Flask Regex Checker Web App with Real-Time Processing

🏠 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

Built a browser-based AI assistant that can execute both Python and JavaScript code directly in the browser - no server required. Key features: 🐍 Python with pandas & matplotlib - Full data analysis capabilities powered by Pyodide (Python compiled to WebAssembly) 📊 Inline chart rendering - Visualizations display directly in the chat 💾 Persistent sessions - Variables persist between executions for iterative analysis ⚡ Auto language selection - AI chooses Python for data/CSV tasks, JavaScript for simple calculations 🔄 Re-run & Edit - Modify and re-execute code blocks with one click ⏱️ Timeout protection - 60s execution limit prevents runaway code Tech stack: Pyodide (Python WASM), Boa Engine (JS sandbox), Web Workers for non-blocking execution Upload a CSV, ask a question, and watch it write & execute code to analyze your data - all running locally in your browser. #AIAgent #AITools #ModelContextProtocol #MCP #Python #JavaScript #WebAssembly #DataAnalysis #Pyodide

🚀 A URL Shortener web application built using Python and Flask! This project lets users convert long URLs into short, shareable links while keeping track of their URL history. It showcases key backend skills like URL validation, Base62 encoding, Flask routing, and database management with SQLite and SQLAlchemy. The clean, responsive UI is built with Bootstrap for an easy user experience. Key Features: Validates URLs before shortening Generates short URLs using Base62 encoding Tracks and displays URL history Responsive and clean UI A big thank you to Innomatics Research Labs for their invaluable guidance and support throughout the development of this project! 🙏 This project was a great learning experience, reinforcing core web development concepts and backend programming. Looking forward to building more exciting projects! #Python #Flask #URLShortener #Coding #Projects

Whether you need to render HTML templates, serve JSON/YAML APIs, or just return plain text, Dyne makes it effortless. 🎨 Dyne features a built-in Jinja2 wrapper for clean template rendering with `resp.html` and an intelligent `resp.media` property that handles content negotiation (JSON/YAML) automatically based on the client's request. Plus, dynamic routing uses familiar f-string syntax to pass arguments directly to your handlers! Build flexible web services: https://lnkd.in/eqEc4Tuw #Python #Jinja2 #WebDev #API #Backend #DyneFramework

𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝗻𝘁𝗼 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝘄𝗶𝘁𝗵 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 From raw Python code to beautiful, interactive dashboards — that’s the power of Streamlit. 🔹 Python handles the logic, data processing, and intelligence 🔹 Streamlit transforms it into a clean, interactive web interface 🔹 Users interact with dashboards, forms, and real-time analytics effortlessly No complex frontend frameworks. No heavy web development setup. Just Python + Streamlit = Rapid deployment. Whether you're building: 📊Data dashboards 🤖 ML model demos 📈 Business analytics tools 🧠 AI-powered apps Streamlit makes it simple, fast, and production-ready. As someone deeply interested in building practical tech solutions, I find tools like Streamlit incredibly powerful for converting ideas into usable products quickly. #SNSInstitutions #SNSDesignThinking #DesignThinkers #Python #Streamlit #WebDevelopment #TechInnovation

Kimi K2.5 is probably going all in on being the next Claude. First of all, it is open sourced. It is 1 trillion parameter count which is pretty insane for a open sourced model. Here are some notable features: 1. It can actually do things, not just talk about them: If you need it to search the web, pull financial data, run calculations in Python, or find images, it can do that right there in the conversation. It doesn't just tell you how to do it—it actually does it. 2. It can tackle complex projects from start to finish: Whether it's researching a topic thoroughly, building a website, creating a presentation, or generating images, it can plan out the steps, use the right tools, and get the job done without you having to guide it through every little detail. 3. There's this feature called Agent Swarm, but I haven't used that yet so no insights on that yet. So, it is going to be meet both of your requirements Claude and could be Lovable as well since there website maker is far greater than decent, I think it might even be better than Lovable.

Why we ripped out the "Agent Frameworks" and went back to raw code. When we started building the Nova engine, we used every popular agent framework abstraction we could find. It felt productive. 3 lines of code to spin up a "Research Agent." 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦? 𝐓𝐡𝐞 "𝐁𝐥𝐚𝐜𝐤 𝐁𝐨𝐱" 𝐓𝐚𝐱. When an agent failed, we couldn't see why. Was it the prompt? The parser? The hidden chain-of-thought injection? Even if we did get those we had to jump through a lot of hoops just to get our own logs, how we wanted and when we wanted. We were debugging library code, not our own logic. 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝: We deleted the abstractions. We went back to raw Python loops and direct API calls. Control: We own exactly what goes into the system_prompt. Visibility: Every loop iteration is a simple log line, not a callback hell. Speed: We shaved 400ms of latency just by removing the wrapper overhead. Frameworks are great for prototypes. But for Production Agents, you need to own the loop. #SoftwareEngineering #AIProduction #Python #CodingAgents #DevOps

Understanding virtualenv made easy ✨ What is it: Virtualenv (or venv) creates isolated Python environments for your projects. It acts like a sandbox, giving each project its own Python interpreter and packages. This keeps things clean without messing up your main Python setup or other work. 🌟 Practical example: Building an e-commerce app with Django 4.2 and an analytics dashboard with Flask 2.3? Create env_ecommerce (python -m venv env_ecommerce, activate, pip install django==4.2) and env_analytics separately—no clashes. 🎯 Popular in: • Avoid package conflicts: Use NumPy 1.24 for ML and 1.26 for data analysis side-by-side. • Keep system clean: Test FastAPI without polluting global Python. • Team collaboration: Share requirements.txt for exact setups. #virtualenv #Technology #Tech #Innovation #LearnTech 📖 Learn more: https://lnkd.in/g_85zaNN

Stop Guessing: Literal vs. Enum in Python Pydantic Ever wondered whether to use Literal or a str Enum for your validation models? Both restrict input, but they serve different masters. Here is the "Cheat Sheet" for your next PR: 🔹 Use Literal when... - The scope is tiny: You only need to restrict a field in one specific model. - Simplicity is king: You don’t want the overhead of a new class for 2 or 3 static values. - One-off validation: "Is this 'asc' or 'desc'?" Example: status: Literal["open", "closed"] 🔸 Use Enum when... - Reusability matters: You need the same options in your Database, your API, and your logic. - Logic is attached: You need to map values to other data (like duration limits or price multipliers). - Refactoring safety: Changing "days" to "Days" in one class updates your entire codebase instantly. - IDE Power: You want full autocomplete and to avoid "Magic Strings" everywhere. 💡 The Pro-Tip: If your project is a FastAPI app, always lean toward Enums. They generate much richer OpenAPI (Swagger) documentation, providing your frontend team with a clear dropdown of choices instead of a raw string field. Which one do you find yourself reaching for most often? 👇 #Python #Pydantic #FastAPI #CodingTips #BackendDevelopment

Found a great workflow for RAG pipelines and quick data extraction today. If you need structured data from Reddit, Inc. but don't want to deal with OAuth flows, just append /.json to the thread URL. It returns the post and comments in a clean JSON format. My workflow: Reddit URL + .json --> Python/GoLang Script --> LLM Context Window. Result: Instant sentiment analysis and topic extraction without the scraping overhead. Note: Be a good citizen of the web. Set a unique User-Agent string to respect their API guidelines and avoid the 10 req/min rate limit. #DevTips #Python #Automation #LLM #DataEngineering #RAG #API #Golang