Python Security Lab: 20 Projects and a Strong Foundation

🐍 Project 2 complete — built a Python network scanner from scratch. No Nmap, no shortcuts. Pure Python using socket, threading, and subprocess: ✅ Threaded ping sweep across full /24 subnet ✅ TCP port scanning on 14 common ports ✅ Service name detection ✅ Reverse DNS hostname resolution ✅ Full scan completes in under 2 seconds The threading implementation was the most interesting part — going from a sequential scan that took minutes to a concurrent scan that finishes in under 2 seconds by launching every ping simultaneously🚀 🔗 GitHub: https://lnkd.in/g66caV73 #Python #Networking #CyberSecurity #PortScanner #Linux

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

If you're not using virtual environments in Python… you're making a big mistake. Here’s why they matter Problem: Different projects need different package versions → This creates conflicts (and headaches) Solution: Virtual Environments They let you: → Isolate dependencies per project → Avoid version conflicts → Keep your system clean How to use: → Create Virtual Environment python -m venv myvenv → Activate Virtual Environment in (Windows) myvenv\Scripts\activate → Activate Virtual Environment in (Mac/Linux) source myvenv/bin/activate → Install packages pip install <package_name> → Deactivate Virtual Environment deactivate Simple habit. Huge impact #Interview_Question: What is a virtual environment, and why is it created? →In Python, a venv (Virtual Environment) is an isolated folder that stores specific Python libraries and versions for a particular project. It is distinct from the main system's Python installation, thereby preventing package version conflicts and providing an independent environment for projects. Do you use virtual environments in your projects? #Python #VirtualEnv #BackendDevelopment #Coding #Developers #Backend #Python #FastAPI #Flask #Django

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

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

AmmonitOR has been upgraded to Debian 13 "Trixie". Debian is a platform we have trusted since 2010 for its legendary stability, security, and open-source values. This update brings further improvements, including Python 3.13 to PostgreSQL 17 - ensuring that AmmonitOR remains fast, secure, and future-proof. Read the full details on our website: https://lnkd.in/d7rRdPKN #AmmonitOR #Windmeasurement #Solarmeasurement #Ammonitdatalogger #Debian #OpenSource

🐍📺 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

💻 I Spent 2 Hours Debugging… for a 2-Minute Fix While practicing Python on Linux, I ran into a simple issue: My script just wouldn’t run. No output. No clear error. After trying multiple things, I finally found the problem 👇 👉 I was running the file incorrectly in the terminal 👉 And missed checking the Python version. Fix: ✔️ Used python3 file.py instead of python file.py ✔️ Verified with python3 --version Simple mistake. But it cost me 2 hours. What I learned: Small details matter a lot in tech Always check basics before overthinking Debugging is where real understanding happens Moments like these can feel frustrating, but they’re also the ones that stick the most. Curious—what’s a “simple mistake” that cost you hours? #Python #Linux #Debugging #DevOps #LearningJourney #TechLearning #Beginners

💡 One Problem, Three Solutions: Python, PowerShell, and Bash As I work on building my automation simulation projects, I recently hit a fascinating crossroad: Should I use Python, PowerShell, or Bash? Coming from a Computer Engineering background, I initially looked for the "one best language." But the more I build, the more I realize that in IT Operations, the "best" tool is the one that fits the environment and the business need. Here are a few lessons I've learned while experimenting with these three: ✅ Python is incredibly intuitive. It’s been my go-to for building the core logic of my simulations. Its readability makes it feel like I’m writing a story, and the libraries for data handling are a lifesaver. ✅ PowerShell is more than just a terminal. I’ve learned that for Windows environments, it’s not just about commands—it’s about objects. It taught me how to look at system management from a structured, "object-oriented" perspective. ✅ Bash scripting is where I feel the raw power of the OS. It takes me back to my Operating Systems classes, where I first opened a Linux shell to analyze system behavior and troubleshoot tasks. Even though those lab days are behind me, the fundamental logic of piping small, efficient tools together to solve a complex problem still feels incredibly satisfying. The biggest takeaway for me is that automation isn't just about writing code; it’s about understanding the "why" behind the system. Whether it's a Windows workstation or a Linux server, I'm learning to choose the tool that ensures stability and efficiency. I’m still at the beginning of my career journey, and every day is a learning lesson. I’d love to know—for those who have been in the industry for years, which tool did you start with, and how has your "toolbox" evolved over time? #LearningJourney #Automation #Python #PowerShell #Bash #Linux #ContinuousLearning #TechCareer

Your Python app on Windows just got a critical attack surface. The Problem: A high-severity vulnerability CVE-2026-3298 was just disclosed in Python's `asyncio` module on Windows. The `sock recvfrom into ` method in `ProactorEventLoop` has a missing boundary check. The Agitation: When a network response exceeds your buffer size, Python fails to enforce limits. This allows an out-of-bounds OOB write into adjacent memory. The impact is severe: - Memory corruption - Application crashes - Potential arbitrary code execution This is Windows-only, but if you're running: - Python web servers on Windows - UDP socket applications - Any async network service with fixed-size buffers Your infrastructure is exposed. Attackers can exploit this to overwrite memory regions and gain control. The Solution: A fix is already in CPython via GitHub PR #148809. The patch adds the missing boundary check. Immediate actions: 1. Monitor CVE-2026-3298 for patched version details 2. Apply the updated Python release immediately 3. Avoid `sock recvfrom into ` with `nbytes` in untrusted environments until patched This vulnerability affects all Windows Python 3.8+ deployments using the default event loop. How is your team securing your infrastructure against this type of exploitation? Let’s discuss in the comments below. #CyberSecurity #Python #VulnerabilityManagement