Violit
Violit is a next-generation Python web framework that perfectly solves Streamlit's critical Full Script Rerun issue with O(1) State Architecture.
https://github.com/violit-dev/violit
Violit is a next-generation Python web framework that perfectly solves Streamlit's critical Full Script Rerun issue with O(1) State Architecture.
https://github.com/violit-dev/violit
GitHub
GitHub - violit-dev/violit: The High-Performance Python Web Framework. The simplicity of Streamlit, minus the reruns
The High-Performance Python Web Framework. The simplicity of Streamlit, minus the reruns - violit-dev/violit
django-polymorphic - Polymorphic models in Django
The video explains Django model inheritance and polymorphic models, showing how django-polymorphic returns the correct subclass types when querying a base model through the ORM. It also covers Django Admin support and integrations with other Django ecosystem packages to make polymorphic models easier to manage.
https://www.youtube.com/watch?v=d8MC2FQ_nqU
The video explains Django model inheritance and polymorphic models, showing how django-polymorphic returns the correct subclass types when querying a base model through the ORM. It also covers Django Admin support and integrations with other Django ecosystem packages to make polymorphic models easier to manage.
https://www.youtube.com/watch?v=d8MC2FQ_nqU
YouTube
django-polymorphic - Polymorphic models in Django
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Tracking 13,000 satellites in under 3 seconds from Python
https://www.reddit.com/r/Python/comments/1qif5o1/tracking_13000_satellites_in_under_3_seconds_from/
https://www.reddit.com/r/Python/comments/1qif5o1/tracking_13000_satellites_in_under_3_seconds_from/
Reddit
From the Python community on Reddit: Tracking 13,000 satellites in under 3 seconds from Python
Explore this post and more from the Python community
7 Things You Didn’t Know Dataclasses Could Do
The video walks through seven lesser-known features of Python’s dataclasses that make code safer, clearer, and easier to maintain, beyond basic use. Each tip shows how dataclasses can reduce boilerplate and improve common patterns in everyday Python code.
https://www.youtube.com/watch?v=YTsJQ4ixq88
The video walks through seven lesser-known features of Python’s dataclasses that make code safer, clearer, and easier to maintain, beyond basic use. Each tip shows how dataclasses can reduce boilerplate and improve common patterns in everyday Python code.
https://www.youtube.com/watch?v=YTsJQ4ixq88
YouTube
7 Things You Didn’t Know Dataclasses Could Do
💡 Learn how to design great software in 7 steps: https://arjan.codes/designguide.
Dataclasses are often treated as nothing more than a shortcut for generating init methods, but there’s a lot more going on under the surface. In this video, I walk through…
Dataclasses are often treated as nothing more than a shortcut for generating init methods, but there’s a lot more going on under the surface. In this video, I walk through…
Asyncio Finally Got Peewee
Charles Leifer introduces an asyncio compatibility layer for the Peewee ORM that uses greenlet to bridge synchronous database logic with asynchronous event loops without requiring a full codebase rewrite. This implementation allows developers to run standard Peewee queries and transactions within async contexts by using a task-local state management system and specific execution helpers ...
https://charlesleifer.com/blog/asyncio-finally-got-peewee/
Charles Leifer introduces an asyncio compatibility layer for the Peewee ORM that uses greenlet to bridge synchronous database logic with asynchronous event loops without requiring a full codebase rewrite. This implementation allows developers to run standard Peewee queries and transactions within async contexts by using a task-local state management system and specific execution helpers ...
https://charlesleifer.com/blog/asyncio-finally-got-peewee/
ruvnet / wifi-densepose
Production-ready implementation of InvisPose - a revolutionary WiFi-based dense human pose estimation system that enables real-time full-body tracking through walls using commodity mesh routers
https://github.com/ruvnet/wifi-densepose
Production-ready implementation of InvisPose - a revolutionary WiFi-based dense human pose estimation system that enables real-time full-body tracking through walls using commodity mesh routers
https://github.com/ruvnet/wifi-densepose
GitHub
GitHub - ruvnet/wifi-densepose: Production-ready implementation of InvisPose - a revolutionary WiFi-based dense human pose estimation…
Production-ready implementation of InvisPose - a revolutionary WiFi-based dense human pose estimation system that enables real-time full-body tracking through walls using commodity mesh routers - ...
webctl
Browser automation for AI agents and humans, built on the command line.
https://github.com/cosinusalpha/webctl
Browser automation for AI agents and humans, built on the command line.
https://github.com/cosinusalpha/webctl
GitHub
GitHub - cosinusalpha/webctl: Browser automation via CLI — for humans and agents
Browser automation via CLI — for humans and agents - cosinusalpha/webctl
dj-celery-panel
Your Celery task inspector inside the Django admin.
https://github.com/yassi/dj-celery-panel
Your Celery task inspector inside the Django admin.
https://github.com/yassi/dj-celery-panel
GitHub
GitHub - yassi/dj-celery-panel: Your Celery task inspector inside the Django admin
Your Celery task inspector inside the Django admin - yassi/dj-celery-panel
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AutoMem
AutoMem is a graph-vector memory service that gives AI assistants durable, relational memory.
https://github.com/verygoodplugins/automem
AutoMem is a graph-vector memory service that gives AI assistants durable, relational memory.
https://github.com/verygoodplugins/automem
GitHub
GitHub - verygoodplugins/automem: AutoMem is a graph-vector memory service that gives AI assistants durable, relational memory:
AutoMem is a graph-vector memory service that gives AI assistants durable, relational memory: - verygoodplugins/automem
Django Time-Based Lookups: A Performance Trap
The article shows how certain Django time-based field lookups can bypass database indexes and turn a simple filter into a full table scan, dramatically slowing queries. It then demonstrates a much faster pattern by computing time boundaries in Python so the database can use the existing index and drop execution time from tens of seconds to under a second.
https://johnnymetz.com/posts/django-time-based-lookups-performance/
The article shows how certain Django time-based field lookups can bypass database indexes and turn a simple filter into a full table scan, dramatically slowing queries. It then demonstrates a much faster pattern by computing time boundaries in Python so the database can use the existing index and drop execution time from tens of seconds to under a second.
https://johnnymetz.com/posts/django-time-based-lookups-performance/
Johnny Metz
Django Time-Based Lookups: A Performance Trap
Django’s field lookups are one of the ORM’s best features, but time-based lookups can quietly bypass database indexes, turning fast queries into expensive full table scans.
A Slow Production Query
I ran into this while debugging a 30 second query on a large…
A Slow Production Query
I ran into this while debugging a 30 second query on a large…
MimicKit
A lightweight suite of motion imitation methods for training controllers.
https://github.com/xbpeng/MimicKit
A lightweight suite of motion imitation methods for training controllers.
https://github.com/xbpeng/MimicKit
GitHub
GitHub - xbpeng/MimicKit: A lightweight suite of motion imitation methods for training controllers.
A lightweight suite of motion imitation methods for training controllers. - xbpeng/MimicKit
Speeding up Pillow's open and save
The author benchmarks and improves Pillow’s image open/save performance by avoiding unnecessary plugin imports and using lazy loading, leading to large speed gains in Python. Results show opening PNG images can be around 2.6 times faster and WebP up to 14 times faster, with similar improvements in saving images, and the changes are included in upcoming Pillow releases.
https://hugovk.dev/blog/2026/faster-pillow/
The author benchmarks and improves Pillow’s image open/save performance by avoiding unnecessary plugin imports and using lazy loading, leading to large speed gains in Python. Results show opening PNG images can be around 2.6 times faster and WebP up to 14 times faster, with similar improvements in saving images, and the changes are included in upcoming Pillow releases.
https://hugovk.dev/blog/2026/faster-pillow/
Hugo van Kemenade
Speeding up Pillow's open and save
Vision-Agents
Open Vision Agents by Stream. Build Vision Agents quickly with any model or video provider. Uses Stream's edge network for ultra-low latency.
https://github.com/GetStream/Vision-Agents
Open Vision Agents by Stream. Build Vision Agents quickly with any model or video provider. Uses Stream's edge network for ultra-low latency.
https://github.com/GetStream/Vision-Agents
GitHub
GitHub - GetStream/Vision-Agents: Open Vision Agents by Stream. Build Vision Agents quickly with any model or video provider. Uses…
Open Vision Agents by Stream. Build Vision Agents quickly with any model or video provider. Uses Stream's edge network for ultra-low latency. - GetStream/Vision-Agents
Oban, the job processing framework from Elixir, has come to Python
https://www.dimamik.com/posts/oban_py/
https://www.dimamik.com/posts/oban_py/
Hi, I'm Dima
Oban.py - deep dive
Oban, the job processing framework from Elixir, has finally come to Python. I spent some time exploring it, and here is how it works.
Let's Build Pipeline Parallelism from Scratch
The tutorial walks through building pipeline parallelism from the ground up, explaining how to split large AI models and training workloads across multiple GPUs to improve training efficiency. It breaks down concepts with step-by-step examples so developers can understand how data and compute are partitioned and coordinated in a distributed training system.
https://www.youtube.com/watch?v=D5F8kp_azzw
The tutorial walks through building pipeline parallelism from the ground up, explaining how to split large AI models and training workloads across multiple GPUs to improve training efficiency. It breaks down concepts with step-by-step examples so developers can understand how data and compute are partitioned and coordinated in a distributed training system.
https://www.youtube.com/watch?v=D5F8kp_azzw
YouTube
Let's Build Pipeline Parallelism from Scratch – Tutorial
Pipeline parallelism speeds up training of AI models by splitting a massive model across multiple GPUs and processing data like an assembly line, ensuring no single device has to hold the entire model in memory.
This course teaches pipeline parallelism from…
This course teaches pipeline parallelism from…
I created a game engine for Django?
The author built a multiplayer Snake game in the browser using only Python and Django LiveView, with no custom JavaScript, by keeping game state on the server and broadcasting rendered HTML over WebSockets.
https://en.andros.dev/blog/6e9e4485/i-created-a-game-engine-for-django/
The author built a multiplayer Snake game in the browser using only Python and Django LiveView, with no custom JavaScript, by keeping game state on the server and broadcasting rendered HTML over WebSockets.
https://en.andros.dev/blog/6e9e4485/i-created-a-game-engine-for-django/
en.andros.dev
I created a game engine for Django? | Andros Fenollosa
TL;DR: Complete multiplayer game in the browser made of 270 lines of Python and 0 lines of JavaScript running on Django thanks to Django LiveView.
Aft
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