Search Results: CPython

Core Python developers Emma Smith and Kirill Podoprigora have proposed incorporating Rust into CPython to boost memory safety and performance, starting with optional standard library modules like base64. Amid community debates on timelines, portability, and build complexity, the plan has been scaled back for a gradual rollout, reflecting Python's cautious evolution. This move could revitalize contributions while addressing long-standing C-related challenges in the interpreter.

A proposal to weave the memory‑safe, thread‑safe language Rust into CPython’s core promises a dramatic reduction in crashes and security holes while opening the door to high‑performance extensions. The plan starts with optional Rust modules and envisions a future where Rust becomes a required dependency, reshaping how the interpreter is built and maintained.

Python core developers have unveiled ambitious plans to accelerate CPython's JIT compiler, targeting 5% faster performance in 3.15 and 10% in 3.16. The roadmap includes trace recording, register allocation, and assembly-level optimizations—while tackling challenges like debugger integration and free-threading support.

Python core developer Antonio Cuni shares critical insights from optimizing PyPy's tracing JIT in high-frequency trading systems, revealing unexpected performance pitfalls that await CPython's emerging JIT. His analysis exposes how trace blockers, data-driven control flow, and generators can cripple optimization gains in real-world code.

The Python 3.14-rc3 release candidate is now available, marking the last test phase before October's official launch. This update introduces groundbreaking features like free-threaded execution and multiple interpreters, alongside measurable performance gains. Developers gain new tools for debugging, compression, and concurrency in one of Python's most significant updates in years.

A deep dive into Python's bytecode reveals why list comprehensions behaved differently across versions 3.10 and 3.12. Discover how CPython's shift from hidden functions to inlined execution resolves scoping mysteries and impacts your code's behavior.