Debugging a Simple Python Issue on Linux

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

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

Why Every Python Developer Should Use Virtual Environments If you're still installing Python packages globally, you're likely creating dependency conflicts without realizing it. Virtual environments (venv) provide a clean and isolated workspace for each project. Why it matters: Avoid version conflicts between projects Keep your global Python installation clean Easily reproduce environments across teams Safely experiment with new libraries Quick setup: python -m venv myenv source myenv/bin/activate # Mac/Linux myenv\Scripts\activate # Windows pip install -r requirements.txt Each project gets its own environment, which makes your development workflow more predictable and maintainable. Pro tip: Always include a requirements.txt or pyproject.toml file so others can replicate your setup. Virtual environments are a foundational best practice in Python development. #Python #SoftwareDevelopment #BackendDevelopment #DevOps #Programming

Is there anyone out there who took their first digital steps on a #Commodore64 (#C64)? I still vividly remember those days—learning to use that machine, which was nothing more than a pure command line, at the computer club in school. No Windows, no buttons to click, just typing code. Maybe that’s why I still prefer the command line over sleek UIs: it forces me to think, to understand what’s happening under the hood. Right now, I’m working through the #100_Days_of_Code #Python course. Instead of relying on PyCharm’s UI for version control for the projects, I’m practicing Git commands in the terminal. Sure, it might be less convenient, but it makes me feel so much more connected to the process. Anyone else feel the same way?

🐍📺 In this video course, you'll learn how to use the terminal on Windows. You'll navigate the file system performing common tasks. If you've never used the terminal, this video course will help you start #python

Did you know you can comment out an entire block of code in R with a simple shortcut? This is super useful when testing or debugging parts of your script. Select the code and press: 🔹 Ctrl + Shift + C (Windows/Linux) 🔹 Cmd + Shift + C (Mac) The same shortcut also uncomments code, making it easy to toggle between active and inactive sections. Check out this tutorial for more details: https://lnkd.in/ezWsUg5f Stay updated on R, Python, and data science. Join my newsletter for hands-on tutorials and useful insights. See this link for additional information: https://lnkd.in/dcyXHzap #RStats #datastructure #R #DataScience

Why Rust? I get this question a lot. Not "why not Python?" or "why not Go?", but specifically: why does Rust exist, and why should you care? Here's the honest answer: Every major language before Rust made a tradeoff you didn't get to opt out of: • C and C++ gave you raw speed, but also memory bugs that have cost billions in security vulnerabilities. • Java and Python gave you safety, but only by adding a garbage collector that pauses your program and uses more memory. • Go made GC fast and simple, but you still don't control when it runs. Rust breaks this tradeoff entirely. It gives you memory safety, no dangling pointers, no use-after-free, no data races ,and it does it at compile time, with zero runtime cost. No GC. No runtime overhead. The compiler just refuses to compile code that would be unsafe. That's it. This is why Rust is showing up in Linux kernels, Windows drivers, web browsers, and embedded firmware. Not for hype. Because nothing else does what it does. Week 1 of the Rust Book series. We start where it matters -the why. What made you curious about Rust? #rustlang #systemsprogramming #memorysafety #programming #rust #systemdesign #softwareengineering #backendengineering #rust #systemsthinking #distributedsystems #scalability #webdevelopment #programming #100daysofcode #techcareers #buildinpublic #devlife #codeeveryday #computerscience #opensource #asyncrust #lowlatency

RISWIS Applied semantic winner ≠ policy winner Ran on a fresh Windows machine (PowerShell, Python 3.12) Clone → install → run. No setup issues. Validation: ✅ general phrasing → no flip ✅ trust-sensitive phrasing → rank flip Behavior: ✅ system does not force overrides ✅ makes ranking changes visible Observed runs: ✅ same query family → different outcomes ✅ ranking changes only when weighting actually matters GitHub below. #RAG #LLM #AIEngineering #MLOps

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

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.