New post: Building Profitable AI Generated Trading Strategies with Python, Rithmic, and LLMs. Explore "Vibe Coding"—how LLMs can produce production-quality Python strategies from plain-language prompts and integrate with Rithmic for live execution. Read the full article: https://wix.to/8YZ966r #QuantTrading #AI #Python #Fintech #MachineLearning

I recently worked on a small Python project that helps download YouTube videos or extract audio using yt-dlp and ffmpeg, running directly on Google Colab. To make the project easier to understand, I used NotebookLM (AI) to generate a short explanation video that breaks down how the script works and the logic behind it. AI tools like NotebookLM are incredibly helpful for quickly understanding code and explaining technical concepts in a clear and structured way. Projects like this are a great way to practice automation, Python scripting, and problem-solving, especially when trying to solve small everyday tasks with simple tools. This project is shared purely for educational and learning purposes, demonstrating how open-source tools and AI can be used together to understand and build practical solutions. If you're interested in exploring the code, you can check it out here: - https://lnkd.in/dm67YcBJ Always open to feedback and suggestions for improving the project.

I built a Voice Personal Assistant using Python 🎙️ The assistant listens to voice commands and performs tasks like opening websites, telling the time, and responding with voice output. Technologies used: • Python • SpeechRecognition • pyttsx3 (Text-to-Speech) • OpenAI API This project helped me understand how speech recognition and AI can be combined to create interactive systems. Looking forward to improving it with LLM-powered conversations and more automation features. 🔗 GitHub Repository: https://lnkd.in/gzkwAn-p #Python #ArtificialIntelligence #VoiceAssistant #SoftwareDevelopment #AIProjects

Developer Launches Python Tool to Convert YouTube Channels into RAG-Ready Datasets 📌 A developer has unveiled a Python tool that transforms entire YouTube channels into RAG-ready datasets in one seamless workflow. Automating transcript extraction, chunking, embedding generation, and FAISS indexing, it bridges a critical gap in AI knowledge retrieval pipelines-enabling fast, semantic search over video content without manual preprocessing. 🔗 Read more: https://lnkd.in/dXUPWfrP #Python #Youtubetranscripts #Faissvectorindex #Ragdatasets #Llmintegration

   OpenCV Python Projects with Source Code      https://lnkd.in/dJazY8jE Contents… 1 Python Programs, to explain cv2, to Capture Video, Create face mesh 2 Python Programs, Flip image horizontally & bluid Face & Hand ... 3 Python programs, to Build Hand & Body Tracking System                                                           

Python is not the problem. Python has become the universal language of scientific R&D for a reason: its flexibility is unmatched. But flexibility without structure leads to chaos. The problem isn't Python, it's the lack of a unified development standard in Deep Tech environments. To move from "lab code" to "industrial code", we don't need to change languages, we need to change how we use them. We need a framework that brings engineering discipline to scientific agility. #TheStructuralShift #DeepTech #ScientificInnovation #ResearchTools #ResearchSoftware #ScientificSoftware #SoftwareEngineering #Python #PythonFramework #OpenSource #TechStandards #RandD #ResearchOps #TranslationalResearch #ScalableResearch #Industrialization #ImpactDriven #TechForImpact #TechForGood #TechLeadership #ScientificExcellence #SUFFISCIENS

Text-to-Speech Conversion Using Python (gTTS) Developed a Python-based Text-to-Speech solution using the Google Text-to-Speech (gTTS) library to convert textual input into audio files (MP3 format). This implementation demonstrates practical use of automation and voice-enabled applications with a focus on accessibility and digital transformation. #Python #gTTS #TextToSpeech #Automation #AI

Day 20 of My Python Learning Journey Today I learned about Shallow Copy and Deep Copy in Python. 📌 Shallow Copy A shallow copy creates a new object, but the nested objects inside it are still referenced from the original object. So, changes in nested elements will affect both copies. Example: import copy list1 = [[1,2,3],[4,5,6]] shallow = copy.copy(list1) shallow[0][0] = 100 print(list1) print(shallow) 📌 Deep Copy A deep copy creates a completely independent copy of the original object including all nested objects. Changes in one object will not affect the other. Example: import copy list1 = [[1,2,3],[4,5,6]] deep = copy.deepcopy(list1) deep[0][0] = 100 print(list1) print(deep) ✅ Key Difference Shallow Copy → Copies only the outer object Deep Copy → Copies outer object + all nested objects Learning these concepts helps in understanding memory handling and object references in Python #Python #PythonLearning #Day20 #CodingJourney #Programming #LearningEveryday

✅ Exploring Generative AI with Python I experimented with Google Generative AI (Gemini) using Python. I built a small script that connects to the Gemini model and generates responses to prompts. In this demo, I used the Gemini 2.5 Flash model to answer the question “What is Python?” ,"what is artificial intelligence?". This simple project helped me understand how to integrate AI models into Python applications. 🔧 Tools & Technologies: • Python • Google Generative AI API • Gemini 2.5 Flash Model This is a small step into the world of AI-powered applications. Excited to explore more projects in Generative AI and Machine Learning! #Python #GenerativeAI #GeminiAI #ArtificialIntelligence 

Yesterday I had to use a humanizer for a text to make it more humanized and less AI-sounding. So the text was something like "I'm building this Python app and I want it to be like so, etc." and the humanizer gave me the Python code as an output instead of humanizing my text for AI detection Using AI to humanize AI-written input literally + - 0 effort - prompt injection? xD