A practical, low-overhead toolbox for Python performance: what to use (and when), how to keep overhead in single digits, and how to read profiles you can trust.

The shortest safe path from Python to C-speed: a practical roadmap for choosing Cython, cffi, HPy, or the raw C API, with a minimal wheelable extension, packaging/ABI mental models, and performance guardrails you can apply today.

A practical guide to Python’s GIL today—where threads help, where they don’t—and what the emerging no-GIL path means for the way you write concurrency, performance, and extension code.

Demystify CPython 3.11's specializing/adaptive bytecode interpreter—quickening, inline caches, and the patterns that help your code hit the fast path without changing a line.

A production-first tour of CPython’s memory model: what refcounts really guarantee, how the cyclic GC works, why RSS doesn’t always go down, and how to reason about growth without guesswork. Part 1 lays the mental model and refcounting truths.

Make Python types carry their weight: combine static checking (mypy/pyright) with fast runtime validation (Pydantic v2) to turn annotations into contracts that prevent bugs, speed up onboarding, and keep hot paths fast.

Build async services that stay responsive under load: apply backpressure with bounded queues, adopt structured concurrency, and make cancellation a contract with deadlines.

Pick the right engine; exploit Apache Arrow and expression pipelines to get faster, more memory‑efficient DataFrames in Python—without painting yourself into a corner.

Async in C without 3 a.m. incidents: understand POSIX AIO, io_uring, and thread-pool strategies; compare latency/throughput/complexity; and build a thin, testable abstraction with real cancellation.

A minimal, production-minded Write-Ahead Log (WAL) in C: append-only records, checksums, segment management, fsync discipline, and recovery guarantees.