Browse the archive by the systems it keeps returning to.

Running large language model inference workloads on Kubernetes at scale—the infrastructure problems that emerge past the prototype stage.

A migration story from Airflow on VMs to cloud-native data pipelines using Argo Workflows and what we'd do differently.

Implementing canary deployments and automated rollbacks using Argo Rollouts with Prometheus analysis templates.

Implementing canary deployments and automated rollbacks using Argo Rollouts with Prometheus analysis templates.

A migration story from Airflow on VMs to cloud-native data pipelines using Argo Workflows and what we'd do differently.

What separates a good internal developer platform from an expensive Kubernetes wrapper—and how to build the former.

How we standardized cluster configuration across a multi-cloud estate using Flux v2 and a monorepo approach.

When someone first asks to run an LLM on Kubernetes, the instinct is reasonable: it's a containerised workload, it exposes an HTTP API, it needs to scale. Deploy it like anything else — a `Deployment`, a `Service`, maybe an `HPA` on CPU. That instinct gets you surprisingly far. Until it doesn't

How we standardized cluster configuration across a multi-cloud estate using Flux v2 and a monorepo approach.

When someone first asks to run an LLM on Kubernetes, the instinct is reasonable: it's a containerised workload, it exposes an HTTP API, it needs to scale. Deploy it like anything else — a `Deployment`, a `Service`, maybe an `HPA` on CPU. That instinct gets you surprisingly far. Until it doesn't

Lessons from building and running three custom operators in production, including the parts the tutorials skip.

A migration story from Airflow on VMs to cloud-native data pipelines using Argo Workflows and what we'd do differently.

What separates a good internal developer platform from an expensive Kubernetes wrapper—and how to build the former.

Running large language model inference workloads on Kubernetes at scale—the infrastructure problems that emerge past the prototype stage.

Lessons from building and running three custom operators in production, including the parts the tutorials skip.

How we standardized cluster configuration across a multi-cloud estate using Flux v2 and a monorepo approach.

When someone first asks to run an LLM on Kubernetes, the instinct is reasonable: it's a containerised workload, it exposes an HTTP API, it needs to scale. Deploy it like anything else — a `Deployment`, a `Service`, maybe an `HPA` on CPU. That instinct gets you surprisingly far. Until it doesn't

Running large language model inference workloads on Kubernetes at scale—the infrastructure problems that emerge past the prototype stage.

Lessons from building and running three custom operators in production, including the parts the tutorials skip.

What separates a good internal developer platform from an expensive Kubernetes wrapper—and how to build the former.

Implementing canary deployments and automated rollbacks using Argo Rollouts with Prometheus analysis templates.