Optimized LeetCode Solution: Continuous Subarray Sum with Prefix Sums and Hashing

🚀 DSA Practice – Array Leaders Problem Today I solved an interesting array problem: Finding Leaders in an Array. 📌 Problem Statement: An element is called a leader if it is greater than or equal to all elements to its right side. The rightmost element is always a leader. 🧠 My Approach: Traverse the array from right to left Keep track of the maximum element seen so far If the current element is greater than or equal to the max, it is a leader Store the leaders and finally reverse the result ⚙️ Algorithm Insight: Start with the last element as the first leader Update max_right while traversing Append elements that satisfy the leader condition 📊 Complexity: Time Complexity: O(n) Auxiliary Space: O(1) (excluding output list) 💻 Language Used: Python Problems like this help improve array traversal logic and optimization thinking. #DSA #Python #ProblemSolving #CodingPractice #Arrays #PlacementPreparation

Tinkering with SKILLs and optimizing like crazy. Funnily enough, the optimizations are all about writing python scripts and minimizing the LLM's task to only the sliver that requires judgement. Also, I moved to running the SKILLs using Gemini 3 Flash instead of Claude Opus 4.6. So my SKILLs is running 5x faster and at 10-20% the cost. Conclusion: SKILLs is another layer for abstracting and democratizing coding, and the smaller models are good enough. #AI #Agentic #SKILL #Gemini #Claude #python

Group Anagrams: Frequency Signature Beats Sorting for Keying Sorting each string as key costs O(k log k) per string. Character frequency array is O(k) and creates same signature for anagrams. Fixed 26-element array converted to tuple serves as hashable HashMap key — cleaner, faster grouping. Frequency as Key: Character counts uniquely identify anagram groups. Tuple conversion enables using array as dict key (lists aren't hashable). This pattern applies to document clustering, duplicate detection. Time: O(n × k) vs O(n × k log k) with sorting | Space: O(n × k) #FrequencySignature #HashMap #Anagrams #KeyOptimization #Python #AlgorithmDesign #SoftwareEngineering

🚀 DSA Practice – Check if Array is Sorted Today I worked on a simple yet important problem: Checking whether an array is sorted. 📌 Problem Statement: Given an array, determine if it is sorted in non-decreasing order. 🧠 My Approach: Traverse the array from left to right Compare each element with the next one If any element is greater than the next, the array is not sorted Otherwise, it is sorted ✅ ⚙️ Key Insight: A single pass through the array is enough — no need for sorting or extra space! 📊 Complexity: Time Complexity: O(n) Space Complexity: O(1) 💻 Language Used: Python ✨ Why this matters? This is a foundational concept used in: Optimizing algorithms Validating inputs Building efficient solutions in real-world problems Small problems like this help build strong fundamentals for coding interviews 💪 #DSA #Python #CodingPractice #Arrays #ProblemSolving #PlacementPreparation

Array Concatenation: Explicit Loops Over Language Shortcuts for Clarity Python offers nums * 2 for array doubling, but explicit nested loops reveal the operation's structure clearly. This approach shows understanding of the underlying iteration (repeat twice, append each element) versus relying on language-specific syntax. Interview Signal: Demonstrates algorithmic thinking over language feature knowledge. Generalizes trivially to n copies (range(n)) or transformations during copy. Production code would use nums + nums for performance. Time: O(n) | Space: O(n) #ExplicitAlgorithms #CodeClarity #ArrayOperations #InterviewStrategy #Python #AlgorithmDesign #SoftwareEngineering

💡 Solved Group Anagrams today! 🧠 Key idea: Instead of comparing strings, I used a frequency array (size 26) to create a unique key for each word. ⚠️ Catch in constraints: All characters are lowercase English letters, which allows us to use a fixed-size array of 26 for efficient hashing. 🚀 Result: Achieved O(n · k) time complexity. Great example of converting a comparison problem into a hashing problem! #LeetCode #DSA #Algorithms #Python #CodingInterview

✅ Day 14 of #DSAPrep >Topic: Time Complexity & Space Complexity > Concept: Big-O Notation Today I focused on understanding how to analyze the efficiency of algorithms. Learned how time complexity represents how the execution time grows with input size, and space complexity shows how much memory an algorithm uses. Covered key complexities: O(1) → Constant Time O(log n) → Logarithmic O(n) → Linear O(n²) → Quadratic Also understood how to compare different approaches and choose the most optimal solution. > Time Complexity: Measures performance > Space Complexity: Measures memory usage #DSAPrep #Algorithms #Python #ProblemSolving #CodingJourney #BigO

Anagram Validation: Frequency Counting Beats Sorting Sorting both strings enables O(n log n) comparison. HashMaps reduce this to O(n) by counting character frequencies — anagrams have identical distributions. Early length check eliminates mismatches instantly. Frequency Pattern: Character counting appears in: group anagrams, substring problems, permutation validation. .get(key, 0) avoids KeyError exceptions cleanly. Time: O(n) | Space: O(1) — max 26 chars #HashMap #FrequencyCounting #Anagrams #StringAlgorithms #Python #AlgorithmOptimization #SoftwareEngineering

DSA Tip: Bubble Sort If you’re comparing every pair of items in a list manually to sort them… there’s a better way. Use Bubble Sort. It repeatedly swaps adjacent elements to “bubble” the largest (or smallest) values to the end of the list. No complicated indexing. No extra storage. Just step-by-step, reliable sorting. Insight: Even simple algorithms like Bubble Sort teach us how repeated small steps lead to order and efficiency. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #Python #BubbleSort #CodingTips #LearnToCode

Unpopular opinion: I don't aim for 100% test coverage. Instead, I write tests where it matters most — after something breaks in production. Every incident becomes a test case. Every edge case that slipped through becomes a regression test. After 10 years, this approach has given me: - A test suite that catches real bugs, not theoretical ones - 40% less time writing tests nobody triggers - Confidence where it actually counts Perfect coverage is a vanity metric. Meaningful coverage is a survival strategy. How do you decide what to test? #SoftwareTesting #BackendEngineering #Python #ProductionEngineering