Python Script Execution Process Explained

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

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

Support engineers who automate become dramatically more effective. Even small automation helps: Gather diagnostics Check service status Verify disk/memory/network Pull logs faster Standardize first-response triage You don’t need massive tooling to add value. A few simple Bash or Python scripts can save hours every week. #CloudSupportEngineer #Automation #Python #Linux #DevOps

REMINDER - The Python programming workshop is THIS SUNDAY Workshop link: https://lnkd.in/eEpsw7de At Hobart Hackerspace. Session 1 will cover how to set up your computer for programming with Python. If you don't know where to start, I'll be able to guide you to having the tools for programming Python set up on your machine. Whether it is Windows or Mac, or even Linux! Or even if your computer is locked down, or you have a tablet, there are still ways to code in Python. Python is often used in data science, AI, web applications, automation, and all kinds of things as it has a vast range of packages available you can just install and work with right away.

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

Ever wondered how Nginx, Redis, and Python's asyncio handle thousands of requests per second? What does await actually do under the hood? The answer is epoll — a Linux syscall. And I built it from scratch to understand it. Raw Python. No frameworks. 52,000 requests/second. Just published my first ever technical article breaking down exactly how it works — the concepts, the code, the real benchmark numbers, and every bug I hit along the way. If you've ever used asyncio and wondered what's actually happening underneath — this one's for you. Link in the comments 👇 #Python #Linux #Backend #SystemsProgramming #SoftwareEngineering

Updated the latest Python Installer on Windows compiled via C# .NET for versions 3.14.3 + 3.13.12 to be run autonomously, if you were to alter code slightly. :) https://lnkd.in/g6NnfiCv

Very common Interview question and it's explanation. Why can += on strings in a loop be inefficient? What is the better approach in high-throughput code paths? Strings are immutable. Each += typically creates a new string, copying old content plus the new part. In a loop, repeated concatenation can become expensive because of repeated allocations and copying. In high-throughput code paths, better approaches are: accumulate parts in a list and use "".join(parts) use io.StringIO for streaming-style construction These approaches reduce repeated copying and are much more efficient for large or repeated string assembly. #python #django #fastapi #systemDesign #linux

Python 3.15 JIT Compiler Surpasses Performance Goals Ahead of Schedule 📌 Python 3.15’s JIT compiler has hit a major milestone - outperforming expectations months early with up to 12% speedups on Apple Silicon and 6% gains on x86_64 Linux. This marks a shift from pure interpretation toward a hybrid model, proving Python can rival compiled languages in performance without external tools. The community-driven effort, despite lost funding, now shows strong promise for enterprise CPU-bound apps. 🔗 Read more: https://lnkd.in/dwSgFXzu #Python315 #Jitcompiler #Cpython #Performanceboost #Macosaarch64

ALL MCP --- ALL THE TIME!!!!! This installment lays the groundwork for Secure MCP in the Rust programming language. Following the same approach as the Go and Python versions- this article builds out a working Rust MCP Server and then secures the connection. MCP Security Series is here: https://lnkd.in/eduv2q6e Medium Article is here: https://lnkd.in/e3-9K5aK dev.to is here: https://lnkd.in/eebBEiu9 #GDE #MCP #GeminiCLI #Rust #GoogleCloud