Every AI team today is balancing the same three pressures: cost, accuracy, and performance. Large language models excel at generating text, but the latest generation relies heavily on step-by-step re...

“How many patients are there?” is not a semantic-search question. Retrieving six relevant patient chunks and asking a language model to infer the total would be fast, fluent, and wrong. It is also un...

A streamed answer feels simple until two conversations run at once, the user changes screens, a token begins with a space, or the user presses Stop before the HTTP request has fully started. Add a be...

Vector search is excellent at finding related language. It is less reliable when a question hinges on an exact identifier, an uncommon drug name, or a clinical code. Full-text search has the opposite...

A health-record index is most vulnerable while it is being rebuilt. A source database can disconnect halfway through a large table. An embedding process can fail after thousands of rows. The applicat...

A RAG store is often introduced as a vector database. That description is too narrow for a real application. Retrieval needs text and vectors, but ingestion also needs checkpoints and publication sta...

  Introduction Field engineers operating in remote substations frequently encounter connectivity limitations when evaluating the safety of proposed grid adjustments. While physics-informed neural...

Introduction I had an Azure model deployment, an endpoint, and an API key. Getting that model into OpenCode looked like a configuration task: register a provider, paste in the connection details, s...

The shortest path to local inference is not to begin with a web server, an agent framework, or a full RAG pipeline. Begin with a spike that proves five things on the target machine: the SDK initializ...