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Human-curated Python news, projects, articles & tools.

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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...

https://blog.python.org/2026/09/language-summit-2026-rust-for-cpython/
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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.

https://github.com/neurosymbolica/clausal-prolog
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Building a lift scheduling algorithm in Python

The post demonstrates how to implement a multi-elevator scheduling algorithm in Python using the LOOK algorithm to prevent out-of-order directional stops. It further explores multi-lift optimization by separating system concerns between a Dispatcher and a LiftController state machine to assign requests based on weighted cost functions.

https://www.yellowduck.be/posts/building-a-lift-scheduling-algorithm-in-python
Python 3.15 for Data Scientists: Lazy Imports, frozendict, and Tachyon

Python 3.15 adds lazy imports, a built-in frozendict, and the Tachyon profiler. Learn how data scientists can use each, with before-and-after code.

https://codecut.ai/python-3-15-features-data-scientists/
Build your own decision model

Nish Tahir shows how to turn a small Qwen model into a fixed-choice decision model by masking outputs to allowed tokens, then evaluates and fine-tunes it on CommonsenseQA. Because the raw token probabilities are overconfident, he applies temperature scaling so the reported confidence better matches actual accuracy.

https://nishtahir.com/build-your-own-decision-model/
Building a GPU Compiler from Scratch

Progressive multi-level IR lowering systematically transforms high-level PyTorch graphs into optimized CUDA code through six explicit compiler stages. By separating graph fusion, hardware scheduling, and autotuning, the architecture makes GPU optimization easier to reason about, measure, and improve.

https://kernelspace.substack.com/p/building-a-gpu-compiler-from-scratch
google / artemis

ARTEMIS turns natural-language instructions into reliable Android automation. It automates end-to-end workflows, captures logs, and integrates seamlessly with AI coding assistants such as Antigravity, Codex, and Claude Code. It also achieves 99%+ success rate on AndroidWorld Benchmark.

https://github.com/google/artemis
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Polars Query Optimization: Lazy vs. Eager Mode

Learn how Polars' lazy mode improves memory efficiency and performance compared to eager mode by building an optimized query plan before execution. By utilizing predicate and projection pushdown, lazy mode significantly reduces resource usage when working with large datasets, making it the preferred approach for most data analysis tasks.

https://www.youtube.com/watch?v=ux7MlXxFDQY
Automating Django deployments with Deployer

The article demonstrates how to implement a multi-lift scheduling algorithm in Python using LOOK to serve requests in the current direction before reversing. It then explores multi-lift optimization by separating responsibilities between a Dispatcher and a LiftController state machine, with requests assigned using weighted cost functions.

https://codewithstein.com/automating-django-deployments-with-deployer/
whatsnewt

Python core developer Barry Warsaw text adventure game to test your knowledge of Python 3.15 by solving puzzles.

https://pypi.org/project/whatsnewt/
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