Beyond Coding

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 Engineers

Guest: 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 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

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

#AmazonNova #AgenticAI #AIEngineering

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 Think

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 Talk

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 Velocity

Mentioned in the episode:
High Output Management by Andy Grove
The Sovereign Individual (1997)
The story of General Magic

Views expressed are Amos's own and do not represent Booking.com.

#AI #SoftwareEngineering #DeveloperExperience

"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 Loop

#softwareengineering #aiagents #aws

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 Cave

Guest - Lindsey Simon, VP of Engineering at Vercel:

https://www.linkedin.com/in/lindseysimon

#softwareengineering #ai #careergrowth

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 Model

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 Engineers

#AIEngineering #TechCareers #SoftwareEngineering