A Tensor API Lab collection
A hosted model API, a deployment platform and a coding environment solve different parts of an AI workflow. These independent guides explain where Anthropic, Azure and Cursor fit, and which responsibilities stay with your application.
Start by identifying the boundary you are building across. Are you sending a message to a hosted model, packaging a model behind an endpoint, or asking a coding assistant to work inside a repository? Similar vocabulary can hide very different interfaces. Each guide helps turn a broad provider question into a concrete task, with an official reference for the documented behavior.
The collection emphasizes requests, configuration, context and verification. It does not assume that one integration exposes another system's internal tensors. Use the companion topic pages for a quick orientation, then read the longer article when you need a workflow you can adapt. Provider names identify the subject of an article; they do not imply endorsement or a commercial relationship.
3 articles in this collection
Use Cursor to develop inference integrations with focused files, durable project rules and testable tasks. Learn how adapter boundaries, realistic fixtures and human review turn generated changes into maintainable code. Finish with a clear implementation handoff.
7 min readChoose the right Azure endpoint for an inference workload, define numerical data contracts and keep preprocessing, model versions, batch recovery and rollout decisions traceable across the full pipeline. Cover both interactive requests and larger asynchronous classification jobs.
7 min readLearn where numerical processing belongs around an Anthropic Messages integration, then design clear application contracts, traceable evidence, validation and evaluation without assuming access to model internals. Keep provider metadata separate from model-generated conclusions.
7 min read
TensorAPI.com
