Antra
A desktop music library builder that turns Spotify, Youtube Music Apple Music, Amazon Music, Tidal, Qobuz, and Deezer links into fully tagged local library in FLAC, ALAC, AAC, or MP3.
https://github.com/anandprtp/Antra
A desktop music library builder that turns Spotify, Youtube Music Apple Music, Amazon Music, Tidal, Qobuz, and Deezer links into fully tagged local library in FLAC, ALAC, AAC, or MP3.
https://github.com/anandprtp/Antra
GitHub
GitHub - anandprtp/Antra: A desktop music library builder that turns Spotify, Youtube Music Apple Music, Amazon Music, Tidal, Qobuz…
A desktop music library builder that turns Spotify, Youtube Music Apple Music, Amazon Music, Tidal, Qobuz, and Deezer links into fully tagged local library in FLAC, ALAC, AAC, or MP3. - anandprtp/A...
labs-OO-Agents
NVIDIA Object Oriented Agents: the Pythonic way to build AI Agents.
https://github.com/nvidia-nemo/labs-OO-Agents
NVIDIA Object Oriented Agents: the Pythonic way to build AI Agents.
https://github.com/nvidia-nemo/labs-OO-Agents
GitHub
GitHub - NVIDIA-NeMo/labs-OO-Agents: NVIDIA Object Oriented Agents: the Pythonic way to build AI Agents.
NVIDIA Object Oriented Agents: the Pythonic way to build AI Agents. - NVIDIA-NeMo/labs-OO-Agents
❤2
Django 6.1 released
Django 6.1 is now available with features including model field fetch modes, database-level ForeignKey delete options, and dictionary-based email settings. Django 6.0 has ended mainstream support and will receive only security and data-loss fixes until April 2027.
https://www.djangoproject.com/weblog/2026/aug/05/django-61-released/
Django 6.1 is now available with features including model field fetch modes, database-level ForeignKey delete options, and dictionary-based email settings. Django 6.0 has ended mainstream support and will receive only security and data-loss fixes until April 2027.
https://www.djangoproject.com/weblog/2026/aug/05/django-61-released/
Django Project
Django 6.1 released
Posted by Jacob Walls on Aug. 5, 2026
💩2
Categorization with NLP
A practical look at building grocery categorization without machine learning, using NLP techniques such as stemming, n-grams, syllable splitting, and spell checking. The author shows how a hand-crafted Python algorithm handles messy real-world inputs and edge cases when training data is scarce.
https://softwaremaniacs.org/blog/2026/07/30/categorization-with-nlp/en/
A practical look at building grocery categorization without machine learning, using NLP techniques such as stemming, n-grams, syllable splitting, and spell checking. The author shows how a hand-crafted Python algorithm handles messy real-world inputs and edge cases when training data is scarce.
https://softwaremaniacs.org/blog/2026/07/30/categorization-with-nlp/en/
💯1
How to Conquer Concurrency in Python
A practical guide to Python concurrency that builds from OS fundamentals, processes, threads, race conditions, and the GIL to choosing between asyncio, threading, and multiprocessing. It also explains concurrency vs. parallelism, CPU vs. GPU tradeoffs, and how profiling and strong mental models help avoid common performance mistakes.
https://www.youtube.com/watch?v=chrOym38pw4
A practical guide to Python concurrency that builds from OS fundamentals, processes, threads, race conditions, and the GIL to choosing between asyncio, threading, and multiprocessing. It also explains concurrency vs. parallelism, CPU vs. GPU tradeoffs, and how profiling and strong mental models help avoid common performance mistakes.
https://www.youtube.com/watch?v=chrOym38pw4
YouTube
How to Conquer Concurrency in Python
Concurrency is arguably the hardest concept for Python developers, because the important ideas already assume you understand the operating system underneath. In this chapter, we start from the kernel and system calls, build up processes, threads, race conditions…
🔥1
Wheels, Bottles and Images
Simon Willison packaged a Go CLI inside Python wheels, showing that PyPI can distribute platform-specific binaries containing no Python at all. The article compares wheels with Homebrew bottles and OCI images, highlighting their shared model of immutable artifacts and client-side platform selection, along with growing convergence in registry infrastructure and Sigstore attestations.
https://nesbitt.io/2026/07/30/wheels-bottles-images.html
Simon Willison packaged a Go CLI inside Python wheels, showing that PyPI can distribute platform-specific binaries containing no Python at all. The article compares wheels with Homebrew bottles and OCI images, highlighting their shared model of immutable artifacts and client-side platform selection, along with growing convergence in registry infrastructure and Sigstore attestations.
https://nesbitt.io/2026/07/30/wheels-bottles-images.html
Andrew Nesbitt
Wheels, Bottles and Images
Any sufficiently advanced package manager is indistinguishable from a container registry.
🔥2
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
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
GitHub
GitHub - arc53/DocsGPT: Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research…
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. - arc53/DocsGPT
🤔1
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/
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/
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/
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/
adamj.eu
Python: how time-machine is O(1) where freezegun is O(n) - Adam Johnson
time-machine is my library for mocking the current date and time in Python tests. Its headline advantage over freezegun, the library that inspired it, is speed.
⚡2
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/
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/
Stanislav Golev
Celery: from first task to advanced recipes
Celery is a mature distributed task queue system for Python. It is useful for integrating various services in a decoupled producer-consumer fashion. In this article, I want to share my experience…
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
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
Data For Science
Building an Advanced Agentic Harness | Data For Science
From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.