Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
The architectural choice is presented as binary; the practical answer is "depends on the workload." The patterns that earn their place and the failure modes we've hit.
Three discounting mechanisms, three different commitments. The rules of thumb we use to pick, and the mistakes we made before settling on them.
Three caching patterns, three failure modes. The one we use most, the one that bit us, and the rule that decides which pattern fits which workload.
Bad resource requests waste money or trigger OOMs. The methodology we use to right-size requests based on actual usage, and the gotchas the autoscalers don't fix.
Edge compute is useless without an edge data layer. Three serverless databases that put data within ms of your edge functions, with the tradeoffs that aren't on the marketing pages.
OIDC federation between AWS, GCP, and CI providers let us delete every long-lived cloud credential we had. The setup, the gotchas, and the trust-relationship discipline.
There are two hard problems in computer science." We've worked on the cache-invalidation one for a while. The patterns that hold up at scale and the ones that look clean and aren't.
We use Step Functions for batch processing, document ingestion, and a few agentic workflows. The patterns that work, the limits we hit, and where we'd reach for something else.
After two years of running Karpenter on production EKS clusters, the NodePool patterns that survived, the ones we replaced, and the tuning that matters.
A working mental model for AWS VPCs — what each piece does, how they connect, and why "VPC" is the wrong mental model if you came from physical networks.
Create your first S3 bucket, upload and download files, and set up the right access controls — without accidentally making everything public.
Write, package, and deploy a Lambda function using only the AWS CLI. Trigger it via a public URL. Understand what serverless actually means.
