💡 From idea → execution 🚀 Ever wondered how chatbots fetch real-time data? I built one! Introducing my Python Weather Chatbot 🌤️ It takes user input and instantly responds with live weather updates. 🔧 What I used: Python + Weather API 🎯 What I learned: Real-world problem solving & API integration Small project. Big learning. 💯 Let’s connect and grow together 🤝 #PythonDeveloper #Projects #Chatbot #LearningByDoing #TechJourney

I’ve been using the chore/agent-friendly-repo-foundation branch(https://lnkd.in/gtypVfA9) in ExcelAlchemy as an experiment in agent-friendly engineering. The interesting part is not just asking an agent to write code. It’s shaping the repo so an agent can actually navigate it: clear docs, explicit architecture, stable public boundaries, linked plans, tests, invariants, and examples. That idea maps closely to OpenAI’s recent post on [harness engineering](https://lnkd.in/gtHiDZbU): humans define intent and constraints; agents execute inside a well-structured environment. ExcelAlchemy itself is a schema-driven Python library for typed Excel workflows with Pydantic: template generation, workbook validation, error mapping, locale-aware result workbooks, and storage-backed integration. This branch is our small testbed for a bigger question: not “can agents code?” but “can we build repos they can reliably understand?” #AgentEngineering #HarnessEngineering #DeveloperTools #RepositoryDesign #Python #OpenSource #Pydantic #ExcelAutomation

I’ve been polishing a personal project called ExcelAlchemy, and it’s now at its first stable public release: 2.0.0. ExcelAlchemy is a schema-driven Python library for Excel import/export workflows. It turns Pydantic models into typed workbook contracts: generate templates, validate uploads, write failures back to rows and cells, and keep workbook-facing output locale-aware. A lot of the work in this project was not just about making it work, but about making it feel like a real library: - modern Python typing and stricter static analysis - a cleaner validation pipeline around Pydantic v2 - protocol-based storage boundaries - pandas removed from the runtime path - contract tests, Ruff, Pyright, and release-focused documentation I also treated the repository as a design artifact: not just code, but a record of architectural tradeoffs, migration strategy, and package design decisions. Repo: https://lnkd.in/gV9jC87W #Python #OpenSource #Pydantic #ExcelAutomation #SoftwareArchitecture #DeveloperTools

If you've ever spent hours manually renaming hundreds of files or updating dizzying amounts of spreadsheet cells, you know exactly how draining it can be. That's why I decided to dive deep into the world of Python Automation to unlock the ultimate game-changer in productivity. I recently completed an intensive course that completely transformed how I approach these challenges. It taught me that writing code isn't just about solving basic problems; it's about crafting true digital artistry. 🎨 Here are some of the key skills I’ve added to my toolkit: 📂 Mastering File Handling: Automating local storage and data organization. 🕸️ Advanced Web Scraping: Extracting data efficiently using Beautiful Soup and Selenium. ⚙️ Robust Scripting: Learning the art of parsing, weaving through command lines, and implementing solid error handling. 🔗 API Integration: Seamlessly connecting different software pieces for robust, automated solutions. I’m incredibly excited to apply these time-saving skills to future projects and build systems that work smarter, not harder. What was the first tedious task you ever automated? Let me know in the comments! 👇 #Python #Automation #Coding #WebScraping #Productivity #TechSkills #ContinuousLearning #PythonDeveloper #processautomation #python.

𝐁𝐮𝐢𝐥𝐝 𝐌𝐨𝐝𝐮𝐥𝐚𝐫 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐀𝐩𝐩𝐬 𝐰𝐢𝐭𝐡 𝐕𝐢𝐳𝐫𝐨 𝐕𝐢𝐳𝐫𝐨 is a Python library from McKinsey, where you can build complex dashboards in minutes. It uses Plotly and Dash components, so the pages look very clean. And deployment is therefore as easy as for Dash apps. Check it out: github(dot)com/mckinsey/vizro

💻 Excited to share my latest project! I’ve built a Streamlit-based Scientific Calculator using Python that performs both basic and advanced mathematical operations. 🧮 Features: ●Addition, Subtraction, Multiplication, Division ●Square Root Calculation ●Power (Exponent) Function ●Logarithmic Operations Clean and interactive web UI using Streamlit 🌐 Tech Stack: Python | Streamlit | Math Library 🚀 I also deployed this project on Hugging Face Spaces, making it accessible as a live web application. This project helped me strengthen my understanding of: ✔ Python functions ✔ UI development with Streamlit ✔ Deployment of web apps ✔ Problem-solving logic 🔗 GitHub Repo: https://lnkd.in/d4n946w7 🌐 Live Demo: https://lnkd.in/dMti6kJX ✨ Always learning, building, and improving one project at a time! #Python #Streamlit #MachineLearning #WebDevelopment #Coding #StudentDeveloper #AI #Projects

A lot of these new AI developer tools can save regular analysts time too and make things like python a lot more approachable… here is GitHub's Copilot’s CLI automating finding unwanted whitespace in Excel easy with a simple prompt: @Example.xlsx use python to tell me if any cell in this workbook has leading white space. give me a summary. then remove it.

🚀 Writing loops in Python for array operations? You’re doing it the hard way ❌ Let’s fix that with NumPy Broadcasting 👇 --- 📌 What is Broadcasting? It allows NumPy to perform operations on arrays of different shapes WITHOUT writing loops 🤯 --- 👀 Example: import numpy as np arr = np.array([1, 2, 3]) result = arr + 10 print(result) ✅ Output: [11 12 13] 👉 NumPy automatically "broadcasts" 10 to match array shape! --- 🔥 Now the real power: arr1 = np.array([[1,2,3], [4,5,6]]) arr2 = np.array([10,20,30]) result = arr1 + arr2 print(result) ✅ Output: [[11 22 33] [14 25 36]] 💡 Smaller array is stretched across rows automatically! --- 📌 Broadcasting Rules (Simple Version): ✔ Shapes must be compatible ✔ Matching from right to left ✔ Dimensions should be equal OR 1 --- 🔥 Why it matters? ✅ No loops → Faster code ✅ Cleaner syntax ✅ Used heavily in ML & Data Science --- ⚠️ Common Mistake: np.array([1,2,3]) + np.array([1,2]) ❌ Error: Shapes not compatible Follow for daily coding clarity 🚀 #Python #NumPy #DataScience #MachineLearning #Coding #Programming #LearnToCode #CodingBlockHisar #Hisar

I built a tool that lets you ask questions about your codebase in plain English. 🧠 Like literally just type — "where is the FAISS vector store initialized?" — and it finds the exact file, function, and code for you. No more ctrl+F. No more digging through 20 files manually. It's called CodeMind. Getting started is super simple too — just paste your GitHub repo link and it'll clone it automatically, or upload a ZIP file if you prefer. That's it, you're ready to start asking questions. Here's how it works under the hood: → Loads your entire codebase → Breaks it into chunks and converts them into embeddings → Stores everything in a FAISS vector store → When you ask something, it pulls the most relevant code and sends it to Groq LLM for a proper answer Built with Python · LangChain · FAISS · Groq · Streamlit 🔗 Try it: https://lnkd.in/gYV8UfC8 🐙 GitHub: https://lnkd.in/gk3F5kZf Still a lot to improve but happy with how v1 turned out. Would love honest feedback from anyone into AI or dev tooling! 🙌 #RAG #LangChain #GenerativeAI #Python #OpenSource #BuildInPublic #AIEngineering

https://lnkd.in/d_sZXwj9 ScriptYT turns any YouTube video into an intelligent knowledge base — paste a URL, and our RAG pipeline instantly extracts, chunks, and vectorizes the transcript using Google Gemini embeddings so you can ask anything and get real-time, cited answers streamed back in seconds. Built on a Python microservice architecture with a precision-engineered Dark Industrial UI, it's not a summarizer — it's a semantic search engine for video content. Try ScriptYT and never scrub through a video again.