Is there life beyond PyUnit/PyTest?
Some years ago, there were many alternatives to just using these: grappa, behave, for instance, with many less-popular alternatives around and thriving.
Today, if you check Snyk Advisor for these, or simply the repo, you will find them abandoned or worse, with security issues. To be sure, checking the Assertions category in Pypi will give you some alternatives, a few interesting ones based in a fluent API, for instance, but none of them are even remotely as popular as these ones. New tutorials don't even bother in telling people to look for alternatives.
Have we arrived to a point where Python is so mature that a single framework is enough to test it all?
/r/Python
https://redd.it/1h0yg58
Some years ago, there were many alternatives to just using these: grappa, behave, for instance, with many less-popular alternatives around and thriving.
Today, if you check Snyk Advisor for these, or simply the repo, you will find them abandoned or worse, with security issues. To be sure, checking the Assertions category in Pypi will give you some alternatives, a few interesting ones based in a fluent API, for instance, but none of them are even remotely as popular as these ones. New tutorials don't even bother in telling people to look for alternatives.
Have we arrived to a point where Python is so mature that a single framework is enough to test it all?
/r/Python
https://redd.it/1h0yg58
PyPI
behave
behave is behaviour-driven development, Python style
Good Linux distribution to choose for a first-time Linux install
FYI: I'm posting this in two sub-reddits, so if this is not the right sub then let me know and I'll delete here.
Generally, the question is per the post.
Context: Long time Mac user who is now studying to be a software engineer, and beginning with Python and Django. 'Everyone' says you should use Linux on your home device, and do your best to get used to it as soon as practical. But without knowing too much about Linux yet (I've only used Mint briefly), let alone the strengths and weaknesses of the different distributions ...What is a good choice for a beginner who both (a) wants to learn a lot, while (b) not getting too frightened too early by something with an immense learning curve or shock vs familiarity with Mac OC and Windows.
Thanks for any tips and advice. Cheers.
/r/djangolearning
https://redd.it/1h125z0
FYI: I'm posting this in two sub-reddits, so if this is not the right sub then let me know and I'll delete here.
Generally, the question is per the post.
Context: Long time Mac user who is now studying to be a software engineer, and beginning with Python and Django. 'Everyone' says you should use Linux on your home device, and do your best to get used to it as soon as practical. But without knowing too much about Linux yet (I've only used Mint briefly), let alone the strengths and weaknesses of the different distributions ...What is a good choice for a beginner who both (a) wants to learn a lot, while (b) not getting too frightened too early by something with an immense learning curve or shock vs familiarity with Mac OC and Windows.
Thanks for any tips and advice. Cheers.
/r/djangolearning
https://redd.it/1h125z0
Reddit
From the djangolearning community on Reddit
Explore this post and more from the djangolearning community
I made a Python signal/slot library that works like Qt but without Qt dependency
Hi everyone!
**What My Project Does:**
I've been working on TSignal, a library that implements Qt-style signals and slots in pure Python. It handles async operations and thread communication automatically, making it easy to build event-driven applications without pulling in heavy dependencies.
**Target Audience:**
This is meant for production use, especially for:
* Python developers who like Qt's signal/slot pattern but don't want Qt as a dependency
* Anyone building async applications that need clean component communication
* Developers working with multi-threaded applications who want easier thread communication
**Comparison:**
While Qt provides a robust signal/slot system, it comes with the entire Qt framework. Other alternatives like PyPubSub or RxPY exist, but TSignal is unique because it:
* Provides Qt-like syntax without Qt dependencies
* Has native asyncio integration (unlike Qt)
* Handles thread-safety automatically (simpler than manual PyPubSub threading)
* Is much lighter than RxPY while keeping the essential event handling features
Here's a quick example:
@t_with_signals
class Counter:
@t_signal
def count_changed(self):
pass
/r/Python
https://redd.it/1h115dx
Hi everyone!
**What My Project Does:**
I've been working on TSignal, a library that implements Qt-style signals and slots in pure Python. It handles async operations and thread communication automatically, making it easy to build event-driven applications without pulling in heavy dependencies.
**Target Audience:**
This is meant for production use, especially for:
* Python developers who like Qt's signal/slot pattern but don't want Qt as a dependency
* Anyone building async applications that need clean component communication
* Developers working with multi-threaded applications who want easier thread communication
**Comparison:**
While Qt provides a robust signal/slot system, it comes with the entire Qt framework. Other alternatives like PyPubSub or RxPY exist, but TSignal is unique because it:
* Provides Qt-like syntax without Qt dependencies
* Has native asyncio integration (unlike Qt)
* Handles thread-safety automatically (simpler than manual PyPubSub threading)
* Is much lighter than RxPY while keeping the essential event handling features
Here's a quick example:
@t_with_signals
class Counter:
@t_signal
def count_changed(self):
pass
/r/Python
https://redd.it/1h115dx
Reddit
From the Python community on Reddit: I made a Python signal/slot library that works like Qt but without Qt dependency
Explore this post and more from the Python community
Comparing AWS S3 with Cloudflare R2: Price, Performance and User Experience
https://kerkour.com/aws-s3-vs-cloudflare-r2-price-performance-user-experience
/r/django
https://redd.it/1h188tg
https://kerkour.com/aws-s3-vs-cloudflare-r2-price-performance-user-experience
/r/django
https://redd.it/1h188tg
Reddit
From the django community on Reddit: Comparing AWS S3 with Cloudflare R2: Price, Performance and User Experience
Posted by The_Naveen - 7 votes and no comments
Thursday Daily Thread: Python Careers, Courses, and Furthering Education!
# Weekly Thread: Professional Use, Jobs, and Education 🏢
Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment.
---
## How it Works:
1. Career Talk: Discuss using Python in your job, or the job market for Python roles.
2. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
3. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally.
---
## Guidelines:
- This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar.
- Keep discussions relevant to Python in the professional and educational context.
---
## Example Topics:
1. Career Paths: What kinds of roles are out there for Python developers?
2. Certifications: Are Python certifications worth it?
3. Course Recommendations: Any good advanced Python courses to recommend?
4. Workplace Tools: What Python libraries are indispensable in your professional work?
5. Interview Tips: What types of Python questions are commonly asked in interviews?
---
Let's help each other grow in our careers and education. Happy discussing! 🌟
/r/Python
https://redd.it/1h1isdo
# Weekly Thread: Professional Use, Jobs, and Education 🏢
Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment.
---
## How it Works:
1. Career Talk: Discuss using Python in your job, or the job market for Python roles.
2. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
3. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally.
---
## Guidelines:
- This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar.
- Keep discussions relevant to Python in the professional and educational context.
---
## Example Topics:
1. Career Paths: What kinds of roles are out there for Python developers?
2. Certifications: Are Python certifications worth it?
3. Course Recommendations: Any good advanced Python courses to recommend?
4. Workplace Tools: What Python libraries are indispensable in your professional work?
5. Interview Tips: What types of Python questions are commonly asked in interviews?
---
Let's help each other grow in our careers and education. Happy discussing! 🌟
/r/Python
https://redd.it/1h1isdo
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
My side project has gotten 420k downloads and 69 GitHub stars (noice!)
Hey Redditors! 👋
I couldn't think of a better place to share this achievement other than here with you lot. Sometimes the universe just comes together in such a way that makes you wonder if the simulation is winking back at you...
But now that I've grabbed your attention, allow me tell you a bit about my project.
# What My Project Does
ridgeplot is a Python package that provides a simple interface for plotting beautiful and interactive ridgeline plots within the extensive Plotly ecosystem.
Unfortunately, I can't share any screenshots here, but feel free to take a look at our **getting started guide** for some examples of what you can do with it.
# Target Audience
Anyone that needs to plot a ridgeline graph can use this library. That said, I expect it to be mainly used by people in the data science, data analytics, machine learning, and adjacent spaces.
# Comparison
If all you need is a simple ridgeline plot with Plotly without any bells and whistles, take a look at this example in their official docs. However, if you need more control over how the plot looks like, like plotting multiple traces per row, using different coloring options, or mixing KDEs and histograms, then I think
/r/Python
https://redd.it/1h1ccfu
Hey Redditors! 👋
I couldn't think of a better place to share this achievement other than here with you lot. Sometimes the universe just comes together in such a way that makes you wonder if the simulation is winking back at you...
But now that I've grabbed your attention, allow me tell you a bit about my project.
# What My Project Does
ridgeplot is a Python package that provides a simple interface for plotting beautiful and interactive ridgeline plots within the extensive Plotly ecosystem.
Unfortunately, I can't share any screenshots here, but feel free to take a look at our **getting started guide** for some examples of what you can do with it.
# Target Audience
Anyone that needs to plot a ridgeline graph can use this library. That said, I expect it to be mainly used by people in the data science, data analytics, machine learning, and adjacent spaces.
# Comparison
If all you need is a simple ridgeline plot with Plotly without any bells and whistles, take a look at this example in their official docs. However, if you need more control over how the plot looks like, like plotting multiple traces per row, using different coloring options, or mixing KDEs and histograms, then I think
/r/Python
https://redd.it/1h1ccfu
GitHub
GitHub - tpvasconcelos/ridgeplot: Beautiful ridgeline plots in Python
Beautiful ridgeline plots in Python. Contribute to tpvasconcelos/ridgeplot development by creating an account on GitHub.
Causal Discovery Competition Winning Paper Discussion D
I’ve recently come across this post: https://thetourney.github.io/adia-report/ which describes the winning method for a casual discovery competition. It’s not really my field but I do have a reasonable understanding of GNNs and Causal Inference. Anyway, from the report I don’t understand precisely what the winning team was doing. Can anyone either link to a full paper or have a good intuitive and potentially step by step explanation of what they are doing?
/r/MachineLearning
https://redd.it/1h1i0ji
I’ve recently come across this post: https://thetourney.github.io/adia-report/ which describes the winning method for a casual discovery competition. It’s not really my field but I do have a reasonable understanding of GNNs and Causal Inference. Anyway, from the report I don’t understand precisely what the winning team was doing. Can anyone either link to a full paper or have a good intuitive and potentially step by step explanation of what they are doing?
/r/MachineLearning
https://redd.it/1h1i0ji
Reddit
From the MachineLearning community on Reddit
Explore this post and more from the MachineLearning community
opennb: Open Jupyter notebooks from GitHub with dependencies, instantly (with uv)!
What My Project Does:
opennb is a tiny CLI tool that lets you open Jupyter notebooks directly from GitHub (or any URL) while automatically handling dependencies in an ephemeral environment. For example:
This single command:
- Creates a temporary environment
- Installs all dependencies (instant with uv's cache!)
- Downloads the notebook
- Opens it in Jupyter
With a cold cache 🥶 it takes 1.5s to do this all, and with a hot cache 🥵 it takes a couple of ms!
GitHub: https://github.com/basnijholt/opennb
Target Audience:
- Data scientists and developers who frequently try out tutorial notebooks
- Anyone learning from Jupyter notebooks in GitHub repositories
- Teachers sharing notebooks with students
- People who want to try notebooks without polluting their environment
It's meant for real use but is intentionally simple and focused on doing one thing well.
Comparison:
Existing workflows typically involve:
1. Cloning the entire repository
2. Creating a virtual environment
3. Installing dependencies
4. Finding and opening the notebook
This can be tedious, especially when you just want to quickly try a notebook. opennb combines these steps into a single command and leverages uv's speed to make it instant.
The closest alternative would be using Binder, but:
- Binder requires waiting for container builds
- opennb works locally and instantly
- opennb integrates with your local Jupyter installation
-
/r/Python
https://redd.it/1h1ecw7
What My Project Does:
opennb is a tiny CLI tool that lets you open Jupyter notebooks directly from GitHub (or any URL) while automatically handling dependencies in an ephemeral environment. For example:
uvx --with "pipefunc[docs]" opennb pipefunc/pipefunc/example.ipynb
This single command:
- Creates a temporary environment
- Installs all dependencies (instant with uv's cache!)
- Downloads the notebook
- Opens it in Jupyter
With a cold cache 🥶 it takes 1.5s to do this all, and with a hot cache 🥵 it takes a couple of ms!
GitHub: https://github.com/basnijholt/opennb
Target Audience:
- Data scientists and developers who frequently try out tutorial notebooks
- Anyone learning from Jupyter notebooks in GitHub repositories
- Teachers sharing notebooks with students
- People who want to try notebooks without polluting their environment
It's meant for real use but is intentionally simple and focused on doing one thing well.
Comparison:
Existing workflows typically involve:
1. Cloning the entire repository
2. Creating a virtual environment
3. Installing dependencies
4. Finding and opening the notebook
This can be tedious, especially when you just want to quickly try a notebook. opennb combines these steps into a single command and leverages uv's speed to make it instant.
The closest alternative would be using Binder, but:
- Binder requires waiting for container builds
- opennb works locally and instantly
- opennb integrates with your local Jupyter installation
-
/r/Python
https://redd.it/1h1ecw7
GitHub
GitHub - basnijholt/opennb: Open Jupyter notebooks from GitHub repositories or URLs directly in Jupyter.
Open Jupyter notebooks from GitHub repositories or URLs directly in Jupyter. - basnijholt/opennb
Python and GoHigh Level Integration
I am looking to have python read a list of names from GoHigh Level CRM and this list contains names that clicked in a link I sent them through email. Then I want python to compare that list to anotherr list that I will be feeding into a folder on my computer on a daily basis and this list contains the names of people from the first list but at this point this new list will say witch of those people actually are confirmed to have signed up. Python will compare these two lists and feed the list of those confirmed people back to the CRM so they can continue on my pipeline. Can this be done?
/r/Python
https://redd.it/1h1pq0x
I am looking to have python read a list of names from GoHigh Level CRM and this list contains names that clicked in a link I sent them through email. Then I want python to compare that list to anotherr list that I will be feeding into a folder on my computer on a daily basis and this list contains the names of people from the first list but at this point this new list will say witch of those people actually are confirmed to have signed up. Python will compare these two lists and feed the list of those confirmed people back to the CRM so they can continue on my pipeline. Can this be done?
/r/Python
https://redd.it/1h1pq0x
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
PixelPurge: Game Cleanup Tool
Hey everyone 👋
I’m excited to introduce you to PixelPurge, my latest side project!
# What PixelPurge Does
PixelPurge is a lightweight Python tool designed to help gamers clean up after trying new games. It’s perfect for anyone who loves testing games but hates the leftover files. It tracks new files and folders created while you play and lets you delete them effortlessly.
You can find images and details in the project repository.
# Target Audience
If you’re a gamer who frequently downloads and uninstalls new noscripts, you’ll love PixelPurge. It’s also great for anyone who wants to reclaim storage space by ensuring no leftover game files clutter your system. While it’s aimed at Windows users, macOS users can use it too.
# Comparison
You might be thinking, “Why not just manually delete leftover files?” Sure, you can, but PixelPurge takes the guesswork out of the process by:
• Automatically tracking changes in monitored directories while games are running.
• Providing an easy GUI for reviewing and selecting files to delete.
• Supporting recursive folder monitoring so nothing is missed.
# Links
• Repository: https://github.com/izaan17/PixelPurge
If you’re curious or have feedback, I’d love for you to check it out. Feel free to contribute, suggest features, or just share your thoughts—this project is all about
/r/Python
https://redd.it/1h1plj9
Hey everyone 👋
I’m excited to introduce you to PixelPurge, my latest side project!
# What PixelPurge Does
PixelPurge is a lightweight Python tool designed to help gamers clean up after trying new games. It’s perfect for anyone who loves testing games but hates the leftover files. It tracks new files and folders created while you play and lets you delete them effortlessly.
You can find images and details in the project repository.
# Target Audience
If you’re a gamer who frequently downloads and uninstalls new noscripts, you’ll love PixelPurge. It’s also great for anyone who wants to reclaim storage space by ensuring no leftover game files clutter your system. While it’s aimed at Windows users, macOS users can use it too.
# Comparison
You might be thinking, “Why not just manually delete leftover files?” Sure, you can, but PixelPurge takes the guesswork out of the process by:
• Automatically tracking changes in monitored directories while games are running.
• Providing an easy GUI for reviewing and selecting files to delete.
• Supporting recursive folder monitoring so nothing is missed.
# Links
• Repository: https://github.com/izaan17/PixelPurge
If you’re curious or have feedback, I’d love for you to check it out. Feel free to contribute, suggest features, or just share your thoughts—this project is all about
/r/Python
https://redd.it/1h1plj9
GitHub
GitHub - Izaan17/PixelPurge: PixelPurge is a simple, efficient tool for gamers who love testing new games but want to keep their…
PixelPurge is a simple, efficient tool for gamers who love testing new games but want to keep their system clean. It allows you to quickly delete game-created files and folders, making it easy to f...
Django Protego - A Flexible and Dynamic Circuit Breaker
Hi folks,
I'm excited to share a project I've been working on: Django Protego, a dynamic and configurable Circuit Breaker for Django applications.
What is Django Protego?
Django Protego is a library that helps to protect your services from cascading failures by providing a Circuit Breaker mechanism. It's simple to integrate, dynamic, and works seamlessly with Django-based applications.
Key Features:
Dynamic Configuration: Configure failure thresholds, reset timeouts, and half-open retries at runtime.
Global Registry: The circuit breaker state is shared across views via a global registry, ensuring centralized control of your application’s fault tolerance.
Easy to Use: Just decorate your views with @/protego.protect to wrap your views in the circuit breaker logic.
Flexible: Supports multiple circuit breakers in the same project, all configurable independently.
In-Memory: Implements a highly efficient in-memory circuit breaker with no external dependencies.
How It Works:
Protego Client: For each service, the circuit breaker maintains its state (open, closed, half-open) and tracks failures.
Thresholds and Timeout: You can dynamically adjust failure thresholds, reset timeouts, and half-open retries via a central configuration in your Django app.
Global Access: Protego ensures that circuit breakers are initialized once and are accessible globally in your project.
/r/django
https://redd.it/1h18ua0
Hi folks,
I'm excited to share a project I've been working on: Django Protego, a dynamic and configurable Circuit Breaker for Django applications.
What is Django Protego?
Django Protego is a library that helps to protect your services from cascading failures by providing a Circuit Breaker mechanism. It's simple to integrate, dynamic, and works seamlessly with Django-based applications.
Key Features:
Dynamic Configuration: Configure failure thresholds, reset timeouts, and half-open retries at runtime.
Global Registry: The circuit breaker state is shared across views via a global registry, ensuring centralized control of your application’s fault tolerance.
Easy to Use: Just decorate your views with @/protego.protect to wrap your views in the circuit breaker logic.
Flexible: Supports multiple circuit breakers in the same project, all configurable independently.
In-Memory: Implements a highly efficient in-memory circuit breaker with no external dependencies.
How It Works:
Protego Client: For each service, the circuit breaker maintains its state (open, closed, half-open) and tracks failures.
Thresholds and Timeout: You can dynamically adjust failure thresholds, reset timeouts, and half-open retries via a central configuration in your Django app.
Global Access: Protego ensures that circuit breakers are initialized once and are accessible globally in your project.
/r/django
https://redd.it/1h18ua0
Reddit
From the django community on Reddit: Django Protego - A Flexible and Dynamic Circuit Breaker
Explore this post and more from the django community
What are you all-time favorite Python talks?
I recently discovered https://pyvideo.org/ with its 19 163 talks from Python conferences.
Do you have any favorite talks or speakers you can recommend?
/r/Python
https://redd.it/1h1qun7
I recently discovered https://pyvideo.org/ with its 19 163 talks from Python conferences.
Do you have any favorite talks or speakers you can recommend?
/r/Python
https://redd.it/1h1qun7
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
I'm using Google Cloud AppEngine to run a flask python app. Is working just fine. Is there any advantage if I create a docker container for this?
Since Google AppEngine is already a container and I will need to install OS dependencies like Microsoft Visual C++ 14.0 for python-Levenshtein-wheels on Windows (if I want to develop in windows). I don't see any advantage on "dockerize" my project. Am'I missing something?
Edit: Just to clarify "When installing the "python-Levenshtein-wheel" package in Python, you might need to install C++ build tools because the package often includes a compiled C++ component that needs to be built during installation, and your system needs the necessary compilers and build tools to compile this component from source code." Extra build is neccesary while enabling this dependency so is harder to create a truly portable docker image. You will need some different OS dependencies in linux to enable this dependency.
/r/flask
https://redd.it/1h1ecgu
Since Google AppEngine is already a container and I will need to install OS dependencies like Microsoft Visual C++ 14.0 for python-Levenshtein-wheels on Windows (if I want to develop in windows). I don't see any advantage on "dockerize" my project. Am'I missing something?
Edit: Just to clarify "When installing the "python-Levenshtein-wheel" package in Python, you might need to install C++ build tools because the package often includes a compiled C++ component that needs to be built during installation, and your system needs the necessary compilers and build tools to compile this component from source code." Extra build is neccesary while enabling this dependency so is harder to create a truly portable docker image. You will need some different OS dependencies in linux to enable this dependency.
/r/flask
https://redd.it/1h1ecgu
Reddit
From the flask community on Reddit
Explore this post and more from the flask community
D Theory behind modern diffusion models
Hi everyone,
I recently attended some lectures at university regarding diffusion models. Those explained all the math behind the original DDPM (Denoiding Diffusion Probabilistic Model) in great detail (especially in the appendices), actually better than anything else I have found online. So it has been great for learning the basics behind diffusion models (slides are available in the link in the readme here if you are interesed: https://github.com/julioasotodv/ie-C4-466671-diffusion-models)
However, I am struggling to find resources with similar level of detail for modern approaches—such as flow matching/rectified flows, how the different ODE solvers for sampling work, etc. There are some, but everything that I have found is either quite outdated (like from 2023 or so) or very superficial—like for non-technical or scientific audiences.
Therefore, I am wondering: has anyone encountered a good compendium of theoretical eplanations beyond the basic diffusion model (besides the original papers)? The goal is to let my team deep dive into the actual papers should they desire, but giving 70% of what those deliver in one or more decent compilations.
I really believe that SEO is making any search a living nightmare nowadays. Either that or my googling skills are tanking for some reason.
Thank you all!
/r/MachineLearning
https://redd.it/1h1vxe1
Hi everyone,
I recently attended some lectures at university regarding diffusion models. Those explained all the math behind the original DDPM (Denoiding Diffusion Probabilistic Model) in great detail (especially in the appendices), actually better than anything else I have found online. So it has been great for learning the basics behind diffusion models (slides are available in the link in the readme here if you are interesed: https://github.com/julioasotodv/ie-C4-466671-diffusion-models)
However, I am struggling to find resources with similar level of detail for modern approaches—such as flow matching/rectified flows, how the different ODE solvers for sampling work, etc. There are some, but everything that I have found is either quite outdated (like from 2023 or so) or very superficial—like for non-technical or scientific audiences.
Therefore, I am wondering: has anyone encountered a good compendium of theoretical eplanations beyond the basic diffusion model (besides the original papers)? The goal is to let my team deep dive into the actual papers should they desire, but giving 70% of what those deliver in one or more decent compilations.
I really believe that SEO is making any search a living nightmare nowadays. Either that or my googling skills are tanking for some reason.
Thank you all!
/r/MachineLearning
https://redd.it/1h1vxe1
GitHub
GitHub - julioasotodv/ie-c4-466671-diffusion-models: Material for lectures on Diffusion models at IE university
Material for lectures on Diffusion models at IE university - julioasotodv/ie-c4-466671-diffusion-models
Creating an AI-powered Image Generation API Service with FLUX, Python, and Diffusers
https://herahaven.ai/blog/creating-an-ai-powered-image-generation-api-service-with-flux-python-and-diffusers/
/r/Python
https://redd.it/1h1tx73
https://herahaven.ai/blog/creating-an-ai-powered-image-generation-api-service-with-flux-python-and-diffusers/
/r/Python
https://redd.it/1h1tx73
HeraHaven AI | Blog
Creating an AI-powered Image Generation API Service with FLUX, Python, and Diffusers
FLUX (by Black Forest Labs) has taken the world of AI image generation by storm in the last few months. Not only has it beat Stable Diffusion (the prior open-source king) on many benchmarks, it has also surpassed proprietary models like Dall-E or Midjourney…
D Why aren't Stella embeddings more widely used despite topping the MTEB leaderboard?
https://huggingface.co/spaces/mteb/leaderboard
I've been looking at embedding models and noticed something interesting: Stella embeddings are crushing it on the MTEB leaderboard, outperforming OpenAI's models while being way smaller (1.5B/400M params) and apache 2.0. Makes hosting them relatively cheap.
For reference, Stella-400M scores 70.11 on MTEB vs OpenAI's text-embedding-3-large 64.59. The 1.5B version scores even higher at 71.19
Yet I rarely see them mentioned in production use cases or discussions. Has anyone here used Stella embeddings in production? What's been your experience with performance, inference speed, and reliability compared to OpenAI's offerings?
Just trying to understand if there's something I'm missing about why they haven't seen wider adoption despite the impressive benchmarks.
Would love to hear your thoughts and experiences!
/r/MachineLearning
https://redd.it/1h1u814
https://huggingface.co/spaces/mteb/leaderboard
I've been looking at embedding models and noticed something interesting: Stella embeddings are crushing it on the MTEB leaderboard, outperforming OpenAI's models while being way smaller (1.5B/400M params) and apache 2.0. Makes hosting them relatively cheap.
For reference, Stella-400M scores 70.11 on MTEB vs OpenAI's text-embedding-3-large 64.59. The 1.5B version scores even higher at 71.19
Yet I rarely see them mentioned in production use cases or discussions. Has anyone here used Stella embeddings in production? What's been your experience with performance, inference speed, and reliability compared to OpenAI's offerings?
Just trying to understand if there's something I'm missing about why they haven't seen wider adoption despite the impressive benchmarks.
Would love to hear your thoughts and experiences!
/r/MachineLearning
https://redd.it/1h1u814
huggingface.co
MTEB Leaderboard - a Hugging Face Space by mteb
Select and customize benchmarks for different tasks like image-text, domain-specific, and language-specific evaluations. Choose from multilingual options, specific languages, and various domains in...
django-fastdev 1.13 released
Django-fastdev is a collection of improvement to Django. The focus is on better error handling/messages.
New since last time I posted:
- Improved TemplateNotFound errors
- Adds a new monkey patch for
- Reintroduced invalid block check with fixes
https://github.com/boxed/django-fastdev/
/r/django
https://redd.it/1h1yjsi
Django-fastdev is a collection of improvement to Django. The focus is on better error handling/messages.
New since last time I posted:
- Improved TemplateNotFound errors
- Adds a new monkey patch for
Model.__repr__ to fix infinite recursion in error messages for DoesNotExist and MultipleObjectsReturned (the first is a fastdev bug and the second is a Django bug)- Reintroduced invalid block check with fixes
https://github.com/boxed/django-fastdev/
/r/django
https://redd.it/1h1yjsi
GitHub
GitHub - boxed/django-fastdev: An app to make it safer, faster and more fun to develop in Django
An app to make it safer, faster and more fun to develop in Django - boxed/django-fastdev
Friday Daily Thread: r/Python Meta and Free-Talk Fridays
# Weekly Thread: Meta Discussions and Free Talk Friday 🎙️
Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related!
## How it Works:
1. Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
2. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
3. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting.
## Guidelines:
All topics should be related to Python or the /r/python community.
Be respectful and follow Reddit's Code of Conduct.
## Example Topics:
1. New Python Release: What do you think about the new features in Python 3.11?
2. Community Events: Any Python meetups or webinars coming up?
3. Learning Resources: Found a great Python tutorial? Share it here!
4. Job Market: How has Python impacted your career?
5. Hot Takes: Got a controversial Python opinion? Let's hear it!
6. Community Ideas: Something you'd like to see us do? tell us.
Let's keep the conversation going. Happy discussing! 🌟
/r/Python
https://redd.it/1h293f6
# Weekly Thread: Meta Discussions and Free Talk Friday 🎙️
Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related!
## How it Works:
1. Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
2. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
3. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting.
## Guidelines:
All topics should be related to Python or the /r/python community.
Be respectful and follow Reddit's Code of Conduct.
## Example Topics:
1. New Python Release: What do you think about the new features in Python 3.11?
2. Community Events: Any Python meetups or webinars coming up?
3. Learning Resources: Found a great Python tutorial? Share it here!
4. Job Market: How has Python impacted your career?
5. Hot Takes: Got a controversial Python opinion? Let's hear it!
6. Community Ideas: Something you'd like to see us do? tell us.
Let's keep the conversation going. Happy discussing! 🌟
/r/Python
https://redd.it/1h293f6
Redditinc
Reddit Rules
Reddit Rules - Reddit
Creating an intranet
So I've created a flask app and I've hosted it through python anywhere. I want to learn how to create and intranet. Any resource or guidance on how I can make a web app that can only be accessed on a specific network? I know this may not be a flask specific question but that my background.
/r/flask
https://redd.it/1h21rrq
So I've created a flask app and I've hosted it through python anywhere. I want to learn how to create and intranet. Any resource or guidance on how I can make a web app that can only be accessed on a specific network? I know this may not be a flask specific question but that my background.
/r/flask
https://redd.it/1h21rrq
Reddit
From the flask community on Reddit
Explore this post and more from the flask community