A Tensor API Lab collection
The most useful AI abstractions start with a few concrete questions: what does this value represent, how is it arranged, and what is allowed to change? These articles make tensors, tokens and embeddings easier to reason about before an implementation gets complicated.
Start with the shape and dtype guide if you are designing a request format or debugging a mismatch. Move to the tokens and embeddings guide when words, integer IDs and numerical representations are being used interchangeably in a conversation. Each article follows the data across a boundary, so the explanation stays connected to something you can inspect.
Read with one of your own examples in mind: a row of measurements, a short message, or a collection of documents. Write down what each axis means, which component owns preprocessing, and how a caller could detect an incompatible input. A shared vocabulary makes the later decisions about inference, evaluation and usage much easier to discuss.
2 articles in this collection
Trace text from vocabulary tokens to embedding vectors and shaped tensors. Understand how masks, pooling and model versions affect API design, memory estimates and reliable similarity search. Keep representation size separate from the meaning of its values.
7 min readA tensor contract needs more than an array of numbers. Learn how to name axes, validate numerical types, preserve feature meaning, and batch compatible examples without hiding errors at the API boundary.
7 min read
TensorAPI.com
