🚀 Excited to Share My Latest Project: Fake News Detection Web App 🧠📰 In today’s digital world, misinformation spreads faster than ever. To tackle this challenge, I built a Machine Learning-based Web Application that helps users identify potential fake news in real-time. 🔍 What this project does: Analyzes news articles or headlines using ML models Provides confidence scores for authenticity Displays visual insights for better understanding Maintains a history of analyzed content Educates users on spotting fake news ⚙️ Tech Stack Used: Frontend: React, TypeScript, TailwindCSS, Chart.js Backend: Python, Flask, Scikit-learn Other: REST API, CORS 💡 This project focuses on combining AI + Web Development to create a practical solution for a real-world problem. ⚠️ Note: This tool is designed to assist users, not replace critical thinking. Always verify information from trusted sources. 🔗 GitHub Repository: https://lnkd.in/gvKsmEij I’d love to hear your feedback and suggestions! 🙌 #MachineLearning #WebDevelopment #Python #ReactJS #AI #FakeNews #TechForGood #OpenSource #Flask #DataScience #FrontendDevelopment #BackendDevelopment #FullStackDeveloper #Innovation

your model can be perfect. your RAG pipeline can be clean. your embeddings can be tuned. but if your UI is a mess, nobody will use it. so here's the honest breakdown of how I think about building web interfaces as an AI engineer in 2026: Streamlit - my default for internal tools and demos if I'm showing something to a client or testing an idea fast, Streamlit wins every time. 10 lines of Python and you have a working app. the tradeoff? it looks like every other AI demo on the internet. Gradio - for ML model demos specifically Hugging Face made this the standard for sharing models. great for quick inference UIs. not great for anything complex. Next.js + React - when it actually needs to ship if the product is real, this is where I land. React is still the most hired framework in the market and Next.js is basically the default stack for startups in 2026. server components changed everything. FastAPI + any frontend - the AI engineer's power move your backend is already Python. FastAPI gives you a production-ready API in minutes. pair it with anything on the frontend. you don't need to master all of these. Streamlit gets you 80% there for AI demos. Next.js gets you the remaining 20% when you're shipping to real users. the best stack is the one you can actually build fast in. what's your go-to for AI project UIs? genuinely curious 👇 #AIEngineering #WebDevelopment #BuildInPublic #Python #React

🚀 Built my first AI-powered Full Stack Application! I’ve just completed a project where I integrated AI into a real-world web app using: 🧠 **AI (Gemini API)** ⚙️ **Backend:** FastAPI (Python) 💻 **Frontend:** React.js --- ### 🔍 What the app does: It’s an **AI News Personalizer** that: * Fetches latest news articles 📰 * Uses AI to generate: * 📄 Summary * 📌 Key bullet insights * 💡 “Why it matters” * 🎭 Tone analysis * Displays everything in a clean React UI --- ### 🧠 Key Learnings: * How to integrate AI APIs into backend services * Structuring AI responses into usable JSON * Handling real-world issues like: * CORS errors * API response mismatches * Frontend-backend integration * Converting raw AI output into meaningful UI --- ### ⚡ Tech Stack: * FastAPI (Python) * React.js (CRA) * Axios * Gemini API --- This project helped me understand how AI can be used beyond chatbots—into real products. More improvements coming soon: * 🔍 Personalized feeds * 🎯 Explain levels (ELI5 / Expert) * ❤️ Save & bookmark --- 🔗 I’d love feedback and suggestions! #AI #FullStackDevelopment #ReactJS #Python #FastAPI #MachineLearning #WebDevelopment #Projects

📚 Weekly Code Check-in: Keep Building! Sometimes the best coding happens in the background. While this week was quiet on the commit front, it doesn't mean I'm not thinking about the next big feature or project. 🎯 This is a good reminder that consistent progress beats perfect productivity. Every developer knows that feeling — taking time to plan, refactor, and brainstorm before the next push. 💪 My current toolkit: ✓ JavaScript & TypeScript — building fast, scalable web apps ✓ Python & ML — experimenting with AI and data ✓ React & Node.js — full-stack web development ✓ AI/ML projects — AI Mock Interview, Jarvis AI, Learning Recommender 🚀 What's next? Keep shipping, keep learning, keep improving. To everyone in their coding journey — celebrate the small wins, embrace the grind, and remember: shipping something imperfect beats shipping nothing perfect. Let's go! 💻 #DeveloperLife #CodingJourney #GitHub #FullStack #WebDevelopment #MachineLearning #StartupMindset #AlwaysLearning

🚀 AI Tool for Developers: Streamlit Recently explored Streamlit, a powerful Python framework used to build and deploy data-driven web applications quickly. 💡 How it works: 🔹 Build web apps using pure Python 🔹 Convert ML models into interactive apps 🔹 Add inputs, buttons, and charts easily 🔹 Deploy apps without complex frontend 💡 Benefits: ✅ Fast AI app development ✅ No need for HTML/CSS/JS ✅ Great for showcasing ML projects ✅ Used in real-world applications As someone learning AI & Machine Learning, tools like Streamlit help me turn models into real applications. Building and deploying AI apps is becoming easier 🚀 Have you tried Streamlit or similar tools? #AI #Streamlit #Developers #MachineLearning #Python

🚀 Node.js vs Go vs Python — Which One Should You Choose in 2026? Choosing the right backend technology can make or break your project. Here’s a simple breakdown 👇 🟢 Node.js Best for: Real-time apps, APIs, startups ✔ Fast development ✔ Huge ecosystem (npm) ✔ Great for microservices & event-driven systems ❌ Not ideal for CPU-heavy tasks 👉 Use Node.js if you want to build fast and scale quickly (think chat apps, streaming, dashboards) 🔵 Go (Golang) Best for: High-performance systems, cloud-native apps ✔ Extremely fast & efficient ✔ Built-in concurrency (goroutines 🔥) ✔ Perfect for scalable backend systems ❌ Smaller ecosystem than Node/Python 👉 Use Go when performance and scalability are your top priority (think DevOps tools, APIs, distributed systems) 🟡 Python Best for: AI/ML, data science, automation ✔ Simple & readable syntax ✔ Massive libraries (AI, ML, data) ✔ Great for rapid prototyping ❌ Slower than Node & Go 👉 Use Python when working with AI, data, or quick MVPs 💡 Final Verdict: Speed & scalability → Go Fast development & flexibility → Node.js AI & data-driven work → Python 🔥 Pro tip: Don’t chase trends — choose based on your use case.

Choosing between #Node.js and #Python isn't just about benchmarks, it’s about the future of your product. 🌟 While #Node.js remains the #startup default for real-time features and high-concurrency workloads, #Python is dominating the 2026 landscape thanks to its "unfair advantage" in the AI/ML ecosystem. 🚀 Quick Guide for Founders & CTOs: ✔️ Go with #Node.js if: You need a unified full-stack JS experience, real-time chat/notifications, or need to handle thousands of concurrent I/O connections. ✔️ Go with #Python if: #AI, #LLMs, or heavy data processing are central to your product. Python’s native libraries like PyTorch and LangChain are the industry gold standard. 💫 The Golden Rule: If you’re pre-product-market-fit, don't pick what's trending, so pick what your team knows best so you can ship. 👉 Read our full breakdown to see which world your product lives in. #TechStack #Startups #NodeJS #Python #AI #WebDevelopment #CTO

I built a Smart Atomic Habit Tracker using Python and Streamlit. And it changed how I think about discipline. Most people don’t fail because they lack motivation. They fail because they don’t track consistency. So I built something simple — but powerful: 🧠 Atomic Habit Tracker App 💡 What it does: • Add daily habits in seconds • Track streaks automatically 📈 • Visual progress insights • AI-based improvement suggestions 🤖 • Clean, distraction-free UI ⚙️ Tech Stack: Python • Streamlit • Logic-based tracking • Deployed on cloud 📌 What I learned: • Building real-world logic with Python • Turning ideas into usable apps • Debugging like a developer • Deploying end-to-end projects 🔥 This project reminded me: “Small habits are easy to ignore, but powerful when tracked consistently.” 🚀 Still improving it with more AI features and smarter insights. 🔗 GitHub: https://lnkd.in/dsq8zDba 🔗 Live App: https://lnkd.in/dkzc6b6a If you’re building in public too — keep going. Consistency wins. #Python #AI #Streamlit #MachineLearning #BuildInPublic #Coding #Projects #DeveloperJourney

Some people unwind from running a full-time business by watching Netflix. I decompress by architecting serverless microservices. 🧪💻 When I’m not managing the daily operations at my business, my roots in software engineering tend to take over. I didn't want to build a boring, static website, so I decided to turn my domain into a live, serverless laboratory just to see what modern browser architectures can actually do. No client deadlines, no roadmaps—just pure engineering experiments. Welcome to 𝗗𝗵𝗮𝗺𝘂𝗱𝗶 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀. Over the last few weeks, I’ve been stitching together Next.js, Python FastAPI, WebGL, and Event-Driven architectures. Here are the four active nodes currently deployed in the lab: 📈 𝟬𝟬𝟭: 𝗧𝗵𝗲 𝗦𝘁𝗼𝗰𝗸 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗲𝗿 A serverless Fintech dashboard that ingests raw 30-day market data and uses generative AI to output sharp, Wall Street-style financial narratives, visualized through Recharts. 👁️ 𝟬𝟬𝟮: 𝗧𝗵𝗲 𝗢𝗺𝗻𝗶-𝗣𝗮𝗿𝘀𝗲𝗿 A Universal Multimodal Insight Engine. I built a Python microservice that takes any image upload and instantly decodes it—whether it's generating culinary recipes, botanical diagnostics, or translating heavy bureaucratic documents. 🌌 𝟬𝟬𝟯: 𝗧𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗦𝘄𝗮𝗿𝗺 An autonomous agent that surfs the live internet, streams data packets via Server-Sent Events (SSE), and dynamically renders its research into a fully interactive 3D WebGL constellation right in the browser. 🎬 𝟬𝟬𝟰: 𝗧𝗵𝗲 𝗞𝗶𝗻𝗲𝘁𝗶𝗰 𝗙𝗼𝗿𝗴𝗲 A deterministic, zero-AI cinematic render engine. It processes raw visual assets into high-fidelity, beat-synced 9:16 vertical reels using pure procedural mathematics, custom typography masks, and server-side frame manipulation. 🛠️ 𝗧𝗵𝗲 𝗦𝗮𝗻𝗱𝗯𝗼𝘅 𝗦𝘁𝗮𝗰𝗸: Next.js 14, React Three Fiber, Python FastAPI, MoviePy, Redis (Upstash) Telemetry, and Firebase App Hosting. I built this infrastructure to be an open playground. If you are an engineer, designer, or tech enthusiast, I’d love for you to drop in, stress-test the servers, and watch the live telemetry dashboard react! 👇 Link to the lab is in the comments below! Let me know which architecture you find most interesting. #SoftwareEngineering #Nextjs #Python #GenerativeAI #Microservices #BuildInPublic