Sowmmya Katari Hariharan’s Post

Pairing GDAL for a geospatial workflow in Python today. 💻 Mac vs Windows (A Love Letter to GDAL… and a Slight Cry for Help 😄) If you’ve ever tried installing GDAL on Windows, you know the struggle. If you’ve ever tried installing GDAL on Windows, you already know… it’s not just installation — it’s a journey. You start with: “pip install gdal” …and suddenly you’re: Installing dependencies you’ve never heard of. Fixing PATH variables like a detective Questioning your life choices. Then I finally typed one line in PowerShell: ✨ BOOM. It works. That moment? Pure joy. Like unlocking a hidden level in life. #python #rosettastone #gisworkflows

Today marked the point where a Flask app that runs became one that runs correctly: replacing the development server with Gunicorn, introducing worker processes, and moving runtime decisions into environment variables. What stood out wasn’t the commands themselves, but how much clearer the boundaries became; what belongs in code, what belongs in configuration, and what belongs to the environment. I’ve realized that these transitions matter more than features at this stage. Understanding them makes everything else easier to reason about later. Next session: tightening up how runtime configuration is persisted. #SoftwareDevelopment #Python #Flask #AWS #Linux #Gunicorn

Just released version 10.8.4 of passagemath, the comprehensive #OpenSource #Mathematics system in #Python, the pip-installable modularized portable fork of SageMath. It is available: - on #PyPI, providing source distributions and binary wheels for Linux and macOS [full functionality of #SageMath, GAP, Macaulay2, PARI/GP, and more] and Windows [partial functionality], - on the prefix.dev emscripten-forge channels, shipping #WebAssembly packages [partial functionality] for serverless in-browser use with Project Jupyter's JupyterLite and Notebook.link, - in the #MSYS2 software distribution for Windows [partial functionality]. In passagemath-10.8.4, thanks to work by Samuel Chen, rich output is now also supported in Jupyter notebooks with standard Python kernels, such as on Google Colab or in JupyterLite (XPython). Example JupyterLite notebook for Algebraic Combinatorics, now with some plots: https://lnkd.in/gveZVfKE https://lnkd.in/g2kXE4Vd

Mellow a distraction-free music player. Built in Rust. No Electron. No Python. What makes it technically interesting: → gtk4-rs + libadwaita-rs : one of the more complete real-world Rust desktop UIs out there → gstreamer-rs : gapless playback, pure Rust bindings all the way down → Adaptive colors from album art, file change detection, removable drive support → Ships as a Flatpak : real distribution, not a hobby binary 1,333 commits in. Still experimental, but genuinely worth watching if you're tracking Rust GUI maturity. https://lnkd.in/gTxmjnKB #Rust #RustLang #OpenSource #Linux #GtkRs

🚀 Day 6/10 — Optimization Series Python Virtual Environments (Why They Matter) 👉 Basics are done. 👉 Now we move from working code → optimized code. You install a package… Everything works fine… Then suddenly 😐 👉 Another project breaks 🔹 The Problem 👉 Different projects need different versions 👉 Global installation = conflicts 🔹 What is a Virtual Environment? 👉 A separate isolated space for your project Each project can have: its own packages its own versions 🔹 Create Virtual Environment python -m venv myenv 🔹 Activate Windows: myenv\Scripts\activate Mac/Linux: source myenv/bin/activate 🔹 Install Packages pip install pandas 👉 Installed only inside this environment 🔹 Why This Matters No version conflicts Clean project setup Easy collaboration 🔹 Real-World Use 👉 Multiple projects 👉 Team development 👉 Production environments 💡 Quick Summary Virtual environment = isolated workspace 💡 Something to remember If everything breaks after installing one package… you’re not using virtual environments. #Python #DataEngineering #LearningInPublic #TechLearning

⚠️ Skipping virtual environments is one of the most common mistakes new Python developers make. Here is why it matters and how to do it right. A virtual environment keeps your project dependencies isolated. Without one, every package you install goes into your global Python installation and things break fast when projects need different versions of the same library. Here is how to set one up for your FastAPI project: 🔧 Step 1: Create the virtual environment python -m venv environmentfoldername ⚡ Step 2: Activate it On Windows PowerShell: .\environmentfoldername\Scripts\Activate.ps1 On Mac, Linux, or Git Bash: source environmentfoldername/Scripts/activate Once activated, your terminal will show the environment name. That is your green light to start installing packages safely. ✅ 🙅 One more thing: always add your environment folder to .gitignore. You do not want to push hundreds of dependency files to GitHub. Virtual environments are a small habit that save enormous headaches. Start doing this from day one. #Python #FastAPI #DeveloperTips #CleanCode #PythonDevelopment

I'm a big fan of Claude Cowork. I was creating a solution where I could get Cowork to interact with external APIs and I learned something new about Cowork generated Python. "The code does run locally -- but "locally" means inside a lightweight Linux VM on your computer, not directly on your Mac/Windows desktop. That VM has network restrictions that block outbound calls to most external APIs. So even though the Python code is running on your machine, it's running inside a sandbox that Anthropic controls, and that sandbox won't let it reach oauth2.googleapis.com." -Claude Cowork

Just released version 10.8.3 of passagemath, the comprehensive #OpenSource #Mathematics system in #Python, the pip-installable modularized portable fork of SageMath. It is available: - on PyPI, providing source distributions and binary wheels for Linux and macOS [full functionality of #SageMath, GAP, Macaulay2, and more] and Windows [partial functionality], - on the prefix.dev emscripten-forge channels, shipping #WebAssembly packages [partial functionality] for serverless in-browser use with Project Jupyter's JupyterLite and Notebook.link, - in the MSYS2 software distribution for Windows [partial functionality]. The release 10.8.3 updates the #CAS Maxima to version 5.49.0, updates GNU MPC (multi-precision complex numbers) to 1.4.0 and OpenBLAS to 0.3.32, and includes various bugfixes backported from #SageMath development. Developers who prefer building passagemath from source will benefit from the initial support for the clang 21 toolchain and autoconf 2.73. passagemath's updated version of the Sage distribution now ships the marimo Jupyter Lab extension for interoperability. It also brings various updates, including CVXPY to version 1.8.2 and NumPy to version 2.4.3. https://lnkd.in/gUmAmzQ8

Got a new laptop last week. Usually this means: reinstall Python, recreate conda environment, pip install everything again, install dependencies, lose half a day. This time I didn't do any of that. I just copied my entire WSL environment from the old machine to the new one: libraries, virtual environments, configs. Opened the terminal on the new laptop and it worked like nothing changed. Here's how: 1. On the old laptop, export the WSL distro: wsl --export Ubuntu D:\Ubuntu_backup.tar 2. Transfer the .tar file to the new laptop with pen drive. 3. On the new laptop, install WSL, then import: wsl --import Ubuntu C:\WSL\Ubuntu D:\Ubuntu_backup.tar 4. Log in with root user: ubuntu config --default-user <your_username> Done. All my Python environments, pip packages, project files remains intact. Zero reinstallation. The .tar file was heavy (7+ GB ), but it saved hours of setup. If you work on WSL and switch machines often, this is the way. #WSL #Python #DevTools #Productivity #Linux

ForzeOS #pentest Build a Linux-style desktop simulator for Windows with Python 3.11, terminal tools, and AI integration for testing and development https://lnkd.in/gPB3WmYf