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News & links about Python programming. https://pythonhub.dev/ Administrator: @rukeba
django-waffle - the best Feature Flipper for Django!
This is an introduction to django-waffle, a production-ready Django package for controlling feature rollouts through flags, switches, and sampling. It enables gradual releases, A/B testing, and quick kill switches for features that need to be disabled without redeploying
https://www.youtube.com/watch?v=K-Y9XHEZwU8
Python Hub Weekly Digest for 2026-08-23
https://pythonhub.dev/digest/2026-08-23/
Comfy-Org / ComfyUI
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
https://github.com/Comfy-Org/ComfyUI
jsonfold: Making Pretty-Printed JSON Compact and Readable in Python
jsonfold is a streaming post-filter that compacts pretty-printed JSON by folding small arrays and objects onto single lines while preserving existing serializers and custom encoders. It processes JSON incrementally without reparsing the document, keeping additional memory usage bounded while producing more compact, human-readable output for large JSON files.
yair.lenga/a-streaming-json-formatter-that-works-with-existing-serializers-eced220da37d">yair.lenga/a-streaming-json-formatter-that-works-with-existing-serializers-eced220da37d" rel="nofollow">https://medium.com/@yair.lenga/a-streaming-json-formatter-that-works-with-existing-serializers-eced220da37d
key-amnesia
Let your AI agent use your passwords and API keys - without ever letting it see them.
https://github.com/fujitoid/key-amnesia
Numba in the Browser: Unlocking a New Scientific Python Stack in JupyterLite
Numba now runs entirely in the browser through JupyterLite, compiling Python functions to WebAssembly with a new LLVM-based execution engine and delivering substantial performance gains without a server. The work also unlocks browser-native support for the broader Numba ecosystem, including PyMC, PyTensor, and other scientific computing libraries.
https://notebook.link/blog/numba-in-the-browser/
Two lines of Python that segfault the interpreter
Two lines of Python could crash CPython 3.14 through 3.16 because SETADD assumed it was always operating on a real set, while PEP 749 madeconditionalannotationsrebindable from Python code. The post explains how that assumption broke, why it caused a type-confusion crash, and why stable releases and the development branch needed different fixes.
https://deadlovelll.github.io/2026-08-10-conditional-annotations-set-add-crash/
From Routing Checks to Trajectory Testing: Evaluating an Agentic Chatbot
Traditional chatbot testing that checks only final responses misses many agent failures, so this article builds an evaluation framework that progresses from routing checks and task completion to full trajectory testing of tool use, conversations, and intermediate actions. It also shows how to combine LLM-as-a-judge evals, multi-turn testing, and telemetry to debug agent behavior and vali...
https://edcrewe.blogspot.com/2026/08/from-routing-checks-to-trajectory.html
earthtojake / text-to-cad
A library of agent skills for CAD, CAE and CAM
https://github.com/earthtojake/text-to-cad
semantica
Graph-Native Infrastructure for Context and Accountable AI Systems.
https://github.com/semantica-agi/semantica
Agent Memory Guard
Agent Memory Guard is an OWASP Incubator Project that prevents AI agents from being weaponized through their own memory. It implements MITRE ATLAS mitigation AML.M0031 (Memory Hardening) to defend against context poisoning attacks (AML.T0080).
https://github.com/OWASP/www-project-agent-memory-guard
What are some Python automations you built for your life?
https://www.reddit.com/r/Python/comments/1vqggbo/what_are_some_python_automations_you_built_for/
Building an Advanced Agentic Harness
From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.
https://data4sci.com/blog/building-an-advanced-agentic-harness
Celery: from first task to advanced recipes
The article introduces Celery, a distributed task queue for Python, then walks through practical patterns for running, routing, batching, timing out, and retrying asynchronous tasks. It also covers advanced recipes such as preventing parallel execution with Redis locks and integrating Celery tasks with Python’s async/await workflows.
https://sgolev.github.io/blog/2026-07-28-celery-recipes/
Gleam for Python Programmers
A practical guide to Gleam for Python programmers, explaining its syntax, static typing, immutability, pattern matching, and functional programming model through Python comparisons. It gives Python developers a quick way to understand how Gleam differs while building on concepts they already know.
https://third-bit.com/gl4py/
Thinking in Python by Bruce Eckel
https://thinkinginpython.com/
pyhctsa
The most comprehensive time-series feature extraction package in Python.
https://github.com/DynamicsAndNeuralSystems/pyhctsa
centaur
Centaur is frontier, agentic infrastructure that you own. Centaur is like Claude Tag, but open source and on steroids.
https://github.com/paradigmxyz/centaur
Prototype on a laptop, scale to 16 billion rows: one Polars query
The post shows how to prototype a data pipeline locally on 97 million Polymarket orderbook rows, then run the same LazyFrame queries on 16 billion rows using distributed execution. The pipeline pre-aggregates the raw data into small Parquet artifacts in S3 that power a responsive Plotly Dash dashboard without scanning the full dataset on each request.
https://pola.rs/posts/market-data-to-plotly-enterprise-dashboard/
python-game-server
A lightweight server and framework for turn-based multiplayer games.
https://github.com/feberts/python-game-server
Composite: The Pattern Behind Menus, File Systems and Games
The Composite pattern lets you treat individual objects and entire object hierarchies through the same interface, making tree-like structures much easier to manage. Using a simple Python game engine, the video demonstrates how this pattern naturally applies to game scenes, UI frameworks, file systems, menus, and similar hierarchical designs.
https://www.youtube.com/watch?v=ss6je4-nDx8
interlock
Circuit breaker for Python: sync + async in one class, sliding-window rate, slow-call detection, Polly-style resilience pipeline, type-safe API.
https://github.com/bagowix/interlock
A quick look at zero-knowledge proofs
In this post, the authors break down non-cryptocurrency zero-knowledge proofs (ZKPs) using graph theory and Python. By implementing Protocol 4 from Goldreich, Micali, and Wigderson, they show how a prover uses randomized color permutations, nonces, and cryptographic hashes to iteratively prove they hold a valid 3-coloring for a graph without revealing the actual solution to the verifier.
https://bernsteinbear.com/blog/zkp/
Python: introducing emojet, a fast emoji lookup library
emojet is an emoji library for Python: it converts between emoji and their names, in both directions, plus the searching and lookup functions that go with that. It covers the core API of the emoji package, a library that been available for this job since 2014, using the same names and the same data. The difference is that emojet does the work in Rust, running 3.5x faster for conversion, ...
https://adamj.eu/tech/2026/08/12/python-introducing-emojet/
Model Genome: Fingerprinting Whether an LLM Was Trained From Scratch or Derived
Outsiders can assess whether a foundation model was truly built from scratch by analyzing architecture configurations, tokenizer overlap, and weight embeddings using a reproducible fingerprinting pipeline. While architecture and tokenizer artifacts provide the strongest evidence, weight analysis has limitations and cannot cleanly distinguish continued pretraining from training from scratch.
https://huggingface.co/blog/mayafree/model-dna
Introducing Flex: Let the Model Write the Code
DSPy’s new Flex module allows optimizers like GEPA to improve programs by rewriting both their instructions and underlying Python code. This lets the optimizer route easy cases to fast deterministic code while reserving model calls for ambiguity, significantly reducing cost and latency while improving accuracy.
https://www.cmpnd.ai/blog/let-the-model-write-the-code.html
Python Hub Weekly Digest for 2026-08-16
https://pythonhub.dev/digest/2026-08-16/
SkyRL
A Modular Full-stack RL Library for LLMs.
https://github.com/NovaSky-AI/SkyRL
Python: how time-machine is O(1) where freezegun is O(n)
A benchmark shows Python’s time-machine library stays O(1) when mocking time, while freezegun scales O(n) with the number of loaded module attributes and becomes dramatically slower as projects grow. The difference comes from time-machine swapping CPython function pointers directly, while freezegun scans loaded modules to replace references to date and time functions.
https://adamj.eu/tech/2026/08/03/python-time-machine-o1-freezegun-on/
arc53 / DocsGPT
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
https://github.com/arc53/DocsGPT