Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A synthesis of this year's major industry surveys (Stack Overflow, GitHub Octoverse, CNCF, DORA, and more), with the actual numbers and what they mean for a working team.
A practitioner's guide to choosing between serverless and provisioned databases based on cost, latency, connections, and load shape.
A practitioner comparison of the leading serverless databases by use case, cold-start behavior, branching, pricing model, and lock-in.
The observability market is huge and the pricing is a minefield. This is the map to the tools that matter, what each is best at, and how to avoid a runaway bill.
Cloud bills grow quietly until someone asks why. This is the map for cutting spend without cutting reliability: where the money actually goes, the levers that work, and the tools worth paying for.
Datadog bills climb quietly until finance forwards the invoice. Here's the playbook we run to cut spend hard while keeping every signal that matters.
Static keys leak. The question isn't if but how fast you notice and how clean your response runbook is when the pager goes off.
Your cloud bill says $80k. Your cluster says nothing about which team burned it. Here's how OpenCost, Kubecost, and Cast AI actually split that number.
One tool is built to answer questions you didn't know you had. The other watches everything at once. Here is how they actually differ in practice.
GCP hands you one discount for free and sells you a deeper one. Here's how sustained and committed use actually stack, and how we size the commitment.
OTel promises no lock-in, vendor agents promise zero-config depth. Here is where each one actually earns its keep once you run it in production.
Edge code runs in hundreds of PoPs, lives for milliseconds, and gives you no shell. Here's how we get logs, traces, and metrics out of it anyway.
