Wrote a blog post to give a status update on WASI support for CPython as PEP 816 got accepted!
snarky.ca/state-of-was...
02.03.2026 19:31
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Wrote a blog post to give a status update on WASI support for CPython as PEP 816 got accepted!
snarky.ca/state-of-was...
I totally get you! Let us know which option you choose in the end.
The newest edition of posit::glimpse() is here! π
Read a roundup of major #RStats & #Python updates: ggplot2 4.0.0, dplyr 1.2.0, ellmer 0.4.0, Positron native Jupyter Notebook support , and more.
ποΈ Plus, registration for posit::conf(2026) is officially OPEN!
Find it here: posit.co/blog/posit-g...
RustPython: A Python Interpreter written in Rust β 21785 https://github.com/RustPython/RustPython
A bar chart comparing the performance of LiteStar (orange bars) and FastAPI (green bars) in Requests Per Second (RPS) as the number of dependencies ('Num Deps') increases from 0 to 5. The data shows that LiteStar consistently maintains a higher throughput than FastAPI across all dependency counts. For example, at 0 dependencies, LiteStar reaches 45573 RPS versus FastAPI's 37004 RPS. At 5 dependencies, LiteStar holds 32877 RPS while FastAPI drops to 26463 RPS.
Testing #FastAPI vs #LiteStar with nested empty deps (0-5 levels).
Both show smooth perf degradation, unlike LiteStar's flat deps with TaskGroup.
Still, 5 deps = 28% drop for LiteStar β DI overhead is significant even when deps do nothing.
#Python
A bar chart comparing performance in Requests Per Second (RPS) between 'TaskGroup' (purple bars) and 'Await' (orange bars) based on the number of dependencies ('Num Deps'). For 1 dependency, TaskGroup achieves 4604 RPS and Await achieves 4628 RPS. For 2 dependencies, TaskGroup is at 4085 RPS while Await is at 4558 RPS. For 4 dependencies, TaskGroup drops to 3817 RPS, whereas Await maintains 4529 RPS, showing that Await scales better as dependency count increases.
Quick perf test: changed #LiteStar dependency resolution from TaskGroup to await.
RPS impact? Minimal vs 17% degradation with TaskGroup at 4 deps. Each request does 4 DB calls.
TaskGroup overhead matters more than expected.
More details in thread π
#Python #Backend
My #PyCon RU 2025 talk (Best Speaker award! π) is now on YouTube: youtu.be/MpQgrhLO6aE
English subtitles available! π¬π§
I break down the Dependency Inversion Principle, compare popular #Python #DI frameworks, and show why they matter for building better applications.
Very interesting talk, thank you!
Wow! I haven't seen it before
This guy is doing a great job.
Flask vs Django: Key Differences Every Python Developer Should Know #programming #webdesign
ift.tt/Kldvhmr
Flask vs Django: a concise guide for Python developers comparing lightweight flexibility with batteries-included completeness. Learn wheβ¦
Great news! Hope it will help to make litestar faster
Thank you for the research, it's very interesting :) Now I will know about this behavior of LiteStar!
Ohh, I'm using litestar, but I haven't known about it!
A picture showing an example output of the program on the city of MontrΓ©al and the autumn color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled MONTRΓAL - QUΓBEC. The latitude and longitude are also written under the title.
A picture showing an example output of the program on the city of Paris and the emerald color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled PARIS - FRANCE. The latitude and longitude are also written under the title.
A picture showing an example output of the program on the city of Tokyo and the japanese ink color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled TOKYO - JAPAN. The latitude and longitude are also written under the title.
A picture showing an example output of the program on the city of Berlin and the noir color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled BERLIN - GERMANY. The latitude and longitude are also written under the title.
Here is what I've been doing this past week: git.olaren.dev/Olaren/mapto...
A little python program that generates poster for cities! You might have seen stuff like this around lately, but I modified it to put an emphasis on rail infrastructure :3
Building High-Performance APIs with FastAPI and Async Python:
dasroot.net/posts/2026/0...
#FastAPI #Async #Python #Redis
That's interesting, thank you!
One Trick That Can Rewrite Pythonβs Behavior: Monkey Patching
Hi, Maxim! Nice to meet you
Oops, of course, no "Paython dev" π I'm Python dev π
That's ΠΏΠΈΠ·Π΄Π΅Ρ
Thank you, I will try it!
If a text column #Python #Pandas data frame uses lots of memory and repeats, make it a category:
df['x'].memory_usage(deep=True)
# result: 8,093,417
df['x'] = df['x'].astype('category')
df['x'].memory_usage(deep=True)
# result: 154,853
A 99% savings!
Hi everyone!
I'm Maria Sosnovskaya, Paython dev
It's my first post :)
I hope, I will find a lot of expert content here!
#Python #IAmNewHere
You can also see to Nim: convenient like Python, but faster and with more language features :)