Backend & Infrastructure

HTTP is a request-response protocol. The client asks, the server answers, the connection closes. For most web applications, this model works perfectly. But when you need the server to push data to the client—live notifications, real-time collaboration, streaming inference results, chat messages, live dashboards—you need

The PostgreSQL-versus-MongoDB debate generates more heat than light. Advocates on both sides argue from first principles (“relational data belongs in a relational database” / “documents are the natural unit of storage”) while ignoring that the right choice depends almost entirely on your specific application’s data

Running an AI/ML development environment involves juggling more services than a typical web application. You need your API server, a vector database, a message queue for async inference jobs, a model registry, a monitoring stack, and probably a PostgreSQL instance for application data. On a

When we started building Harbor Software’s core inference platform in late 2021, the backend framework decision felt unusually consequential. We were building something that needed to serve ML model predictions with sub-200ms latency, handle concurrent long-running inference jobs, and remain approachable enough that a team