Mastering Backtracking with N-Queens Problem

“I have so much gratitude to people who wrote extremely complex software character-by-character… Thank you for getting us to this point.” Sam Altman. The line between "thank you" and "farewell" is thinning. For forty years we carved logic one keystroke at a time. Compiler errors were our daily blood-letting. That meticulous craft got us here, and I still respect every late-night semicolon. But the game changed. The compiler is now an intern with infinite stamina. The scarce resource is no longer typing skill; it's the clarity to ask the right question before the model starts guessing. I've already stopped polishing loops and started polishing prompts. My pull-request reviews talk more about risk and user value than trailing commas. The winners on my team are the ones who can 𝑓𝑟𝑎𝑚𝑒 𝑡ℎ𝑒 𝑝𝑟𝑜𝑏𝑙𝑒𝑚, 𝑑𝑒𝑠𝑖𝑔𝑛 𝑡ℎ𝑒 𝑠𝑦𝑠𝑡𝑒𝑚, and 𝑐𝑜𝑛𝑑𝑢𝑐𝑡 𝑎 𝑠𝑤𝑎𝑟𝑚 𝑜𝑓 𝑎𝑔𝑒𝑛𝑡𝑠 the way we once conducted sprints. The craft isn't gone-it just grew a new layer. Are you ready to move up, or are you still counting brackets? #SoftwareLeadership #FutureOfWork #AIEngineering #ProductStrategy

Most developers don’t have a coding problem. They have a thinking problem. And it shows up every time they write code that works… but doesn’t scale. Take this simple problem: Find two numbers that add up to a target. A lot of people solve it with nested loops. And yes, it works. But it’s O(n²). Now imagine running that on real data. Here’s where great developers think differently. Instead of repeatedly searching, they ask: “What if I store what I’ve already seen?” That one question introduces a hash map. Now the solution becomes: → One pass → O(n) time → Instant lookups Same problem. Completely different performance. This is the power of Hash Tables & Sets. They don’t just optimize your code, they change how you think. Once you understand this, you start spotting patterns everywhere: → Counting frequencies → Detecting duplicates → Finding pairs instantly → Grouping related data → Solving subarray problems And here’s the shift that separates good from great: You stop asking “How do I solve this?” And start asking “What should I store?” Because in many cases: The fastest solution isn’t about searching better… it’s about avoiding the search entirely. If this clicked for you, you’re thinking like a problem solver. #DataStructures #Algorithms #DSA #SoftwareEngineering #TechGrowth #CodingInterview #LearnToCode #web3

Sometimes learning doesn’t come from assigned tasks. It comes from *just sitting and building something for yourself.* Recently, I was playing around with **rate limiting** — not as a requirement, just out of curiosity. Tried implementing algorithms like: • Token Bucket • Leaky Bucket But more than the algorithms, I focused on **how I design the code.** My approach was simple: → Keep it modular → Keep it extensible → Keep it plug-and-play So instead of hardcoding logic, I designed it using: • Strategy Pattern → to switch algorithms dynamically • Builder Pattern → to configure and create limiter cleanly Each algorithm is isolated. No tight coupling. Easy to extend, easy to test. You can literally change the behavior of the system just by switching the algorithm — no code rewrite. No production pressure. No deadlines. Just experimenting, breaking things, and observing behavior. And that’s where the real insight came: Rate limiting is not just about restricting requests. It’s about understanding **how systems behave under different traffic patterns.** • Burst traffic behaves differently • Steady traffic behaves differently • Each algorithm has its own trade-offs Sometimes the best learning happens when: You’re not told *what to build* But you explore *how to design it right* That’s where engineering thinking evolves. #BackendEngineering #SystemDesign #RateLimiting #Java #DesignPatterns #SoftwareArchitecture #LearningByDoing

𝗧𝗵𝗶𝘀 𝗧𝗿𝗲𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲 𝗗𝗙𝗦… 𝗨𝗻𝘁𝗶𝗹 𝗡𝘂𝗺𝗯𝗲𝗿 𝗧𝗵𝗲𝗼𝗿𝘆 𝗕𝗿𝗲𝗮𝗸𝘀 𝗜𝘁 𝗢𝗽𝗲𝗻 🌳 Today’s problem looked like a simple tree traversal - until a quiet condition appeared: Count ancestors where nums[i] * nums[ancestor] is 𝗮 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝘀𝗾𝘂𝗮𝗿𝗲. Brute force (walking up ancestors for every node) is too slow. The real win comes from 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲. 💡 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 A product is a perfect square iff every prime factor appears an even number of times. So reduce every number to its square-free form: keep only primes with odd exponent. Examples: 12 = 2^2 × 3 -> 3 18 = 2 × 3^2 -> 2 Now the condition becomes: Two numbers form a perfect square product iff their square-free forms are equal. 🚀 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 For each node, count how many ancestors have the same square-free value. That’s just DFS + frequency map on the path. 🛠️ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 1. Build adjacency list 2. Precompute square-free value for each node 3. DFS from root while maintaining a hashmap of frequencies 4. At each node, add map.get(k[node]) to answer 5. Backtrack (remove from map) No ancestor traversal. No pair checks. ✨ 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 1. Perfect square checks → parity of prime exponents 2. Convert math conditions into hashable signatures 3. DFS + hashmap is powerful for ancestor path problems 4. Optimize by transforming the condition, not the traversal A beautiful mix of Trees + Number Theory + Hashing. #algorithms #datastructures #dfs #numbertheory #trees #hashmap #graph #primefactorization #coding #programming #leetcode #codinginterview #interviewprep #problemSolving #competitiveprogramming #developer #devlife #engineering #tech #learning #growth #100DaysOfCode #career #faangprep

Day 71 on LeetCode Find Peak Element ⛰️✅ Today’s problem introduced a Binary Search approach, but I first solved it using a brute-force traversal to build intuition. 🔹 Approach Used in My Solution (Brute Force) The goal was to find an element that is greater than its neighbors. Key idea: • Traverse the array from index 1 to n-1 • Check if current element is greater than its neighbors • Handle edge cases like last element separately • Return the index once a peak is found This approach is simple and works reliably. 🔹 Optimized Approach (Binary Search Insight) • If nums[mid] < nums[mid+1] → peak lies on the right side • Else → peak lies on the left side (including mid) • Continue narrowing until left == right ⚡ Complexity: • Brute Force: O(n) • Binary Search: O(log n) 💡 Key Takeaways: • Building intuition with brute force helps understand the problem deeply • Learned how to transition from linear scan → binary search optimization • Reinforced that multiple approaches can lead to the same result, but efficiency matters 🔥 Step by step, moving from basic logic to optimized patterns! #LeetCode #DSA #Algorithms #DataStructures #BinarySearch #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

A script solves a problem once. A tool solves it forever. Most R&D teams never make the leap. Not because they lack talent, but because nobody ever showed them how. Here's what a typical research script looks like: Written fast, in a notebook or a .py file, by one person, for one purpose. It works, brilliantly, sometimes. Then it gets used once, saved somewhere, and forgotten. Six months later, nobody can run it. Including the person who wrote it. Sound familiar? A research tool is something fundamentally different: > It's documented — not for yourself, but for a stranger. > It's tested — not just "it ran on my machine", but verified against known results. > It's modular — so others can extend it without breaking everything. > It's versioned — so you can trace every result back to the exact code that produced it. > It's collaborative — so the whole team builds on the same foundation. The difference isn't about the quality of the underlying science. It's about whether the science can grow beyond the person who created it. This is exactly what nuRemics is built for. An open-source Python framework that brings these software engineering practices into scientific development, without requiring a team of software engineers to make it work. The goal isn't perfection. It's durability. What does your R&D toolchain look like today? #ScientificSoftware #Python #SoftwareEngineering #ComputationalScience #nuRemics #OpenSource #ResearchTools #DeepTech #Reproducibility #SUFFISCIENS

You don't need a CS degree to understand Big O. It just answers one question: "As my data grows bigger does my code stay fast or get slow?" That's it. Here's all 6 types, explained like you're 15: O(1) — Always instant ✅ Imagine a dictionary. No matter how thick it is, you can jump straight to page 200. Size doesn't matter. Always same speed. Real code: looking up a value by key in a list O(log n) — Gets a little slower, not much ✅ Think of guessing a number between 1–1000. Each guess you cut it in half. You never need more than 10 guesses. Super efficient. Real code: searching a sorted list O(n) — Grows one step at a time 🟡 Like reading every page of a book to find one word. 100 pages = 100 steps. 1000 pages = 1000 steps. Fair enough. Real code: checking every item in a list O(n log n) — A bit slower, still okay 🟠 Like sorting a messy deck of cards smartly split, sort, merge. Takes more effort but still manageable. Real code: merge sort, most sorting algorithms O(n²) — Starts hurting at scale 🔴 Like comparing every student in a class with every other student. 10 students = 100 comparisons. 100 students = 10,000 comparisons. Ouch. Real code: two nested loops bubble sort. O(n!) — Never use this 🚫 Trying every single possible arrangement. With just 20 items, that's more operations than atoms in the universe. Your computer will cry. Real code: brute-force travel route finder 💡 The simple takeaway: The further down this list your code is the more it will struggle when your users grow from 100 to 1 million. Good engineers don't just write code that works. They write code that works at scale. 🎯 At Mocklingo, we help you practice explaining concepts like this out loud so in your next interview, you sound sharp and confident, not confused. mocklingo.com 💬 Which one surprised you the most? Drop it in the comments! ♻️ Share this with a friend learning to code — this took me years to understand and it's all right here. #BigO #LearnToCode #Mocklingo #CodingForBeginners #TechInterview #Programming #SoftwareEngineering #CareerGrowth #100DaysOfCode

🔁 Recursion isn’t magic — it’s just a Stack in action A lot of developers struggle with recursion… until they understand what’s happening behind the scenes: 👉 The Call Stack 💡 Let’s simplify it with a classic example — Factorial factorial(4) = 4 × factorial(3) = 4 × 3 × factorial(2) = 4 × 3 × 2 × factorial(1) = 4 × 3 × 2 × 1 = 24 📦 What actually happens internally? Every recursive call is pushed onto a stack (LIFO — Last In, First Out) 🔼 Stack Build Phase (Going Down) factorial(4) factorial(3) factorial(2) factorial(1) 🔽 Stack Unwind Phase (Coming Back) factorial(1) → returns 1 factorial(2) → 2 × 1 = 2 factorial(3) → 3 × 2 = 6 factorial(4) → 4 × 6 = 24 🧠 Key Insight: Recursion has 2 phases: Expansion (calls keep stacking) Resolution (stack starts unwinding) ⚠️ Important: Without a base case, recursion = 💥 Stack Overflow 🎯 Real-world analogy: It’s like asking a question down a chain of people — The last person answers, and the response travels back up. 🔥 Once you understand the stack, Recursion becomes predictable — not scary. 💬 How did you finally understand recursion? Was it also the stack moment for you? #Recursion #DataStructures #Coding #SoftwareEngineering #BackendDevelopment #LearningInPublic

Can’t solve coding problems? You’re probably missing this Big O + Arrays + Strings foundation. Before you solve problems, you need to understand what’s happening under the hood. Let’s start simple. Arrays store data in continuous memory That’s why: • Access is fast → O(1) • Insert/Delete is slow → O(n) (because elements shift) Strings are just arrays of characters So every string problem is secretly an array problem. Now let’s talk about Big O (this is where most people get confused) Big O tells you: “How does my code behave as input grows?” Examples: • O(1) → constant (fastest) • O(n) → loop once • O(n²) → nested loops (slow) • O(log n) → very efficient Here’s the mistake most people make: They memorize Big O, but don’t see it in code. Example: for i in range(n): print(i) That’s O(n) because it runs n times. The goal is simple: Don’t just write code. Understand what it costs. Because once you understand cost, You start writing better solutions naturally. In my next post, I’ll break down the two patterns that solve most problems: → Two Pointers & → Sliding Window Trust me, this is where things start to click. Follow along #Web3 #DataStructures #Algorithms #LearnToCode #TechGrowth

Day 69 of #100daysofcoding | Deep Dive into Linked List Sorting Consistency is starting to compound. Today, I tackled one of the most important Linked List problems: Sorting a Linked List efficiently — not just making it work, but making it optimal. 🔍 Problem Solved: Sort a linked list in ascending order 💡 Approach Used: Merge Sort (optimized for linked lists) 🧠 What made this problem interesting? Unlike arrays, linked lists don’t allow random access — which makes algorithms like Quick Sort inefficient here. 👉 That’s where Merge Sort shines: * No need for indexing * Works naturally with node splitting * Maintains O(n log n) time complexity * Uses constant extra space(in-place pointer manipulation) ⚙️ What I implemented: ✔️ Finding middle using slow & fast pointers ✔️ Splitting the list into two halves ✔️ Recursive sorting of sublists ✔️ Merging two sorted linked lists efficiently 📈 Key Takeaways: * Writing code is one thing, but choosing the right algorithm is what differentiates a developer * Learned how to handle edge cases like empty lists and single nodes * Improved my understanding of recursion + pointers together * Realized how important clean modular functions (like merge) are in scaling logic --- 💭 Honest Reflection: At first, breaking the linked list correctly without losing nodes felt confusing. But after debugging step-by-step, I finally reached a point where everything clicked. And that moment? That’s why I love coding. 🎯 Why this matters (Recruiter POV): This problem demonstrates: * Strong Data Structures & Algorithms fundamentals * Ability to write efficient and optimized code * Understanding of time & space complexity trade-offs * Problem-solving mindset with clean implementation 🔥 Progress Update: 69/100 days completed — and the growth is visible. Not just solving problems anymore, but understanding why one solution is better than another. -l 🚀 What’s next? Diving deeper into: * Dynamic Programming * Advanced Linked List problems * Real-world problem solving patterns #100DaysOfCode #Day69 #LeetCode #DSA #CodingJourney #LinkedList #MergeSort #SoftwareEngineer #PlacementPreparation #TechCareers #Developers #CodeNewbie #Consistency #GrowthMindset #FutureEngineer #OpenToOpportunities #LearningInPublic #BuildInPublic #ProgrammersLife #EngineeringStudent