Have you ever wondered how much of your LinkedIn feed is AI? I built a small Python utility that scans your feed and estimates likely AI usage in posts. Source code: https://lnkd.in/gytB8x7j I ran it on my own feed and visualized the results below. In my sample, 17% of posts looked highly AI-assisted, and another 39% showed moderate AI usage. That means over half of the posts in my feed used some level of AI. #AI #Python #DataAnalysis
AI Usage in LinkedIn Feed Estimated at 56%
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I built an AI agent that can search Google + remember conversations. And it changed how I see AI. This isn’t just: “Ask → Get answer” It’s becoming: “Ask → Understand → Remember” That’s the shift. Tech stack: Python + LangChain + Groq Still simple. But powerful. 💬 What would you add to this? #AI #Python #LangChain #BuildInPublic
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🚀 Learning AI with Python: My Journey Begins! Artificial Intelligence is no longer the future — it’s the present. And one of the best ways to dive into it is through Python 🐍 Here’s why I started learning AI using Python: ✅ Simple and beginner-friendly syntax ✅ Powerful libraries like NumPy, Pandas, and TensorFlow ✅ Huge community support ✅ Endless real-world applications What I’m focusing on: 🔹 Machine Learning fundamentals 🔹 Data preprocessing & visualization 🔹 Building small AI models 🔹 Exploring deep learning One thing I’ve realized: 👉 Consistency beats intensity. Even 1 hour daily compounds massively over time. If you're thinking about getting into AI, just start. You don’t need to know everything — you just need to take the first step. Let’s grow together in this AI journey 💡 #ArtificialIntelligence #Python #MachineLearning #AI #LearningJourney #TechGrowth #Developers #100DaysOfCode
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🚀 Workflows built with AI and backed by Python = Determinism AI workflows are great, but where they fail business use cases is when the same prompt can generate different results for different users. Mito let's you anyone create full workflows/automations from a prompt, but it also generates the Python for each step. This means when you share the workflow or rerun it, you get the same results. Determinism is key for enterprises to trust AI. More to come! #AI #Determinism #EnterpriseAI #Data #Python
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A single “Hello, World” in 4 languages. For a non‑coder, they all look similar. But for building AI & ML, the difference is huge. Most AI breakthroughs you read about are built with Python. Not because it’s “cooler,” but because it’s faster to learn, test, and pivot. 🔹 Weeks of coding in other languages → days in Python 🔹 Easier to turn an idea into a working prototype 🔹 Huge libraries (TensorFlow, PyTorch, scikit‑learn) = no need to reinvent the wheel For founders: Python lowers the risk and time to discover if AI can actually solve your problem. You don’t need your team to be elite engineers. You need them to move fast. That’s Python. #AI #MachineLearning #Python #LLM
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Here is a mini project I’ve built an interactive AI assistant using Google Gemini, LangChain, and LangGraph in Python. 💡 Features: Real-time AI responses streamed to the console Handles multi-turn conversations with context Easy-to-extend with additional tools Perfect for learning AI agent development and experimenting with state-of-the-art conversational AI. Github: https://lnkd.in/d6-xww3v #Python #AI #LangChain #GoogleGemini #ConversationalAI #TechProjects #DeveloperLife
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Day 6/20 my AI/ML journey 🚀 One thing I’m starting to appreciate more is how much work happens before machine learning models are even involved. This week I spent time working on reading and exploring datasets using Python. Simple things like: Understanding the structure of the dataset Checking the data types of each column Looking for missing values Inspecting how different features are distributed At first it seemed basic, but the more I explore datasets the more I realize how important this stage is. If you don’t understand your dataset, you can’t build a reliable model. Data first. Models later. #africaagility #learninginpublic #AI #MachineLearning #DataScience #Python
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There’s a growing shift in AI: optimize inference, not just training. InferScale documentation is a great example of this shift. It enables better LLM outputs using inference-time scaling—making open-source models far more competitive. The docs are concise and actionable, making it easy to experiment and integrate into your stack. If you're building with LLMs, don’t skip this. https://lnkd.in/gRmY5Gc8 #AITrends #LLM #OpenSourceAI #Python #AIEngineering #GenAI #FutureOfAI
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Artificial Intelligence is no longer the future — it is the present. 🤖 The real question is: Are you building AI or just watching it evolve? Mastering AI with Python: Build Your Own Artificial Intelligence Step by Step gives students, developers, and tech enthusiasts a practical roadmap to understand AI and create intelligent systems from scratch using Python. From basic concepts to real AI models, this book turns complex ideas into clear, hands-on learning you can apply immediately.https://https://lnkd.in/dysDw3tW Don’t just use AI. Build it. Train it. Master it. 📘 Start your AI journey today and become part of the technology shaping tomorrow. #ArtificialIntelligence #Python #MachineLearning #AI #TechEducation #FutureSkills 🚀
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Python continues to be the backbone of modern Artificial Intelligence—and for good reason. From building scalable machine learning models to powering advanced deep learning frameworks, Python offers an ecosystem that accelerates innovation. Libraries like TensorFlow, PyTorch, and scikit-learn have transformed how developers approach complex problems. But beyond tools, what makes Python truly powerful in AI is its accessibility. It lowers the barrier to entry, enabling more professionals to experiment, build, and deploy intelligent systems. As AI continues to evolve, one thing is clear: those who understand both Python and data-driven thinking will lead the next wave of technological transformation. #Python #ArtificialIntelligence #MachineLearning #DataScience #Innovation
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Day 9/20 of my AI/ML journey 🚀 Today I explored web scraping and why it matters for machine learning. A lot of people assume datasets magically exist somewhere ready for analysis. In reality, data often has to be collected first. Web scraping makes that possible. Using Python, you can extract information from websites and convert it into structured data that can be analyzed later. This opens the door to building your own datasets instead of relying only on pre-packaged ones. No data → no machine learning. #AGIT20DayChallenge #LearningInPublic #AI #MachineLearning #WebScraping #Python
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