Beyond Coding

Patrick Akil

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

  1. 1 dzień temu

    Niantic Spatial CTO: How The Best Engineers Solve Problems Most Give Up On

    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 B******t: 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

    Niantic Spatial CTO: How The Best Engineers Solve Problems Most Give Up On

  2. 26 sie

    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 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 insteadRehearsing multiple futures: what judgment looks like in a staff engineerThe case method: building judgment from incident reports and system design history instead of waiting years for itCognitive coordination, code review load, and the surprise ask for more meetings at staff levelTiger teams vs scaled teams, trust as architecture, and building evals from a spreadsheetSplitting planning from execution so engineers stop falling behind with agentsTaste vs judgment, and how to build both outside of softwareIf 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 New Staff Engineers Build Judgment Without Years of Experience

  3. 19 sie

    How Amazon Turns Real Failures Into Better AI 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 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 deleteRL gyms: training models on real environments like migrations, DevOps, and pen testingModel routing, cost-per-token trade-offs, and why routing isn't solvedThe agent stack of an Amazon product lead: Claude Code, Codex, and KiroAutonomous migrations, trust, and how much human-in-the-loop survivesFor 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 - Intro00:00:36 - The Agents an Amazon Product Lead Uses Daily00:03:36 - Why Nobody's Heard of Amazon Nova00:04:55 - Model Costs and the Routing Problem00:08:10 - Why Building Good Evals Is So Hard00:10:05 - When Evals Saturate and Get Deleted00:12:17 - Turning Real Failure Modes Into Hundreds of Evals00:15:26 - Improving Models Without Training on Customer Data00:18:26 - If Everyone Uses Agents, You Need Agents00:20:22 - The Bottleneck Is No Longer Engineering Hours00:23:20 - Ship Fast to Validate the Right Thing00:26:44 - Staying at the Frontier Amid Constant Noise00:29:37 - Spend 10-20% of Your Time Experimenting00:32:54 - RL Gyms: How Models Learn From Failure00:37:09 - Will Migrations Become Fully Autonomous? Guest: Michael Giannangeli - Head of Product, Agentic AI & Amazon Nova at Amazon #AmazonNova #AgenticAI #AIEngineering

    How Amazon Turns Real Failures Into Better AI Models

  4. 12 sie

    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 development, and what still requires human thinking. In this conversation: The limits of generative UI and AI-generated designAgent loops, harnesses, and cheaper AI modelsThe rising cost of AI coding and the case for local hardwareWhy developer education is shifting from syntax to problem-solvingPersonal branding, conferences, newsletters, and AI-generated contentFor 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

    Wes Bos: How Developers Stand Out When AI Writes the Code

  5. 5 sie

    Career 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, 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

    Career Advice Every Software Engineer Needs Right Now

  6. 29 lip

    DX 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 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 nextBuilding an event store that separates KTLO from real feature deliveryWhy static dashboards create the metric they measure, and the cobra story behind itAgent cost, model routing, and why Booking ignores token maxing entirelyRunning a developer survey with a 92% response rate across 3k+ engineersWho 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

    DX Expert: What The Best Engineers Solve After The Code Review Bottleneck

  7. 22 lip

    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 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 commandSpec-driven development with Open Spec and why vanilla setups failThree model tiers: SOTA for planning, mid-tier for implementation, cheap models for reviewsMerge checks with adversarial reviewers and attestations that catch agents fabricating test resultsThe /retro command: using the Socratic method to make your workflow more deterministicIf 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 Up00:00:43 - Amazon Hypergrowth: 11 Years, 8 Different Roles00:03:29 - Learning From the Trenches as a Technical Account Manager00:08:38 - Developer Identity and the Birth of Lambda Powertools00:10:20 - The Hard Parts of Working in Public00:13:12 - How Powertools Hit 230 Billion API Calls a Week00:16:42 - Career Advice: Learn Adjacent Roles, Not More Tech00:19:37 - When Leadership Decisions Don't Make Sense to You00:23:21 - The Product Loop Starts With Discovery00:25:22 - From Whiteboard to /roadmap00:27:37 - Why Humans Plan First and Agents Come Second00:30:33 - Commands vs Skills Across 32 Different Models00:33:38 - Adversarial Reviewers on Every Plan00:36:07 - The Socratic Method, Explained00:40:29 - Why He Only Takes Paper Notes00:44:43 - The Five-Line Paper Trick for High-Stakes Meetings00:48:18 - /new-work: Capturing Scope Creep Without Derailing00:54:03 - The Dev Loop Begins: Open Spec Explore00:56:34 - Three Model Tiers: SOTA, Mid, Cheap00:57:43 - The $5,000/Month Per Engineer Question00:58:57 - Guardrails vs Autonomy for 1,400 Engineers01:04:22 - Auto-Sizer: Does This Task Even Need a Spec?01:07:26 - Decision Fatigue and Why Frameworks Win01:09:10 - The Plan Phase: Specs, Design, Formal Verification01:13:07 - The Refactor That Cost 200 Million Tokens01:15:11 - When Agents Forge Evidence They Ran Your Tests01:17:27 - Local-First Architecture Explained01:23:04 - The Apply Phase: Fully Autonomous Loops01:24:30 - Coding Was Never the Bottleneck01:26:39 - Why This Workflow Is an Investment01:27:39 - Decision Logs and the /onboarding Command01:29:06 - Running Agents Locally With Enterprise Governance01:32:42 - Hooks: Making Quality Gates Deterministic01:36:02 - Merge Checks: 15 Adversarial Reviewers Per Change01:38:30 - /retro: Interviewing Yourself to Improve the Loop01:43:12 - Trust, Loss of Trust, and Recovery With Agents01:48:02 - Experience, Scars, and Critical Thinking01:49:32 - Why Right Now Is the Time to Experiment01:52:04 - Conviction Comes From Being in the Loop #softwareengineering #aiagents #aws

    AWS Veteran: The New Software Development Life Cycle

  8. 15 lip

    Vercel 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 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 humansThe scope-of-impact ladder and what the best engineers systematizeLearning how to learn: closing gaps to 100% understandingWhy writing is the skill that scalesIf 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. TIMESTAMPS00:00:00 - Impact the Business00:00:31 - FOMO all the time: The 2006 Google Interview00:01:52 - Engineering Roles Are Consolidating00:02:52 - The "Member of Technical Staff" trend in SF00:03:33 - Interns Demo to the CTO00:04:33 - How New Grads Out-Prepare Senior Engineers00:06:10 - Ask Your Agent Before You Ask a Human00:08:01 - Digging Backwards Into Fundamental Understanding00:09:22 - "We're All Junior Engineers Again"00:10:22 - Management Is Not Leadership00:12:04 - Losing PR Throughput as Your #1 Lever00:13:11 - Fulfillment Beyond Shipping Features00:14:32 - Building for Fickle Engineers: Telemetry Beats Opinions00:16:11 - Watching Users Struggle With Your Product00:18:12 - Have Expectations for Seniors Actually Changed?00:19:57 - Claude Says a Month, It Takes Two Hours00:20:33 - What the Best Engineers Do Differently00:21:30 - How Vercel React Skill Came to Be00:22:23 - Why Conference Conversations Hit Different00:23:35 - Learning How to Learn: Close Gaps to 100%00:25:41 - The Case for Liberal Arts in Tech00:27:03 - 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

    Vercel VP: What Senior Engineers do Differently
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O programie

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

Informacje

  • Twórca
    Patrick Akil
  • Lata aktywności
    2021 - 2026
  • Odcinki
    266
  • Klasyfikacja
    Dla dorosłych
  • Prawa autorskie
    © Patrick Akil
  • Witryna programu
    Beyond Coding

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