Mastering Kivy TextInput for Enhanced User Input

🤔 What is Currying? It’s a functional programming technique where a function with multiple arguments is transformed into a series of functions that each take a single argument. I’ve explored two ways to implement this: 1️⃣ Currying with .bind() Useful when you want to "preset" some arguments of an existing function. javascript let multiply = function(x, y) { console.log(x * y); } let multiplyByTwo = multiply.bind(this, 2); // 'x' is now locked as 2 multiplyByTwo(3); // Result: 6 2️⃣ Currying with Closures The more modern and flexible approach. javascript let addition = function(x) { return function(y) { console.log(x + y); } } let addTwo = addition(2); addTwo(3); // Result: 5 Why is Currying a Game-Changer for Scalable Apps like Naukri? 🧩 Ever wondered how large-scale platforms handle complex filters, custom logs, or reusable logic without repeating code? The secret often lies in Function Currying. Where is Currying used in Real-Time Projects (like Naukri.com)? In a massive application like Naukri.com, currying is used to create specialised functions from generic ones. Here are three real-world use cases: Search Filters (The Most Common): Naukri has a generic getJobs function. Instead of passing all filters every time, they use currying to create specific search functions: const getJobsByLocation = getJobs("Hyderabad") const getJobsInHydByRole = getJobsByLocation("Frontend Developer") Now, the getJobsInHydByRole function is ready to be called whenever a user clicks "Search." Form Validation: Validation logic often repeats. You can curry a validate function: const minLength = (min) => (value) => value.length >= min; const passwordValidator = minLength(8); const userNameValidator = minLength(5); Logging & Analytics: When tracking user behavior (e.g., "Applied to Job," "Saved Job"), you can curry the event type: const trackEvent = (category) => (action) => console.log(category, action); const jobTracker = trackEvent("Job Interaction"); jobTracker("Apply Clicked"); 💼 Real-World Application: The "Naukri.com" Example Imagine you are building a job search filter. Instead of writing a new function for every filter combination, you curry it! ✅ Generic Function: getJobs(Location)(Role)(Experience) ✅ Specialized Function: const getHyderabadJobs = getJobs("Hyderabad") ✅ Ready to Use: getHyderabadJobs("React Developer")("2 years") This makes your code highly declarative, reusable, and easier to test. Are you using Currying in your React or Node.js projects? Let’s discuss in the comments! 👇 #JavaScript #WebDevelopment #Currying #CleanCode #FrontendEngineering #NaukriTech #CodingBestPractices

🚀 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 𝐄𝐯𝐞𝐫𝐲 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰! 🐍 If you're learning Python or already working with it, mastering the right libraries can 10x your productivity. Here’s a quick cheat sheet to guide your journey 👇 Python Certification Course :- https://lnkd.in/dWm-vfKQ 🔹 Pandas → Data Manipulation Clean, transform, and analyze data efficiently with powerful DataFrame operations. 🔹 Scikit-learn → Machine Learning Build, train, and evaluate ML models with ease—even as a beginner. 🔹 Matplotlib → Data Visualization Create basic charts and graphs to understand your data visually. 🔹 Seaborn → Advanced Visualization Make your plots more attractive and insightful with minimal code. 🔹 Flask → Web Development & APIs Lightweight framework to build web apps and REST APIs quickly. 🔹 Pygame → Game Development Turn your ideas into simple 2D games and interactive experiences. 🔹 Kivy → Mobile App Development Build cross-platform mobile apps using Python. 🔹 Tkinter → GUI Development Create desktop applications with user-friendly interfaces.

There is a website you use regularly that has no Application Programming Interface (API). You want to automate your interactions with it — searching for people, managing your profile, sending messages. The traditional approach would be to spend days reverse-engineering the site’s Hypertext Markup Language (HTML) structure, writing a scraper, building a command-line interface (CLI), setting up tests, configuring Continuous Integration (CI), and deploying. What if you could do all of that in under ninety minutes, producing a well-tested, well-structured Python package with over two hundred tests and nearly five thousand lines of code? This essay describes exactly that process. It is not theoretical. Every claim here is verifiable from session logs, git history, and GitHub pull request records. The project is a Python CLI tool managed with uv (a fast Python package manager from Astral) for a website that has no public API. The tool uses authenticated Hypertext Transfer Protocol (HTTP) requests, scrapes HTML responses with BeautifulSoup, validates data with Pydantic models, and presents results through a Click command-line interface. It was built from a template project to a working tool with auth, search, and member-view commands in eighty-four minutes of wall-clock time. https://lnkd.in/giC9RzsP

Built something small, but genuinely useful 👇 🎥 YouTube Playlist Analyzer I often save long playlists (courses, tutorials, lectures)… and then wonder: “How long will this actually take me to finish?” So I built a simple web app to answer that. It takes a playlist link and gives: • Total number of videos • Total duration • Average video length • Longest & shortest videos • Estimated watch time at different speeds (1x → 2x) ⚙️ Tech: Python, Flask, yt-dlp, HTML/CSS What I liked about building this: Turning a real personal problem into a tool Working with yt-dlp to extract structured data Keeping the UI minimal and focused This is just a starting point. Planning to add: • Export (PDF/CSV) • Smarter insights (like topic breakdown) • Better UI/UX + loading states If you’re someone who learns from YouTube a lot, this might actually save you time. Would love feedback or ideas to improve it! GitHub repo link in the comments !!

𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗣𝘂𝘀𝗵 𝗡𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗜𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 You want to keep your users informed about their orders. Push notifications are a great way to do this. They send messages to users' devices to inform them about important updates. Here are some ways you can use push notifications in a food delivery app: - Order confirmation - Order status updates - Delivery updates You can use Firebase Cloud Messaging (FCM) to send notifications. FCM is a free service that allows you to send notifications to Android, iOS, and web applications. To implement push notifications in Python, follow these steps: - Set up a Firebase project - Install the Firebase Admin SDK in Python - Initialize Firebase in your Python backend - Send push notifications using FCM You can send notifications when an order is updated. For example, you can send a notification when an order is out for delivery. To receive notifications on the client side, you need to handle them on Android, iOS, or web. You can use the Firebase SDK to handle incoming notifications. You can also trigger notifications automatically based on order status updates. For example, you can send a notification when an order is marked as "Out for Delivery". Source: https://lnkd.in/gRCas64W

🚀 Deploy Python Apps in Minutes with PythonAnywhere 🚀 Recently I deployed one of my applications using PythonAnywhere, and honestly it was one of the easiest deployment experiences I've had as a developer. If you're working with Python projects and want a simple way to host them online, this platform is definitely worth exploring. 🔹 What is PythonAnywhere? PythonAnywhere is a cloud-based platform that allows developers to run, build, and deploy Python applications directly from the browser without managing servers or complex infrastructure. It supports frameworks like Flask and Django and is perfect for quickly hosting Python web apps. 🔹 How we use it The deployment process is surprisingly simple: 1️⃣ Upload your project or connect GitHub 2️⃣ Create a virtual environment 3️⃣ Configure your web app (Flask/Django) 4️⃣ Set WSGI configuration 5️⃣ Reload the app and your project is live 🌐 Within minutes your Python application is accessible online. 🔹 Where it is useful ✔ Hosting Flask or Django apps ✔ Running automation scripts ✔ Data science tools ✔ Developer portfolios ✔ Quick MVP deployments ✔ Learning cloud deployment 🔹 Why I liked it • Very beginner friendly • No server setup required • Simple dashboard • Free tier available • Perfect for small to medium Python projects 💡 If you want a complete step-by-step guide to deploy apps using PythonAnywhere, comment "Python" and I will send you the full blog guide. #Python #PythonAnywhere #WebDevelopment #Flask #Django #CloudComputing #Developers #Programming #DevTools #SarotechHub

Which Python modules should we learn first? 🤔 As a web developer, using the right tools can save time and level up your workflow Here’s a quick guide to essential Python modules: 🔹 Data Handling: Pandas – Analyze and manage data efficiently 🔹 Machine Learning: scikit-learn – Build smart models 🔹 Web Scraping: BeautifulSoup – Extract website data 🔹 Web Development: Flask / Django – Create powerful web apps 🔹 Automation: Selenium – Automate repetitive browser tasks 🔹 Desktop Apps: Tkinter – Build simple GUI applications Each of these tools solves a specific problem — and mastering them can elevate both your development efficiency and project quality. If you're building modern web solutions, this stack is worth having in your toolkit. Follow me for more web dev tips, coding insights, and practical guides! #Python #WebDevelopment #Coding #Developers #Programming #Tech #LearnToCode #Frontend #Backend #ReactJS #Django #ShumailaMujahid #ShumailaDev #Flask #Github #GitLab #Code #Job #TechJob #FullStackDev #DeveloperJourney #TechRoadmap

🚀 Built & Deployed: URL Redirect Checker Web App 🔗 Live App: https://lnkd.in/e-x2gpPR I created a web-based tool to analyze bulk URL redirects using Python. Key features: ✔ Upload CSV with URLs ✔ Detect full redirect chains ✔ Capture final URL + HTTP status codes ✔ Parallel processing using ThreadPoolExecutor ✔ Download processed results Tech Stack: • Python • Pandas • Requests • Streamlit Deployed using Streamlit Cloud for easy access and sharing. This was a great hands-on project combining backend logic with a simple UI. Open to feedback and ideas for improvements! Note : file is in CSV Format and first row mention "url" and URLs mentioned after 1st row(start from 2nd row) #Python #Automation #Streamlit #Developers #Projects #BuildInPublic Test Automation Python Coding Automation Anywhere

The week starts with some big news. The era of PHP Coding Agents is officially here. Meet Maestro. 🐘🚀 For a long time, the industry thought that building autonomous coding agents was a playground reserved for Python or Node.js. Today, we are breaking that myth. We are proud to introduce Maestro, the first professional-grade AI Coding Agent built entirely in PHP. Powered by the Neuron AI framework, Maestro brings the power of autonomous development directly into your terminal. It’s not just a wrapper; it’s a sophisticated orchestrator that understands your codebase, plans changes, and executes them—all while staying within the ecosystem you love. Why leave PHP to build the future of AI? With Maestro, you don't have to. 👉 Check it out on GitHub: https://lnkd.in/db9tzTEX 👉 Read the launch story: https://lnkd.in/dK5tkgGi #NeuronAI #Maestro #PHP #AgenticAI #Laravel #OpenSource #Innovation

A few developers recently are reaching for Python in places that surprised me a bit. Embedded systems, mobile apps, infrastructure tooling - not the usual data and scripting territory it's known for. It's made me think about how far Python's footprint has actually spread, and where it genuinely fits vs where it's just... familiar. Places it makes complete sense: AI and data tooling: still its home turf, no argument there Backend scripting and automation: natural fit DevOps workflows: growing fast and largely works well Places it's turning up that raised an eyebrow: Mobile apps where performance overhead is real Embedded systems where memory constraints matter Frontend-adjacent tooling that probably wanted something else Not a criticism, Python is brilliant in the right context. But when a language gets big enough, momentum starts doing the decision-making. Developers and Python right now, (let the image do the rest) Curious what others are seeing - where's the most unexpected place you've come across Python recently?