Anthropic invests $1.5 million in the Python Software Foundation to support Python ecosystem security and core development.
Anthropic invests $1.5 million in the Python Software Foundation to support Python ecosystem security and core development.
Benchmark results comparing the performance of Python 3.14 against older Python versions, PyPy, Node.js, and Rust on pure Python code.
Benchmarking Python 3.11's performance improvements by testing common code patterns like loops, comprehensions, and sorting.
Explores how to mutate Python tuples, which are normally immutable, by using the C Foreign Function Interface (ctypes) to directly manipulate memory.
Analyzes performance limitations in scikit-learn due to CPython internals, memory hierarchy issues, and lack of low-level data structures.
A series exploring techniques to write faster Python code, covering idiomatic patterns, performance tools, and benchmarking.
A tutorial on writing a simple Python-to-C compiler in Python, using libpython to compile and run a Fibonacci program.
Explains how data is represented as objects in CPython, covering types, identity, binding, and mutability.
Compares assignment operations and parameter passing in C (copying) vs. CPython (binding), focusing on mutable/immutable data.
A guide to using Python's platform module to retrieve system information on Linux, covering Python 2 and 3 differences.
Explains the compilation process in C versus the compilation and interpretation process in CPython, with practical examples.
A technical deep dive into exposing and accessing Python 3.6's private dictionary version number using ctypes.
A deep dive into Python exception handling, covering the BaseException hierarchy, internal implementation, and best practices for defining custom exceptions.
An in-depth technical explanation of why Python is slower than compiled languages, focusing on dynamic typing, interpretation, and object overhead.
A developer shares their journey to becoming a Python core contributor, emphasizing perseverance and the welcoming nature of the project.
A PyPy developer details optimizing Python code for the Computer Language Shootout benchmarks, revealing CPython-specific hacks and cross-implementation challenges.
Explores fixing compatibility issues between Django and the PyPy Python interpreter, detailing specific bugs and solutions.
A 2009 benchmark comparing CPython, Unladen Swallow, and PyPy performance on a Mandelbulb application, analyzing JIT patch results.
A developer details two performance optimizations for Unladen Swallow, a Python implementation, focusing on argument parsing and JIT compilation.
