OAUTH on client-side with Python
An overview of OAUTH in non-web apps and the general data-flow of an OAUTH Client.
https://jakabszilard.work/posts/oauth-in-python
An overview of OAUTH in non-web apps and the general data-flow of an OAUTH Client.
https://jakabszilard.work/posts/oauth-in-python
jakabszilard.work
Personal Profile
Rendering 18,000 videos in real-time with Python
Learn how the author used game engine tech to solve a video streaming problem.
https://madebymohammed.com/pysaic
Learn how the author used game engine tech to solve a video streaming problem.
https://madebymohammed.com/pysaic
Madebymohammed
Rendering 18,000 videos in real-time with Python
How I used game engine tech to solve a video streaming problem
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github / awesome-copilot
Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot.
https://github.com/github/awesome-copilot
Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot.
https://github.com/github/awesome-copilot
GitHub
GitHub - github/awesome-copilot: Community-contributed instructions, agents, skills, and configurations to help you make the most…
Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. - github/awesome-copilot
iNTERCEPT
A free and open-source platform that unites the best signal intelligence tools into a single, accessible interface.
https://github.com/smittix/intercept
A free and open-source platform that unites the best signal intelligence tools into a single, accessible interface.
https://github.com/smittix/intercept
GitHub
GitHub - smittix/intercept: iNTERCEPT, a free and open-source platform that unites the best signal intelligence tools into a single…
iNTERCEPT, a free and open-source platform that unites the best signal intelligence tools into a single, accessible interface. - smittix/intercept
❤1
WSE
Rust-powered WebSocket engine for Python. Up to 14M msg/s JSON, 30M msgpack/zlib fan-in, 2.1M del/s fan-out, zero-GIL JWT, E2E encryption.
https://github.com/silvermpx/wse
Rust-powered WebSocket engine for Python. Up to 14M msg/s JSON, 30M msgpack/zlib fan-in, 2.1M del/s fan-out, zero-GIL JWT, E2E encryption.
https://github.com/silvermpx/wse
GitHub
GitHub - silvermpx/wse: WSE - Rust-powered WebSocket engine for Python. Up to 5M del/s fan-out, native cluster binary protocol…
WSE - Rust-powered WebSocket engine for Python. Up to 5M del/s fan-out, native cluster binary protocol, zero-GIL JWT, E2E encryption - silvermpx/wse
Does it still make sense to learn python or any programming language in 2026
https://www.reddit.com/r/learnpython/comments/1ritmiw/does_it_still_make_sense_to_learn_python_or_any/
https://www.reddit.com/r/learnpython/comments/1ritmiw/does_it_still_make_sense_to_learn_python_or_any/
Reddit
From the learnpython community on Reddit
Explore this post and more from the learnpython community
CLI subcommands with lazy imports
The post explains that Python 3.15’s new lazy imports feature is great for CLI tools, but naïve argparse subcommand patterns accidentally force all lazy imports to execute early, negating the benefit. It then shows two simple patterns that preserve laziness for subcommands: dispatching with a match statement, or wrapping lazy-imported callables in lambdas so they are only reified when th...
https://snarky.ca/subcommands-with-lazy-imports/
The post explains that Python 3.15’s new lazy imports feature is great for CLI tools, but naïve argparse subcommand patterns accidentally force all lazy imports to execute early, negating the benefit. It then shows two simple patterns that preserve laziness for subcommands: dispatching with a match statement, or wrapping lazy-imported callables in lambdas so they are only reified when th...
https://snarky.ca/subcommands-with-lazy-imports/
Tall, Snarky Canadian
CLI subcommands with lazy imports
In case you didn't hear, PEP 810 got accepted which means Python 3.15 is going to support lazy imports! One of the selling points of lazy imports is with code that has a CLI so that you only import code as necessary, making the app a bit more snappy
taylorwilsdon / google_workspace_mcp
Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace / G Suite MCP Server & CLI Tool
https://github.com/taylorwilsdon/google_workspace_mcp
Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace / G Suite MCP Server & CLI Tool
https://github.com/taylorwilsdon/google_workspace_mcp
GitHub
GitHub - taylorwilsdon/google_workspace_mcp: Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search &…
Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace / G Suite MCP Server & CLI Tool - taylorwilsdon/goo...
Capturing Plots in R and Python: A Tale of Two Architectures
This post explains how plot capture works differently in Python and R: in Python (especially Jupyter) all figure output goes through a single display system that makes automatic capture reliable, whereas in R the graphics architecture has no central display layer so capturing plots is harder and often requires explicit publishing. It contrasts the architectural reasons behind these diffe...
https://blog.gofigr.io/posts/capturing-plots-in-r-and-python
This post explains how plot capture works differently in Python and R: in Python (especially Jupyter) all figure output goes through a single display system that makes automatic capture reliable, whereas in R the graphics architecture has no central display layer so capturing plots is harder and often requires explicit publishing. It contrasts the architectural reasons behind these diffe...
https://blog.gofigr.io/posts/capturing-plots-in-r-and-python
blog.gofigr.io
Capturing Plots in R and Python: A Tale of Two Architectures | GoFigr Blog
ai-functions
Python functions powered by AI agents - with runtime post-conditions for reliable agentic workflows.
https://github.com/strands-labs/ai-functions
Python functions powered by AI agents - with runtime post-conditions for reliable agentic workflows.
https://github.com/strands-labs/ai-functions
GitHub
GitHub - strands-labs/ai-functions: Python functions powered by AI agents - with runtime post-conditions for reliable agentic workflows.
Python functions powered by AI agents - with runtime post-conditions for reliable agentic workflows. - strands-labs/ai-functions
Properties vs Methods in Python: What Your Code Is Promising
The video explains when to use a property versus a method in Python, highlighting how each communicates different expectations around cost, side effects, and behavior. It also argues that async properties are typically a design smell and shows how to keep asynchronous work explicit while maintaining clean object design.
https://www.youtube.com/watch?v=wHLZ_uTrCYA
The video explains when to use a property versus a method in Python, highlighting how each communicates different expectations around cost, side effects, and behavior. It also argues that async properties are typically a design smell and shows how to keep asynchronous work explicit while maintaining clean object design.
https://www.youtube.com/watch?v=wHLZ_uTrCYA
YouTube
Python Properties vs Methods: The Contract You Didn’t Know You Were Making
🧱 Build software that lasts. Join the Software Design Mastery waiting list → https://arjan.codes/mastery.
In this video, I break down when something in Python should be a property and when it should be a method, and why that choice matters more than it looks.…
In this video, I break down when something in Python should be a property and when it should be a method, and why that choice matters more than it looks.…
Timber – Ollama for classical ML models, 336x faster than Python
https://github.com/kossisoroyce/timber
https://github.com/kossisoroyce/timber
GitHub
GitHub - kossisoroyce/timber: Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost…
Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 33...
Validating data with pointblank in python
One of the most common tasks of any organization is reviewing data to ensure that it is accurate and does not contain errors. Commonly, this is done by producing graphs or summary information like a median or mean and confirming that it looks reasonable. Pointblank is a newer tool that allows you to really dig into a dataset and task assumptions in a robust and reproducible manner.
https://www.markpitblado.me/blog/validating-data-with-pointblank-in-python
One of the most common tasks of any organization is reviewing data to ensure that it is accurate and does not contain errors. Commonly, this is done by producing graphs or summary information like a median or mean and confirming that it looks reasonable. Pointblank is a newer tool that allows you to really dig into a dataset and task assumptions in a robust and reproducible manner.
https://www.markpitblado.me/blog/validating-data-with-pointblank-in-python
Mark Pitblado
Validating data with pointblank in python
One of the most common tasks of any organization is reviewing data to ensure that it is accurate and does not contain errors. Commonly, this is done by producing graphs or summary information like a median or mean and confirming that it looks reasonable.…
Update on array API adoption in scikit-learn
The article explains how scikit-learn is adopting the Python Array API standard, allowing machine-learning code to run on different array libraries (NumPy, PyTorch, CuPy) without rewriting algorithms. This enables the same scikit-learn models to automatically leverage accelerators like GPUs and improves interoperability across the scientific Python ecosystem.
https://labs.quansight.org/blog/array-api-scikit-learn-2026
The article explains how scikit-learn is adopting the Python Array API standard, allowing machine-learning code to run on different array libraries (NumPy, PyTorch, CuPy) without rewriting algorithms. This enables the same scikit-learn models to automatically leverage accelerators like GPUs and improves interoperability across the scientific Python ecosystem.
https://labs.quansight.org/blog/array-api-scikit-learn-2026
Python 3.12.13, 3.11.15 and 3.10.20 are now available!
New security releases for 3.10, 3.11 and 3.12 are now available. (As these Python versions are now in security-fix-only mode, these are source-only releases, and there is no pre-set release cadence.)
https://blog.python.org/2026/03/python-31213-31115-31020/
New security releases for 3.10, 3.11 and 3.12 are now available. (As these Python versions are now in security-fix-only mode, these are source-only releases, and there is no pre-set release cadence.)
https://blog.python.org/2026/03/python-31213-31115-31020/
Python Insider
Python 3.12.13, 3.11.15 and 3.10.20 are now available! | Python Insider
Python Releases For Your Security! New security releases for 3.10, 3.11 and 3.12 are now available.
MedKit
MedKit is a high-performance, unified SDK that transforms fragmented medical APIs into a single, programmable platform. It provides a clean interface for OpenFDA, PubMed, and ClinicalTrials.gov, augmented with a clinical intelligence layer and relationship mapping.
https://github.com/interestng/medkit
MedKit is a high-performance, unified SDK that transforms fragmented medical APIs into a single, programmable platform. It provides a clean interface for OpenFDA, PubMed, and ClinicalTrials.gov, augmented with a clinical intelligence layer and relationship mapping.
https://github.com/interestng/medkit
GitHub
GitHub - interestng/medkit: Unified Python SDK for OpenFDA, PubMed, and ClinicalTrials.gov with clinical intelligence, interaction…
Unified Python SDK for OpenFDA, PubMed, and ClinicalTrials.gov with clinical intelligence, interaction detection, and research tools. - interestng/medkit
Serving Private Files with Django and S3
The article shows how to securely serve private user files stored in Amazon S3 from a Django app by keeping the bucket private and generating time-limited pre-signed URLs when a user is authorized to access a file. This approach lets S3 handle file delivery directly while Django controls access, avoiding slow proxying through the app server and making the system more scalable.
https://lincolnloop.com/blog/serving-private-files-with-django-and-s3/
The article shows how to securely serve private user files stored in Amazon S3 from a Django app by keeping the bucket private and generating time-limited pre-signed URLs when a user is authorized to access a file. This approach lets S3 handle file delivery directly while Django controls access, avoiding slow proxying through the app server and making the system more scalable.
https://lincolnloop.com/blog/serving-private-files-with-django-and-s3/
Lincoln Loop
Serving Private Files with Django and S3 | Lincoln Loop
Django’s FileField and ImageField are good at storing files, but on their own they don’t let us control access. When …