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?
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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
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💻 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
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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
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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
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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
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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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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
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🚀 Just Launched: My C++ Learning Repository on GitHub! This is more than just code — it’s my journey of learning and improving every day. From: ❌ Basics confusion ➡️ Understanding OOP ➡️ Exploring STL ➡️ Solving problems with DSA 💡 What’s inside: • C++ fundamentals • OOP concepts • STL (vectors, maps, iterators) • File handling • Practice problems 🎯 Goal: To build strong problem-solving skills and grow as a developer. 🔗 GitHub Repo: https://lnkd.in/gWrtZDCU 💬 I’d love your feedback! 🔥 Small steps daily = big results. #cplusplus #programming #github #dsa #oop #codingjourney
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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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The most underrated part of any ML project is setup. I've seen great repos abandoned because the README had 15 steps, required three environment variables nobody explained, and broke on Windows. For System-RAG I built a small orchestration layer, a script that handles environment setup, dependency installation, config scaffolding, and validation automatically. On Windows it's a double-click. On Mac/Linux it's one command. The goal: from clone to running in under 5 minutes, for someone who has never touched Python packaging. This isn't glamorous engineering. But it's the difference between a project you can share and one that sits in your own machine forever. If you're building something in the open — invest in the setup experience. It's the first thing anyone sees. Repo here https://lnkd.in/gUS69Whu #MLEngineering #OpenSource #BuildInPublic #Python #RAG
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