Beyond Coding

Hosted by Patrick Akil

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Oct 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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October 21, 2026Episode 27342 min

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, 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 Want Guest: Kerim Satirli, Senior Developer Advocate at HashiCorp, an IBM company. https://www.linkedin.com/in/ksatirli #Terraform #CodingAgents #DevOps

October 14, 2026Episode 2721 hr 15 min

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. 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 AI #CodingAgents #SoftwareEngineering #AgenticEngineering

October 7, 2026Episode 2711 hr 14 min

What 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 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/mathewwjones #codingagents #softwareengineering #platformengineering

September 30, 2026Episode 27045 min

Why 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 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 Machine Dex Horthy: https://x.com/dexhorthy Dex's talks and deep dives: https://aithatworks.bold.video #AgenticEngineering #AICoding #SoftwareEngineering

September 23, 2026Episode 23843 min

How 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 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 Ceiling #AIAgents #SoftwareEngineering #PullRequests

September 16, 202633 min

How 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 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 Career #softwareengineering #techjobs #careeradvice

September 9, 2026Episode 26740 min

Why 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 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 duck #MotherDuck #DuckDB #SoftwareEngineering

September 2, 2026Episode 26637 min

How 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 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

August 26, 2026Episode 26549 min

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 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

August 19, 2026Episode 26441 min

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 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