🚀 𝗗𝗮𝘆 𝟭𝟵/𝟯𝟬 – 𝟯𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Continuing my journey of building one Python project every day to improve consistency and real-world problem-solving. Today’s focus: **Networking & GUI Applications** 🧠 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗦𝗽𝗲𝗲𝗱 𝗧𝗲𝘀𝘁𝗲𝗿 Built a Python GUI application that measures internet speed (download & upload) in real-time using an interactive interface. ✨ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Measures Download Speed in Mbps • Measures Upload Speed in Mbps • Uses best server selection for accurate results • Responsive GUI using threading (no UI freeze) • Simple one-click speed testing 💡 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗨𝘀𝗲𝗱: • GUI development with Tkinter • Multithreading in Python • Working with external libraries (`speedtest-cli`) • Handling network-based operations • Error handling and debugging 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/d5RX2qyT From checking it in on Internet to Building it gave a happy experience Building discipline through code — one project at a time. Follow along as I complete 30 Python projects in 30 days 🚀 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #PythonProjects #Networking #Tkinter #Automation #Coding #SystemProgramming

I’ve just published my first Python project on GitHub. This is a command-line Contact List application developed to practice core programming concepts such as data structures, input validation, and basic CRUD operations. Key features: • Add, edit, and delete contacts • Search functionality • Automatic ID generation • Duplicate prevention (email/phone) This project is part of my transition into software engineering, and I’ll continue building more applications to strengthen my skills. Repository: https://lnkd.in/g7kFu8PD #Python #GitHub #SoftwareEngineering #Programming #CareerTransition

𝗬𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘆𝗼𝘂𝗿 𝗳𝗮𝘃𝗼𝘂𝗿𝗶𝘁𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗮𝗻𝗱 𝗮 𝗴𝗿𝗲𝗮𝘁 𝘀𝗼𝗹𝘃𝗲𝗿. 🧩🚀 #Python optimization modeling ecosystem is rich: PuLP, Pyomo, Linopy, PyOptInterface, CVXPY, ... — each with its own style and strengths. With #FICO #Xpress as the backend, you get enterprise-grade LP, QP, and MIP solving where supported, along with many advanced features. In our latest "𝗫𝗽𝗿𝗲𝘀𝘀 𝗘𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲" blog (Part 2), Francesco Cavaliere and I solve the same #portfolio #optimization problem five different ways, highlighting how each library exposes Xpress features like warm starts, SOS constraints, solver parameters, and solver callbacks. https://lnkd.in/eyAS-68r 🗂️ 𝗪𝗲 𝗮𝗹𝘀𝗼 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗚𝗶𝘁𝗛𝘂𝗯 𝗿𝗲𝗽𝗼 with complete, runnable examples for all integrations — including OR-Tools from Part 1. You can even run them on GitHub Codespaces with zero local setup, Xpress #Community #License pre-configured. 📖 𝗥𝗲𝗮𝗱 𝗣𝗮𝗿𝘁 𝟭 (OR-Tools, with Daniel Junglas): https://lnkd.in/eqdCZwCH 📖 𝗥𝗲𝗮𝗱 𝗣𝗮𝗿𝘁 𝟮 (Python ecosystem): https://lnkd.in/eyAS-68r 💻 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 #GitHub 𝗿𝗲𝗽𝗼: https://lnkd.in/ea-UcFMB

𝐃𝐚𝐲 𝟔/𝟑𝟎: 𝐇𝐨𝐰 𝐝𝐨 𝐏𝐲𝐭𝐡𝐨𝐧 𝐝𝐞𝐯𝐬 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐢𝐬? 🐍 Coming from C++, I’m used to the compiler being my safety net. If I try to add a string to an int there, the code won’t even run. ➡️ But yesterday I realized Python is... a different world! def add(a, b): return a + b add(10, "5")💣 Boom! Runtime Error In a tiny script, this is a 2-second fix.. But in a massive codebase with thousands of functions? This feels like a 𝐡𝐢𝐝𝐝𝐞𝐧 𝐫𝐢𝐬𝐤 𝐰𝐚𝐢𝐭𝐢𝐧𝐠 𝐭𝐨 𝐡𝐚𝐩𝐩𝐞𝐧. So, the real question for the experts here: How do you guys stop these "sneaky" errors before they hit production? Is it: A) Just remember everything B) Test everything thoroughly (Unit tests for every single edge case) C) Use Python type hints (a: int, b: int) (👀but do they actually stop the crash?) D) Something else(MyPy? Pylint?) I’ve been digging into this 𝐜𝐥𝐚𝐬𝐡 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐬𝐚𝐟𝐞𝐭𝐲 𝐭𝐨𝐝𝐚𝐲. I’m hunting for a way to make Python feel just as safe as C++. I'll share what I find tomorrow! Drop your choice below 👇 What’s the standard industry workflow for catching these? #Python #Cpp #LearningInPublic #30DaysOfCode #SoftwareEngineering #Programming

🚀 Just wrapped up my latest Python project: A smart Command-Line Task Tracker! I wanted to move beyond basic scripts and tackle real-world data management. Here are the top 3 concepts I leveled up while building this: 💾 File Persistence: Built a permanent "filing cabinet" using Python's with open() to safely serialize, save, and reload user data. ⏳ Dynamic Data vs. Static Data: Used the datetime module to recalculate "days remaining" every time the app boots, rather than hard-coding static dates into the save file. 🌿 Clean Git Workflow: Managed isolated feature testing using git checkout -b and used .gitignore to keep system files out of my remote repo. This project really solidified my understanding of how data flows in and out of a program seamlessly. On to the next build! 💻 #Python #SoftwareDevelopment #CodingJourney #Git

🚀 Just Published My First Python Library on PyPI! Excited to share that I’ve built and published "common-fun" — a modular Python utility library designed to simplify everyday development tasks. 📦 Install: pip install common-fun 🖥️ Try CLI: common-fun help 🔗 GitHub: https://lnkd.in/gjWRyhpq 🔧 What it includes: • Number utilities (prime, gcd, factorial, etc.) • String processing (palindrome, slugify, etc.) • Array helpers (flatten, chunk, rotate) • Validators (email, URL, password) • File utilities • Performance decorators (timer, retry, caching) • 🔥 CLI support for direct terminal usage 💡 Why I built this: While working on multiple projects, I realized I was repeatedly writing similar utility functions. So I decided to consolidate everything into a clean, reusable, and structured library. ⚙️ Key highlights: • Fully modular architecture • Optimized implementations • CLI tool for quick access • PyPI-ready packaging • Clean documentation This project helped me understand: ✔️ Library design ✔️ Packaging & publishing ✔️ CLI development ✔️ Clean code practices Would love your feedback and suggestions! #Python #OpenSource #Developer #Programming #PyPI #SoftwareDevelopment

I’ve been spending my recent free time in building an Event-Driven Backtesting Engine from scratch for Options. Backtesting complex option strategies requires processing massive amounts of market data, calculating Greeks, and tracking portfolio metrics simultaneously. To handle this without latency bottlenecks, I decided to architect the entire core engine in C++. for now I have mostly tried to make it very flexible like modular commission and slippage and ability to write custom strategies instead of editing the core engine itself I completely decoupled most of the core things so The entire C++ backend is compiled as a standalone library. I am also trying to Integrate a python bridge using pybind11 exposing this compiled library directly to Python. The goal for this is to make the engine to do all the computation in the background, allowing anyone to write, test, and plug in custom strategies dynamically using simple Python scripts without ever needing to modify the core engine files. Getting the C++ event loop to work good with Python scripting is proving to be a little complicated right now! I'll be pushing a final README and some sample strategies once I get the bindings fully stabilized. You guys can check out the code here : https://lnkd.in/gRSgd4gs #quantfinance #cpp #python #algorithmictrading #options #pybind11 #derivatives

🚀 𝗗𝗮𝘆 𝟮𝟱/𝟯𝟬 – 𝟯𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Continuing my journey of building one Python project every day to improve consistency and real-world problem-solving. Today’s focus: File Handling & PDF Automation 🧠 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗣𝗗𝗙 𝗣𝗮𝗴𝗲 𝗦𝗽𝗹𝗶𝘁𝘁𝗲𝗿 Built a simple Python tool that splits a multi-page PDF into individual pages and saves them automatically in the same directory. ✨ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Split PDF into single-page files 📄 • Automatically saves files in the original folder 📂 • Fast and lightweight ⚡ • Clean and minimal code structure 🧩 💡 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗨𝘀𝗲𝗱: • File handling in Python • Working with PDFs using pikepdf • OS module for path management • Looping and automation 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/dmiQStmG A small but powerful utility that solves a real-world problem efficiently. Building discipline through code — one project at a time. Follow along as I complete 30 Python projects in 30 days 🚀 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #PythonProjects #Automation #PDF #Coding #Learning

"ImportError: cannot import name 'FastMCP'" I stared at this for way too long. The library was installed. The version was correct. The import path was right. pip install mcp → success from mcp.server import FastMCP → ImportError Here's what happened: My project had a folder called mcp/. Python found it first. That's it. Python's import system checks the local directory before installed packages. My mcp/ folder — which held config files — was silently hijacking every import call. The fix was one line: sys.path.remove(os.path.dirname(__file__)) The debugging? Over an hour of reinstalling packages, checking versions, and questioning my sanity. Name your folders carefully. Python's import system doesn't care about your intentions — only your directory structure. If your installed package suddenly "can't be found," check if you accidentally created a folder with the same name. Ever had a naming collision silently break your project? #Python #Debugging #DeveloperLife #SoftwareEngineering

I got tired of waiting… Installing heavy Python packages over a slow internet connection was killing my momentum — especially with libraries like torch. So I looked for a better way and came across devpi. Set it up on our local server, and honestly — it’s been worthy. Now I spin up a new virtual environment and install gigabytes of packages in under a minute ⚡ If you’re working with Python and large ML dependencies, setting up devpi locally is 100% worth it. Sometimes the best productivity boost isn’t new tools — it’s fixing the friction. link to github: https://lnkd.in/djJMNNnr