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
Rust Desktop Music Player with gtk4-rs and gstreamer-rs
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The Backslash That Cost Me 25 Minutes "venv\Scripts\activate" vs "venv/Scripts\activate" Windows devs: ❌ Forward slash = instant fail ✅ Backslash = works Lesson: Environment setup = 50% of project time Windows devs - YOUR worst venv story? 👇** Tomorrow: API key disaster... Windows devs - YOUR worst venv activation story? Comment your answer below 👇 #Python #WindowsDev #Techstruggles
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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
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Built capsule, a container runtime (similar to Docker). A custom Alpine Linux image with Python installed boots in under 3µs. Also wrote a blog about it (https://lnkd.in/dxzT7AVE) :p GitHub Repo: https://lnkd.in/d4nmCTS8 #golang #LearnInPublic #BuildInPublic
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Docker containers are the new node_modules. We went from: > 500MB dependencies in package.json > To 2GB base images with Alpine Linux > To 8GB with "lightweight" Python ML stacks We're just moving the bloat around. Same shit, different abstraction layer.
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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
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Built a tiny open source tool this week — cursor-jump / screen-hop⚡ If you use a laptop + external monitor, you know the pain of dragging your cursor across screens every time you need to switch. This script lets you jump your cursor instantly with Alt + Arrow keys. That's it. No third-party apps. No admin access. Just Python and one pip install. ✅ Windows support ✅ Zero CPU usage ✅ One pip install 🔗 GitHub: https://lnkd.in/gv5VVatd Would love any feedback or contributions! #Python #OpenSource #Productivity #100DaysOfCode
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here's the initial release of sandboxing solution I've been working on 💥 check it out and let me know what you think hardware isolation, wasi components and opt-in file and networking capabilities (off by default) all in 10-15ms 🚀 sandbox = Sandbox(backend="wasm", module="python_guest.path") sandbox.allow_domain("https://httpbin.org") sandbox.register_tool("echo", host_echo) code = 'print(call_tool("echo", message="python guest Hello world!"))' result = sandbox.run(code) https://lnkd.in/gN_iNphk
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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
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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
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Today I gained an interesting insight into Docker image sizes. When I use python:3.12, the image size is around 1.82GB. But when I switch to python:3.12-alpine, it’s amazing to see that the size drops to just 293.42MB. This happens because python:3.12 is based on a Debian image, whereas Alpine is built on Alpine Linux, which uses a minimal operating system. Of course, there are some trade-offs, such as missing pre-installed packages. As a beginner learning Docker, I’ve realized that Docker is not just about containers, tools, or commands. It’s about understanding the core concepts of operating systems how cgroups and namespaces work in Linux, and why tools like Docker Desktop are needed. It’s truly about understanding the underlying system and its architecture. I’m grateful to be learning this through Habib vai’s tutorial. #Happy_Learning #Docker #DevOps
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