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.

https://adamj.eu/tech/2026/10/02/django-app-links/

Pixora

A Python Library for Pixel Art Conversion.

https://github.com/sepandhaghighi/pixora

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.

https://triangulatedexistence.mataroa.blog/blog/making-a-mathematical-model-for-better-indic-keyboards/

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.

https://www.youtube.com/watch?v=H5o1P8RMiMw

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.

https://pola.rs/posts/release-polars-2/

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.

https://github.com/tradertanmay/ai-agents-zero-to-hero

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/

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

TurboPython

A Python-to-C++ compiler. Typed Python to native binaries with no Python runtime, no GC, no GIL.

https://tpy-lang.org/

WagtailRocket

A boilerplate for CMS websites developed in Wagtail to save you tens of hours of work.

https://github.com/AlexBrence/WagtailRocket

pytorch / pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

https://github.com/pytorch/pytorch

RuleFlow

A Python research framework and terminal-based IDE (RuleFlow Studio) for modeling, evolving, and analyzing discrete complex systems like ECA and SSS.

https://github.com/RuleFlow-OSS/RuleFlow

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/

security-suite

Open-source security suite for OSINT, web scanning, API testing, SIEM integration, and AI-powered analysis.

https://github.com/TheSecuredAnalyst/security-suite

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