DaleStudy/leetcode-study
Nov 2025 – Feb 2026
4 Months active
Languages Used
Python
Technical Skills
Python programmingalgorithm designalgorithm developmentbacktrackingdynamic programmingproblem solving
socow
PROFILE
Socow
Over four months, SoCoWJH developed a robust suite of algorithmic solutions for the DaleStudy/leetcode-study repository, focusing on core data structures and problem-solving patterns. Using Python, they implemented features such as dynamic programming for climbing stairs, backtracking for combination sums, and efficient algorithms for binary tree manipulation, including inversion and lowest common ancestor detection. Their work included optimized linked list operations, cycle detection, and string manipulation utilities, all designed for correctness, clarity, and maintainability. By emphasizing clean code, complexity analysis, and disciplined version control, SoCoWJH delivered a reliable foundation for algorithm practice and future extension without introducing regressions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
20Total
Bugs
0
Commits
20
Features
16
Lines of code
814
Activity Months4
Your Network
251 people
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 — DaleStudy/leetcode-study: Delivered the Lowest Common Ancestor in Binary Search Tree (BST) feature, leveraging BST properties for efficient navigation and correct ancestor return (LeetCode 235). The feature is backed by commits 728519d2fc0a9320af946794cff298f62fab240c and 5f668eaea619756ffadf650a44459500e65a1659. No major bugs reported for this repo in February 2026. Overall impact: enhances problem-solving speed for BST-based LeetCode challenges and provides a reusable LCA algorithm. Technologies/skills demonstrated include BST traversal/navigation, algorithm design, and disciplined version control through focused commits.
2 Commits • 1 Features
Feb 1, 2026February 2026 — DaleStudy/leetcode-study: Delivered the Lowest Common Ancestor in Binary Search Tree (BST) feature, leveraging BST properties for efficient navigation and correct ancestor return (LeetCode 235). The feature is backed by commits 728519d2fc0a9320af946794cff298f62fab240c and 5f668eaea619756ffadf650a44459500e65a1659. No major bugs reported for this repo in February 2026. Overall impact: enhances problem-solving speed for BST-based LeetCode challenges and provides a reusable LCA algorithm. Technologies/skills demonstrated include BST traversal/navigation, algorithm design, and disciplined version control through focused commits.
February 2026
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for DaleStudy/leetcode-study focusing on reliability, correctness, and performance of core data-structure algorithms. Implemented robust cycle detection for linked lists to prevent infinite loops, core binary tree operations (inversion) and a utility to verify tree identity, and efficient methods to identify a missing number in 0..n using both a summation approach and an XOR-based approach. These deliverables strengthen the practice problem library, improve runtime characteristics, and demonstrate solid algorithm design and version-controlled delivery.
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for DaleStudy/leetcode-study focusing on reliability, correctness, and performance of core data-structure algorithms. Implemented robust cycle detection for linked lists to prevent infinite loops, core binary tree operations (inversion) and a utility to verify tree identity, and efficient methods to identify a missing number in 0..n using both a summation approach and an XOR-based approach. These deliverables strengthen the practice problem library, improve runtime characteristics, and demonstrate solid algorithm design and version-controlled delivery.
December 2025
9 Commits • 8 Features
Dec 1, 2025December 2025: Delivered a robust algorithmic problem-solving library for the DaleStudy/leetcode-study repo, focusing on performance, robustness, and maintainability. Key features include DFS-based Binary Tree Maximum Depth with edge-case handling and documented time/space complexity; optimized Merge Two Sorted Linked Lists; single-pass Best Time to Buy and Sell Stock I; Group Anagrams; and Reverse Linked List with both iterative and recursive implementations plus an iterative-only follow-up. Impact includes improved candidate readiness, faster solution prototyping, and a stronger foundation for future extensions. Demonstrated skills in data structures (trees, linked lists, arrays), algorithm design (two-pointer, sliding window, hashmap-based approaches), complexity analysis, and clean code/documentation. Commits reflect a disciplined approach to incremental work and traceability.
9 Commits • 8 Features
Dec 1, 2025December 2025: Delivered a robust algorithmic problem-solving library for the DaleStudy/leetcode-study repo, focusing on performance, robustness, and maintainability. Key features include DFS-based Binary Tree Maximum Depth with edge-case handling and documented time/space complexity; optimized Merge Two Sorted Linked Lists; single-pass Best Time to Buy and Sell Stock I; Group Anagrams; and Reverse Linked List with both iterative and recursive implementations plus an iterative-only follow-up. Impact includes improved candidate readiness, faster solution prototyping, and a stronger foundation for future extensions. Demonstrated skills in data structures (trees, linked lists, arrays), algorithm design (two-pointer, sliding window, hashmap-based approaches), complexity analysis, and clean code/documentation. Commits reflect a disciplined approach to incremental work and traceability.
December 2025
November 2025
5 Commits • 4 Features
Nov 1, 2025November 2025 (DaleStudy/leetcode-study): Focused on expanding the algorithmic problem-solving toolkit with four core features, delivering reusable utilities for study material and code challenges. Implementations emphasize correctness, efficiency, and clarity to support learning throughput and code quality across the repository.
November 2025
5 Commits • 4 Features
Nov 1, 2025November 2025 (DaleStudy/leetcode-study): Focused on expanding the algorithmic problem-solving toolkit with four core features, delivering reusable utilities for study material and code challenges. Implementations emphasize correctness, efficiency, and clarity to support learning throughput and code quality across the repository.
Activity
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Quality Metrics
Correctness100.0%
Maintainability98.0%
Architecture99.0%
Performance98.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PythonPython programmingalgorithm designalgorithm developmentbacktrackingbinary tree manipulationbinary treesbreadth-first searchdata structuresdepth-first searchdynamic programminglinked listsproblem solvingrecursionrecursive programming
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