Building Scalable Systems: Debugging and Improving Performance

🚀 Day 24 of My Python Journey – Reverse String (In-Place) 🔁💻 Today’s problem was simple on the surface but a great way to strengthen fundamentals. 📌 Problem Reverse a string given as an array of characters in-place using O(1) extra space. Example: ["h","e","l","l","o"] → ["o","l","l","e","h"] 🔍 Approach I Used I implemented the two-pointer technique: One pointer starts from the beginning Another pointer starts from the end Swap elements and move both pointers towards the center 👉 Keep doing this until both pointers meet 💡 Key Learning ✔ Two-pointer technique is extremely powerful ✔ In-place operations help optimize space complexity ✔ Even simple problems build strong fundamentals ✔ Clean swapping logic improves code readability ⏱ Complexity Time Complexity: O(n) Space Complexity: O(1) Small problem, but big concept reinforcement 🔥 Consistency is what matters 💪 #Python #DSA #TwoPointers

🎯 Built a Simple Recommendation System Created a system that suggests items based on user preferences using content-based filtering. 🔹 Features: • Recommends similar items • Uses basic genre matching logic • Fast and easy to scale 💡 What I learned: • Recommendation system fundamentals • Content-based filtering • How platforms like Netflix/Amazon suggest items 🛠️ Tech: Python This project helped me understand how personalization works in real systems. #RecommendationSystem #Python #MachineLearning #AIProjects #Learning

🚀 Day 68 | Python Revision (Up to Recursion) Today I focused on revising all Python concepts up to recursion 📘 🔹 What I Revised: • Basics → variables, data types, input/output • Control statements → if-else, loops • Functions → user-defined functions, arguments • Built-in functions → len(), sum(), min(), max(), etc. • String methods → strip(), split(), replace(), join() • List & Dictionary operations • Lambda functions and functional programming basics • Recursion → factorial, list flattening 💡 Key Learning: • Revision helps in connecting all concepts together • Improved clarity on when to use loops vs recursion • Strengthened understanding of problem-solving approaches 🔥 Takeaway: 👉 Strong fundamentals come from consistent revision Consistency + Revision = Confidence 🚀 #Day68 #Python #Revision #Recursion #ProblemSolving #CodingJourney #10000Coders #PythonDeveloper #SravanKumarSir

🚀 Day 14 – Sort a List Without Using sort() (Python) 💻 Today’s task: Sort a list without using the built-in sort() function. 🔍 Explored alternative approaches: • Using lambda functions 🧠 • Using slicing techniques 🔪 📌 This exercise helped me understand: • Custom sorting logic ⚙️ • How Python handles data manipulation internally 🔍 • Writing optimized and flexible code ✨ ✨ Challenging myself to go beyond built-in functions and strengthen problem-solving skills. 📈 Consistency is key — learning something new every day. #Python #100DaysOfCode #CodingJourney #Programming #ProblemSolving #Developer #LearnToCode #Tech #PythonTips

Most developers think generators are just about saving memory. That’s true, but it misses the more interesting part. Generators give you control over when work happens. Nothing runs until the next value is requested. That small detail changes how you design data flows, especially when you’re dealing with streams, pipelines, or external systems. In this week’s video, I show how generators act as small state machines, how to build clean data pipelines with them, and how features like `send()` and async generators extend that model even further. If you want to get better at designing data flows in Python, this is worth understanding properly. 👉 Watch the full video here: https://lnkd.in/eHrPzaQJ. #python #softwaredesign #cleancode #generators #developers #arjancodes

Most developers think generators are just about saving memory. That’s true, but it misses the more interesting part. Generators give you control over when work happens. Nothing runs until the next value is requested. That small detail changes how you design data flows, especially when you’re dealing with streams, pipelines, or external systems. In this week’s video, I show how generators act as small state machines, how to build clean data pipelines with them, and how features like `send()` and async generators extend that model even further. If you want to get better at designing data flows in Python, this is worth understanding properly. 👉 Watch the full video here: https://lnkd.in/eztrHhmx. #python #softwaredesign #cleancode #generators #developers #arjancodes

Amazon, YouTube, Google — all have bugs in production right now. 🐛 And that's completely fine. The difference between a server that crashes and one that keeps running isn't perfect code — it's knowing how to catch failures before they become disasters. I just published a deep-dive on the Python concept that powers it all: 🔥 try / except / else / finally — the full anatomy 🔥 Every built-in exception you'll hit in production 🔥 Logging that actually alerts you in real-time 🔥 Custom exceptions & real-world patterns used at scale Small concept. Massive impact. If you're building anything in Python — this one's for you. 👇 https://lnkd.in/dxDqRyNv #Python #SoftwareEngineering #BackendDevelopment #Programming

🚀 Day 21 – Merge Two Sorted Lists (Python) 💻 Today’s task: Write a program to merge two sorted lists into a single sorted list. 🔍 The goal is to combine both lists while maintaining the sorted order. 📌 This exercise helped me understand: • Two-pointer technique 🔁 • Efficient list traversal ⚙️ • Writing optimized and clean logic ✨ ✨ A classic problem that builds a strong foundation for algorithms like merge sort. 📈 Learning step by step and improving consistency every day. #Python #100DaysOfCode #CodingJourney #Programming #ProblemSolving #Developer #LearnToCode #Tech #PythonTips #DataStructures

𝐓𝐡𝐞 𝐨𝐧𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐜𝐨𝐧𝐜𝐞𝐩𝐭 𝐭𝐡𝐚𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐡𝐨𝐰 𝐈 𝐭𝐡𝐢𝐧𝐤 𝐚𝐛𝐨𝐮𝐭 𝐌𝐞𝐦𝐨𝐫𝐲 In Python, every value in memory has an identity called Id. And this tiny detail is the key to understanding one of Python's most important concepts: 𝐌𝐮𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲. Here's the insight that clicked for me: 🔴 Immutable means Python creates a brand new object in memory every time you "change" a value. The old one stays untouched. The Id changes. 🟢 Mutable means Python updates the existing object in place. Same memory address. Same Id. Before this, I thought mutability was just about whether a value "can change". But now I understand it's really about whether Python touches the same memory location or creates a new one. That's a much deeper way to think about it. Still in a learning journey, but these small 𝒂𝒉𝒂 moments are what make it exciting.

Ever tried posting nested data in DRF and hit the dreaded “writable nested fields not supported” error? In my latest article, I break down how to fix it cleanly — from overriding create() to adding transaction safety and optimizing with bulk_create(). You’ll learn: Why nested serializers fail on POST How .pop('items') saves your day The right way to separate read/write serializers Production-ready patterns for clean, scalable APIs Read the full breakdown here 👇 🔗 https://lnkd.in/d-3qARwR #Django #RESTFramework #Python #BackendDevelopment #APIs #SoftwareEngineering