Building a website: Front-end, Back-end, and Infrastructure

Currently building a Geographic Information System(GIS)-based application and it has pushed me slightly outside my usual stack. I mostly work with JavaScript (React, modern frontend tools), but for this project I need OpenCV, which works better with Python. So I’m learning Flask to integrate the computer vision component with the web app. Interesting challenges so far: . Structuring communication between Python backend and JS frontend • Managing performance when processing images • Keeping the architecture simple while mixing technologies One thing I’m learning again is that real-world projects often require flexibility beyond your primary stack. If anyone has experience combining Flask + frontend frameworks, I’d appreciate any tips or best practices.

Lately I have been working on building crazy looking websites using streamlit, and i'll let you guys know the crazy secret on how to build amazing ,animated looking web apps Streamlit is a Python framework used to build interactive web apps for data, ML, and dashboards without needing frontend skills. You write pure Python Streamlit automatically creates the UI in the browser, Every time your code runs, the UI updates instantly In simple terms: Python code - Streamlit - Web app UI What is import streamlit.components.v1 as components (The crazy secret) This line imports the Components API of Streamlit. import streamlit.components.v1 as components What it does: Gives you access to functions that allow custom HTML, CSS, and JavaScript Lets you go beyond Streamlit’s built-in UI Why you need it Normally Streamlit: Does not allow JavaScript Controls UI rendering With components: You can run HTML + CSS + JS Embed custom frontend code Use external libraries #data #streamlit #cloud #dataengineer #cloudengineer

I’m excited to share a project I’ve been working on! I developed a full-stack Car Price Prediction System that estimates the market value of a vehicle based on its features. The Tech Stack: 🐍 Backend: Python & Flask 📊 Data Science: Pandas & Scikit-Learn (Linear Regression) 💻 Frontend: HTML5, Bootstrap 5, & JavaScript (AJAX) Key Challenges Solved: Data Cleaning: Processed a raw dataset to handle missing values and inconsistent naming. Dynamic UI: Built a dependent dropdown system using JavaScript so users only see models corresponding to the selected brand. Asynchronous Prediction: Used AJAX to deliver real-time predictions without refreshing the page. Check out the demo below! I'd love to hear your thoughts on how to improve the model accuracy or the UI experience. Link the GitHub: https://lnkd.in/dHCUggPY #Python #DataScience #WebDev #MachineLearning #Flask #PortfolioProject

🚀 Stop Guessing: Django vs. Node.js in 2026! Which one is actually winning the backend war? 🧐 Choosing the wrong backend stack can cost you months of refactoring. I’ve broken down the Top 10 Key Differences between the two giants: Django (The Python Powerhouse) and Node.js (The JavaScript Speedster). Whether you are building a data-heavy AI app or a high-traffic real-time chat, the choice isn't always obvious. Inside this carousel: ✅ Architecture: MVT vs. Event-Driven ✅ Performance: CPU-bound vs. I/O-bound ✅ Security: Built-in vs. Manual ✅ Scalability: Vertical vs. Horizontal The Bottom Line: There is no "better" framework—only the right tool for your specific problem. 👇 Which one are you using for your next project? Let’s discuss in the comments! #WebDevelopment #Backend #Django #NodeJS #Python #JavaScript #CodingTips #FullStack #SoftwareEngineering #TechTrends2026 #Programming #WebDev #GeeksforGeeks

👉 Read the full article here: https://lnkd.in/dPBDH8fZ 🚀 New Article Published: Array Flatten in JavaScript Understanding how to work with nested arrays is an important skill for every JavaScript developer. In this article, I explained: • What nested arrays are • Why flattening arrays is useful • The concept of array flattening • Different approaches (built-in methods, recursion, loops) • Common interview scenarios I also included step-by-step explanations and visual thinking to make the concept easy to understand. This topic really helped me improve my problem-solving mindset while working with real-world data structures. Big thanks to Hitesh Choudhary Sir, Piyush Garg Sir and Chai Aur Code for continuous learning and guidance 🙌 #JavaScript #WebDevelopment #Coding #LearningInPublic #Developers #Frontend #100DaysOfCode

NestJS vs. FastAPI: Different Languages, Same Soul? 🤯 If you are moving from TypeScript to Python (or vice versa), you might be expecting a total culture shock. But if you look under the hood of NestJS and FastAPI, you’ll find they are actually long-lost twins. Here are the 4 ways they are practically the same: 1. The "Contract" (DTOs vs. Schemas) 🤝 In both worlds, we hate "guessing" what’s in a JSON body. NestJS uses DTOs (Class-validator). FastAPI uses Pydantic Schemas. Both ensure that if a user sends a string instead of a price, the API shouts "400 Bad Request" before your code even runs. 2. Dependency Injection (DI) 💉 Both frameworks move away from "Hardcoding" dependencies. In NestJS, you inject services into constructors. In FastAPI, you use the Depends() function. This makes swapping a "Mock Database" for a "Production Database" a breeze during testing. 3. Decorators are King 👑 Whether it’s @Get() in NestJS or @app.get() in FastAPI, both use decorators/annotations to handle the heavy lifting of routing and metadata. It keeps the code readable and declarative. 4. Built for Speed (Async/Await) ⚡ Both are "non-blocking" by nature. NestJS rides on the Node.js Event Loop, while FastAPI is built on Python’s anyio/asyncio. They both handle thousands of concurrent connections (like search queries) without breaking a sweat. The Real Difference? It's all about Freedom vs. Structure. NestJS is "Opinionated." It tells you exactly where to put your files (Modules, Controllers, Services). Great for big teams! FastAPI is "Unopinionated." It gives you the tools but lets you decide the structure. Great for speed and AI integration! The takeaway? If you master the concepts in one, you’ve already mastered 80% of the other. The syntax is just a detail. #WebDev #FastAPI #NestJS #Python #TypeScript #SoftwareArchitecture #Backend #CodingTips

🚀 Starting my journey in web development! I’m happy to share my first project — a Flask CRUD Web Application 💻 🔹 Built using: Flask, MySQL, HTML, CSS 🔹 Features: Create, Read, Update, Delete operations 🔹 Deployed using Render 🔹 Source code available on GitHub 🌐 Live Demo: https://lnkd.in/gXdhjUYY 💻 GitHub Repo: https://lnkd.in/gRbe_2YB This is just the beginning — looking forward to building more advanced and impactful projects ahead! #Flask #Python #WebDevelopment #BeginnerProject #svhec Dr. Muralisankar Kumaresan Guide KABILESH RAMAR

Flask vs FastAPI: A Comprehensive Performance Comparison - As experienced web developers and software engineers, we constantly seek the most efficient tools for our projects. When it comes to Python web frameworks for API development, Flask and FastAPI are two prominent contenders, each offering distinct advantages. This comprehensive comparison article dives deep into their core differences, exploring their respective features, syntax, and real-world performance benchmarks. We will analyze how each framework handles API requests, contrasting Flask's lightweight, unopinionated design with FastAPI's modern, high-performance approach built on asynchronous capabilities and Pydantic for data validation. Our discussion will extend beyond theoretical benchmarks, offering practical tips for implementation and delving into specific real-world examples where Flask's simplicity and vast ecosystem might be preferable, versus scenarios where FastAPI's speed, automatic documentation, and robust type checking provide an undeniable edge. We aim to provide a detailed Flask and FastAPI comparison that helps you understand their performance differences and make an informed decision when choosing between Flask and FastAPI for API development, ensuring your projects are built on the most suitable foundation for scalability and maintainability. Read the full article > https://lnkd.in/gQCc3m5R #iPixelInsights #WebDesignTips #DigitalMarketingStrategy #FrontendDevTalks #UIUXDesign #GoogleAdsHelp #TechForCreatives #SEOForBusiness #DesignVsDev #MarketingTechExplained

Stop fighting framework churn to deliver client value faster. Last week on a critical Supabase integration, we leaned hard on established Django REST endpoints instead of chasing the newest async Python library. Pure, boring reliability won. The shiny new thing often costs more in debugging time than it saves in theoretical speed. In agency life, predictable performance isn't optional; it’s the product. Our Next.js frontend handles the innovation; the backend needs rock-solid stability. If it just works across 50,000 daily users, it's genius. Don't over-engineer stability away. What's one piece of "boring" tech you refuse to replace because it just keeps delivering? #SoftwareArchitecture #TechLeadership #Reliability #EngineeringPragmatism