Philosophy

matten is a developer-experience-first tensor library for Rust. It is shaped for learning, teaching, early prototypes, small numerical workflows, and data exploration where clear tensor code matters more than exposing every specialized engine concern up front.

The project optimizes for time to first understanding: create a tensor, see its shape, transform it, and keep moving. That does not mean hiding Rust. It means using Rust’s packaging, explicit errors, and predictable ownership while keeping the public tensor surface narrow enough to learn.

What matten is

matten gives Rust users a small, concrete Tensor-centered path for vectors, matrices, axes, broadcasting, reductions, simple statistics, dynamic ingestion, and small table-to-tensor workflows. It is useful when the goal is to explore an idea, explain an operation, teach tensor shape, or build a readable proof of concept before choosing heavier tools.

The intended feel is the family car: practical and predictable, comfortable to start, explicit about boundaries, and honest about when another library is a better next step.

Core principles

One primary type. Ordinary numeric work starts with Tensor. The public API avoids generic dtype parameters and lifetime-bearing tensor views in common examples so a new reader can focus on the operation and its shape.

Concrete before abstract. Core Tensor computation is numeric and f64-based by default. Mixed external data enters through the dynamic ingestion path, where cleanup and numeric conversion are explicit steps instead of hidden coercion.

Small surface, visible meaning. Shape, axis, and data movement should be inspectable. Examples and visual explanations are part of the learning path, not decoration.

Panic locally, return Result at boundaries. Trusted local math conveniences may panic with actionable messages. Anything that reads files, parses JSON/CSV, accepts user-provided shapes, or crosses an external boundary returns Result.

Evidence without ranking. Benchmarks and reports exist to explain tradeoffs and catch regressions, not to claim universal speed leadership.

What matten is not

matten is not a dataframe engine, an ML framework, a GPU backend, a sparse tensor library, an automatic differentiation system, or a broad wrapper around external numeric crates. Companion crates and migration docs can help users connect to other ecosystems, but core matten stays small and tensor-centered.

When to move on

If a workflow grows beyond the small, readable, educational, or prototype-oriented scope, move the hot path to the tool that owns that domain. Use ndarray for broader Rust N-D array work, nalgebra for linear algebra structures, Polars or Pandas for dataframe workflows, and Candle for ML tensor/model workflows. The migration guide explains those paths.

The goal is not to keep every project inside matten; the goal is to make the first model clear enough that the next decision is informed.