Beyond Coding

Afleveringen

1. Meta Staff Engineer: What the Best Engineers Do Differently

   28 oktober 2026 · 33 min

   Afspelen
   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. Lees meer

   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 Startups

   Guest - Sajal Rustagi, Staff Engineer at Meta: https://www.linkedin.com/in/sajalrustagi

   #StaffEngineer #SoftwareEngineering #EngineeringL…

2. How Senior Engineers Use Coding Agents On Prod Infrastructure

   21 oktober 2026 · 42 min

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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. Lees meer

   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 A…

3. Lead Architect Reveals His Rules for Coding Agents

   14 oktober 2026 · 1 uur 15 min

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   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. Lees meer

   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 …

4. What the Best Engineers Built for Their Coding Agents

   7 oktober 2026 · 1 uur 15 min

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   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. Lees meer

   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 #s…

5. Why Coding Agents Keep Making Your Codebase Worse

   30 september 2026 · 45 min

   Afspelen
   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. Lees meer

   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

6. How Amp Ships 50 Times a Day With AI Agents

   23 september 2026 · 43 min

   Afspelen
   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. Lees meer

   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

7. How Top Engineers Still Get Hired When Most Get Ghosted

   16 september 2026 · 34 min

   Afspelen
   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. Lees meer

   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

   #softwaree…

8. Why an Ex-Googler Bet Everything on an Open-Source Database

   9 september 2026 · 40 min

   Afspelen
   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 Lees meer

   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 #SoftwareEngi…

9. How the Best Engineers Build a World Model (Google Earth Creator & Niantic Spatial CTO)

   2 september 2026 · 37 min

   Afspelen
   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. Lees meer

   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 McClendo…

10. How New Staff Engineers Build Judgment Without Years of Experience

    26 augustus 2026 · 50 min

    Afspelen
    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. Lees meer

    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…

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