Python Load Testing with Locust and Kubernetes

I spent time going deep on Python's asyncio, not just "use await" but actually understanding what's happening underneath. Built a step-by-step workshop repo that walks you through the whole journey: My previous post shows why your threaded server slows down under load, Today you will know - The event loop is literally a while True, no magic - Trampolines, the real mechanism behind async/await - Debug mode that tells you exactly what blocked your loop, where, and for how long. - uvloop in one line, 2x to 4x faster, with the same API Each concept is one file. Run them in order. Watch the mental model build. Visit: https://lnkd.in/guNetZMH If you've ever been confused by asyncio or hit a wall with it, start here. #Python #AsyncIO #Backend #SoftwareEngineering #OpenSource

Day 1 of my LeetCode journey 🚀 🧩 Problem: Minimum Distance to the Target Element (1848) 💡 Approach: Looped through the array and tracked the minimum absolute difference whenever the target appeared. 📚 What I learned today: • How to iterate through a list efficiently • Using abs() to compute absolute difference • Tracking minimum values during iteration • Initializing variables correctly using infinity • Better understanding of loops and conditional checks in Python 🛠 Language Concepts Used: • for-loop • if condition • abs() function • min() logic / running minimum ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) 🔗 GitHub: https://lnkd.in/gZ23dWkV #LeetCode #Python #DSA #100DaysOfCode #LearningInPublic

🚀 Week 2 — DSA + System Design This week I focused on making my basics stronger. 📚 DSA (C++ & Python): • Practiced Arrays again • Worked more on Time & Space Complexity • Learned Binary Search and solved different types of problems • Solved around 30–35 questions 🏗️ System Design (LLD): • Revised OOP concepts • Practiced writing cleaner and better structured code ❌ Challenges: Binary Search was confusing in different problems Applying time complexity in real questions was not easy 💡 Realization: Now I’m starting to understand when to use which approach 🎯 Week 3 Goal: Start Linked List and Recursion Staying consistent. Chandan Kumar #DSA #SystemDesign #BuildInPublic #LearningInPublic

When you start working with APIs in Python 🧪 one of the most common ways to see results is with Flask. Just like in a laboratory, you experiment with flasks and ampoules. Here, the Flask is your container. You pour in routes, logic, requests. You observe what comes out. Simple. Lightweight. Immediate feedback. No heavy setup. No complex structure at the beginning. Just you… testing ideas in real time. And that’s exactly why it works so well early on. Because before scaling, before architecture, before optimization… you need a place to experiment. Flask is that place. #Python #Flask #APIs #SoftwareEngineering #BackendDevelopment #DeveloperLife #ContinuousLearning #RotterdamTech

🚀 LeetCode Progress Update Just solved LeetCode 2828 – Check if a String Is an Acronym of Words ✅ 💡 Key Idea: Compared the first character of each word with the corresponding character in the given string — simple logic, but a great reminder that clarity beats complexity! 🧠 What I learned: Importance of index-based comparison Writing clean and readable conditions Thinking in terms of patterns, not just code ⚡ Result: Accepted with 1 ms runtime 💻 Consistency is the goal — one problem at a time! 🔥 #LeetCode #100DaysOfCode #Python #CodingJourney #ProblemSolving #Consistency #DataStructures

I built a PyTorch edge detection package. Created by Mason Andrew Harrison Just joined. I created my first Python package. Its is is able to do different types of edge detection using PyTorch. I would love to know what other people think about it. Here are the links to it if anyone is interested. https://lnkd.in/eVQTyUEW https://pypi.org/p... link https://lnkd.in/eFPiDAZp pubDate Wed, 08 Apr 2026 03:25:18 +0000

New weekend build: AI Incident Copilot (CLI) in Python. It collects recent logs from systemd (journalctl) and Docker in one command, detects high-signal patterns (timeouts, OOM kills, 5xx errors, disk pressure), and generates a copy-pasteable incident report in under 60 seconds. Zero external APIs. No keys. One pip install. Full write-up (with architecture diagram + step-by-step build): https://lnkd.in/eMKY5hP8 #DevOps #Python #Linux #Docker #SRE #Automation #Incident

🚀 LeetCode Progress Update – Problem Solved! ✅ Problem: Remove Trailing Zeros From a String 💡 Approach: Used reverse traversal to find the first non-zero digit and sliced the string accordingly. 🔍 Key Learning: Efficient string manipulation can avoid unnecessary conversions. Traversing from the end helps solve trailing-based problems quickly. 💻 Code Insight: Instead of removing zeros one by one, I identified the breakpoint and sliced the string — making it optimal and clean. ⏱️ Performance: Runtime: 3 ms ⚡ Beat: 66%+ users Memory: 19.23 MB 📈 Consistency is key — one problem closer to mastery! #LeetCode #CodingJourney #Python #ProblemSolving #DSA #100DaysOfCode

For years, we accepted the GIL as a tax on Python performance. But with the "No-GIL" movement officially maturing in Python 3.14 and 3.15, we are finally unlocking true multi-core parallelism. It is a massive shift in how we think about CPU-bound tasks. We no longer have to default to multiprocessing and the memory overhead that comes with it just to bypass the lock. Seeing a single Python process actually saturate multiple cores without the "ceremony" of older workarounds feels like a new era for the language. The performance gap with Go or Rust is narrowing where it matters most, making Python an even stronger contender for high-throughput backends. Are you already experimenting with free-threaded builds for your heavy processing, or are you waiting for library support to catch up? #Python315 #PerformanceEngineering #BackendDevelopment #NoGIL #ProgrammingTrends

I wrote about building a RAG pipeline in Go from scratch. Not the architecture. Not the tech stack. The part that actually taught me something staring at scores that looked like this: 0.0164, 0.0161, 0.0158 for every query, across every chunk, and figuring out why. The article covers: - Why I built this in Go instead of Python - Why vector search alone wasn't enough and how RRF solves the score normalization problem - The reranking bug and what flat scores actually tell you - Two bugs I introduced myself that were invisible until I read the raw JSON - What I still haven't built (the eval directory is empty) Link in comments. #golang #rag #buildinpublic