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Join us in listening to the experienced experts discuss cutting edge challenges in the world of DevOps. From applying the mindset at your company, to career growth and leadership challenges within engineering teams, and avoiding the common antipatterns. Every episode you'll meet a new industry veteran guest with their own unique story.

Adventures in DevOps

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We grabbed Donald Nguyen, co-founder and CTO at Corvic, to discuss the absurd complexities of enterprise data and multimodal inference. We explore how organizations habitually hoard mountains of useless, "dead" data just out of the sheer fantascy that someone might ask for it later. We highlight the fundamental disconnect where data collectors using tools like Airbyte and Kafka speak a completely different language than the business consumers analyzing it in Excel.
 

True scale isn't just about managing petabytes; it's the absolute nightmare of extracting subjective business meaning from flat PDFs and invoices. In the deep-end of vector embeddings, we're challenging translating data into a different semantic universe requires imposing a heavy business bias. Auditors and artists will view the exact same invoice completely differently, meaning your embedding model selection is incredibly subjective to the business context.
 

The industry's desperate search for actual AI success stories beyond basic workflow automation is still ongoing as we laugh—and cry—at the reality that companies are likely budgeting 50% of an engineer's salary for LLM token usage, effectively enabling product managers to burn cash on infinite loops to generate prototype code. Reasonable or unreasonable?
 

And lastly, we tackle the existential dread of securing autonomous AI agents. Because fine-grained access control for agent actions is basically an unsolved fantasy, we must treat their execution environments as entirely untrusted, relying on rigid sandboxes like AWS Firecracker VMs. Prompt injection attacks are an inevitable flaw of the transformer architecture, and the industry's best defense mechanism seems to be wrapping models inside of other models to validate the outputs. It is quite literally turtles all the way down, and the winner of enterprise security is simply the organization that manages to put one more turtle ahead of the attackers. 
 
💡 Notable Links: 
Kuuk Thaayorre Aboriginal Tribe - Cardinal Directions✨ Episode: Generating automatic integrations at scale🎯 Picks: 
 • Warren - 
Dr. NEMO: Clockwise circle pit • Donald - 
Book: InvestiGators

Automatic Data Pipelining: One More Turtle Ahead

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Down to business with GitHub's Cassidy Williams, Senior Director of Developer Advocacy at GitHub, where we try to untangle the existential dread of modern software development. It includes the sheer absurdity of managing a platform that officially crossed the one billion commit mark in 2025. Currently absorbing a completely unreasonable 275 million commits per week, GitHub's technical debt is naturally showing its age under the weight of AI agents aggressively creating pull requests. And with company's own copilot advocating for more, we explore the daily reality of being the internet's punching bag during an outage, and how the "Tiny Wins" buy back developer affection by still shipping the critical features.
 

Which of course is a small signal in the sea of the industry's collective identity crisis: vibe coding and the valley of AI-generated garbage. Discussed is one suggested solution of strongly typed languages which are skyrocketing in popularity because we desperately need rigid guardrails to babysit the hallucinated code our non-human agents are frantically pushing to production. Things have gotten so dire that we commiserate on missing the good old days of Stack Overflow, where instead of a chatbot agreeably telling you your terrible idea is great, a grumpy human engineer would just ruthlessly roast your architecture honestly.
 
💡 Notable Links: 
Cassidy's post on Typed LanguageFermat's Last TheoremCassidy's newsletterBook: 4-Hour Work Week✨ Episode: Typed Languages✨ Episode: Vibecoding✨ Episode: Productivity Isn't Real🎯 Picks: 
 • Warren - 
Book: The Light Eaters • Cassidy - 
Obsidian Offline Wiki

The Human Value Versus AI Legacy Code

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Founder of Bespinian and long-time cloud solutions architect, Lena Fuhrimann, sits down with us to clarify the widespread confusion around serverless architecture. We discuss how serverless is often incorrectly equated solely with Function as a Service (FaaS), when it actually represents a broader spectrum on the abstraction ladder—including managed AI inference, container platforms, and databases.
 

Lena shares her early career traps of building a fragmented landscape of sixty "nano-services" and explains why starting with a well-architected monolith and progressively breaking out microservices based on distinct resource or lifecycle requirements is a much saner approach. Then we shift to drivers behind cloud migrations, emphasizing that the primary financial benefit of serverless isn't necessarily shrinking the monthly cloud provider bill, but rather optimizing your most expensive resource: engineering time. By offloading mundane infrastructure patching to the cloud provider, teams can focus entirely on delivering tangible business value to customers. But cost is still there too. 
 

We also explore the psychological challenges of adopting new paradigms, sharing a fascinating story of bridging the gap for a VM-loving engineer by introducing immutable infrastructure concepts through Packer and Ansible before fully transitioning them to containers. And of course we tackle the dreaded topic of "cold starts" and why complex workarounds—like building custom Lambda warmers to periodically call APIs—often defeat the core benefits of reduced total cost of ownership.
 
💡 Notable Links: 
BespinianBook: Drive — Motivation 3.0✨ Episode: Typed Languages, Haskell, and building monoliths🎯 Picks: 
 • Warren - 
Better thank coffee: Himmelstau tea • Lena - 
Home Assistant open source project • and 
Awtrix Clocks

Who needs a server?

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We sit down with Ian Duncan, senior staff engineer on the stability team at Mercury, to discuss the delicate balance of choosing your tech stack and the implications. That means explore the concept of the novelty budget or frequently known as "Choose Boring Technology". It emphasizes why companies should carefully spend their innovation tokens on things that actually move the needle, rather than reinventing the wheel.
 

Mercury leverages simple technology like Postgres and EC2 instances alongside high-innovation bets like Haskell and Nix to maintain stability. The conversation unpacks the hidden complexities of over-relying on standard tools, sharing a cautionary tale about using a Postgres table as a massive queuing system until it consumed all the database resources and caused login failures. To solve architectural scaling without descending into nanoservice madness, we jump to discussing monolithic build systems. By leveraging hermetically sealed, modular build targets, teams can achieve massive parallelism and avoid endless local rebuilds while maintaining a single coherent view of the codebase.
 

We also advocate for separating management tools from primary systems by utilizing dedicated control planes, and touch on the rising popularity of durable execution frameworks like Temporal to handle resilient workflows. And it turns out Ian might be a bigger advocate of microservices that he thought!
 
💡 Notable Links: 
Ian's blogBook: Blah Blah BlahUsing Innovation TokensNovelty budgetBuck2🎯 Picks: 
 • Warren - 
Why Archers Didn’t Volley Fire • Ian - 
Band - Gloryhammer

How to build a monolith the right way

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We explore the past and AI-driven future of Infrastructure as Code with Cloud Posse's Erik Osterman, discussing various IaC traumas. Erik maintains the world's largest repository of open-source IaC modules. Looking back at the dark ages of infrastructure, from the early days of raw CloudFormation and Capistrano to the rise and fall of tools like Puppet and Chef, we discuss the organic, messy growth of cloud environments. Where organizations frequently scale a single AWS account into a tangled web rather than adopting a robust multi-account architecture guided by a proper framework.
 

The conversation then shifts to the modern era of rapid integration of infrastructure development. While generating IaC with large language models can be incredibly fast, it introduces severe risks if left unchecked, and we explore how organizations can protect themselves by relying on Architectural Decision Records (ADRs) and predefined "skills". The hopeful goal of ensuring autonomous deployments are compliant, reproducible, and secure instead of relying on hallucinated architecture.
 

Finally, we tackle the compounding issue of code review in an age where developers can produce a year's worth of engineering slop progress in a single week.
 
💡 Notable Links: 
Atmos frameworkCheckov - IaC ValidationCode Rabbit✨ Episode: Agent Skills✨ Episode: All about MCPs🎯 Picks: 
 • Warren - 
Project Hail Mary • Erik - 
Everybody's free to wear sunscreen • & 
Book: The 10X Rule

Infrastructure as code: why you can never avoid thinking

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We sit down with Jaikumar Ganesh, Head of Engineering at AnyScale, to explore the intricacies of heterogeneous compute. He unpacks the growing CPU/GPU divide, detailing how ML pipelines require precise orchestration — using CPUs for data reading and writing while leveraging expensive, massive-die GPUs for chunking and embedding.
 

Warren brings the insight that, with AI agents rapidly changing how software is created, building is now a requirement of the business-focused team. And our guest shares how sales and marketing departments are increasingly using tools like Cursor and Claude to develop their own workflow automations. We discuss the challenges that this shift begs: what is engineering really doing for us?
 

JK emphasizes that the core responsibility of the engineering organization is reliability. While anyone can generate code, running stable production software requires the deep "battle scars", robust observability, and meticulous release processes that only a dedicated engineering team can provide.
 

That results in needing to find the right talent. But, finding the talent to maintain this critical infrastructure isn't easy, which is why JK advocates for highly creative hiring strategies. He shares incredible success stories of bypassing traditional recruiting by running hiring ads in foreign-language movies at local movie theaters and setting up booths at social food festivals to find uniquely qualified candidates.
 
🎯 Picks: 
 • Warren - 
Archer's Don't Fire Volleys • JK - 
Book: The Explorer's Gene

GPU versus CPU: What is engineering really doing for us

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In this adventure, we sit down with Dan Wahlin, Principal of DevRel for JavaScript, AI, and Cloud at Microsoft, to explore the complexities of modern infrastructure. We examine how cloud platforms like Azure function as "building blocks". Which of course, can quickly become overwhelming without the right instruction manuals. To bridge this gap, one potential solution we discuss is the emerging reliance on AI "skills"—specialized markdown files. They can give coding agents the exact knowledge needed to deploy poorly documented complex open-source projects to container apps without requiring deep infrastructure expertise. 
 

And we are saying the silent part outloud, as we review how handing the keys over to autonomous agents introduces terrifying new attack vectors. It's the security nightmare of prompt injections and the careless execution of unvetted AI skills. Which is a blast from the past, and we reminisce how current downloading of random agent instructions to running untrusted executables from early internet sites. While tools like OpenClaw purport to offer incredible automation, such as allowing agents to scour the internet and execute code without human oversight, it's already led us to disastrous leaks of API keys. We emphasize the critical necessity of validating skills through trusted repositories where even having agents perform security reviews on the code before execution is not enough.
 

Finally, we tackle the philosophical debate around AI productivity and why Dorota's LLMs raise the floor and not the ceiling is so spot on. The standout pick requires mentioning, a fascinating 1983 paper titled "Ironies of Automation" by Lisanne Bainbridge. This paper perfectly predicts our current dilemma: automating systems often leaves the most complex, difficult tasks to human operators, proving that as automation scales, the need for rigorous human monitoring actually increases, destroying the very value that was attempting to be captured by the original innovation.
 
💡 Notable Links: 
Agent Skill MarketplaceAI Fatigue is realEpisode: Does Productivity even exist?🎯 Picks: 
 • Warren - 
Paper: Ironies of Automation (& AI) • Dan - 
Tool: SkillShare

Upskilling your agents

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How much do you really know about the protocol that everything is built upon? This week, we go behind the scenes with Simone Carletti, a 13-year industry veteran and CTO at DNSimple, to explore the hidden complexities of DNS. We attempt to uncover why exactly DNS is often the last place developers check during an outage, drawing fascinating parallels between modern web framework abstractions and network-level opaqueness.
 

Simone shares why his team relies on bare-metal machines instead of cloud providers to run their Erlang-based authoritative name servers, highlighting the critical need to control BGP routing. We trade incredible war stories, from Facebook locking themselves out of their own data centers due to a BGP error, to a massive 2014 DDoS attack that left DNSimple unable to access their own log aggregation service. The conversation also tackles the reality of implementing new standards like SVCB and HTTPS records, and why widespread DNSSEC adoption might require an industry-wide mandate.
 

And of course we have the picks, but I'm not spoiling this weeks, just yet...
 
💡 Notable Links: 
Episode: IPv6SVCB + HTTPS DNS Resource Records RFC 9460Avian Carrier RFC 1149🎯 Picks: 
 • Warren - 
Book: One Second After • Simone - 
Recommended diving locations in Italy • and 
Wreck diving projects

There's no way it's DNS...

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We are joined by Daan Boerlage, CTO at Mavexa as we tackle the long-awaited arrival of IPv6 in cloud infrastructure. Here, we highlight how migrating to an IPv6-native setup eliminates public/private subnet complexity and expensive NAT gateways natively. As well as entirely sidestepping the nightmare of IP collisions during VPC peering.
 

Beyond the financial savings of ditching IPv4 charges, we explore the technical superiority of IPv6. Daan breaks down just how mind-bogglingly large the address space is, and focuses on how it solves serverless IP exhaustion while systematically debunking the pervasive myth that NAT is a security feature. We also discuss how IPv6's end-to-end connectivity, paving the way for next-generation protocols like QUIC, HTTP/3, and WebTransport.
 

The episode rounds out with a cathartic venting session about legacy architecture, detailing a grueling nine-year migration away from a central shared database that ironically culminated in a move to Salesforce. Almost by design, Daan recommends his pick, praising its intuitive use of signals and fine-grained reactivity over React. And Warren's pick explores storing data in the internet itself by leveraging the dwell time of ICMP ping packets.
 
💡 Notable Links: 
FOSDEM talk on the internet of threadsHilbert Map of IPv6 address space🎯 Picks: 
 • Warren - 
Harder Drive: what we didn't want or need • Daan - 
SolidJS

Getting better at networking

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In this episode, we examine how the software industry is fundamentally changing. We're joined by our expert guest, Matt Edmunds, a long-time UX director, principal designer, and Principal UX Consultant at Tiny Pixls. The episode kicks, analyzing how early AI implementation in Applicant Tracking Systems (ATS) created rigid hiring processes that actively filter out the varied candidates who actually bring necessary diversity to engineering teams.
 

Of course we get to the world of "vibe coding", and revisit the poor LLM usage highlighted in the DORA 2025 report, exploring how professionals without traditional software engineering backgrounds are leveraging models to generate functional code.
 

Matt details his hands-on experience using the latest models of Claude Opus and Gemini Pro, successfully building low-level C virtual audio driver in 30 minutes drive by personal needs. We discuss the inherent challenges of large context windows, and coin the term "guess-driven development". To combat these hallucinations, Matt shares his strategy of using question-based prompting and anchoring the AI with comprehensive test files and documented schemas, which the models treat as an undeniable source of truth.
 

Beyond the code, we look at the broader economic and physical limitations of the current AI boom, noting that AI providers are operating at massive financial losses while awaiting hardware efficiency improvements.
 
💡 Notable Links: 
Oatmeal on hating AI ArtEpisode: DORA 2025 Report🎯 Picks: 
 • Warren - 
Book: Start With Why • Matt - 
Book: Creativity, Inc.

Varied Designer Does Vibecoding: Why testing always wins

Will Button

Warren Parad

Episode Summary
On this week's Adventures in DevOps, Chuck talks about the recent Twitter thread about 10x engineers. He goes through each of the points in the tweet and talks about each of them in turn. There are only two points he sort of agrees with, and believes the rest to be absolute garbage. One of the issues with this tweet is that it doesn't define what a 10x engineer is.Defining a 10x engineer is difficult because it is also impossible to measure a truly average engineer because there are many factors that play into measuring productivity. Chuck turns the discussion to what a 10x engineer is to him and how to find one. A 10x engineer is dependent on the organization that they are a part of, because they are not simply found, they are made.When a 10x engineer is added to a team, the productivity of the entire team increases. Employers have to consider firstly what you need in your team and how a person would fit in. You want to avoid changing the entire culture of your organization. Consider also that a 10x engineer may be hired as a 2x engineer, but it is the employer that turns them into a 10x engineer.Overall, Chuck believes these tweets are asinine because it's impossible to measure what makes a 10x engineer in the first place, and hiring a person that fits the attributes in the list would be toxic to your company.Panel • Charles Max Wood

DevOps 010: What Makes a 10x Engineer

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