I built my first AI Smart Study Assistant using Python and Streamlit. It’s a simple project where I tried to understand how AI apps actually work with a clean user interface. ✨ What it can do: 📄 Summarize text (demo mode) 🧠 Explain topics in simple words ❓ Generate quiz questions 🎨 Simple and interactive web UI 🛠️ Tech used: Python, Streamlit While building this, I understood how user input flows into logic and how AI-based applications are structured. Right now this is a demo version, but I designed it in a way that it can be upgraded later with real AI models and a chat interface. Next step for me is to improve this project further and keep building more AI-based applications. Would love feedback or suggestions 🙌#AI #Python #Streamlit #MachineLearning #LearningByDoing #ArtificialIntelligence #TechJourney #WomenInTech #DataScience Microsoft Google OpenAI https://lnkd.in/eA-xwtqG

Python: The Brain Behind Your Favorite Apps Ever wonder how apps "know" exactly what you need? Python is the engine under the hood of the world’s smartest websites, using AI to create personalized experiences just for you. From recommending your next favorite movie to providing instant customer support, it’s the technology making the web feel more human. Discover how we’re building a smarter digital world at Artemee.com!

I saw someone asking the Maccas app how to reverse a linked list. The result? A perfect, monster-level Python script. It turns out the assistant is running on Opus 4.7, which is absolute state-of-the-art for coding. But here’s the problem…. It’s a Guardrail Failure. While it’s cool that I can get data structures with my Big Mac, this is a major "UX Logic Leak." From a product perspective, your AI shouldn't be a polymath - it should be a specialist. Have you seen other examples of AI agents doing the unexpected? #AIAgents #UXDesign #UserTestingLab #Maccas #AIUX

Most content creators and teams don’t struggle with ideas they struggle with managing them. I personally faced this too ideas scattered across notes, planning in different tools, and no clear system to stay consistent. To solve this, I developed Content OS a system that brings content planning, organization, and scheduling into one place, making the workflow simple and structured. ⚙️Built with a full-stack setup using Next.js (TypeScript) + FastAPI (Python) + SQLite #developer #Innovation #Technology #python #AI

📅 Day 17/30 — House Recommendation System (Python + ML + Streamlit) 🏡🤖 🔹 Project Overview: Built a House Recommendation System that helps users find properties based on their specific requirements using Machine Learning. Designed an interactive system with Streamlit to take user inputs and return relevant house suggestions. 🔹 Tools Used: Python | Machine Learning | Streamlit 🔹 Key Features: • Personalized house recommendations based on user preferences 🏡 • User input-driven filtering (budget, location, features) 🎯 • Real-time property suggestions ⚡ • Data preprocessing and feature engineering 🔧 • Efficient recommendation logic using similarity techniques 🤖 🔹 What I Learned: • Building practical recommendation systems using ML • Handling user input and mapping it to meaningful outputs • Feature engineering for improving recommendations • Creating interactive applications with Streamlit • Applying ML to solve real-world user problems 🔗 GitHub Repository: https://lnkd.in/dH79ives #Python #MachineLearning #Streamlit #RecommendationSystem #DataScience #MLProjects #TechProjects #30DaysOfCode 🚀

🏡 House Price Prediction App! I developed a Machine Learning-based web app using Python & Streamlit that estimates house prices based on important factors such as: ✔️ Area (in square feet) ✔️ Number of bedrooms ✔️ Property age ✔️ Distance from the city center ✨ Highlights of the project: Interactive and clean UI built with Streamlit Easy-to-use input fields for users Integrated ML model for accurate predictions Instant results display 🎯 What I learned while building this: Data cleaning & preprocessing techniques Working with regression algorithms Deploying ML models into web apps Designing simple and effective user interfaces 🛠️ Tech Stack: Python | Pandas | NumPy | Scikit-learn | Streamlit 🔗 Check out the project here: https://lnkd.in/ghhUHEGM 💡 Next improvements I’m working on: 🔹 Enhancing model performance 🔹 Adding visual analytics & charts 🔹 Improving UI with advanced features #MachineLearning #Python #AI #DataScience #Streamlit #Projects #BuildInPublic #Learning

🎬 Excited to share my Movie Recommender System project! Built an interactive web app that recommends movies based on similarity using Machine Learning and Streamlit. ✨ Features: • Select any movie from the list • Get instant movie recommendations • Simple and user-friendly interface 🧠 How it works: Movie descriptions are converted into numerical vectors, and a Cosine Similarity Matrix is created to measure similarity between movies. When a movie is selected, the system finds other movies with the highest cosine similarity scores and recommends them. Tech Stack: Python | Pandas | Scikit-learn | Streamlit Learning by building real-world ML applications 🚀 #MachineLearning #Python #Streamlit #AIProjects #StudentDeveloper

Happy to share that I’ve recently started exploring Python 🐍 — and I’m genuinely having a lot of fun with it! Coming from a frontend background, it’s interesting to see how expressive and concise Python can be. A few things that stood out to me so far: ✔ Clean and readable syntax (feels almost like writing plain English) ✔ Powerful built-in data structures like lists, sets, and dictionaries ✔ List comprehensions — simple yet very elegant ✔ Strong ecosystem for AI/ML, automation, and scripting ✔ Great for quickly prototyping ideas It’s refreshing to step outside your primary tech stack and look at problems from a different perspective. Still early in the journey, but definitely enjoying the process of learning something new. Would love to hear from the community: **What’s one Python feature or use case that you found most powerful?** #Python #Learning #DeveloperJourney #AI #Programming #SoftwareDevelopment

Small workflow issues add up fast in production. A missed export, a wrong camera setting, or a render sitting unnoticed can slow everything down. 🎬 In our latest blog post, we explore how Blender’s Python API lets you listen to events and automate these tasks in the background. From render complete handlers to interactive viewport tools, these patterns help remove manual steps and keep pipelines consistent. ⚙️ Check out the full blog post here:

🚀 I built and deployed a Credit Card Default Prediction App 💳 This project analyzes customer behavior and predicts default risk using Machine Learning. 🔗 Live App: https://lnkd.in/dAsNnung 📊 Key idea: Late payments and high credit usage increase default risk. Happy to hear your feedback! #DataScience #MachineLearning #Python #Streamlit