Amara

Accelerating Python with native code — choosing an interface layer (C-API, cffi, Cython, nanobind, Rust), modern CPython C-API hygiene, free-threading, the stable ABI, and (the hard part) building/shipping compiled extensions under a hatchling project. Use when adding, reviving, or packaging a C/C++/Rust acceleration module in a Python library.

Python 3.12+ backend and library development — packaging, testing, pyproject.toml, uv, hatchling, asyncio, and repo hygiene. Use when working on Python services, CLIs, or libraries.

Fast, cheap, deterministic test suites — the unit/integration split, marking and excluding tests that hit unowned code or services, and the "don't mock what you don't own" alternative-implementations pattern. Use when writing, organizing, or speeding up tests for Python libraries or webapp back ends.