than the old apprenticeship allowed.
Recorded at the AI4 conference 2026.
Timestamps:
00:00:00 - How AI Is Changing Senior Engineering Careers
00:00:41 - Why "How Does AI Affect Engineers" Is the Wrong Question
00:03:26 - What Judgment Actually Is: Rehearsing Multiple Futures
00:05:24 - Why Data Structures and Algorithms Are No Longer Enough
00:07:22 - Learning Judgment From Incident Reports Like the 2017 S3 Outage
00:11:13 - The New Staff Engineer's Core Challenge: Cognitive Coordination
00:14:48 - What Managers, Universities, and Shakespeare Each Owe You
00:17:55 - Code Review Load, Meeting Notes, and the Surprise Ask for More Meetings
00:23:59 - Trust as Architecture: Why Evals Started as a Spreadsheet
00:27:09 - Tiger Teams vs Big Teams: Product Managers Reviewing Code
00:32:39 - Why Some Engineers Can't Keep Up With Agents
00:35:46 - Local AI Champions and Splitting Planning From Execution
00:38:38 - Go Deep or Go Broad? Search in a World of Agents
00:44:12 - Taste vs Judgment: Thinking in 50 Layers
Guest: Mallika Rao, engineering leader in big tech.
Rehearshing the Future framework
If by Rudyard Kipling
Amazon AI Lead: What Differentiates The Best AI Coding Models
How does Amazon build its agentic AI? Michael Giannangeli, Head of Product for Amazon Nova and Agentic AI, breaks down evals, RL gyms, and model routing. He also explains why the bottleneck in software has …
Shownotes
How does Amazon build its agentic AI? Michael Giannangeli, Head of Product for Amazon Nova and Agentic AI, breaks down evals, RL gyms, and model routing.
He also explains why the bottleneck in software has shifted away from engineering hours and what takes its place.
In this video, we cover:
- The eval lifecycle: building from real failure modes, saturation, and why 100% means delete
- RL gyms: training models on real environments like migrations, DevOps, and pen testing
- Model routing, cost-per-token trade-offs, and why routing isn't solved
- The agent stack of an Amazon product lead: Claude Code, Codex, and Kiro
- Autonomous migrations, trust, and how much human-in-the-loop survives
For engineers and product people building with AI agents who want to see how a frontier lab actually closes its feedback loops.
Recorded at the AI4 conference 2026.
Timestamps:
00:00:00 - Intro
00:00:36 - The Agents an Amazon Product Lead Uses Daily
00:03:36 - Why Nobody's Heard of Amazon Nova
00:04:55 - Model Costs and the Routing Problem
00:08:10 - Why Building Good Evals Is So Hard
00:10:05 - When Evals Saturate and Get Deleted
00:12:17 - Turning Real Failure Modes Into Hundreds of Evals
00:15:26 - Improving Models Without Training on Customer Data
00:18:26 - If Everyone Uses Agents, You Need Agents
00:20:22 - The Bottleneck Is No Longer Engineering Hours
00:23:20 - Ship Fast to Validate the Right Thing
00:26:44 - Staying at the Frontier Amid Constant Noise
00:29:37 - Spend 10-20% of Your Time Experimenting
00:32:54 - RL Gyms: How Models Learn From Failure
00:37:09 - Will Migrations Become Fully Autonomous?Guest: Michael Giannangeli - Head of Product, Agentic AI & Amazon Nova at Amazon
Wes Bos: How Developers Stand Out When AI Writes the Code
AI is changing what developers build, but code alone is no longer enough to prove what you can do. Wes Bos explains why engineers need to solve problems beyond syntax, how agent workflows are reshaping software …
Shownotes
AI is changing what developers build, but code alone is no longer enough to prove what you can do. Wes Bos explains why engineers need to solve problems beyond syntax, how agent workflows are reshaping software development, and what still requires human thinking.
In this conversation:
- The limits of generative UI and AI-generated design
- Agent loops, harnesses, and cheaper AI models
- The rising cost of AI coding and the case for local hardware
- Why developer education is shifting from syntax to problem-solving
- Personal branding, conferences, newsletters, and AI-generated content
For developers navigating AI-assisted coding, this episode explores the skills and signals that still help you stand out.
This podcast was recorded at JSNation, the key web dev conference.
OUTLINE
00:00:00 - Code Is Not Enough for Developers
00:00:32 - Why Generative UI Still Feels Unfinished
00:04:35 - How Agent Loops Improve AI Coding
00:07:06 - When Agent Workflows Become Standard Tools
00:08:19 - Are Cheaper AI Models Good Enough?
00:10:44 - Can AI Coding Costs Stay Sustainable?
00:12:24 - What Engineers Need To Learn Now
00:14:23 - Why Fundamentals Matter Beyond Syntax
00:15:34 - How Non-Coders Are Building Production Tools
00:16:21 - Why In-Person Conferences Still Matter
00:18:11 - Personal Branding When Code Isn't Enough
00:20:37 - Can Newsletters Beat The Attention Crisis?
00:22:02 - Why AI-Generated Content Feels Insulting
00:24:12 - Use AI To Scaffold, Not ThinkCareer Advice Every Software Engineer Needs Right Now
Answering engineer questions on AI pressure, career growth, product thinking and impact. Including the production incident I'm glad happened, and the mindset I refuse to accept when things break.In this video …
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Answering engineer questions on AI pressure, career growth, product thinking and impact. Including the production incident I'm glad happened, and the mindset I refuse to accept when things break.
In this video, we cover:
- Whether managers are really demanding more output because of AI
- Balancing fundamentals with AI coding tools and agents early in your career
- Specialist vs generalist and when to lean into each
- Visibility, personal branding and who gets credit for your work
- Product thinking, evaluating impact and what I got wrong about content being king
For software engineers at any level who want honest answers on career strategy in the agent era, from someone doing both engineering and product.
Timestamps:
00:00:00 - How to Spot the Next Big Thing
00:03:15 - The Saying I Hate Most
00:04:27 - The Production Mistake I'm Glad I Made
00:07:32 - Are Managers Demanding More Because of AI?
00:13:39 - Learning Fundamentals vs AI Coding Tools
00:19:00 - Will AI Ever Get Good at Distributed Systems?
00:20:51 - Specialist vs Generalist: When to Lean In
00:26:35 - How to Become More Visible in Your Org
00:31:49 - I Was Wrong: Content Isn't King
00:35:03 - Workflows, Priorities and Hiring an Editor
00:37:08 - What Being a Force Multiplier Really Means
00:41:26 - How to Evaluate What's Worth Building
00:45:01 - Product Thinking Without Years of Experience
00:48:13 - Energy Management, Curiosity and Defining Success
00:54:21 - Hair TalkDX Expert: What The Best Engineers Solve After The Code Review Bottleneck
How do you prove AI is shipping more features? Amos Haviv leads the Developer Workflow teams at Booking.com, supporting 4000 engineers operating 8000 repos.Everybody is burning through their AI budget right now …
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How do you prove AI is shipping more features? Amos Haviv leads the Developer Workflow teams at Booking.com, supporting 4000 engineers operating 8000 repos.
Everybody is burning through their AI budget right now and almost nobody can answer what it bought them. Amos can, because his team spent four years building an event system to debug their own SDLC before AI upped the urgency.
In this video, we cover:
- Why verification is the bottleneck right now, and where it moves next
- Building an event store that separates KTLO from real feature delivery
- Why static dashboards create the metric they measure, and the cobra story behind it
- Agent cost, model routing, and why Booking ignores token maxing entirely
- Running a developer survey with a 92% response rate across 3k+ engineers
- Who should own skills and MCPs: a central platform team or the domain experts?
For platform engineers, engineering leaders, and anyone being asked to prove ROI on AI tooling this quarter.
Timestamps:
00:00:00 - Everyone is burning through their budget
00:00:32 - Verification Is the Bottleneck Every Team Hit
00:03:35 - 4,000 Engineers and 8,000 Repos at Booking.com
00:06:48 - Why Copying Google and OpenAI Will Break You
00:09:21 - Verification Is a Stack of Agents, Not One Review
00:13:27 - Cost Is Becoming a Bottleneck of Its Own
00:17:14 - Was the Internet a Bubble? What That Teaches Us
00:25:32 - What Working With the Frontier Labs Looks Like
00:28:26 - Debugging the SDLC With Four Years of Event Data
00:30:24 - Do Engineers Using AI Actually Ship More Features?
00:37:13 - Where to Start If You Measure Nothing Today
00:45:01 - The Cobra Effect: When a Metric Becomes a Target
00:52:23 - Everyone Is a Builder Now, and Everything Needs Support
01:01:21 - Is AI Turning Every Engineer Into a Manager?
01:03:46 - The Developer Survey With a 92% Response Rate
01:10:09 - Who Owns Skills, MCPs, and the Enterprise Harness
01:17:46 - Great Developer Experience Is High VelocityMentioned in the episode:
High Output Management by Andy Grove
The Sovereign Individual (1997)
The story of General MagicViews expressed are Amos's own and do not represent Booking.com.
AWS Veteran: The New Software Development Life Cycle
"I need to stop using Opus. This doesn't work." That was Heitor Lessa's conclusion after a refactor cost him 200 million tokens, and it forced him to rebuild the entire agent workflow now available for 1400 …
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"I need to stop using Opus. This doesn't work." That was Heitor Lessa's conclusion after a refactor cost him 200 million tokens, and it forced him to rebuild the entire agent workflow now available for 1400 engineers. Heitor spent 11 years at AWS, built Lambda Powertools to 230 billion API calls a week, and in this episode he walks through the full SDLC workflow on screen, from discovery to merge check.
In this episode, we cover:
- The product loop: discovery, whiteboarding, and the /roadmap command
- Spec-driven development with Open Spec and why vanilla setups fail
- Three model tiers: SOTA for planning, mid-tier for implementation, cheap models for reviews
- Merge checks with adversarial reviewers and attestations that catch agents fabricating test results
- The /retro command: using the Socratic method to make your workflow more deterministic
If you're an engineer figuring out how to work with agents at team scale without losing trust in your codebase, this is the workflow to steal. This is also the first Beyond Coding episode with visuals on screen, so let me know what you think of the format.
Timestamps:
00:00:00 - The Math Doesn't Add Up
00:00:43 - Amazon Hypergrowth: 11 Years, 8 Different Roles
00:03:29 - Learning From the Trenches as a Technical Account Manager
00:08:38 - Developer Identity and the Birth of Lambda Powertools
00:10:20 - The Hard Parts of Working in Public
00:13:12 - How Powertools Hit 230 Billion API Calls a Week
00:16:42 - Career Advice: Learn Adjacent Roles, Not More Tech
00:19:37 - When Leadership Decisions Don't Make Sense to You
00:23:21 - The Product Loop Starts With Discovery
00:25:22 - From Whiteboard to /roadmap
00:27:37 - Why Humans Plan First and Agents Come Second
00:30:33 - Commands vs Skills Across 32 Different Models
00:33:38 - Adversarial Reviewers on Every Plan
00:36:07 - The Socratic Method, Explained
00:40:29 - Why He Only Takes Paper Notes
00:44:43 - The Five-Line Paper Trick for High-Stakes Meetings
00:48:18 - /new-work: Capturing Scope Creep Without Derailing
00:54:03 - The Dev Loop Begins: Open Spec Explore
00:56:34 - Three Model Tiers: SOTA, Mid, Cheap
00:57:43 - The $5,000/Month Per Engineer Question
00:58:57 - Guardrails vs Autonomy for 1,400 Engineers
01:04:22 - Auto-Sizer: Does This Task Even Need a Spec?
01:07:26 - Decision Fatigue and Why Frameworks Win
01:09:10 - The Plan Phase: Specs, Design, Formal Verification
01:13:07 - The Refactor That Cost 200 Million Tokens
01:15:11 - When Agents Forge Evidence They Ran Your Tests
01:17:27 - Local-First Architecture Explained
01:23:04 - The Apply Phase: Fully Autonomous Loops
01:24:30 - Coding Was Never the Bottleneck
01:26:39 - Why This Workflow Is an Investment
01:27:39 - Decision Logs and the /onboarding Command
01:29:06 - Running Agents Locally With Enterprise Governance
01:32:42 - Hooks: Making Quality Gates Deterministic
01:36:02 - Merge Checks: 15 Adversarial Reviewers Per Change
01:38:30 - /retro: Interviewing Yourself to Improve the Loop
01:43:12 - Trust, Loss of Trust, and Recovery With Agents
01:48:02 - Experience, Scars, and Critical Thinking
01:49:32 - Why Right Now Is the Time to Experiment
01:52:04 - Conviction Comes From Being in the LoopVercel VP: What Senior Engineers do Differently
What senior engineers do differently has less to do with output than most career ladders suggest, and Lindsey Simon, VP of Engineering at Vercel, has watched the distinction sharpen as everyone in the valley …
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What senior engineers do differently has less to do with output than most career ladders suggest, and Lindsey Simon, VP of Engineering at Vercel, has watched the distinction sharpen as everyone in the valley becomes a "member of technical staff." From why new grads with hackathon years might out-prepare engineers with six years on the job, to what happens when PR throughput stops being your lever, this is a conversation about what earns seniority now.
In this episode, we cover:
- Why engineering roles are consolidating into "member of technical staff"
- How to ask agents first and frame better questions to humans
- The scope-of-impact ladder and what the best engineers systematize
- Learning how to learn: closing gaps to 100% understanding
- Why writing is the skill that scales
If you're wondering whether your years of experience still compound, or you're early-career and tired of the "woe is the juniors" narrative, this one reframes both.
This podcast was recorded at TechLead Conference, a conference for engineering leaders on adopting AI.
TIMESTAMPS
00:00:00 - Impact the Business
00:00:31 - FOMO all the time: The 2006 Google Interview
00:01:52 - Engineering Roles Are Consolidating
00:02:52 - The "Member of Technical Staff" trend in SF
00:03:33 - Interns Demo to the CTO
00:04:33 - How New Grads Out-Prepare Senior Engineers
00:06:10 - Ask Your Agent Before You Ask a Human
00:08:01 - Digging Backwards Into Fundamental Understanding
00:09:22 - "We're All Junior Engineers Again"
00:10:22 - Management Is Not Leadership
00:12:04 - Losing PR Throughput as Your #1 Lever
00:13:11 - Fulfillment Beyond Shipping Features
00:14:32 - Building for Fickle Engineers: Telemetry Beats Opinions
00:16:11 - Watching Users Struggle With Your Product
00:18:12 - Have Expectations for Seniors Actually Changed?
00:19:57 - Claude Says a Month, It Takes Two Hours
00:20:33 - What the Best Engineers Do Differently
00:21:30 - How Vercel React Skill Came to Be
00:22:23 - Why Conference Conversations Hit Different
00:23:35 - Learning How to Learn: Close Gaps to 100%
00:25:41 - The Case for Liberal Arts in Tech
00:27:03 - Get Feedback Early, Don't Hide in the CaveGuest - Lindsey Simon, VP of Engineering at Vercel:
Cracked Solo Dev: Why the Fastest Engineers Are Falling Behind
The fastest engineers are falling behind, and Kitze was one of them. He built his reputation on raw coding speed, then realized his coding wasn't competing with anyone's coding anymore, it was competing with …
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The fastest engineers are falling behind, and Kitze was one of them. He built his reputation on raw coding speed, then realized his coding wasn't competing with anyone's coding anymore, it was competing with their setups. Wake-up call for developers: Kitze now runs 140 projects solo with agent loops, and in this episode he breaks down what separates the engineers pulling ahead from the ones getting left behind.
In this episode, we cover:
- Vibe coding vs vibe engineering, and how to get better results from your agents
- Police files: Self-correcting loops that end every agent turn with zero errors
- Why teams of 10 are collapsing into teams of 2, and who survives
- The rude awakening coming for engineers who refuse to adapt
- The number one advice to stay on track and fight FOMO
For individual contributors, tech leads, and principal engineers who don't plan on falling behind
This podcast was recorded at React Summit, the biggest React conference worldwide.
TIMESTAMPS:
00:00:00 - Intro
00:00:40 - Vibe Coding vs Vibe Engineering: The Real Difference
00:02:16 - Police Files: The Self-Correcting Loop on Every Turn
00:05:23 - Capture Every Frustration as a Rule
00:06:53 - Why Being the Fastest Coder Stopped Mattering
00:09:45 - Problem Solver vs Problem Lover: Pick One
00:10:43 - The Rude Awakening Engineers Don't Want
00:12:05 - Why Teams of 10 Become Teams of 2
00:13:09 - Loop Engineering: The Edge Anyone Can Build
00:16:08 - Why No Agent Orchestrator Works Yet
00:17:07 - Starting a Fresh Codebase: What Kitze Transfers
00:19:14 - No Sidebars: Inventing an Agentic OS
00:21:07 - How Kitze Shipped 300 Changes Across 200 Repos
00:23:40 - We Are Becoming the Bottleneck
00:24:25 - Why Leadership Must Give Engineers Room to Experiment
00:26:16 - The Token Divide: Not Everyone Can Compete
00:27:41 - Learn Now or Lose Access Later
00:29:33 - The Culling: Coasting Is Going Away
00:30:45 - Why LLM Code Reviews Beat Tired Seniors
00:33:21 - Solo Engineers With Agent Swarms vs Teams
00:34:53 - Agents Climbing the Org Chart to CEO
00:36:03 - What Distinguishes the Best Engineers: Unblocking
00:36:50 - Ego Is the Real Bottleneck
00:37:55 - Kitze's #1 Advice: Stick to One ModelAI Cloud CTO: Why These Engineering Skills Get You Hired No Matter What
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 …
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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 out
- Why an AI cloud CTO restricts Claude Code inside his own company
- Dan's rule for merging any AI-written code into production
- Why working with an agent is like managing a junior engineer
- The interview question that surfaces top tier engineer qualities
- Why he still runs algorithm interviews today
If 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 EngineersTech Career Expert: Why Applying to Jobs No Longer Works
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 …
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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 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 weeksGuest: Keki Mwaba, career and recruitment expert:
https://www.linkedin.com/in/keki-mwaba#techjobs #softwareengineering #careeradvice
AI Frontrunners: Why Coding is Solved But Engineering is Not
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 …
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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
AI Architect: Why The Best Software Engineers Are Solving Code Review Bottlenecks Now
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 …
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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 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.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 WeekGoogle AI Lead: The New Rules of Software Engineering
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 …
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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 WeekAddy Osmani: Top Tier Software Engineers vs. AI Agents. The Mindset You Need
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 …
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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
What World Class Software Engineers Do That You Don't
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 …
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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 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
What Separates Cracked Software Engineers From Everyone Else
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 …
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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 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?
DevOps Expert: How Elite Software Engineers Are Using Agents to Get Sh*t Done
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 …
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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 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/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
OSS Expert: Why World Class Engineers Get Jobs on Easy Mode
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 …
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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 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/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
Veteran Architect: How To Design And Build Systems That Survive
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 …
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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 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
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