ArjanCodes’ Post

I refactored some business logic, cleaned up the code… and accidentally changed what the software does. In this video, I go through the mistakes I made, why they’re so easy to introduce during refactoring, and why passing tests and good coverage don’t automatically mean your logic is correct. Especially when business rules are involved, assumptions sneak in fast. This one is less about a specific bug and more about a reality of software engineering: there often is no single ground truth. Not in the code, not in the tests, and not in the requirements. If you’ve ever felt slightly uneasy after a "successful" refactor, this will probably look familiar. Watch the video here: https://lnkd.in/erZQ7w3j #softwareengineering #refactoring #cleanCode #python #testing #softwareDesign #programming #developers

Optimization Matters: How I solved LeetCode 1390 (Four Divisors) 🚀 When solving algorithmic problems, the difference between a "working" solution and an "accepted" solution usually comes down to one thing: Time Complexity. The Challenge: The problem asks for the sum of divisors of numbers that have exactly four divisors. With values up to 10⁵and an array size of 10⁴, a brute-force approach (checking every number from 1 to n for every element) would result in O(N×M) complexity. In the worst case, that's 10⁹ operations—way too slow for most environments. My Evolution of Logic: Initially, I considered a simple linear scan for divisors. However, I realized I needed a more mathematical approach to stay within the time limits. The Optimized Approach: I shifted to a Square Root of x optimization. Here’s why it works: Divisor Pairs: Divisors always appear in pairs. For a number x, if i is a divisor, then x/i is also a divisor. One will always be < √xand the other will be >=√x Efficiency: By only iterating up to √x, I reduced the operations per number from 100,000 to just 316. The Logic: Loop from 1 to √x If (x% i == 0), identify if it’s a perfect square (i == x/i) or a distinct pair. Early Exit: If the divisor count exceeds 4, I immediately break the loop. This "pruning" saves significant CPU cycles. Only if the final count is exactly 4, do I add the sum to the total. Key Takeaway: Writing code that works is the first step, but writing code that is performant is the goal. This approach brought the complexity down to O(N√M}), making the solution run almost instantly. #SoftwareEngineering #Coding #Optimization #Java #LeetCode #DSA #ContinuousLearning #DailyCoding #LeetCodeChallenge

UV Package Manager - The Python Tool Revolution 🔧 Spent 3 hours debugging a Python environment issue last week? You're using the wrong tools. I've watched developers waste DAYS on: → Conda conflicts → pip dependency hell → Version mismatches → Broken virtual environments There's a better way: UV Package Manager ⚡ Why UV Changes Everything: 1. SPEED: Built in Rust → 10-100x faster than pip → Nearly instant installs → No more coffee breaks while packages download 2. SIMPLICITY: One tool, all tasks → Create environments in seconds → Switch Python versions effortlessly → No more conda vs. pip confusion 3. RELIABILITY: Modern architecture → Better dependency resolution → Fewer conflicts → Reproducible builds 📊 Here's what you can do with UV: Create environment  → 2 seconds Add dependencies   → 5 seconds   Switch Python version → 3 seconds vs. traditional tools that take minutes (or crash entirely). 🔄 Real-world Impact: BEFORE UV: 30 mins setting up project Frequent environment issues Team onboarding = nightmare AFTER UV: 2 mins setup Zero environment problems New devs productive in hours 📈 The Adoption Curve: Early 2024: Curious developers trying it Mid 2024: Smart teams switching Late 2024: Industry standard forming 2026: Not using UV is a red flag 🚩 💭 My Take: If you're still using conda/pip as your primary tools, you're coding like it's 2020. UV isn't just "another package manager"—it's the reset button Python needed. 🚀 Getting Started: 1. Install UV (takes 30 seconds) 2. Create your first project 3. Never look back 👉 Have you made the switch yet? What's holding you back? 👇 Let me know in the comments! #Python #DevTools #Programming #SoftwareDevelopment #Productivity #Coding #TechTools

We’ve all stared at the screen, ready to blame the compiler, the framework, or the universe for a broken feature. The sheer frustration of debugging complex logic, only to realize hours later... you never actually called the function. 🤦♂️ In Software Engineering, we often fall into the trap of over-engineering the problem before validating the basics. We look for deep architectural failures when the reality is often a missing semicolon, a typo, or an uncalled function. Two strategies I use to avoid this specific flavor of burnout: Occam’s Razor for Code: Always assume the error is simple and user-generated before assuming it’s complex and systemic. Rubber Duck Debugging: explain your code line-by-line out loud. You’ll usually catch the "obvious" mistake before you finish the sentence. Sometimes, the most sophisticated problem-solving tool is just slowing down. What is the most embarrassing "simple" bug that kept you stuck for hours? Let’s comfort each other in the comments. 👇 #SoftwareEngineering #DeveloperLife #Coding #Debugging #TechHumor #ProblemSolving #WebDevelopment #Python #JavaScript #TechCommunity #ProgrammerLife #DevOps #CodeNewbie #SeniorDev

We’ve all stared at the screen, ready to blame the compiler, the framework, or the universe for a broken feature. The sheer frustration of debugging complex logic, only to realize hours later... you never actually called the function. 🤦♂️ In Software Engineering, we often fall into the trap of over-engineering the problem before validating the basics. We look for deep architectural failures when the reality is often a missing semicolon, a typo, or an uncalled function. Two strategies I use to avoid this specific flavor of burnout: Occam’s Razor for Code: Always assume the error is simple and user-generated before assuming it’s complex and systemic. Rubber Duck Debugging: explain your code line-by-line out loud. You’ll usually catch the "obvious" mistake before you finish the sentence. Sometimes, the most sophisticated problem-solving tool is just slowing down. What is the most embarrassing "simple" bug that kept you stuck for hours? Let’s comfort each other in the comments. 👇 #SoftwareEngineering #DeveloperLife #Coding #Debugging #TechHumor #ProblemSolving #WebDevelopment #Python #JavaScript #TechCommunity #ProgrammerLife #DevOps #CodeNewbie #SeniorDev

🏎️ Day 4 of The Product-Engineer-Max Challenge > Reflections: - Rust’s ownership model replaces garbage collection with compile-time guarantees - Memory safety is enforced before runtime, not patched after crashes - Ownership is not about syntax, it’s about clear responsibility for memory - Stack memory is fast, predictable, and scope-bound - Heap memory is flexible but requires explicit ownership tracking - Rust decides safety based on where data lives (stack vs heap) - Every value has exactly one owner at any point in time - Moves are the default; copies are explicit and intentional - The Copy trait explains why primitives behave differently than String - Passing values to functions is conceptually the same as assignment - Ownership can be transferred, borrowed immutably, or borrowed mutably - Borrowing lets you use data without owning it - Immutable references are unlimited; mutable references are exclusive - Rust prevents data races by design, not by convention - Reference scopes end at last use, not at block boundaries - Dangling pointers are impossible in safe Rust - The compiler rejects references to data that won’t live long enough - Lifetimes exist to prove validity, not to confuse developers - Slices solve the “index out of sync” problem - Returning a slice ties data and logic together safely - &str is more powerful than String for APIs - String literals are already slices - Designing functions around &str improves flexibility and correctness - Slices generalize beyond strings to arrays and collections Progress till Now: - LeetCode | 9q Mastery | https://lnkd.in/g46vGtfU - CodeForces | 12q Mastery | https://lnkd.in/gWcjPeaW - Whack-A-Mole | Java Game Dev | https://lnkd.in/giq8PCEy - C++ STL | https://lnkd.in/gVQzFMhm Coding_is_meditation #ProductEngineer #RustLang #SystemsProgramming #MemoryManagement #SoftwareEngineering #BackendDevelopment #EngineeringMindset #LearnBuildShip #CareerJourney #TechLeadership

Stop building "Big" projects until you've mastered the "Small" script. I used to think more features = more revenue. I was wrong. My most successful "product" this year wasn't a massive platform. It was a simple Python script that automated a single, annoying task for a client. The Math: Big Project: 400+ hours. High maintenance. Low ROI. Small Script: 3 hours. Zero maintenance. High ROI. In a world of bloated software, simplicity is a premium service. Build tools that save time, not tools that require more of it. Don't fall in love with the code. Fall in love with the problem. Sometimes, 50 lines of Python are more valuable than 50,000 lines of Full-stack development. #Efficiency #Programming #BuildInPublic #TechTrends

✏️ DSA Diary Day 14/100 📊➗: Solving LeetCode’s “Maximum Dot Product of Two Subsequences” 🚀✨ Today I worked on a Dynamic Programming + Recursion + Optimization problem on LeetCode 🔥👇 👉 Maximum Dot Product of Two Subsequences This problem is a great example of how DP helps handle complex choices efficiently 🧠💡 🔹 My Approach 🛠️🧠 I used Recursion + Memoization (Top-Down DP) 🔁👇 🔸 Step 1: Define DP State 📌 dp(i, j) = Maximum dot product using elements from nums1[i…] elements from nums2[j…] 🔸 Step 2: Choices at Each Step 🔍 At every (i, j) position, we have 4 options: 1️⃣ Take both elements & continue nums1[i] * nums2[j] + dp(i+1, j+1) 2️⃣ Take both & stop here nums1[i] * nums2[j] 3️⃣ Skip nums1[i] dp(i+1, j) 4️⃣ Skip nums2[j] dp(i, j+1) We take the maximum of all these options 🔝✨ 🔸 Step 3: Memoization 🧠 To avoid recomputation, I used a 2D memo array This reduces time complexity from exponential → O(n*m) 🚀 🔸 Why NEG_INF? ⚠️ To ensure at least one pair is selected, I returned a very small value when indices go out of bounds. 🔹 Key Learnings 📚✨ ✅ DP is perfect for subsequence + optimization problems ✅ Always consider all possible choices ✅ Memoization saves massive computation time ✅ Handling negative values carefully is crucial ✅ Clean recursion = clear logic 🧠💯 🔥 This problem felt like a perfect mix of: 📊 Arrays + 🔁 Recursion + 🧠 DP + 📈 Optimization #LeetCode 🚀 #Java ☕ #DSA 🧠 #DynamicProgramming 📊 #ProblemSolving 💡 #CodingChallenge 💻 #100DaysOfCode 🔥 #DSADiaryByRethanya ✨ #Recursion 🔁 #Memoization 🧩 #LearnInPublic 📢 #TechJourney 🚀

🚀 Day 36/100 of My LeetCode Challenge: Mastering Greedy & Dynamic Programming! Just solved LeetCode 3507: Minimum Pair Removal to Sort Array I – an interesting problem that beautifully blends greedy operations with dynamic programming thinking! 🧠 🔍 The Challenge: Given an array, repeatedly replace the adjacent pair with the minimum sum until the array becomes non-decreasing. Return the minimum number of such operations required. 💡 Key Insights: This isn't just about blindly following the operation description (selecting minimum sum pairs) The optimal solution requires recognizing this as a dynamic programming problem We need to find the minimum operations to partition the array into segments that can be merged while maintaining the non-decreasing property Each merge operation happens only when the left segment's sum exceeds the right segment's sum 🛠️ My Approach: Implemented a memoized DP solution that explores all possible partition points For each possible split position, recursively solve left and right subarrays Add a merge cost when the left segment's sum is greater than the right segment's sum Use memoization to avoid redundant computations for O(n²) time complexity 📊 Performance: Runtime: 66.06% Memory: 44.62 MB Beats: 32.51% of Java submissions 🎯 Why This Matters: This problem is a great example of how: Problem understanding matters more than just following instructions Dynamic programming can elegantly solve what seems like a greedy problem Memoization dramatically improves performance for recursive solutions Real-world scenarios often require transforming problem statements into solvable patterns ✨ The Journey Continues: Every day of this 100-day challenge brings new learning opportunities. Today reinforced that sometimes the direct approach isn't optimal, and we need to think one level deeper about the underlying structure of the problem. #LeetCode #CodingChallenge #100DaysOfCode #ProblemSolving #DynamicProgramming #GreedyAlgorithms #Java #SoftwareEngineering #TechCareer #DeveloperJourney #Algorithm #DataStructures #CodingInterview #Programming