🏠 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

From ML Model to Live Web App 📈 I just deployed my Gold Price Predictor, a full-stack project that moves beyond the notebook and into production. The Stack: ML: Random Forest Regressor (Scikit-learn) for price forecasting. Backend: FastAPI (Python) for high-performance, async API serving. Frontend: React & Tailwind CSS for a modern user dashboard. Deployment: Fully hosted on Render. Key Takeaway: Building this taught me how to bridge the gap between Python data science and a production-ready web UI, specifically handling CORS and model serialization. Check it out: Live: https://lnkd.in/dTKdfkP5 GitHub: https://lnkd.in/dfxnez7A #MachineLearning #FastAPI #ReactJS #Python #FullStack #BuildInPublic

Python had its run. For years, we built AI agents in two worlds. TypeScript for the app, and Python for the "AI stuff." It was clunky. It felt like a translation. Mastra v1 ends that. It’s not a port. It’s the TypeScript Renaissance. Native primitives, memory, and tools in the language we actually use to build the web. I spent 6 hours breaking down the architecture and the v1 changes. The Architect’s Playbook is back. What do you think?

Meta built a Python type checker in Rust that makes mypy feel like dial-up. 🔥 Pyrefly already has 5.4k stars, and it's not hard to see why. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗲𝗮𝗹: Python's type checking story has always been slow and clunky. Pyrefly rewrites the whole thing from scratch in Rust (92% of the codebase). Giving you a lightning-fast type checker AND a full language server in one tool. Install it with pip, point it at your code, done. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 👉🏽 Three-step pipeline: resolve module exports, convert modules to bindings, solve bindings across your entire project 👉🏽 Uses Type::Var placeholders for recursive and unknown types that resolve later 👉🏽 Module-centric design prioritizing raw speed over fine-grained identifier solving 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁 👉🏽 Type inference that auto-detects variables and return types without annotations 👉🏽 Flow-sensitive types that track control flow (if/else, isinstance checks) 👉🏽 Incremental checking and parallel processing for large codebases 👉🏽 IDE support for VSCode, Neovim, and Zed out of the box 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 👉🏽 Modular Rust crate architecture (pyrefly_util, pyrefly_types, pyrefly_config) 👉🏽 MIT licensed, 153 contributors, 11k+ commits 👉🏽 Browser sandbox so you can try it without installing anything 👉🏽 Biweekly office hours and an active Discord community Python type checking has been an afterthought for too long. A Rust-powered checker that doubles as your language server could finally make it a first-class experience. 𝘈𝘳𝘦 𝘺𝘰𝘶 𝘴𝘵𝘪𝘭𝘭 𝘶𝘴𝘪𝘯𝘨 𝘮𝘺𝘱𝘺, 𝘰𝘳 𝘩𝘢𝘷𝘦 𝘺𝘰𝘶 𝘴𝘸𝘪𝘵𝘤𝘩𝘦𝘥 𝘵𝘰 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘧𝘢𝘴𝘵𝘦𝘳? 🔗 Link in comments #python #rust #opensource #meta

🚀 Why I’m Exploring Reflex – A Pure Python Web Framework As someone who works deeply in Python-based systems, I’m always looking for ways to move faster from idea → prototype → production. Recently, I’ve been exploring Reflex, a modern Python web framework that lets you build full-stack web apps using only Python — no separate frontend JavaScript required. 💡 What makes Reflex interesting? ✅ Build UI components using Python ✅ Automatic frontend + backend integration ✅ Real-time updates with reactive state ✅ Clean developer experience For Python developers who want to build dashboards, internal tools, AI apps, or data-driven web platforms — Reflex removes a lot of the traditional frontend complexity. Instead of switching between: Python (backend) JavaScript (frontend) API wiring You stay inside Python and ship faster. 🔥 My Take For AI-powered apps, admin panels, and rapid internal tooling, Reflex feels lightweight, productive, and aligned with Python-first teams. Still early in exploration, but definitely promising. #Python #WebDevelopment #Reflex #FullStack #AIApps #DeveloperExperience

Built a Full-Stack AI Powered Recipe Platform Features: • Create and share recipes • AI recipe assistant • External recipes using TheMealDB API • Save favorite recipes • Social interaction features • Fully responsive mobile UI Tech Stack: Django • Python • HTML • CSS • JavaScript • APIs 🔗 Live Demo: https://lnkd.in/gJ4CBpqR 🔗Git Hub : https://lnkd.in/gyimC-W5 #django #python #fullstack #webdevelopment #backenddeveloper #projects

I recently deepened my skills in React Native, Python, and AI through hands‑on projects. I built a cross‑platform mobile app that integrates REST APIs for real‑time data, and applied machine‑learning models to enhance user experiences. In parallel, I explored data analytics and Power BI to visualize insights from the app’s usage patterns. These experiences reinforced the importance of clean code, iterative testing, and clear data storytelling. #machinelearning #PowerBI What recent project has helped you grow the most?

Let’s know the core difference between JavaScript (JS) and Python! JavaScript (JS) Mainly used for web development (client-side and server-side) Runs directly in the browser Supports asynchronous programming Dynamic typing, event-driven al for interactive websites and web apps Python Known for its simplicity and readability Widely used in data science, AI, and automation Works on the server-side Synchronous programming, interpreted language Ideal for scientific computing, machine learning, and back-end development So, which one should you choose? JavaScript is your go-to for web development! Python shines in data-driven fields like AI and machine learning! #JavaScript #Python #Coding codingProgramming dataScience softwareDevelopment webdevelopment daily_info #codingProgramming #dataScience #softwareDevelopment #webdevelopment February 7, 2026

Let’s know the core difference between JavaScript (JS) and Python! JavaScript (JS) Mainly used for web development (client-side and server-side) Runs directly in the browser Supports asynchronous programming Dynamic typing, event-driven Ideal for interactive websites and web apps Python Known for its simplicity and readability Widely used in data science, AI, and automation Works on the server-side Synchronous programming, interpreted language Ideal for scientific computing, machine learning, and back-end development So, which one should you choose? JavaScript is your go-to for web development! Python shines in data-driven fields like AI and machine learning! #JavaScript #Python #Coding codingProgramming dataScience softwareDevelopment webdevelopment daily_info #codingProgramming #dataScience #softwareDevelopment #webdevelopment February 7, 2026

Build Fast, Perform Faster! Meet FastAPI – the modern, lightning-fast API framework for Python that developers are raving about! Here’s how ridiculously simple it is to get started: from fastapi import FastAPI app = FastAPI() @app.get("/") def read_root(): return {"message": "Hello World"} Key Features 🔥 • Data Validation • Auto Docs (interactive Swagger UI) • Asynchronous support • High Performance What You Can Build ✅ REST APIs ✅ Microservices ✅ AI & ML applications ✅ Real-Time Apps ✅ Auth Systems Why Choose FastAPI? ⚡ Blazing Fast ✅ Easy Syntax 🔄 Scalable for Microservices 📋 Built-In OpenAPI If you're into Python, backend development, or shipping production-grade APIs at record speed, FastAPI is an absolute must-add to your toolkit. #FastAPI #Python #APIDevelopment #BackendDevelopment #Microservices #WebDevelopment #PythonDeveloper #Tech #Developer #OpenSource #fullstackdevelopment