Beyond Coding

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.
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