Akhila Katragadda’s Post

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

🚀 Day 5 — DSA with Python Solved the classic “Product of Array Except Self” problem today. This one introduced me to an important concept: 👉 Precomputation (Prefix & Suffix Pattern) Instead of recalculating products again and again, I learned how to: • Store prefix products (left side) • Store suffix products (right side) • Combine them to get the result efficiently 💡 Key Learning: Optimizing brute-force solutions using precomputation can significantly reduce time complexity. ⚡ What challenged me: Understanding how to manage two passes (left → right and right → left) without using extra space initially felt confusing — but breaking it step by step helped. 📈 Growth Insight: DSA is less about memorizing solutions and more about recognizing patterns like this one. On to Day 6 🔥 #DSA #Python #CodingJourney #ProblemSolving #100DaysOfCode

🚀 Day 2 of #100DaysOfCode Today I learned how to check whether a number is a Palindrome using Python 🐍 🔍 Problem: A number is called a palindrome if it reads the same forward and backward (like 121, 1331). 💡 Approach: Reverse the number using a loop Compare it with the original number 🐍 Code: num = int(input("Enter a number: ")) original = num reverse = 0 while num > 0: digit = num % 10 reverse = reverse * 10 + digit num = num // 10 if original == reverse: print("Palindrome Number") else: print("Not a Palindrome Number") 📌 Key Learning: Learned how loops and basic logic can solve interesting problems. 💬 Next: Armstrong Number 🔥 #Python #Coding #100DaysOfCode #Learning #CSE

Day 7 of my Python learning journey: Today I focused on classic array and string patterns, and tried to keep solutions clean and efficient. What I solved: Two Sum using brute-force and hash map Valid Palindrome with two pointers Move Zeros with an in-place two-pointer approach Container With Most Water in O(n) Big takeaway: Correctness first, clarity second, optimization third. Small design choices, like in-place updates vs extra arrays, really affect code quality. GitHub link: https://lnkd.in/gGPw8_js #Python #ProblemSolving #Algorithms #TwoPointers #LearningInPublic #CleanCode

Simple way to understand vector search in RAG I made a small Python example using SentenceTransformers + FAISS to understand how retrieval works in RAG. What happens here: A few documents are converted into embeddings Those embeddings are stored in FAISS A user question is also converted into an embedding FAISS finds the most similar document chunks This is the basic idea behind RAG: store meaning as vectors, then retrieve the most relevant context before generation Very small code, but it explains a very important concept. Text → Embedding → Similarity Search → Relevant Chunks That is why vector databases are so important in RAG systems. #RAG #FAISS #Embeddings #AIEngineering #Python #LLM Code source: https://lnkd.in/g-cm4BB2

🚀 Day 14 of My Python + DSA Journey Tried a different approach today and optimized my solution 👇 ✅ Majority Element II (#229) 💡 Majority Element II Find elements appearing more than n/3 times 🔍 Approach: Used Boyer-Moore Voting Algorithm → tracked candidates and validated counts ⏱ O(n) time | O(1) space 🔥 What I learned today: • Same problem can have multiple approaches • Optimization reduces space from O(n) → O(1) • Smart algorithms > brute-force thinking Learning to not just solve… but solve better ⚡ #Day14 #LeetCode #Python #DSA #CodingJourney #100DaysOfCode

While learning LangGraph, one small Python concept suddenly became much more important to me: TypedDict. At first, I thought it was just “type annotations for dictionaries.” Useful, sure—but nothing special. Then I started thinking about state. When multiple nodes in a workflow keep reading and updating shared data, an unstructured dict becomes chaos very quickly. - Missing keys. - Unexpected values. - Confusing debugging. TypedDict solves that by forcing structure into state. That was my takeaway: - Sometimes tools that look “optional” become essential once systems start growing. #Python #BackendDevelopment #LangGraph #AIEngineering #BuildInPublic

Understanding Python’s core data structures is the first step toward writing efficient code. 🐍 • List → Ordered, mutable, and allows duplicate elements. Perfect when you need a collection that can change. • Tuple → Ordered but immutable, meaning once created it cannot be modified. Ideal for fixed data. • Dictionary → Stores data in key–value pairs, where keys are unique and values can be accessed quickly. Choosing the right data structure can make your code cleaner, faster, and more efficient. 🚀 #Python #PythonProgramming #DataStructures #Coding #LearnPython #Programming #TechLearning #DeveloperJourney Akhilendra Chouhan Sanjana Singh Radhika Yadav Skillcure Academy

Day 3 — DSA with Python Solved Group Anagrams today. Yesterday was about understanding what makes two words anagrams. Today was about applying that concept at scale. Key Insight: Sorting transforms all anagrams into the same representation. "eat" → "aet" "tea" → "aet" "ate" → "aet" Same sorted key → same group. A simple hash map does the job efficiently. What stood out today: DSA isn’t about isolated problems. It’s a chain. Concepts compound. Yesterday’s understanding becomes today’s solution. That’s when learning shifts from memorizing to thinking. On to Day 4. #DSA #Python #100DaysOfCode #PlacementPrep #LearningInPublic

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/e9Y3xGNF. #python #softwaredesign #designpatterns #statemachine #cleancode