11 posts · 6 companies · newest first
A follow-on from the smash hit that was “My First 4 Months at Bazaarvoice.” While my first post focused on onboarding and adapting to a global organization, this post shifts focus to developing at scale, navigating major organizational shifts, and my journey to becoming a Senior Engineer. What’s Been Going On? (The High-Level View) To […]
Recorded September 3, 2026. Quotes are lightly edited for clarity. Maybe this sounds familiar. You run infrastructure at a company that isn’t American. Your workloads are on AWS, Azure, or Google Cloud, probably more than one, because that is what everyone picked. Until recently nobody asked you where the data lives or who can reach it. Now you’re getting questions. Legal wants to know what NIS2 means for where your systems run. Someone on the leadership team read that the US government locked the cloud accounts of judges at the International Criminal Court and wants to know if that could…
There is no single best Kubernetes infrastructure as code tool, because “Kubernetes IaC” actually spans three different jobs. For provisioning the cluster and its cloud dependencies, Pulumi and Terraform (or OpenTofu) are the strongest general-purpose options. For templating and packaging workloads, Helm and Kustomize dominate. For continuous reconciliation once things are running, Argo CD and Flux lead the GitOps category. The right stack usually combines one tool from each layer, not a single tool that claims to do all three. What counts as infrastructure as code for Kubernetes? Kubernetes…
Yes, Terraform can manage Kubernetes: the official hashicorp/kubernetes provider lets you declare Deployments, Services, and other objects as HCL resources, and community providers like kubectl fill in the gaps. It works well for many teams. The friction shows up around two well-documented limits — provider ordering and plan-time API access — and around testing, where a general-purpose language changes what’s possible. That friction matters more in 2026 than it did a few years ago. Kubernetes infrastructure now sits next to AI-driven engineering workflows: agents that propose changes, run…
Kubernetes has become the default place teams run agentic AI workloads: CNCF’s 2026 annual survey found that 66% of organizations hosting generative AI models use Kubernetes to manage some or all of their inference workloads.1 An entire ecosystem has grown up around that fact — agent runtimes, model servers, GPU schedulers — and most of it assumes the infrastructure underneath is already handled. It usually isn’t. An AI agent is not a stateless web service, and provisioning for one takes more than copying a Deployment YAML and swapping the image. I spend a lot of my time these days thinking…
Making large pricing changes safe to run, safe to resume, and safe to undo.Most days, shipping code to production at Thumbtack is a non-event. You merge your change, a deployment pipeline takes it from there and it rolls out while you monitor the rollout. If a deploy stalls, the system already knows which step it stalled on. If it goes wrong, rolling back is a button away. It’s the return on years of platform work, so routine now that we mostly forget it’s there. Which is the point: Safety is built into the road, not into how carefully each person drives.Changing pricing data never had that…
The strongest Terraform alternatives in 2026 split into three groups: general-purpose-language platforms like Pulumi and AWS CDK, HCL-compatible forks like OpenTofu, and cloud- or platform-specific tools like AWS CloudFormation, Azure Bicep, and Crossplane. (Pulumi also supports HCL directly and can serve as a Terraform-compatible state backend, so teams don’t have to choose between staying in HCL and gaining Pulumi Cloud’s platform capabilities.) Which one fits depends less on syntax preference than on how well it lets your team, and increasingly your AI coding agents, read, test, and safely…
By AI Platform’s Model Runtime team and Inference teamIntroductionMost organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of those decisions weren’t obvious, and a few revealed their trade-offs only under production load.This post focuses on the choices where alternatives were seriously considered: engine selection, model packaging, API surface design, deployment strategy, and output constraints enforcement. The goal is…
Five weeks ago I wrote that the least glamorous piece of an agent loop is also the one that decides whether it compounds: memory. A markdown file outside the context window that holds what is done, what is next, and what was learned, because the model forgets all of it between runs. Write the memory file before the loop. What I left open, because there was nothing to point at, was the format. My memory file looked nothing like yours, and neither of our agents could read the other’s. Three days after that post went live, Google shipped an answer. The pattern everyone copied Andrej Karpathy…
Learn how Github uses eBPF to detect and prevent circular dependencies in its deployment tooling. The post How GitHub uses eBPF to improve deployment safety appeared first on The GitHub Blog.
Expedia Group Technology — EngineeringWhen your platform’s next user isn’t humanPhoto by Alex Vasey on UnsplashEarlier this month I hosted a town hall for Expedia Group™ Platform Engineering organization, focused on the rapid progress happening in the agentic coding space, and what it means for us as engineers and as a platform team.Our teams are responsible for the horizontal foundations that power Expedia Group: AI and analytics, data, user experience platforms, edge and API platforms, cloud and infrastructure, as well as EG’s developer experience.In other words, we own the “platform of…