Java Integer Storage: Binary, Octal, Hexadecimal Explained

Day 63 of #100DaysOfLeetCode 💻✅ Solved #2085. Count Common Words With One Occurrence problem in Java. Approach: • Iterated through each word in the first array • Avoided duplicate checks by ensuring each word is processed only once • Counted occurrences of the current word in both arrays • If the word appears exactly once in both arrays, incremented the result • Returned the final count Performance: ✓ Runtime: 88 ms (Beats 7.45% submissions) ✓ Memory: 46.06 MB (Beats 93.07% submissions) Key Learning: ✓ Practiced handling duplicates and frequency counting ✓ Improved understanding of string comparison in arrays ✓ Learned importance of optimizing nested loop solutions Learning one problem every single day 🚀 #Java #LeetCode #DSA #Strings #Arrays #ProblemSolving #CodingJourney #100DaysOfCode

𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗝𝗮𝘃𝗮’𝘀 𝘃𝗮𝗿 Recently, I explored how Java introduced Local Variable Type Inference (var) in Java 10 and how it transformed the way developers write cleaner and more expressive code. Java has traditionally been known for its verbosity. With the introduction of var through Project Amber, the language took a major step toward modern programming practices—balancing conciseness with strong static typing. 𝗪𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: • var allows the compiler to infer types from initializers, reducing boilerplate without losing type safety. • It is not a keyword, but a reserved type name, ensuring backward compatibility. • Works only for local variables, not for fields, method parameters, or return types. • The inferred type is always static and compile-time resolved—no runtime overhead. • Powerful in handling non-denotable types, including anonymous classes and intersection types. Must be used carefully: • Avoid when the type is unclear from the initializer • Prefer when the initializer clearly reveals the type (e.g., constructors or factory methods) • Enhances readability only when the initializer clearly conveys the type. 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: var is not just about writing less code—it’s about writing clearer, more maintainable code when used correctly. The real skill lies in knowing when to use it and when not to. A special thanks to Syed Zabi Ulla sir at PW Institute of Innovation for their clear explanations and continuous guidance throughout this topic. #Java #Programming #SoftwareDevelopment #CleanCode #Java10 #Developers #LearningJourney

Day 64 of #100DaysOfLeetCode 💻✅ Solved #724. Find Pivot Index problem in Java. Approach: • Calculated the total sum of the array • Maintained a variable left to track left sum • For each index, computed right sum as total - left - nums[i] • Compared left and right sums • If both are equal, returned the index as pivot • Updated left sum by adding current element Performance: ✓ Runtime: 1 ms (Beats 98.68% submissions) 🚀 ✓ Memory: 47.58 MB (Beats 32.00% submissions) Key Learning: ✓ Learned prefix sum technique for efficient calculations ✓ Practiced reducing time complexity from O(n²) to O(n) ✓ Strengthened understanding of array traversal and sum logic Learning one problem every single day 🚀 #Java #LeetCode #DSA #Arrays #PrefixSum #ProblemSolving #CodingJourney #100DaysOfCode

Solved LeetCode 17 – Letter Combinations of a Phone Number using backtracking in Java. Approach: Mapped each digit (2–9) to its corresponding characters using a simple array for O(1) access. Then used backtracking to build combinations digit by digit. For every digit: Pick each possible character Append → explore next digit → backtrack Key idea: Treat it like a tree of choices, where each level represents a digit and branches represent possible letters. Key learnings: Backtracking = build → explore → undo StringBuilder helps avoid unnecessary string creation Problems like this are about systematic exploration of choices Time Complexity: O(4^n * n) Space Complexity: O(n) recursion stack + output Consistent DSA practice is strengthening pattern recognition day by day. #Java #DSA #Backtracking #LeetCode #CodingInterview #SoftwareEngineering

Day 90 of #100DaysOfLeetCode 💻✅ Solved #42. Trapping Rain Water problem in Java. Approach: • Used Two Pointer technique • Maintained leftMax and rightMax boundaries • Calculated trapped water based on smaller side • Accumulated water while traversing Performance: ✓ Runtime: 0 ms (Beats 100.00% submissions) 🚀 ✓ Memory: 47.70 MB (Beats 72.36% submissions) Key Learning: ✓ Mastered Two Pointer approach for complex problems ✓ Learned optimal water trapping strategy ✓ Improved understanding of boundary-based calculations Learning one problem every single day 🚀 #Java #LeetCode #DSA #TwoPointers #Arrays #ProblemSolving #CodingJourney #100DaysOfCode

Day 67 of #100DaysOfLeetCode 💻✅ Solved #91. Decode Ways problem in Java. Approach: • Checked if the input string is empty or starts with '0' • Initialized a DP array to store number of ways to decode up to each index • Used dp[i] = dp[i-1] if single digit is valid (1–9) • Added dp[i-2] if two-digit number is valid (10–26) • Returned dp[n] as the total number of decoding ways Performance: ✓ Runtime: 1 ms (Beats 99.90%) ✓ Memory: 41.5 MB (Beats 55%) Key Learning: ✓ Practiced dynamic programming with strings ✓ Strengthened understanding of single vs double digit constraints ✓ Reinforced how to build DP solutions incrementally Learning one problem every single day 🚀 #Java #LeetCode #DSA #DynamicProgramming #ProblemSolving #CodingJourney #100DaysOfCode

Day 44-What I Learned In a Day(JAVA) Today I revised pattern programming in Java to strengthen my core logic and understanding of loops. What I practiced: • Star patterns • Number patterns • Pyramid patterns • Inverted patterns • Nested loop logic Pattern programming helped me improve: • Loop control (for/while) • Logical thinking • Understanding of rows & columns Every pattern I solve makes my logic stronger step by step. Consistency is the key #Java #CodingJourney #PatternProgramming #Learning #StudentDeveloper

🚀 Day 51 of My Java DSA Journey Today was a mix of Dynamic Programming 💡 Problem I solved: 📌 Longest Increasing Subsequence (LIS) • Used Dynamic Programming • Built a dp[] array to track longest subsequence ending at each index • Compared all previous elements to extend the sequence 🔍 Key idea: For each element: Find all smaller previous elements and extend the longest subsequence. ⚡ DP Relation: dp[i] = max(dp[j] + 1) where j < i and nums[i] > nums[j] 🎯 Takeaway: Dynamic Programming helps break complex problems into smaller subproblems. #Day51 #90DaysOfCoding #Java #DSA #DynamicProgramming #LIS #leetcode

Day 70 of #100DaysOfLeetCode 💻✅ Solved #3136. Valid Word problem in Java. Approach: • Checked if the word length is at least 3 • Traversed each character in the string • Ensured all characters are either letters or digits • Checked for presence of at least one vowel • Checked for presence of at least one consonant • Returned true only if all conditions are satisfied Performance: ✓ Runtime: 1 ms (Beats 99.62% submissions) 🚀 ✓ Memory: 43.08 MB (Beats 93.16% submissions) Key Learning: ✓ Practiced string validation with multiple conditions ✓ Learned efficient use of built-in character functions ✓ Strengthened logic building for edge case handling Learning one problem every single day 🚀 #Java #LeetCode #DSA #Strings #ProblemSolving #CodingJourney #100DaysOfCode

Day 66 of #100DaysOfLeetCode 💻✅ Solved #3427. Sum of Variable Length Subarrays problem in Java. Approach: • Iterated through each index of the array • Determined the starting index using i - nums[i] • Ensured the start index does not go below 0 • Calculated sum of elements from start to current index i • Added each subarray sum to the total Performance: ✓ Runtime: 1 ms (Beats 99.90% submissions) ✓ Memory: 45.22 MB (Beats 56.30% submissions) Key Learning: ✓ Practiced handling variable-length subarrays ✓ Improved understanding of index-based range calculations ✓ Strengthened nested loop logic for array problems Learning one problem every single day 🚀 #Java #LeetCode #DSA #Arrays #PrefixSum #ProblemSolving #CodingJourney #100DaysOfCode