Adventures in DevOps

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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:
Bespinian
Book: 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

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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 blog
Book: Blah Blah Blah
Using Innovation Tokens
Novelty budget
Buck2
🎯 Picks:
Warren - Why Archers Didn’t Volley Fire
Ian - Band - Gloryhammer

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We explore the past and AI-driven future of Infrastructure as Code with Cloud Posse's Eric 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 framework
Checkov - IaC Validation
Code Rabbit
✨ Episode: Agent Skills
✨ Episode: All about MCPs
🎯 Picks:
Warren - Project Hail Mary
Erik - Everybody's free to wear sunscreen & Book: The 10X Rule

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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

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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 Marketplace
AI Fatigue is real
Episode: Does Productivity even exist?
🎯 Picks:
Warren - Paper: Ironies of Automation (& AI)
Dan - Tool: SkillShare