Get a 1-Year Perplexity Pro Code for $25 (regular price $200)

This includes access to models like:

» GPT-4o, o1 Mini for Reasoning & Llama 3.1

» Claude 3.5 Sonnet, Claude 3.5 Haiku, Grok-2

» Image generators: Flux.1, DALL-E 3, Playground v3 Stable Diffusion XL

Works globally as long as you don't have an active Pro subscription.

How It Works:

1. Join the 🔗Discord of 330+ Members
2. Pay via PayPal for buyer protection.
3. I'll send you a link to redeem.

Vouch from Buyers,  Feedback 2,  Feedback 3,  Feedback 4,  Feedback 5

Hi all, seeing as my last post with these 3 courses led to all spaces filling up quickly, and Udemy courses are not on sale right now, I thought I'd generate some more free spaces, pretty useful if you want to start Python from scratch, learn functional code or learn OOP from scratch (or all 3!)

FYI: Only valid for the next 5 days!

https://www.udemy.com/course/python-programming-for-the-total-beginner/?couponCode=0BBB34FF2AB808789898

https://www.udemy.com/course/object-oriented-programming-in-python-3/?couponCode=10579ED6FCE853C84329

https://www.udemy.com/course/functional-programming-with-python-comprehensions/?couponCode=D5BA520B6BC7388F027C

https://preview.redd.it/5vk0me9ngg5e1.jpg?width=1280&format=pjpg&auto=webp&s=3c01025d40dad625eae8c82c49b3a87a5e988bad

👁️ CNN Image Classification for Retinal Health Diagnosis with TensorFlow and Keras! 👁️

How to gather and preprocess a dataset of over 80,000 retinal images, design a CNN deep learning model , and train it that can accurately distinguish between these health categories.

What You'll Learn:

🔹 Data Collection and Preprocessing: Discover how to acquire and prepare retinal images for optimal model training.

🔹 CNN Architecture Design: Create a customized architecture tailored to retinal image classification.

🔹 Training Process: Explore the intricacies of model training, including parameter tuning and validation techniques.

🔹 Model Evaluation: Learn how to assess the performance of your trained CNN on a separate test dataset.

You can find link for the code in the blog : https://eranfeit.net/build-a-cnn-model-for-retinal-image-diagnosis/

You can find more tutorials, and join my newsletter here : https://eranfeit.net/

Check out our tutorial here : https://youtu.be/PVKI_fXNS1E&list=UULFTiWJJhaH6BviSWKLJUM9sg

Enjoy

Eran

#Python #Cnn #TensorFlow #deeplearning #neuralnetworks #imageclassification #convolutionalneuralnetworks #computervision #transferlearning

I have no idea where to start or anything. I was not taught anything. Please help maybe be able to pay if helped proprely

hello everyone! just launched doc2exam on ProductHunt

a place to turn any material into live exams -- for students prepping or professors setting official certifications

ofc it's made in django for the backend

https://reddit.com/link/1h6e955/video/v6xr6la0mt4e1/player

https://2333-2603-7081-22f0-8c50-84d0-e431-11b2-2d51.ngrok-free.app/auth

Hi all, seeing as the Udemy sale has ended I'm giving away 3 of my courses, including my brand new OOP one, these are mainly aimed at beginners but the functional programming one is a little trickier.

Feel free to message me via the Udemy Q&A if you get stuck on any of the challenges, quizzes or projects.

Cheers!

OOP

https://www.udemy.com/course/object-oriented-programming-in-python-3/?couponCode=38ACDBC1065F9DE5C090

Functional Code

https://www.udemy.com/course/functional-programming-with-python-comprehensions/?couponCode=A6E6EF5175DD9075B425

Python Basics

https://www.udemy.com/course/python-programming-for-the-total-beginner/?couponCode=03AA07ABFA5CD6846EE7

https://preview.redd.it/thokr57zo13e1.png?width=1212&format=png&auto=webp&s=1a7843729bc36e5ae57a1413ef304f39a6246bca

• Github. https://github.com/Djdefrag/QualityScaler

Qualityscaler is a Windows app powered by AI to enhance, upscale and de-noise photographs and videos.

QualityScaler 3.12 changelog.

▼ NEW

Video upscale STOP&RESUME
⊡ Now is possible to stop and resume the video upscale process at any time
⊡ When restarting (with same settings) the app will resume from the interrupted point
⊡ NOTE - If video temporary files are deleted, upscaling will start over again

User settings save
⊡ The app will now remember all the options of the user (AI model, GPU, GPU VRAM etc.)
⊡ NOTE - In case of problems, delete the file _UserPreference.json in Documents folder

AI multi-threading improvements
⊡ Optimized upscaling speed when using AI multi-threading
⊡ Is now possible to select up to 6 threads (6 video frames simultaneous)

Keep frames widget
⊡ Added new widget to choose whether to save upscaled video frames
⊡ Selecting “Enabled”, upscaled frames will not be deleted
⊡ This allows you to re-encode upscaled video with different extension without upscaling again

AI models update
⊡ Updated AI models using updated tools
⊡ Improved upscale quality
⊡ Improved GPU compatibility and upscaling performance

GPU Auto selection
⊡ Added new "Auto" option in GPU Widget
⊡ Selecting “Auto,” the app automatically choose the most powerful GPU in the PC
⊡ This solves a problem with GPU processing on notebooks with 2 GPUs

▼ BUGFIX / IMPROVEMENTS

FFMPEG audio passthrough
⊡ This feature allows audio to be processed without any alterations (lossless quality)
⊡ Supports multiple audio streams (when a video contains multiple audio tracks)
⊡ This function fix an issue where audio could not be applied to upscaled videos

Video upscale improvements
⊡ Improved video upscale stability and memory usage
⊡ Updated FFMPEG to version 7.1 (video encoding bugfix and performance improvements)
⊡ Now the app automatically removes the temp folder when the video upscale is finished

Video encoding improvements
⊡ Updated MoviePy to version 2.0
⊡ A long list of bugfixes and optimizations for video encoding

General improvements
⊡ Bug fixes, code cleaning, performance improvements
⊡ Updated dependencies

▼ NOTE

Nvidia GPUs optimizations
⊡ Is essential to enable Windows Hardware Accelerated GPU scheduling option
⊡ This option can dramatically improve upscale performance
⊡ Enable it in Windows 10 / Windows 11 settings > Graphic Settings menu

Slack Bridge API is a Flask-based REST API designed for user management, email-based user search, and Slack message integration.

• https://github.com/password123456/slackbridge-api

Birdeye is a cryptocurrency data aggregator. Their API is public, but they do not provide any language SDKs, so I decided to create a Python one. Project contains modern tooling like ruff/uv, CI with GutHub actions, clean architecture, and 100% code coverage. Can be found here https://github.com/nickatnight/birdeye-py

Ever tried to look for an open source project to contribute to but got lost?

Me too. So I created my own.
Get hands-on experience contributing to open-source projects, sharpen your Python networking skills, and explore the world of sockets and encryption! 🚀

I’ve just started an open-source project called Network_Phrasebank, a beginner-friendly networking program built in Python. The goal is simple: to store and retrieve encrypted phrases over a local network while making open-source contributions approachable and fun!

Whether you’re an aspiring developer, someone wanting to strengthen their Python fundamentals, or a seasoned contributor looking for a cool side project, I beg you to please join! Ugly crying begging you outside your house all night please join.

Why This Project Is Perfect for You

• Beginner-Friendly: Designed for newcomers to Python networking and open-source contributions.
• Low Barrier to Entry: No VMs, extra hardware, or complex setups. If you have a computer, you’re good to go!
• Learn by Doing: Dive into real-world Python networking concepts while contributing to a live project.
• Collaborative Environment: Work with contributors from different levels and backgrounds.

Current Roadmap

We’ve broken the project into bite-sized tasks so anyone can jump in, regardless of experience level.

1️⃣ Level 1: Basic socket communication (send and receive messages).
2️⃣ Level 2: Handle multiple simultaneous connections.
3️⃣ Level 3: Encrypt and decrypt messages using custom ciphers.
4️⃣ Level 4: Expand functionality to store, retrieve, and update phrases.
5️⃣ Level 5: Create a simple command-line interface (CLI).

...and so much more in the pipeline!

The README details how to get started and clone the repo, how to contribute, etc.

Communication will NOT be done on reddit, but on the repo's DISCUSSIONS page. thanks!

Python only

Here is the github:
https://github.com/ernbernie/network_phrasebank

Hi everyone!

Just wanted to share my first Python package: pytest-case.
It’s designed to make writing and organizing test cases with pytest more intuitive and readable.

I love writing tests, but while working I found myself repeating patterns when testing multiple input-output scenarios.
I wanted a simple, elegant solution to keep my test cases concise and readable, without sacrificing flexibility.
And so, I came with pytest-case as a solution.

Key Features:

• Concise Code: Reduce repetitive test logic while keeping everything clean.
  • No need to specify the test parameters
  • Each test case in another decorator (or use an iterable / geneartor for your cases)
• Simple API: Just use the case decorator :)

The package:
You can read the code here on GitHub (https://github.com/eitanwass/pytest-case)
You can install it from Pypi (https://pypi.org/project/pytest-case)

I'd love you feedback!
I would love to hear your feedback on the package - do you see usage for it? things that could be done better? Things that are missing...

Thanks

Hi there, I have been developing and using this package to speed up a few personal projects involving the extraction of data from Transfermarkt and I thought I could share it. The library provides a declarative interface that eases the search and retrieval of data and allows basic querying of TM's content, I intend to expand and improve it if there is some interest, all feedback is welcome

https://github.com/franz38/tmquery

Example

scrape data from players in 2010-11 FC Barcelona:

TMQuery().search_club("barcelona").get_players(season="2010-11").csv()

return player's transfers:

TMQuery().search_player("morata").get_transfers().csv()

UML schema

https://preview.redd.it/fr2xbtyvcm2e1.png?width=674&format=png&auto=webp&s=413673039626defe059fce9e8f0f8c95fb4e0782

Hi all, I've just released my 3rd Udemy course, this time it's covering OOP!

There's some free sign-ups available here:

https://www.udemy.com/course/object-oriented-programming-in-python-3/?couponCode=38ACDBC1065F9DE5C090

Cheers

Hey everyone! 👋

I’m excited to introduce MetaDataScraper, a Python package designed to automate the extraction of valuable data from Facebook pages. Whether you're tracking follower counts, post interactions, or multimedia content like videos, this tool makes scraping Facebook page data a breeze. No API keys or tedious manual effort required — just pure automation! 😎

Usage docs here at ReadTheDocs.

Key Features:

• Automated Extraction: Instantly fetch follower counts, post texts, likes, shares, and video links from public Facebook pages.
• Comprehensive Data Retrieval: Get detailed insights from posts, including text content, interactions (likes, shares), and multimedia (videos, reels, etc.).
• Loginless Scraping: With the LoginlessScraper class, no Facebook login is needed. Perfect for scraping public pages.
• Logged-In Scraping: The LoggedInScraper class allows you to login to Facebook and bypass the limitations of loginless scraping. Access more content and private posts if needed.
• Headless Operation: Scrapes data silently in the background (without opening a visible browser window) — perfect for automated tasks or server environments.
• Flexible & Easy-to-Use: Simple setup, clear method calls, and works seamlessly with Selenium WebDriver

Example Usage:

1. Installation:

Simply install via pip:

pip install MetaDataScraper

2. Loginless Scraping (no Facebook login required):

from MetaDataScraper import LoginlessScraper

page_id = "your_target_page_id"
scraper = LoginlessScraper(page_id)
result = scraper.scrape()

print(f"Followers: {result['followers']}")
print(f"Post Texts: {result['post_texts']}")

3. Logged-In Scraping (for more access):

from MetaDataScraper import LoggedInScraper

page_id = "your_target_page_id"
email = "your_facebook_email"
password = "your_facebook_password"
scraper = LoggedInScraper(page_id, email, password)
result = scraper.scrape()

print(f"Followers: {result['followers']}")
print(f"Post Likes: {result['post_likes']}")
print(f"Video Links: {result['video_links']}")

Comparision to existing alternatives

• Ease of Use: Setup is quick and easy — just pass the Facebook page ID and start scraping!
• No Facebook API Required: No need for dealing with Facebook's complex API limits or token issues. This package uses Selenium for direct web scraping, which is much more flexible.
• Better Data Access: With the LoggedInScraper, you can scrape content that might be unavailable to public visitors, all using your own Facebook account credentials.
• Updated Code Logic: With Meta's code updating quite often, many of the now existing scraper packages are defunct. This package is continuously tested and monitored to make sure that the scraper remains functional.

Target Audience:

• Data Analysts: For tracking page metrics and social media analytics.
• Marketing Professionals: To monitor engagement on Facebook pages and competitor tracking.
• Researchers: Anyone looking to gather Facebook data for research purposes.
• Social Media Enthusiasts: Those interested in scraping Facebook data for personal projects or insights.

Dependencies:

• Selenium
• WebDriver Manager

If you’re interested in automating your data collection from Facebook pages, MetaDataScraper will save you tons of time. It's perfect for anyone who needs structured, automated data without getting bogged down by API rate limits, login barriers, or manual work. Check it out on GitHub, if you want to dive deeper into the code or contribute. I’ve set up a Discord server for my projects, including MetaDataScraper, where you can get updates, ask questions, or provide feedback as you try out the package. It’s a new space, so feel free to help shape the community! 🚀

Looking forward to seeing you there!

Hope it helps some of you automate your Facebook scraping tasks! 🚀 Let me know if you have any questions or run into any issues. I’m always open to feedback!

Hi everyone! I just released Memoripy, a Python library designed to give AI applications memory capabilities, from short-term to long-term storage. It works with APIs like OpenAI and Ollama to store and retrieve contextual information, making your AI smarter and more context-aware over time.

The library uses Faiss for similarity searches, supports semantic clustering, and includes adaptive memory decay and reinforcement. It’s flexible too—you can define your own storage, whether that’s local files, cloud, or even custom databases.

If you’re building AI agents, assistants, or anything requiring context retention, Memoripy might be a game-changer for you. Would love to hear your thoughts or see what you build with it!

GitHub: github.com/caspianmoon/memoripy

Hello, I wanted to share that I am sharing free courses and projects on my YouTube Channel. I have more than 200 videos and I created playlists for learning Data Science. I am leaving the playlist link below, have a great day!

Data Science Full Courses & Projects -> https://youtube.com/playlist?list=PLTsu3dft3CWiow7L7WrCd27ohlra_5PGH&si=6WUpVwXeAKEs4tB6

Data Science Projects -> https://youtube.com/playlist?list=PLTsu3dft3CWg69zbIVUQtFSRx_UV80OOg&si=go3wxM_ktGIkVdcP

... well to answer this question we have to go back in time. Most likely around 100 Million years (according to the current theories). There might have been a moon that went too close to Saturn and was fragmented apart, by something called Tidal Forces.

After some equation magic one finds 2 rather simple equations for the so called critical distance: a distance between a planet and a smaller object where the smaller object is ripped by strong gravitationally induced tidal disturbances.

Why are there 2 solutions? Well, one equation determines the distance for a rigid object and the other one for a deformable object (a more realistic scenario).

Considering a slightly higher density than ice and the light gas density of Saturn, an icy object would be destroyed at around 120,000 km distance from Saturn's centre. Well... check out the following image of the ring system and the distances shown below: https://en.wikipedia.org/wiki/Wikipedia:Featured_pictures/Space/Panorama#/media/File:Saturn's_rings_dark_side_mosaic.jpg

But how can we compute this critical distance? Well, we can use Python and a small script I created:

GitHub: https://github.com/ThomasAlbin/Astroniz-YT-Tutorials/blob/main/CompressedCosmos/CompressedCosmos_Tidal_Forces.ipynb

If you like, I made a short video about it: https://youtu.be/7HNNSAykw4U

No, I am not a big YouTuber. I am a former space scientist and astrophysicist that likes to share some knowledge :-)

https://i.redd.it/jd6r28dspq0e1.gif

Hey all, I've been experimenting with Streamlit + Claude and wanted to see if I could generate a Tetris clone.

Some comments:

- Claude was unable to generate a full working game with a single prompt

- Instead I went step by step and asked the model to first create the logic that moves the blocks

- Then I asked to generate the controls

- I spent like 30 mins debugging an error that caused lines to to clear correctly. Claude was unable to spot the issue, but once I found which function was causing the issues, I send it to Claude and fixed it

Here's the app: https://editor.ploomber.io/editor/tetris-clone-85c8

Hey everyone! I wanted to share a Python project I've been working on.

What My Project Does

VideoForge AI is a desktop application that automates video content creation by combining multiple AI services:

• Generates scripts using Claude AI (supports Romanian/English)
• Creates images via Stability AI
• Converts text to speech using ElevenLabs
• Automatically combines everything using FFmpeg
• Handles both short-form (≤60s) and long-form videos
• Manages aspect ratios, transitions, and audio mixing automatically

You can see it in action here:

• Live Channel: Istorie Mistere
• Example Short: Mystery Short

Target Audience

This is a production-ready tool designed for:

• Content creators who want to automate their video production
• YouTube channel managers handling multiple content streams
• Anyone looking to create professional-quality videos without video editing experience
• History/educational content creators (currently optimized for this niche)

Comparison with Alternatives

Unlike existing solutions:

• vs Traditional Video Editors: Fully automated process vs manual editing
• vs Other AI Tools:
  • Integrates multiple AI services instead of just one
  • Handles the entire pipeline from script to final video
  • Supports both shorts and long-form content
  • Includes project management and scene regeneration
  • Local storage and processing (no cloud dependencies except APIs)

🛠️ Tech Stack:

• Python 3.8+
• PyQt6 for the GUI
• FFmpeg for video processing
• Integration with Claude AI, Stability AI, and ElevenLabs APIs

📦 Source Code: GitHub Repository

Would love to hear your thoughts and feedback! Feel free to ask any questions.

This tool allows you to view your code as it is executed line by line.

I realized that most people(including myself) are visual learners meaning that they will understand concepts better if presented visually rather than in purely written form.

I understand that there are similar tools for debugging, but this tool is purely for educational purposes. Beginners and people learning Python, can use it to understand basic Python concepts more easily.

The visualizer indicates the line that was executed in each step, displays its output values and updates the scope details to reflects the changes made by the line.

Link: Python Visualizer

Please share feedback, how it can be improved and whether it is actually useful.

https://preview.redd.it/r14ol73gjqzd1.jpg?width=1280&format=pjpg&auto=webp&s=c81de7258465f3f67e23d7a6e8c8ec61a9eccdda

📽️ In our latest video tutorial, we will create a dog breed recognition model using the NasLarge pre-trained model 🚀 and a massive dataset featuring over 10,000 images of 120 unique dog breeds 📸.

What You'll Learn:

🔹 Data Preparation: We'll begin by downloading a dataset of of more than 20K Dogs images, neatly categorized into 120 classes. You'll learn how to load and preprocess the data using Python, OpenCV, and Numpy, ensuring it's perfectly ready for training.

🔹 CNN Architecture and the NAS model : We will use the Nas Large model , and customize it to our own needs.

🔹 Model Training: Harness the power of Tensorflow and Keras to define and train our custom CNN model based on Nas Large model . We'll configure the loss function, optimizer, and evaluation metrics to achieve optimal performance during training.

🔹 Predicting New Images: Watch as we put our pre-trained model to the test! We'll showcase how to use the model to make predictions on fresh, unseen dinosaur images, and witness the magic of AI in action.

Check out our tutorial here : https://youtu.be/vH1UVKwIhLo&list=UULFTiWJJhaH6BviSWKLJUM9sg

You can find link for the code in the blog : https://eranfeit.net/120-dog-breeds-more-than-10000-images-deep-learning-tutorial-for-dogs-classification/

You can find more tutorials, and join my newsletter here : https://eranfeit.net/

Enjoy

Eran

Target Audience:

Audio playback in Python is pretty niche, but is a really fun an interesting way for newer programmers to integrate exciting feature feedback into their projects, but is also a good choice for seasoned projects to consider, if it meets the feature requirements of their existing solutions.

What It Does:

• Non-blocking Audio Playback: Unlike traditional audio libraries that may block your program’s main thread, Rpaudio runs in a non-blocking manner. This means it works seamlessly with Python’s async runtimes, allowing you to handle audio in the background without interrupting other tasks.
• Simple and Intuitive API: I wanted to make sure that using Rpaudio is as simple as possible. With just a few lines of code, you can easily load, play, pause, and resume audio. For more complicated needs, it also provides abstractions such as AudioChannel's, which act as a queue manager, and can apply different effects such as fades or speed changes to any AudioSink object played from its queue, and can even apply the effects dynamically, over time.
• Lightweight and Efficient: Built with Rust, Rpaudio brings the performance benefits of a compiled language to Python. This ensures safe and efficient thread handling and memory management.
• Cross-Platform: Rpaudio is designed to work smoothly on Windows, macOS, and Linux.

I built this because I wanted a way to use Rust’s power in Python projects without having to deal with the usual awkwardness that come with Python’s GIL. It’s especially useful if you’re working on projects that need to handle audio in async applications.

Why I Think It’s Useful:

During my work with Python and audio, I found that many libraries were either too cumbersome or didn’t play well with async applications. Libraries like PyAudio often require dealing with complicated dependencies, and others don’t handle concurrency well, leading to blocking calls that mess with async code. Rpaudio was born out of the need for a lightweight, easy-to-use solution that works well with Python’s async ecosystem and offers simple, efficient audio control.

Comparison:

Pyaudio and other popular libraries like it, dont seem to support async functionality natively, which is one of the ways I normally like to interact with audio since it's naturally just kind of a blocking thing to do. Audio libraries are often more complex than necessary, requiring additional dependencies and setup that just isn’t needed if you’re working on a simple audio player or sound management tool. Additionally, they don’t always work well with async Python applications because they rely on blocking calls or the overhead of larger libraries..

I’d Love Your Feedback:

Im not a professional developer, so any feedback is well appriciated.

Code, docs and other info available in the repo:

https://github.com/sockheadrps/rpaudio

Or if youd like a short, video-form glimpse, I uploaded a short video explaining the uses and API a bit.

https://www.youtube.com/watch?v=DP7iUC5EHHQ

pls DM me need help for a great project.....

Looking for some great coders in python to help me finish my passion project(already got one but looking for another)

I'll explain you everything in DM...what I'm,and what I'm doing

https://reddit.com/link/1gjf3is/video/bt6fo64i5wyd1/player

Originally this is an idea that sparked from the requirement of making a novel submission for a Data Structures and Algorithms course assignment at my university. The project though, grew much more from there outside the scope of a univ assignment when I saw the potential in using animated visuals to demonstrate the nuances of sorting algorithms.

Each visualiser shows a live tracking of what part of the pseudocode is currently being executed, and tracks important variables and iterators throughout the sorting process.

The need to use pygame was purely a consequence of my familiarity with the library and how easy it made implmenting animation ideas like these for me.

Planning to do so much more from here - visualising trees, graphs and more of the kind. I will very soon be releasing the github repo for this project, making some final tweaks :)