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Python Programming Learning Circle
Python Programming Learning Circle
Nov 13, 2025 · Backend Development

Why Python 3.12 Triggers App Store Rejection and How CPython Is Fixing It

Upgrading a Python project from 3.11 to 3.12 can cause macOS App Store rejections because the new standard library embeds an "itms-services" URL string that Apple’s automated review flags, prompting CPython core developers to propose patches, distribution‑tool options, and a new compliance flag to resolve the issue.

App StoreCPythoncompliance
0 likes · 12 min read
Why Python 3.12 Triggers App Store Rejection and How CPython Is Fixing It
Code Mala Tang
Code Mala Tang
Aug 30, 2025 · Fundamentals

Why Python Never Overflows: The Secret of Arbitrary‑Precision Integers

Unlike fixed‑size integers in languages like C or Java, Python uses arbitrary‑precision integers that automatically expand, preventing overflow; this article explains the underlying CPython implementation, demonstrates the behavior with examples, and discusses the memory and performance trade‑offs of such limitless integers.

Arbitrary PrecisionCPythonPython
0 likes · 5 min read
Why Python Never Overflows: The Secret of Arbitrary‑Precision Integers
Python Programming Learning Circle
Python Programming Learning Circle
Jun 6, 2025 · Fundamentals

Why Making Python Faster Is Hard—and Worth the Effort

Optimizing Python is challenging due to its dynamic nature, but various strategies—from using NumPy, Numba, and Cython to upcoming CPython enhancements like adaptive specialization, JIT, and GIL‑free versions—show promise for improving performance while preserving the language’s flexibility.

CPythonJITPerformance Optimization
0 likes · 7 min read
Why Making Python Faster Is Hard—and Worth the Effort
DevOps Engineer
DevOps Engineer
Jan 20, 2025 · Fundamentals

Simplifying CPython Copyright Notices: Lessons from Issue gh-126133

The article examines CPython issue gh‑126133 where Hugo van Kemenade proposed simplifying the project's copyright notice by keeping only the first publication year, discusses legal feedback from the PSF, compares practices of major open‑source projects, and concludes with the accepted change merged into the main branch.

CPythonCopyrightlegal
0 likes · 9 min read
Simplifying CPython Copyright Notices: Lessons from Issue gh-126133
Satori Komeiji's Programming Classroom
Satori Komeiji's Programming Classroom
Oct 28, 2024 · Fundamentals

How Python Implements Static Lookup for Local Variables and Its Relationship to the Local Namespace

The article explains that CPython stores function local variables in a statically‑indexed array (f_localsplus), accesses them via GETLOCAL/SETLOCAL macros, and builds the locals() dictionary on demand, showing how exec, variable assignment order, and the hidden local namespace interact with this mechanism.

CPythonPythonbytecode
0 likes · 27 min read
How Python Implements Static Lookup for Local Variables and Its Relationship to the Local Namespace
Satori Komeiji's Programming Classroom
Satori Komeiji's Programming Classroom
Sep 18, 2024 · Fundamentals

How Python Implements Iterators: From __iter__ to __next__

This article explains Python's iterator protocol by showing how objects become iterable through __iter__, how the built‑in iter function creates iterator objects, the CPython internals of PyObject_GetIter, and how the __next__ method advances elements, with concrete code examples and source‑level snippets.

CPythonIterableIterator
0 likes · 16 min read
How Python Implements Iterators: From __iter__ to __next__
Satori Komeiji's Programming Classroom
Satori Komeiji's Programming Classroom
Jul 29, 2024 · Fundamentals

Which sequence operations does Python’s list support and how are they implemented?

The article explains the various sequence‑type operations that Python lists provide—concatenation, repetition, indexing, slicing, and element assignment—detailing how each is realized in CPython through the tp_as_sequence and tp_as_mapping slots and the corresponding C functions such as list_concat, list_repeat, list_subscript, and list_ass_subscript.

CPythonListc-implementation
0 likes · 13 min read
Which sequence operations does Python’s list support and how are they implemented?
21CTO
21CTO
Jan 12, 2024 · Fundamentals

How CPython’s New JIT Will Boost Python 3.13 Performance

The upcoming CPython 3.13 release will optionally include a JIT compiler built on LLVM, offering modest speed gains of 2%‑9% today and promising larger improvements in future versions through advanced optimization techniques.

CPythonJITLLVM
0 likes · 6 min read
How CPython’s New JIT Will Boost Python 3.13 Performance
21CTO
21CTO
Oct 5, 2023 · Fundamentals

What’s Coming in Python 3.12? Inside the Faster CPython Roadmap

The article outlines the major performance and architectural goals for Python 3.12, describing the Faster CPython project’s specialized opcodes, multithreading proposals, object size reductions, memory‑management improvements, and API stability efforts that aim to boost speed and maintainability.

CPythonMemory ManagementPEP
0 likes · 8 min read
What’s Coming in Python 3.12? Inside the Faster CPython Roadmap
IT Services Circle
IT Services Circle
Jul 14, 2023 · Fundamentals

Meta Supports PEP 703 to Make the CPython GIL Optional

Meta is promoting PEP 703, which proposes adding a --disable-gil build option to CPython so Python can run without the Global Interpreter Lock, and has pledged three engineer‑years by 2025 to help implement the change, while also noting its Threads product already runs on a heavily modified CPython backend.

CPythonGILMeta
0 likes · 3 min read
Meta Supports PEP 703 to Make the CPython GIL Optional
MaGe Linux Operations
MaGe Linux Operations
Jan 13, 2023 · Fundamentals

Why Python’s GIL Slows Multithreading and How It Actually Works

This article explains the purpose and mechanics of Python’s Global Interpreter Lock (GIL), how it serializes bytecode execution on single- and multi‑core CPUs, its impact on I/O‑bound versus CPU‑bound workloads, and why certain operations remain thread‑safe despite the lock.

CPythonGILmultithreading
0 likes · 8 min read
Why Python’s GIL Slows Multithreading and How It Actually Works
21CTO
21CTO
Nov 14, 2022 · Fundamentals

How Microsoft’s Faster CPython Team Is Boosting Python Performance by Up to 60%

Microsoft assembled a six‑engineer team, including Guido van Rossum, to accelerate CPython according to a multi‑phase plan, resulting in Python 3.11 delivering 10‑60% speed improvements and showcasing how coordinated open‑source effort can overcome Python’s historic performance limitations.

CPythonLanguage DevelopmentMicrosoft
0 likes · 6 min read
How Microsoft’s Faster CPython Team Is Boosting Python Performance by Up to 60%
21CTO
21CTO
Nov 9, 2022 · Fundamentals

Exploring Python’s Major Interpreters: CPython, Jython, IronPython, PyPy, and Cython

An overview of Python’s primary interpreter implementations—including CPython, Jython, IronPython, PyPy, and Cython—highlights their origins, execution models, compatibility, performance characteristics, and unique advantages, helping developers choose the right runtime for their specific needs.

CPythonCythonInterpreters
0 likes · 4 min read
Exploring Python’s Major Interpreters: CPython, Jython, IronPython, PyPy, and Cython
Programmer DD
Programmer DD
Nov 1, 2022 · Fundamentals

How Microsoft’s Faster CPython Team Is Boosting Python Performance

Microsoft’s Faster CPython team, formed with Guido van Rossum and six engineers, aims to accelerate CPython by up to five‑fold through staged improvements, already delivering 10‑60% speed gains in Python 3.11 while emphasizing collaborative development and community value.

CPythonMicrosoftPython
0 likes · 6 min read
How Microsoft’s Faster CPython Team Is Boosting Python Performance
21CTO
21CTO
Jun 1, 2022 · Fundamentals

How Python 3.11 Boosts Speed by Up to 60% – The Secrets Behind Faster CPython

Python 3.11, slated for release in October 2022, delivers a 10‑60% performance boost over 3.10 thanks to the Microsoft‑backed Faster CPython project, which introduces adaptive specialization, memory‑allocator refinements, and several low‑level optimizations that trade a modest memory increase for faster execution.

CPythonInterpreter OptimizationMemory Management
0 likes · 5 min read
How Python 3.11 Boosts Speed by Up to 60% – The Secrets Behind Faster CPython
ByteDance Terminal Technology
ByteDance Terminal Technology
Feb 22, 2022 · Fundamentals

Optimizing CPython for True Parallel Execution: Implementing a Multi-Interpreter Architecture

This article details a novel approach to overcoming CPython's Global Interpreter Lock by implementing a multi-interpreter architecture that isolates execution states, manages shared variables through thread-specific data, and introduces a subinterpreter pool to significantly enhance multi-core CPU utilization and algorithm execution performance.

CPythonGIL OptimizationMulti-Interpreter Architecture
0 likes · 15 min read
Optimizing CPython for True Parallel Execution: Implementing a Multi-Interpreter Architecture
21CTO
21CTO
Jan 18, 2022 · Fundamentals

Why Python 3.11 Is Twice as Fast as 3.10 – Inside the Shannon Plan

On January 18, 2022 the Python Software Foundation released Python 3.10, 3.9, and a preview of 3.11, detailing performance gains, Windows installer issues, and a critical Cython‑related memory leak fixed in the upcoming 3.10.3 release.

CPythonCythonVersion Release
0 likes · 5 min read
Why Python 3.11 Is Twice as Fast as 3.10 – Inside the Shannon Plan
MaGe Linux Operations
MaGe Linux Operations
May 31, 2021 · Mobile Development

Why Python Struggles on Mobile and How BeeWare Is Trying to Fix It

The article examines Python's popularity in machine learning, the difficulties of running Python apps on iOS and Android, the BeeWare project's milestones and challenges, funding issues, and Guido van Rossum’s perspective on focusing Python on backend and scientific workloads.

AndroidBeeWareCPython
0 likes · 7 min read
Why Python Struggles on Mobile and How BeeWare Is Trying to Fix It
MaGe Linux Operations
MaGe Linux Operations
May 20, 2021 · Fundamentals

How the “Shannon Plan” Aims to Double Python’s Speed in 3.11

Guido van Rossum revealed that the new “Shannon Plan” backed by Microsoft will target a 2× speed boost for CPython 3.11 through a specialized bytecode interpreter, stack optimizations, and other enhancements, while preserving ABI compatibility and open‑source principles.

CPythonGuido van RossumPEP 659
0 likes · 4 min read
How the “Shannon Plan” Aims to Double Python’s Speed in 3.11
Python Crawling & Data Mining
Python Crawling & Data Mining
May 20, 2021 · Fundamentals

How the Shannon Plan Aims to Make CPython 5× Faster

Guido van Rossum’s recent “Making CPython Faster” talk reveals the Shannon Plan—a four‑year effort, backed by Microsoft, to boost Python’s performance up to five times, with the first milestone targeting a 2× speedup in the upcoming Python 3.11 release through adaptive bytecode interpreters and other optimizations.

CPythonGuido van RossumPEP 659
0 likes · 4 min read
How the Shannon Plan Aims to Make CPython 5× Faster
21CTO
21CTO
May 15, 2021 · Fundamentals

Can Python’s Creator Double Its Speed? Inside Guido’s New CPython Push

Guido van Rossum, now a Microsoft Distinguished Engineer, promises to boost CPython performance by up to five times without breaking existing code, detailing the team, funding, and security efforts behind the ambitious speed upgrades slated for Python 3.11 and beyond.

CPythonGuido van RossumMicrosoft
0 likes · 6 min read
Can Python’s Creator Double Its Speed? Inside Guido’s New CPython Push
Python Programming Learning Circle
Python Programming Learning Circle
Jun 3, 2020 · Fundamentals

Understanding CPython's Memory Management (Python 2.7)

This article explains how CPython implements its own memory‑management scheme—including the layered allocation model, pool and arena structures, block size classes, and reference‑count‑based reclamation—by dissecting the source code of Python 2.7’s obmalloc module.

CPythonGarbage CollectionMemory Management
0 likes · 23 min read
Understanding CPython's Memory Management (Python 2.7)
MaGe Linux Operations
MaGe Linux Operations
Oct 21, 2019 · Fundamentals

Explore CPython Internals: Build and Understand Python’s Core

This comprehensive guide walks you through the CPython source tree, shows how to clone, configure, and compile version 3.8.0b3 on macOS, explains the language grammar, tokenization, memory management, reference counting, and garbage collection, and provides practical code snippets for each step.

CPythonCompilationPython interpreter
0 likes · 22 min read
Explore CPython Internals: Build and Understand Python’s Core
MaGe Linux Operations
MaGe Linux Operations
Mar 4, 2019 · Fundamentals

Which Python Implementation Is Right for You? A Comparative Guide

An overview of major Python implementations—including CPython, Jython, IronPython, PyPy, and Pyston—explains their underlying technologies, performance characteristics, platform integrations, and ideal use cases, helping developers choose the most suitable runtime for their projects.

CPythonIronPythonJython
0 likes · 6 min read
Which Python Implementation Is Right for You? A Comparative Guide
Efficient Ops
Efficient Ops
Dec 27, 2018 · Fundamentals

Why Python Lists Aren’t Pre‑Allocated: Inside CPython’s Memory Management

Python’s list type appears flexible, but its underlying CPython implementation uses a dynamic resizing strategy without a pre‑allocated memory pool, allocating memory on demand via list_resize and PyMem_RESIZE, which this article explains through code analysis, memory size measurements, and practical recommendations.

CPythonListMemory Management
0 likes · 13 min read
Why Python Lists Aren’t Pre‑Allocated: Inside CPython’s Memory Management
MaGe Linux Operations
MaGe Linux Operations
May 19, 2018 · Fundamentals

How a Four‑Year Hunt Fixed a Hidden Python GIL Race Condition

The article recounts a four‑year investigation that uncovered and repaired a subtle race‑condition bug in Python's Global Interpreter Lock, detailing the bug's origin, the implemented fixes, performance testing, and the decision to make the GIL creation unconditional in Python 3.7.

CPythonGILPython
0 likes · 10 min read
How a Four‑Year Hunt Fixed a Hidden Python GIL Race Condition
MaGe Linux Operations
MaGe Linux Operations
Feb 10, 2018 · Fundamentals

How Python Generators Work: From Yield to Bytecode Execution

This article explains the concept of Python generators, how the `yield` keyword creates iterator objects, the basic operations for iterating, practical examples such as infinite sequences, and dives into CPython's internal implementation including the call stack, generator creation, the `send`/`next` mechanisms, and bytecode execution details.

CPythonGeneratorsIterators
0 likes · 12 min read
How Python Generators Work: From Yield to Bytecode Execution
MaGe Linux Operations
MaGe Linux Operations
Mar 14, 2017 · Fundamentals

Why Python’s Double Underscore Doesn’t Guarantee True Privacy

This article explains Python’s double‑underscore name‑mangling for private attributes, how they can still be accessed via class methods, the pitfalls of attribute shadowing, and why deleting dict entries may raise errors due to CPython’s underlying C implementation.

CPythonPythonattribute shadowing
0 likes · 4 min read
Why Python’s Double Underscore Doesn’t Guarantee True Privacy