Beyond Coding

What do coding agents need from engineers to be trusted? AI consultant Fausto Albers builds production software with agents and doesn't believe anyone still reads the code line by line. Here is what he relies on instead.

We cover:

• Why newer models are "eating the harness", and the shift from context engineering to loop engineering
• The state a coding agent should be in before it runs for hours, and the gates its output has to pass
• Observability with open-source MLflow when you can't read every trace
• A shared ontology and a three-layer memory stack: a Karpathy-style Markdown wiki in Obsidian, graph memory and a local vector database in Docker
• Disposable vs durable software, and why software built by intent threatens traditional SaaS
• Why AI makes attacks cheaper than defense

This episode is for software engineers and tech leads who want to give coding agents real autonomy without giving up responsibility for what gets built.

Timestamps:
00:00:00 - Intro
00:00:49 - Why Newer Models Keep Eating the Harness
00:02:26 - Will SaaS Survive the Next Ten Years?
00:03:39 - From Sociology to Cocktail Bartender to Restaurant Owner
00:07:19 - Why Human Preference Can't Be Reduced to Data
00:10:10 - What I Learned From a Failed Restaurant Chatbot
00:14:16 - Learning Faster With AI and Who Gets Left Behind
00:18:38 - What State Should Your Coding Agent Be In?
00:22:09 - Building a Shared Understanding Before Kicking Off a Loop
00:26:58 - Why Nobody Really Reads AI Code Line by Line
00:31:34 - The Real Reason Engineers Push Back on AI
00:33:48 - Sloppy, Disposable or Durable: How to Judge Code
00:35:18 - A Three-Layer Memory System for Coding Agents
00:39:44 - Why Domain Experts Matter When Execution Gets Cheap
00:42:23 - How AI Makes Attacks Cheaper Than Defense

Guest: Fausto Albers, AI consultant at Wonder Why
https://www.linkedin.com/in/stepintoliquid

#codingagents #softwareengineering #aiagents

What is the most future-proof engineering skill to learn now with AI and Coding agents? Time is up for traditional development cycles as Agents shrink vulnerability exploit windows from 200 days to just 18 hours.

In this video, IBM HashiCorp Field CTO Anna McDougall shares why brilliant engineers have been avoiding the single most resilient career path in tech and how it's time to change that. It's now part of what software engineering is becoming:

• Why 20% of software engineers are grieving hands-on IDE coding (and why manual code is becoming like carpentry)
• The #1 future-proof engineering specialty that protects your career as AI agents write more code
• How to stand out in interviews using the "Pumpkin" CV strategy, high-signal personal achievements, and human networking
• Why non-deterministic coding agents ignore skills.md files and why 88% of breaches come down to credentials
• How engineering leaders can implement deterministic runtime guardrails, workload identity, and scoped credentials at scale

Whether you are looking to future-proof your career or a software engineer navigating agentic workflows, you will walk away with a clear roadmap for what comes next.

This episode was recorded at TechLead Conference. Join for the next edition of the event in Amsterdam on May 20: https://techleadconf.com

Guest - Anna Mcdougall:
https://www.linkedin.com/in/annajmcdougall

Timestamps:
00:00:00 - From 200 Days to 18 Hours: Why Time Is Up
00:00:47 - What a Field CTO Actually Does Every Day
00:03:53 - Why 20% of Engineers Are Grieving Hands-On Coding
00:06:55 - Why Hand-Crafted Code Is Becoming Like Carpentry
00:08:26 - The Most Future-Proof Engineering Skill Nobody Wants
00:10:53 - How to Network by Becoming the Connective Tissue
00:13:55 - How to Stand Out When AI Writes Every Resume
00:18:31 - The "Pumpkin" Strategy to Be Remembered in Interviews
00:22:25 - Why Brilliant Engineers Avoid This Career Path
00:26:42 - Why Skills Files Are Not Enough for Coding Agents
00:31:10 - Building Deterministic Guardrails Without Slowing Teams
00:36:32 - Finding Your People in Engineering Leadership

#SoftwareEngineering #CareerAdvice #AIEngineering

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.

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 #EngineeringLeadership

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

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

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

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

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

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

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