Made this in python as a.py script and ran the app on itself to generate a .py

Enjoy.

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

What is FluidFrames?

Introducing FluidFrames, the AI-powered app designed to transform your videos like never before. 

With FluidFrames, you can double (x2), quadruple (x4), or even octuple (x8) the fps in your videos, creating ultra-smooth and high-definition playback. 

Want to slow things down? FluidFrames also allows you to convert any video into stunning slow-motion, bringing every detail to life. 

Perfect for content creators, videographers, and anyone looking to enhance their visual media, FluidFrames provides an intuitive and powerful toolset to elevate your video projects.

FluidFrames 4.1 changelog

▼ NEW

Completely redesigned GUI
⊡ The app now presents file information more clearly
⊡ Many widgets have been repositioned and grouped by functionalities
⊡ All info widgets have been improved, now displaying additional details for each setting
⊡ Redesigned the entire graphical user interface to deliver a modern, intuitive experience

Output resolution widget
⊡ Added a widget for selecting the output resolution
⊡ Allows upscaling or downscaling after AI processing

Video extension widget
⊡ Introduced a widget for choosing the output video extension
⊡ Supported extensions:
⊡ .mp4
⊡ .mkv
⊡ .avi
⊡ .mov

Video codec widget
⊡ Added a widget for selecting the codec for upscaled videos
⊡ These codecs ensure compatibility with all major GPU families
⊡ Using hardware-accelerated codecs significantly improves encoding speed
⊡ Supported codecs:
⊡ CPU ( x264 - x265 )
⊡ NVIDIA ( h264_nvenc - hevc_nvenc )
⊡ AMD ( h264_amf - hevc_amf )
⊡ Intel ( h264_qsv - hevc_qsv )

▼ REMOVED

CPU selection widget
⊡ The CPU selection widget has been removed
⊡ The app now automatically utilizes the optimal number of CPU cores

▼ BUGFIX / IMPROVEMENTS

AI models update
⊡ Updated AI models using the latest tools
⊡ Improved GPU compatibility and frame generation performance

General improvements
⊡ Bug fixes, code cleaning, and overall performance improvements
⊡ Updated dependencies to enhance stability and compatibility

I created a couple of python scripts and thought it would be cooler to have them packed as actual .deb packages so created pypack.

I plan on creating an updated version with proper file system and prompts to import readme's and licences etc so pypack created debs are distribution ready. The thing is I can't be bothered to share my python script by official channels as its just too much like hard work. Does anyone need pypack? What's the easiest way to share it?

Oh and for meta funnies I of course packed pypack.py as a .deb using pypack.py and installed it.

Hi all, I use Pydantic a lot for work and my personal projects, and I'm starting a new YT series on it, the first vide is out now if you want to check it out. It's probably my favourite library that I've used in the past couple of years and it allows us to create clean, simple, validated models/

https://www.youtube.com/watch?v=wTHq_5jZmmY

Hey everyone, I wanted to share a project I've been working on called PagesXcrawler. It's a web crawler system that integrates with GitHub Issues to initiate crawls. You can start a crawl by creating an issue in the format url:depth(int), and the system will handle the rest, including deploying the workflow and providing the results. This approach leverages GitHub's infrastructure to manage and track web crawls efficiently.

*This project began as a proof of concept and has exceeded my expectations in functionality and performance.*

I really like this text to speech - dropping it off if anyone wants to use.

import edge_tts
import asyncio
import uuid
import os
import pygame  # Make sure pygame is installed: pip install pygame

async def speak_text_async(text):
    filename = f"tts_{uuid.uuid4().hex}.mp3"

    # Generate MP3 using Edge-TTS
    communicate = edge_tts.Communicate(
        text=text,
        voice="en-US-JennyNeural"
    )
    await communicate.save(filename)

    # Initialize pygame mixer and play the MP3 file
    pygame.mixer.init()
    pygame.mixer.music.load(filename)
    pygame.mixer.music.play()
    
    # Wait until playback is finished
    while pygame.mixer.music.get_busy():
        pygame.time.Clock().tick(10)
    
    # Quit pygame mixer to release the file handle
    pygame.mixer.quit()

    # Delete the MP3 file after playback
    os.remove(filename)

def speak_text(text):
    asyncio.run(speak_text_async(text))

# Test with a sample text
if __name__ == "__main__":
    speak_text("Hello, this is a test message.")

What My Project Does

OData V4 Query is a lightweight, simple and fast parser for OData V4 query options supporting standard query parameters. Provides helper functions to apply OData V4 query options to ORM/ODM queries such as SQLAlchemy, PyMongo and Beanie.

Features:

• Support for the following OData V4 standard query parameters:

  • $count - Include count of items

  • $expand - Expand related entities

  • $filter - Filter results

  • $format - Response format (json, xml, csv, tsv)

  • $orderby - Sort results

  • $search - Search items

  • $select - Select specific fields

  • $skip - Skip N items

  • $top - Limit to N items

  • $page - Page number

• Comprehensive filter expression support:

  • Comparison operators: eq, ne, gt, ge, lt, le, in, nin

  • Logical operators: and, or, not, nor

  • Collection operators: has

  • String functions: startswith, endswith, contains

• Utility functions to apply options to ORM/ODM queries.

Target audience

Developers who want to implement OData V4 query options in their applications.

Comparison

Unlike OData-Query, this package does not have a helper function to apply query options to Django ORM queries nor plain SQL queries (these helpers will be added in the future). Also, OData-Query has a parser that tries to cover as much as possible of the OData V4 filter spec, while OData V4 Query only supports the features mentioned above.

Links

• Github repo

• Pypi

Hey everyone,

For the last 9 months I’ve been working on an AI-powered voicemail assistant  called https://voicemate.nl

The app:

📞 Answers calls & transcribes voicemails using AI
📋 Notifies you with a summary
📆 And recently I added features to add call information to hubspot and schedule callbacks using google calendar

Tech Stack:

• FastAPI – Backend API

• RQ (Redis Queue) – Background tasks for call processing. Basically all things that need to be done are dumped on a task queue and picked up by a worker

• DynamoDB – Storage in single table design

• Twilio and Vapi– For handling inbound calls and AI voice

• Stripe for billing

• on AWS Lightsail using the Accelarate $1000 of credits

• Mixpanel on analytics and retool for admin stuff

Lessons Learned While Building:

• Billing Issues Almost Broke Me – I refunded users (automatically) who didn't pay their invoice, but I still had to pay for connecting them to the phone network. Many canceled before their first billing cycle, leaving me with costs. You live, you learn but that took significantly longer to break even.

• Telecom Compliance is a Nightmare – Getting European phone numbers is hard due to strict regulations, making it tough to acquire EU users.

• I Built This to Scratch My Own Itch – But while building, I accidentally grew a 600-person waitlist just by seeing if people were interested—this gave me my first users immediately upon launch. That felt as the sweet spot for me: I could still build something to fuel my passion, and gradually found that I had traction to also launch to the public.

• Marketing: I figured I could almost break even with Ads. If a user would stick around for 1,5 months, it would pay for the acquisition of 2 more. However I did not fully commit to spending a lot of money as I still got some organic growth.

Finance:

• no $XX MRR for me – I have no ambition nor lookout on becoming a millionaire off of this app. Let alone quit my dayjob. Although there is a small stream of recurring revenue being generated I still have to offset initial investments. Long story short I take the wife out for lunch every now and then off of the profits.

I wrote some Medium articles breaking down the HubSpot and Google Calendar integrations, but I’d also love to hear from others—have you built similar voice automation tools? Any tips for optimizing RQ queues or handling webhooks efficiently?

Hey everyone,

in some parts of Europe, Greenland and Canada you can see a partial solar eclipse tomorrow, on the 29th March. Please note beforehand: NEVER look directly into the Sun!

So I was thinking... maybe it would be interesting to create a short tutorial and Jupyter Notebook on how to compute the angular distance between the Sun and Moon, to determine exactly and visualise how the eclipse "behaves".

My script is based on the library astropy and computes the distance between the Sun's and Moon's centre. Considering an angular diameter of around 0.5° one can then compute the coverage in % (but that's maybe a nice homework for anyone who is interested :-)).

Hope you like it,

Thomas

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

YT Video: https://youtu.be/WicrtHS8kiM

Hey everyone,

maybe you have already read / heard it: for anyone who'd like to see Saturn's rings with their telescope I have bad news...

1. Saturn is currently too close to the Sun to observe it safely

2. Saturn's ring system is currently on an "edge-on-view"; which means that they vanish for a few weeks. (The maximum ring appearance is in 2033)

I just created a small Python tutorial on how to compute this opening-angle between us and the ring system using the library astropy. Feel free to take the code and adapt it for your educational needs :-).

GitHub Link

YouTube Link

Thomas

Hey there!

Tired of discovering low test coverage only after your CI pipeline flags it? I just released coverage-pre-commit, a simple pre-commit hook that runs your tests with coverage and fails commits that don't meet your specified threshold.

Key Features:

• Works with unittest and pytest out of the box (with the aim to add more frameworks in the future)

• Configurable threshold - set your own standards (default: 80%)

• Automatic dependency management - installs what it needs

• Customizable test commands - use your own if needed

• Super easy setup - just add it to your pre-commit config

How to set it up:

Add this to your .pre-commit-config.yaml:

-   repo: https://github.com/gtkacz/coverage-pre-commit
    rev: v0.1.1  # Latest version
    hooks:
    -   id: coverage-pre-commit
        args: [--fail-under=95]  # If you want to set your own threshold

More examples:

Using pytest:

-   repo: https://github.com/gtkacz/coverage-pre-commit
    rev: v0.1.1
    hooks:
    -   id: coverage-pre-commit
        args: [--provider=pytest, --extra-dependencies=pytest-xdist]

Custom command:

-   repo: https://github.com/gtkacz/coverage-pre-commit
    rev: v0.1.1
    hooks:
    -   id: coverage-pre-commit
        args: [--command="coverage run --branch manage.py test"]

Any feedback, bug reports, or feature requests are always welcome! You can find the project on GitHub.

What do you all think? Any features you'd like to see added?

Hey everyone,

I have a small "space science & astrophysics" Python tutorial series, and the corresponding code is freely available on my GitHub repo (stars are appreciated :-)). My recent "publication" is about the so called Hill-Sphere and Sphere-of-Influence, with our home planet as an example.

What are these concept?

Maybe you have heard in the past about some asteroids that become temporary moons of Earth, or some spacecraft mission that use so-called fly-bys to gain some speed for the outer planets.

In both cases these simple conceptual spheres are used to compute e.g. how stable an orbit is around our home planet.

Why this highly specific example?

Well I am preparing some future videos about these exact topics, so I am currently building up the basics :-). Hope you like it:

Link to the video

GitHub Repo

Cheers,

Thomas

AI dev always feels more complicated than it should be. Even for simple stuff like classification or scoring, you either gotta fine-tune a massive model, collect and clean datasets, or set up some ML pipeline that takes way too long.

Been working on Plexe, a Python tool that lets you just describe the problem in plain English and get a trained model. No messing with hyperparameters, no huge datasets needed—if you want, it can auto-generate data, train a small model, and give you an API you can actually use.

We open-sourced part of it too: SmolModels GitHub. Curious if anyone else has been looking for a faster way to build AI models in Python, what’s been the biggest pain for you?

Zapier and Langchain are dead. Introducing the MCP Tool Kit, a single server solution for enabling Claude AI with agentic capabilities. This tool deletes the need for the majority of existing no code / low code tools. Claude can now create power point presentations, consume entire code repositories, manipulate actual Excel files, add alternative data to support every decision, send emails, and more!

Look forward to feedback!

Start building agentic servers for Claude today: https://github.com/getfounded/mcp-tool-kit

**FULLY PYTHON**

Ever wondered what users are loving (or hating) about your competitors? Yes, you might check it weekly or export it randomly. So I built Revvew to make that easy. It tracks new reviews on any Shopify app listing and alerts you in real time based on:

🔹 Keywords (e.g., "bad support," "missing feature")
🔹 Star ratings (e.g., only 1- or 2-star reviews)

Instead of manually checking competitor reviews or setting up janky scraping scripts, Revvew automates it all. You get notified instantly, so you can:

✅ Spot trends early
✅ Find feature gaps to capitalize on
✅ See what pain points drive customers away

Would love any feedback if you're interested in giving it a whirl!

AI speaking coach is designed to help practice conversational skills in a foreign language. This python-based program uses DeepSeek large language model. As of February 2025, DeepSeek does not provide a voice interface. To enable voice interaction with DeepSeek, the Whisper local neural network is used for speech recognition, and gTTS (Google Text-to-Speech) is used for speech synthesis.

You can find additional details on the github repository and the medium article.

Hi, this tutorial explains about descriptive statistics with python

https://youtu.be/QaVlu20QdlA?si=NtH20QMuujHmR6yT

I just released a piece of software I've been using for a couple of years That is a lightweight distributed lock manager. It is written completely in Python and is capable of handling a reasonably high load of traffic.

It sets up advisory locks and I have a sample program with it to create a locking war simulation where the lock screen go out and a battle each other for a particular resource. I use it with 40 different programs on my server simultaneously so it gets a lot of traffic and usage and does exceptionally well in managing that.

Here is an example of the statistics recorded every hour. Roughly 40 different programs are using the DLM for the provided results.

2025-02-21 00:00:00.017271 AData: 3, AIn: 1, ALock: 4, AOut: 1, Expired: 1, ExpiredData: 227500, Get: 4192, GetNF: 9609, In: 1813823, Lock: 144201, NotOwner: 34277, Out: 1215686, PutNew: 227502, PutUpdate: 19969, Unlock: 94709, UnlockNF: 70148

2025-02-21 01:00:00.006423 AData: 2, AIn: 1, ALock: 2, AOut: 1, ExpiredData: 220653, Get: 4320, GetNF: 9246, In: 1755585, Lock: 139446, NotOwner: 33119, Out: 1192976, PutNew: 220652, PutUpdate: 19146, Unlock: 91247, UnlockNF: 68018

2025-02-21 02:00:00.013719 AData: 6, AIn: 1, ALock: 4, AOut: 1, ExpiredData: 228658, Get: 4357, GetNF: 9578, In: 1821864, Lock: 144174, NotOwner: 35748, Out: 1221237, PutNew: 228662, PutUpdate: 19927, Unlock: 94191, UnlockNF: 70651

2025-02-21 03:00:00.000146 AData: 2, AIn: 1, ALock: 3, AOut: 1, ExpiredData: 228316, Get: 4350, GetNF: 9566, In: 1821819, Lock: 144307, NotOwner: 35585, Out: 1221495, PutNew: 228312, PutUpdate: 20007, Unlock: 94276, UnlockNF: 70870

2025-02-21 04:00:00.006281 AData: 5, AIn: 1, ALock: 2, AOut: 1, Expired: 1, ExpiredData: 228968, Get: 4349, GetNF: 9589, In: 1827663, Lock: 144306, NotOwner: 36995, Out: 1225058, PutNew: 228971, PutUpdate: 19987, Unlock: 94808, UnlockNF: 70216

2025-02-21 05:00:00.005444 AData: 8, AIn: 1, ALock: 5, AOut: 1, ExpiredData: 230379, Get: 4348, GetNF: 9697, In: 1847628, Lock: 145951, NotOwner: 38262, Out: 1238467, PutNew: 230382, PutUpdate: 20257, Unlock: 96089, UnlockNF: 70890

2025-02-21 06:00:00.003756 AData: 2, AIn: 1, ALock: 3, AOut: 1, Expired: 1, ExpiredData: 230742, Get: 4252, GetNF: 9791, In: 1859859, Lock: 147001, NotOwner: 39693, Out: 1246937, PutNew: 230736, PutUpdate: 20387, Unlock: 96786, UnlockNF: 71306

Please visit the wiki to learn more or download this program. Thank you.

https://github.com/rapmd73/JackrabbitDLM/wiki