
Performance Tuning for Serverless Workloads
If you have shipped enough serverless workloads, you know the moment. You deploy a “simple” function. It passes tests, scales effortlessly, and looks clean on paper. Then production teaches you

If you have shipped enough serverless workloads, you know the moment. You deploy a “simple” function. It passes tests, scales effortlessly, and looks clean on paper. Then production teaches you

If you have ever been paged for “elevated error rate” and then spent 45 minutes arguing with dashboards, you already know the dirty secret of distributed systems: the failure is

You do not “add multi-tenancy” to a database. You design a system where a tenant boundary is as real as a network boundary, even though everything might be sharing the

If you have operated a production system long enough, you can probably map your career by production incidents rather than job titles. The first cascading failure you debug at 3

You have been there. Alerts firing, dashboards half red, Slack exploding with theories and hot takes. Someone asks for a rollback while another person is already changing configs in production.

If you have ever deployed more than a handful of containers in production, you already know the uncomfortable truth. Running containers is easy. Operating them at scale, across failures, upgrades,

You usually notice your message processing pipelines are inefficient the same way you notice a leaky roof, not during the sunny days, but the first time traffic spikes, a downstream

If you have ever been on call for a system you did not design, you have felt it. The expectations were never written down, but they were absolutely enforced. Which

Mobile robots are no longer a futuristic abstraction floating around in R&D departments. They’re multiplying in warehouses, chirping in hospitals, and, let’s be honest, sometimes causing headaches in airports. What