Beyond Coding

Patrick Akil
  • 0.0 (0)
  • 科技
  • 每星期更新

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. 4日前

    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

  2. 8月12日

    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

  3. 8月5日

    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

  4. 7月29日

    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

  5. 7月22日

    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

  6. 7月15日

    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

  7. 7月9日

    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 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 agentsPolice files: Self-correcting loops that end every agent turn with zero errorsWhy teams of 10 are collapsing into teams of 2, and who survivesThe rude awakening coming for engineers who refuse to adaptThe number one advice to stay on track and fight FOMOFor 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 - Intro00:00:40 - Vibe Coding vs Vibe Engineering: The Real Difference00:02:16 - Police Files: The Self-Correcting Loop on Every Turn00:05:23 - Capture Every Frustration as a Rule00:06:53 - Why Being the Fastest Coder Stopped Mattering00:09:45 - Problem Solver vs Problem Lover: Pick One00:10:43 - The Rude Awakening Engineers Don't Want00:12:05 - Why Teams of 10 Become Teams of 200:13:09 - Loop Engineering: The Edge Anyone Can Build00:16:08 - Why No Agent Orchestrator Works Yet00:17:07 - Starting a Fresh Codebase: What Kitze Transfers00:19:14 - No Sidebars: Inventing an Agentic OS00:21:07 - How Kitze Shipped 300 Changes Across 200 Repos00:23:40 - We Are Becoming the Bottleneck00:24:25 - Why Leadership Must Give Engineers Room to Experiment00:26:16 - The Token Divide: Not Everyone Can Compete00:27:41 - Learn Now or Lose Access Later00:29:33 - The Culling: Coasting Is Going Away00:30:45 - Why LLM Code Reviews Beat Tired Seniors00:33:21 - Solo Engineers With Agent Swarms vs Teams00:34:53 - Agents Climbing the Org Chart to CEO00:36:03 - What Distinguishes the Best Engineers: Unblocking00:36:50 - Ego Is the Real Bottleneck00:37:55 - Kitze's #1 Advice: Stick to One Model

    Cracked Solo Dev: Why the Fastest Engineers Are Falling Behind

  8. 7月1日

    AI 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 wants most, and it is not the one you are grinding on. We get into which engineering skills are actually scarce and well paid today, and which ones are quietly on the way out. In this episode we cover: The engineering skills in highest demand right now and which ones are on the way outWhy an AI cloud CTO restricts Claude Code inside his own companyDan's rule for merging any AI-written code into productionWhy working with an agent is like managing a junior engineerThe interview question that surfaces top tier engineer qualitiesWhy he still runs algorithm interviews todayIf you are an engineer trying to work out where the value sits now that agents write the easy code, this is a straight answer from the person building the infrastructure underneath all of it. Timestamps: 00:00:00 - AI Agents doing everything is a lie 00:00:44 - What Nebius Actually Does 00:04:31 - The Engineers In Highest Demand Right Now 00:06:58 - Inside the Hiring Process 00:08:12 - The Bootcamp: You Join the Company, Not a Team 00:10:51 - Why You Can't Use AI in Their Interviews 00:16:31 - Why He Banned the Word "Headcount" 00:22:25 - Why a CTO Is Not a Technical Role 00:24:49 - The One Skill Every Manager Needs 00:25:48 - Why Smart People Fail at This 00:28:17 - "The Promise of Agents Is B******t" 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

    AI Cloud CTO: Why These Engineering Skills Get You Hired No Matter What
查看全部 (264)

關於

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

資料

  • 創作者
    Patrick Akil
  • 活躍年份
    2021年 - 2026年
  • 單集
    264
  • 分級
    兒童不宜
  • 版權
    © Patrick Akil
  • 節目網站
    Beyond Coding

你可能也會喜歡