Python Hub Weekly Digest for 2026-10-18
This week, TurboPython was introduced as a Python-to-C++ compiler that converts typed Python to native binaries without a runtime. Python 3.15.0 was released, featuring enhancements like lazy imports and a built-in immutable dictionary. The ai-agents-zero-to-hero project offers a comprehensive guide to building AI agents using standard Python 3.11+. Have a good week and happy coding!
đź’– Most Popular
TurboPython
A Python-to-C++ compiler. Typed Python to native binaries with no Python runtime, no GC, no GIL.
ai-agents-zero-to-hero
Learn AI agents from first principles to production. Zero mandatory dependencies, pure standard Python 3.11+, and no magic frameworks.
clausal-prolog
Clausal Prolog is a Python implementation of Prolog for neurosymbolic AI, combining symbolic reasoning with Python and neural models in the same process without a separate runtime.
Pixora
A Python Library for Pixel Art Conversion.
đź“– Articles
How to Take a Website Screenshot with Python
Vendor-authored Site-Shot tutorial: Python SDK, CSV batch captures, and a local HTML index. Requires a paid Site-Shot API plan.
Python Hub Weekly Digest for 2026-10-11
apsw: Another Python SQLite Wrapper
How we built an async-aware Python profiler
Learn how Datadog made Python profiling async-aware while reducing profiler overhead and preserving context across asyncio tasks.
First Steps into Memory Management in Python
Polars 2.0
Polars 2.0 introduces initial out-of-core spill-to-disk support, major performance improvements, first-class SQL, a new Map dtype, and stricter dtype handling. With the SQL and performance work, Polars reports leading DataFusion and DuckDB on TPC-H and TPC-DS benchmarks.
Jev for Python engineers
Vercel’s Python AI SDK adds support for Jev, a fast universal classifier that answers structured choice, scoring, and probability questions without task-specific training. The post tests Jev on classifying Python vs. English and generating Python ASTs, showing that it works well for narrow decisions but still struggles when pushed into general code generation.
Django: serve apple-app-site-association and assetlinks.json
Django can serve the apple-app-site-association and assetlinks.json files needed for Apple Universal Links and Android App Links, letting installed apps securely claim website URLs and support password autofill and passkeys. The post walks through the required JSON, Django views, tests, caching, and production verification with Apple’s CDN and Google’s Digital Asset Links tools.
The things you own end up owning you
NumPy is being ported toward Python’s Limited API so it could eventually ship abi3 wheels that work across multiple Python versions, reducing the large per-version wheel matrix and easing support for new Python releases. The same work is also pushing NumPy toward subinterpreter support by replacing static types and global state with heap types and per-module state, though multiarray rema...
AI Development on Windows: from PyTorch and llama.cpp to Windows ML
Microsoft is expanding local AI development on Windows with experimental GGUF support in Windows ML via llama.cpp, new native inference APIs, and an OpenAI-compatible endpoint for running models locally. The broader stack also adds native PyTorch and Triton support on Windows Arm64, giving developers a path to train and optimize models with familiar tools and deploy them through Windows ...
Making np.searchsorted up to 25Ă— Faster in NumPy 2.5
The article speeds up binary search by batching independent searches with NumPy’s vectorized operations, then reformulates the algorithm to use only O(1) extra memory. After porting it to C++, the approach achieves up to a 25× speedup over NumPy 2.4’s implementation.
Rust for CPython (Python Language Summit 2026)
CPython developers are exploring a gradual introduction of Rust, starting with an optional Rust implementation of zlib in Python 3.16 before considering broader use in areas like I/O, JSON, parsing, and memoryview. The proposal aims to improve safety and performance without rewriting CPython, while addressing concerns around platform support, dependencies, build complexity, and whether c...
Making a mathematical model for better Indic keyboards
The article develops a mathematical model for more efficient Indic touchscreen keyboards, starting with Tamil and mapping script structure into Boolean logic and compact input states. It uses Python with SymPy to derive and simplify the logic, then implements the resulting input engine in TypeScript.
Can Jev save my inbox from the Democrats?
The author outlines an end-to-end ML project evaluating whether TypeSafe’s Jev, accessed through OpenRouter, and smaller local models can effectively filter political fundraising emails from a personal inbox. He ultimately builds a 23.4 MB quantized ONNX SetFit model using mdbr-leaf-mt and confidence thresholding, creating a fast, CPU-friendly local classifier while exposing the remainin...
Orchestrating Disposable Browser
Glazer replaced race-prone parallel startup with readiness signals between Python and Docker containers, allowing components to start concurrently without assuming that running means ready. That design cut cold starts from 8–10 seconds to about 4.5 seconds, and caching the Tor data directory brought startup down to a consistent 1.9–2.1 seconds.
Building a Faster (Rust-Based) Python Driver for ScyllaDB
ScyllaDB is building a new Python driver on top of its Rust driver using PyO3, with most serialization, deserialization, request execution, and concurrency handled in Rust for better performance and maintainability. Benchmarks show especially large gains under concurrent workloads, while the project still needs production hardening, broader feature coverage, and a compatibility layer for...
Architecture Deep Dive: Startup Time & Import Optimization
The post explains how the VEX shell AI agent reduced its cold start time from about 4 seconds to ~2 seconds by bypassing heavy initialization for quick CLI commands and deferring expensive imports until they are needed. It used tools like tuna to identify import bottlenecks, moved type-only imports behind TYPE_CHECKING, and lazily imported heavy dependencies inside function scopes.
⚙️ Projects
openTPU
An open-source AI accelerator, developed by AI: RTL, ISA, simulator, compiler and profiler in one repo. Runs Qwen3, LFM2.5 and Qwen3.5 on a Kintex-7 PCIe card.
rGPU
Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation.
ldraw-nova
Agent tooling for generative LEGO models building, built with Astra and Opus 5.5, powered by Jev
Pyronaut
Pyronaut is a high-performance polyglot application platform that brings Micronaut features like HTTP routing, dependency injection, configuration, validation, and Java library access to Python via GraalPy.
pytorch / pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
WagtailRocket
A boilerplate for CMS websites developed in Wagtail to save you tens of hours of work.
RuleFlow
A Python research framework and terminal-based IDE (RuleFlow Studio) for modeling, evolving, and analyzing discrete complex systems like ECA and SSS.
🎬 Videos
Build Your Own Agentic Harness in Python - Full Tutorial
This tutorial demonstrates how to build a custom AI agentic harness in pure Python by progressively integrating LLM communication, tool calling, and Model Context Protocol (MCP) support. It covers the full development loop from scratch, enabling persistent memory and model routing to create functional, autonomous agents.
8 Python 3.15 Features You Need to Know
Python 3.15 introduces practical enhancements such as lazy imports, a built-in immutable dictionary, and streamlined unpacking in comprehensions. These updates, alongside a new sampling profiler and default UTF-8 encoding, aim to improve developer productivity and application performance.
Build & Train a GLM-5.3-Flash Model From Scratch with Python
This tutorial demonstrates how to build and train a compact 25-million parameter multimodal language model from scratch using modern techniques like mixture-of-experts and sparse attention. It further explores the AI researcher's role by guiding viewers through pre-training, reinforcement learning experiments, and the critical importance of designing effective data-driven environments.
đź‘ľ Reddits
What are some features in Python that you discovered many years later?
Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.