Microservices were supposed to make software easier to scale. Instead, many engineering teams discovered that they had scaled their architecture...
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GraphQL changed how developers think about APIs. Instead of maintaining multiple endpoints for different resources, clients could request exactly the...
Retrieval-Augmented Generation has become one of the standard architectures for building AI applications that need access to private, current, or...
LLM applications have a latency problem that traditional backend optimization cannot completely solve. A backend API can be carefully tuned...
LLM APIs made it remarkably easy to add intelligence to a backend application. A developer can connect an API, send a prompt, receive a response, and...
There is a dangerous assumption around legacy software: if the code is old, the answer must be to replace it. That sounds logical until you look at...
AI has moved from generating text to making decisions, calling APIs, updating records, and triggering workflows. For backend developers, that shift...
“Move fast and break things” made sense when breaking things mostly meant fixing a buggy interface, rolling back a deployment, or...
For years, the standard approach to building AI applications was simple. The application lived on the user’s device, but the intelligence lived...
For years, backend architecture followed a predictable model: the client sent a request, the server processed it, the database stored the result, and...
