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 engineeringThe state a coding agent should be in before it runs for hours, and the gates its output has to passObservability with open-source MLflow when you can't read every traceA shared ontology and a three-layer memory stack: a Karpathy-style Markdown wiki in Obsidian, graph memory and a local vector database in DockerDisposable vs durable software, and why software built by intent threatens traditional SaaSWhy AI makes attacks cheaper than defenseThis 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 - Intro00:00:49 - Why Newer Models Keep Eating the Harness00:02:26 - Will SaaS Survive the Next Ten Years?00:03:39 - From Sociology to Cocktail Bartender to Restaurant Owner00:07:19 - Why Human Preference Can't Be Reduced to Data00:10:10 - What I Learned From a Failed Restaurant Chatbot00:14:16 - Learning Faster With AI and Who Gets Left Behind00:18:38 - What State Should Your Coding Agent Be In?00:22:09 - Building a Shared Understanding Before Kicking Off a Loop00:26:58 - Why Nobody Really Reads AI Code Line by Line00:31:34 - The Real Reason Engineers Push Back on AI00:33:48 - Sloppy, Disposable or Durable: How to Judge Code00:35:18 - A Three-Layer Memory System for Coding Agents00:39:44 - Why Domain Experts Matter When Execution Gets Cheap00:42:23 - How AI Makes Attacks Cheaper Than DefenseGuest: Fausto Albers, AI consultant at Wonder Whyhttps://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 codeHow to stand out in interviews using the "Pumpkin" CV strategy, high-signal personal achievements, and human networkingWhy non-deterministic coding agents ignore skills.md files and why 88% of breaches come down to credentialsHow engineering leaders can implement deterministic runtime guardrails, workload identity, and scoped credentials at scaleWhether 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.comGuest - Anna Mcdougall:https://www.linkedin.com/in/annajmcdougallTimestamps:00:00:00 - From 200 Days to 18 Hours: Why Time Is Up00:00:47 - What a Field CTO Actually Does Every Day00:03:53 - Why 20% of Engineers Are Grieving Hands-On Coding00:06:55 - Why Hand-Crafted Code Is Becoming Like Carpentry00:08:26 - The Most Future-Proof Engineering Skill Nobody Wants00:10:53 - How to Network by Becoming the Connective Tissue00:13:55 - How to Stand Out When AI Writes Every Resume00:18:31 - The "Pumpkin" Strategy to Be Remembered in Interviews00:22:25 - Why Brilliant Engineers Avoid This Career Path00:26:42 - Why Skills Files Are Not Enough for Coding Agents00:31:10 - Building Deterministic Guardrails Without Slowing Teams00:36:32 - 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 MetaHow top engineers treat on-call firefighting as a system-wide ownership opportunityWhy rotating between Engineering Manager (EM) and Staff IC sharpens your business and technical influenceTransitioning from Developer Experience (DX) to Agent Experience (AX) using MCP, Claude, and markdown contextThis 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 Skills00:00:44 - 2 Behaviors That Separate Mediocre and Staff Engineers00:02:16 - How the Best Engineers Build an Ownership Mindset00:04:26 - What Staff Engineers Do Differently During On-Call00:06:26 - Senior vs. Staff Engineer: Expanding Your Sphere of Influence00:09:56 - Why Switching Between Manager and Staff IC Builds Better Engineers00:14:04 - Why Unblocking Engineers Now Means Unblocking Agents00:16:41 - Managing Multi-Agent Handoffs Without Losing Control00:19:02 - Shifting from Developer Experience to Agent Experience00:21:47 - Why the Empty Engineering Backlog Is Still a Myth00:25:03 - How to Build Custom Agent Workflows and Reusable Skills00:28:31 - The #1 New Skill Software Engineers Must Build Today00:31:10 - How Meta Engineering Culture Compares to StartupsGuest - 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 harnessesWhy AI skills files and “make no mistakes” prompts are only requests, and what replaces them in production pipelinesHow 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 SaaSFor 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 Request00:00:38 - How Open Source Terraform Changed Provisioning00:01:59 - Why API-First Services Matter Beyond AWS and Azure00:04:19 - How AI Agents Changed Terraform Provider Updates00:07:03 - Why “Make No Mistakes” Won’t Stop Your Agent00:10:31 - Bringing Determinism Back to Agentic Workflows00:13:07 - How AI Pull Requests Are Overwhelming Open Source00:15:41 - Why Every Engineer Is Building a Software Factory00:18:52 - Why AI-Generated Documentation Slows Down Humans00:23:12 - Using Agents to Filter Noise and Summarize Specs00:27:02 - Escape Hatches for Automated Terraform Applies00:29:03 - Where the Joy in Engineering Lives Now00:31:24 - Spec-Driven Development and the Return of Architecture00:34:51 - Open-Source Rust Clones, Reverse Engineering, and SaaS00:38:19 - How to Build Software Products People Actually WantGuest: 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 executionHow to manually scaffold code, types, and folder boundaries before letting sub-agents runDesigning a distributed Markdown and indexed-search graph harness for progressive context disclosureUsing Git worktrees to run parallel coding agents and merge the best implementationsWhy dynamic memory and the heap need to disappear in favor of deterministic programming languagesHow AI bubbles and solo coding impact junior engineers and team collaborationThis 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-4016b8225Timestamps:00:00:00 - Why Carlos Throws Away Working AI Code00:00:53 - Why Pull Requests and Team Collaboration Are Dying00:03:50 - The Trap of Starting Projects You Never Finish00:06:06 - Escaping the Solo AI Developer Bubble00:11:54 - How to Scaffold Code Before Using AI Agents00:15:54 - Learning New Programming Languages With AI Agents00:21:14 - Why We Must Get Rid of the Heap00:28:32 - Building a Shared Language Across Engineering Teams00:31:31 - Vibe Coding vs Deep Software Engineering00:40:12 - Code Is Cheap: Git Worktrees for Parallel Agents00:41:45 - How Deep to Scaffold Before the Agent Runs00:45:09 - Democratizing Software, Missing Juniors, and the AI Bubble00:53:33 - Designing a Graph Harness for AI Agent Context00:58:18 - Why His Main Agent Never Touches Files01:03:07 - Throw Away Code You Don't Understand01:06:25 - Rethinking Code Reviews and Mutation Testing for AI#CodingAgents #SoftwareEngineering #AgenticEngineering