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Shared 12 articles, 12 open-source projects, 2 hot discussions
Here are the title summaries for this issue:
① CPython to remove experimental JIT
② The state of Python profiling tools in 2026
③ Why DuckDB 2.0 is faster
④ UUIDv7 doesn't have the properties you think it does
⑤ Build a coding agent in 306 lines of Python
⑥ Orchestrating disposable browsers with Python
⑦ Production practices for caching in Django at scale
⑧ Storing Django static and media files on Cloudflare R2
⑨ From loops to vectorization: 7 key mindsets for speeding up NumPy
⑩ pyinfra: server automation with Python code
⑪ A 3D game built entirely by AI: the full production process revealed
⑫ PyScript enters maintenance mode: what's left of Python in the browser
① taste-skill: making coding agents settle on an art direction first
② mcode-stack: the MiniMax team's AI coding workflow and skill set
③ BMAD-METHOD: an AI-driven agile development methodology
④ artemis: a natural-language-driven agent for Android automation
⑤ ego-lite: handing an already-logged-in browser to an AI agent
⑥ agent-zero: giving an agent a Linux computer
⑦ Hy-Memory: a multi-scenario memory library for agents
⑧ jeff: a 0.8B small model for fast decisions with local LLMs
⑨ KittenTTS: an open-source speech synthesis model that runs on CPU
⑩ Open-Source-Face-Recognition-SDK: local offline face recognition
⑪ TalkLikeYou-Streaming-RealTime-Demo: real-time digital human generation in the browser
⑫ deepopen-3d-webs: generating explorable 3D scenes from a single image
① PEP 845: leading-dot value patterns
② PEP 851 proposes adding discard() to list
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Below is the issue published one year ago (Issue 124, 2025-10-25) —
much of it is still worth reading today.
Issue 124 shared 12 articles, 12 open-source projects, 2 audio/videos.
🦄Articles & Tutorials
1、Beyond the AI hype: Guido van Rossum on Python philosophy, simplicity, and the future of programming
Guido van Rossum interview: emphasizes that code still requires human review; Python's "humanist" philosophy makes it easy for LLMs to understand; holds a rational view on GIL removal, believing that big-company demands are making Python more complex; recommends type hints only for code over 10,000 lines, keeping Python beginner-friendly.
2、The no-GIL future of Python web services
Benchmarks FastAPI and Flask on the Python 3.14 no-GIL build. The author finds the no-GIL version's performance loss dropped from 35% to 5-10%, with significantly reduced memory usage. In I/O-bound scenarios the no-GIL build slightly outperforms the standard build, bringing true multithreaded concurrency to Python web services and potentially reducing reliance on multiprocess architectures.
3、Goodbye GIL: exploring Python 3.14's no-GIL mode
A deep benchmark report shared by the author at PyCon JP 2025. Comparing the Python 3.14 standard build with the no-GIL build, it finds about 15% performance loss for single-threaded code, but significant gains for multithreaded CPU-bound tasks. It also covers how to set up no-GIL mode, performance behavior, and practical use cases.
4、Lazy imports: making Python start up 3x faster
For the explicit lazy import feature proposed in PEP 810, the author tested the pypistats CLI tool and found that lazy imports cut startup time from 104ms to 36ms, nearly a 3x speedup. This is especially beneficial for command-line tools, test suites, and applications with large dependency graphs, significantly reducing startup time and memory usage.
5、Python lazy import techniques you can use today
Introduces practical techniques for implementing lazy imports in existing Python versions by moving import statements inside functions to defer module loading. The article demonstrates several approaches, including using importlib.import_module() and diagnosing slow imports with python -X importtime.
6、Diskcache: more than just a cache
An in-depth look at diskcache, a SQLite-based local caching library for Python. It offers microsecond-level key-value storage plus advanced features like transactions, tags, locking, and queues. A sharding mechanism enables multiprocess concurrent writes, and it includes built-in thundering-herd protection and function decorators, making it a near drop-in replacement for lightweight Redis use cases.
7、Lessons from using Postgres as a task queue
The author shares practical experience using PostgreSQL as a task queue, achieving atomic locking via FOR UPDATE SKIP LOCKED and avoiding extra infrastructure setup and complex retry mechanisms. Compared with traditional message queues, this approach reduces system complexity, offers better observability, and makes it easy to add retries, delayed execution, and more.
8、Why performance matters in Python development
Starting from Python interpreter characteristics and GIL limitations, the article analyzes common performance pitfalls in detail, including unnecessary loops, string concatenation, and misuse of data structures. It emphasizes that performance optimization affects not only user experience and cloud costs but also directly impacts system scalability and developer efficiency.
9、Async Django: a solution in search of a problem?
The author questions the practical value of Django's async features. Although the Django team spent six years developing async support, a 2024 survey shows only 14% of developers use async views. The article notes that async Django adds framework complexity and creates a dual API, while traditional background task solutions (such as Celery) remain the best choice for resolving performance bottlenecks.
10、The Django bulk_update memory problem
While updating hundreds of thousands of database records, the author discovered a memory trap in Django bulk_update: despite setting the batch_size parameter, Django still prepares all batches of UPDATE statements in advance, causing memory to balloon to 4.8GB and trigger SIGTERM. The solution is to manually implement a batching loop, reducing memory usage to just 62MB.
11、An empirical evaluation of property-based testing in Python
An OOPSLA 2025 paper provides the first systematic evaluation of property-based testing in Python. The study finds that property-based testing excels at finding bugs, especially for boundary conditions and exceptional inputs. Compared with traditional unit tests, property-based testing automatically generates large numbers of test cases, significantly improving code coverage and bug detection.
12、From Python to Kotlin: a migration worth trying
The article analyzes the advantages of migrating from Python to Kotlin. Although the two languages have similar syntax, Kotlin offers static typing, immutability, and a stronger concurrency model. Benchmarks show Python is 10-100x slower than Java, while Kotlin inherits the JVM's high performance; its type safety and modern language features can significantly boost developer efficiency.
🐿️Projects & Resources
1、deepwiki-open: AI-driven codebase documentation generator
Automatically generates beautiful interactive documentation for GitHub, GitLab, or BitBucket code repositories, using AI to analyze code structure and produce comprehensive docs and visual diagrams. Supports private repositories, Mermaid diagram generation, and RAG-based AI Q&A. (11.5K stars)
2、tidal-dl-ng: multithreaded TIDAL music downloader
A powerful TIDAL music downloader supporting multithreaded and chunked downloads, capable of downloading music and videos up to HiRes Lossless / TIDAL MAX 24-bit 192 kHz quality, with Dolby Atmos support and both CLI and GUI. (1.4K stars)
3、hexstrike-ai: AI-driven cybersecurity automation platform
An advanced MCP server that lets AI agents autonomously run 150+ cybersecurity tools for penetration testing, vulnerability discovery, and security research. Uses a multi-agent architecture with an intelligent decision engine, real-time visualization interface, and vulnerability intelligence analysis. (4K stars)
4、maltrail: malicious traffic detection system
Identifies malicious network activity using public blacklists and threat intelligence. Supports detecting threat indicators such as domains, URLs, and IP addresses, integrates dozens of threat intelligence sources, and uses heuristics to discover unknown threats, with real-time monitoring and detailed reporting. (7.6K stars)
5、hyperflask: full-stack web development framework
A full-stack development framework built on Flask that pre-selects the entire tech stack and integrates file-based routing, SQL ORM, deep htmx integration, and more. Supports mixing multiple frontend technologies, enabling indie developers and small teams to quickly build and deploy web apps.
6、modshim: Python package enhancement tool
Override and customize Python module functionality without modifying source code. By creating "shim" modules that combine original code with enhancements, it uses AST rewriting to ensure consistency and avoid the global pollution issues of monkey-patching.
7、django-keel: versatile Django project template
A production-ready Django project template designed around 12-Factor App principles, supporting Django 5.2 and Python 3.12+. Offers optional features like REST Framework, GraphQL, HTMX+Tailwind, and Celery, and generates customized projects through interactive configuration.
8、buttonpad: cross-platform button grid GUI toolkit
A cross-platform Python GUI toolkit designed for button interfaces, built on Tkinter. It uses CSV-style layout strings to quickly create grid interfaces, supports callbacks, tooltips, and hotkey mapping, and includes built-in demos like a calculator and a 2048 game.
9、django-bolt: high-performance Django API framework
A high-performance Rust-based Django API framework achieving 60k+ RPS throughput. It uses Actix Web to handle HTTP requests and bridges Python and Rust via PyO3. Fully compatible with the Django ORM and existing packages, and deployable directly without gunicorn.
10、burla: large-scale Python distributed computing platform
An open-source Python distributed computing platform that scales code to 10,000 CPUs or GPUs with a single function. It supports a local development experience, automatically syncs local packages to the cluster, and includes a built-in web platform for monitoring long-running tasks.
11、klavis: AI agent tool integration platform
An MCP integration platform that lets AI agents reliably use a wide range of tools at scale. It offers 100+ prebuilt integrations and OAuth authentication, uses progressive discovery to avoid tool overload, and supports cloud hosting and self-deployment. (5.3K stars)
12、computer_use_ootb: out-of-the-box desktop GUI agent solution
An out-of-the-box desktop GUI agent solution supporting an API mode (Claude 3.5 Computer Use) and a local mode (ShowUI, UI-TARS). No Docker required; supports both Windows and macOS, with a friendly Gradio-based interface and support for multiple monitors, any resolution, and remote control. Local mode costs 30-200x less than Claude Computer Use, and the computer can be controlled remotely via a mobile browser. (1.8K stars)
🐢Podcasts & Videos
1、Armin Ronacher interview: Python, Go, Rust, TypeScript, and AI
An interview with Flask creator Armin Ronacher on The Pragmatic Engineer Podcast, sharing insights on programming language choice, the impact of AI tools, and error handling. He believes Rust, while excellent, isn't suited to early-stage startups, that the Python 2 to 3 migration was more challenging than expected, and that AI tools are changing the need for a unified codebase. He also discusses why different programming languages still matter and the downsides of agentic programming.
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