Just deployed: A Full-Stack Scientific Calculator using Flask! I am excited to share my latest personal project, the Muntazer Scientific Calculator. I designed this application to replicate the sleek interface of modern scientific calculators while adding powerful backend logic using Python. Technical Highlights: Backend: Python (Flask) for handling complex scientific math logic (trigonometry, logarithms, factorials). Frontend: Responsive HTML5/CSS3 grid layout with a "Google-style" aesthetic. Features: Real-time calculation history sidebar, dual-mode (Degrees/Radians), and a smart "Ans" memory key. Version Control: Managed via Git & GitHub. This project was a great way to refine my full-stack skills, specifically focusing on state management between the frontend (JS) and backend (Python). Check out the code here: 🔗 https://lnkd.in/dpUbua_i #Python #Flask #WebDevelopment #SoftwareEngineering #Portfolio #OpenSource #Coding

🚀 My Python Playground is back, and it’s better than ever! 🐍 They say you can’t keep a good project down. After a short hiatus (and a little "adventure" migrating the backend after Shuttle.dev shut down), I’ve spent the last few days working with AI to self-host the infrastructure. The AsyncMove Python Playground is officially live with a working URL shortener! 🛠️✨ If you need a fast, browser-based Playground to test ideas, teach, or share code, this is built for you. Here’s what’s under the hood: ⚡ Ruff Linting: Lightning-fast linting to catch bugs instantly. 🧪 Pytest Support: Built-in testing to ensure your logic is bulletproof. 📦 Package Management: Install and import Python packages on the fly. 🔗 Short URLs: Share your entire workspace with one clean, portable link. 💅 Auto-Formatting: Keep your code PEP 8 compliant with a single click. 📖 README.md: Annotate your snippets with full Markdown support. 💯 Type hints and Autocomplete: Hover over your codes to see the docstrings. It feels great to have this back in the wild. I’d love for you to give it a spin and share a snippet! Link in the Comments section. #Python #BuildInPublic #OpenSource #Coding #PythonProgramming #WebDev #SoftwareEngineering

🚀 New Project Alert: Movie Recommendation System 🎥🍿 Excited to share my latest project — Movie Recommendation System built using Python + Streamlit + Google Gemini API 🤖✨ ✅ What it does: 🎯 Recommends movies based on user preferences ⚡ Gives quick and relevant suggestions 🖥️ Clean and simple Streamlit UI 🛠️ Tech Used: Python | Streamlit | Gemini API (API Key Integration) This project gave me hands-on experience in GenAI integration + web app deployment. Feedback is welcome! 💯 #PythonDeveloper #StreamlitApp #Gemini #GenAI #AIProjects #MachineLearning #LinkedInProjects ✅ Caption Option 3 (Short & Powerful)

🚀 I just published a new technical article! How to Connect CopilotKit to a Python Backend Using Direct-to-LLM (FastAPI Guide) In this post, I walk through: Why Direct-to-LLM can be a simpler alternative to heavy orchestration (e.g., LangGraph) How CopilotKit’s Remote Backend Endpoint works A real FastAPI + CopilotKit setup using the official Python SDK A practical in-production use case with streaming responses When this architecture actually makes sense in real systems If you're building AI copilots, agentic apps, or integrating LLMs with existing Python backends, this pattern can save you complexity while keeping things production-ready. Would love your thoughts and feedback! 👇 🔗 Link in comments #CopilotKit #FastAPI #DirectToLLM #Python #AIEngineering #AgenticAI

Today’s Learning: Nested Loops in Python Today, I focused on understanding and practicing nested loops in Python, which are essential when working with repetitive logic inside another loop. Key takeaways from today’s practice: How an inner loop executes fully for each iteration of the outer loop Using nested loops for pattern printing Improving logical thinking and flow control Understanding real-use cases like matrix operations and iterations Hands-on practice helped me clearly visualize how nested loops work step by step and how they are applied in problem-solving scenarios. 🔗 GitHub Repository (Day 13 – Nested Loops Practice): https://lnkd.in/gvjitfr8 Consistent practice is helping me strengthen my Python fundamentals and move closer to building more complex programs and projects. #Python #NestedLoops #PythonLearning #Consistency #DailyLearning #CodingJourney #SoftwareDevelopment #CareerGrowth #GitHub #LearningByDoing

🏠 House Pricing Prediction Web App Developed a full-stack ML web application that predicts house prices using a regression model trained on the Boston housing dataset. Built a responsive frontend with HTML/CSS, integrated the model via a Flask backend, and deployed the app to Heroku. Implemented CI/CD with GitHub Actions for automated deployment, gaining hands-on experience with Python, scikit-learn, Flask, and end-to-end ML application deployment. Repository: https://lnkd.in/ghnXYT6y #MachineLearning #Python #Flask #WebDevelopment #DataScience #MLDeployment #Heroku #GitHubActions #FullStack #HousePricePrediction #AI #Projects

Today’s learning was focused on Object-Oriented Programming in Python, diving deeper into Inheritance and its practical applications. Topics covered: Concept of Inheritance in Python Types of Inheritance: Single Inheritance Multiple Inheritance Multilevel Inheritance Hierarchical Inheritance Hybrid Inheritance Use of the super() keyword to access parent class methods and constructors How inheritance improves code reusability, scalability, and maintainability Hands-on practice helped me understand how child classes interact with parent classes and how super() simplifies method overriding. GitHub code reference: https://lnkd.in/gqqeGJM5 #Python #PythonProgramming #ObjectOrientedProgramming #OOP #Inheritance #SuperKeyword #LearningByDoing #CodingJourney #SoftwareDevelopment #ProgrammingPractice #ContinuousLearning #CareerGrowth #Upskilling #TechSkills #DeveloperJourney

🚀 The Happy Path: The "Magic" of Deleting Code There is no cleaner code than the code that no longer exists. We often think "progress" means adding more: more features, more abstractions, more lines. But one of the most pleasant "wins" in a developer's day is finding a way to solve a problem by removing complexity. Replacing a custom, buggy function with a single built-in library call? Pure bliss. Deleting a 100-line workaround because a dependency finally updated? Chef’s kiss. 👨🍳 It’s lighter, it’s faster, and it’s one less thing for your future self to debug. What’s the most satisfying chunk of code you’ve ever been able to delete? Let’s hear your "deletion wins" in the comments! 👇 #TheHappyPath #SoftwareEngineering #CleanCode #ProgrammingLife #Python #Django

Today’s learning was all about Polymorphism in Python Polymorphism is a powerful OOP concept that allows the same function or method name to behave differently based on the object or context. It helps in writing flexible, scalable, and maintainable code. Key takeaways from today: Understanding method overriding How the same interface can work with different data types Real-world use cases of polymorphism in object-oriented design I’ve practiced these concepts with hands-on Python examples and shared the code in my GitHub repository GitHub: https://lnkd.in/gxSwxixq Consistent learning and practice are helping me strengthen my Python OOP fundamentals step by step. #Python #Polymorphism #OOP #ObjectOrientedProgramming #PythonLearning #SoftwareDevelopment #CodingJourney #LearningEveryDay #CareerGrowth #Upskilling #GitHub #DeveloperJourney

 LeetCode Daily Challenge – Minimum Cost to Convert String 🔍 Problem Summary We are given two strings source and target. We can convert one character to another using some given rules, each with a cost. Our goal is to find the minimum total cost to convert source into target. If it’s not possible, return -1. 💡 Pattern Used Graph + Shortest Path (Dijkstra) Characters (a–z) are nodes Conversion rules are weighted edges Find the minimum cost path between characters 🛠️ Approach Build a graph from original → changed with costs Use Dijkstra to find shortest paths between all characters Store results in a 26 × 26 cost matrix For each index in source: Add the cost to convert source[i] → target[i] If conversion is impossible, return -1 ⏱️ Time & Space Complexity Time Complexity Dijkstra runs 26 times Each run: O(E log V) Since V = 26, total time is very small 👉 Overall: O(26 × E log 26) ≈ O(E) Space Complexity Cost matrix: 26 × 26 Graph storage 👉 Overall: O(26² + E) 🧠 Tricks & Tips Always precompute shortest paths for character problems Use ord(char) - ord('a') to map characters to indices Avoid iterating directly over defaultdict.keys() while running Dijkstra Skip Dijkstra updates if current distance is already larger (heap optimization) ✅ Key Takeaway When a problem involves transformations + costs, 👉 Think of it as a graph problem 👉 Shortest path algorithms save the day Happy coding 🚀 #LeetCode #DailyChallenge #Graphs #Dijkstra #Python