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
Aft
PicoFlow – a tiny DSL-style Python library for LLM agent workflows
https://news.ycombinator.com/item?id=46706535
https://news.ycombinator.com/item?id=46706535
Microcode
Microcode is an efficient terminal-based AI agent with an internal REPL environment for coding assistance. It leverages Reasoning Language Models (RLMs) to help developers with coding tasks directly from the command line.
https://github.com/modaic-ai/microcode
Microcode is an efficient terminal-based AI agent with an internal REPL environment for coding assistance. It leverages Reasoning Language Models (RLMs) to help developers with coding tasks directly from the command line.
https://github.com/modaic-ai/microcode
GitHub
GitHub - modaic-ai/microcode: context-efficient terminal agent
context-efficient terminal agent. Contribute to modaic-ai/microcode development by creating an account on GitHub.
Prototyping a Live Product Recommender With Python
The article shows how to build a real-time product recommender prototype in Python using Contextual Multi-Armed Bandits to simulate user behavior and validate online learning algorithms like LinUCB. It explains why bandits handle cold-start and context better than traditional models, walks through data generation, feature engineering, offline evaluation, and sets up a live simulation as ...
https://jaehyeon.me/blog/2026-01-29-prototype-recommender-with-python/
The article shows how to build a real-time product recommender prototype in Python using Contextual Multi-Armed Bandits to simulate user behavior and validate online learning algorithms like LinUCB. It explains why bandits handle cold-start and context better than traditional models, walks through data generation, feature engineering, offline evaluation, and sets up a live simulation as ...
https://jaehyeon.me/blog/2026-01-29-prototype-recommender-with-python/
jaehyeon.me
Prototyping a Live Product Recommender with Python
Traditional recommenders struggle with cold-start users and short-term context. Contextual Multi-Armed Bandits (CMAB) continuously learns online, balancing exploitation and exploration based on real-time context. In Part 1, we build a Python prototype to…