DaleStudy/leetcode-study
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
PythonPython programmingalgorithm designalgorithm developmentbinary treesdata structures
오왕택_T6102(Wang-Taek Oh)
PROFILE
오왕택_t6102(wang-taek Oh)
During March 2026, the developer contributed to the DaleStudy/leetcode-study repository by building an Algorithm Practice Library focused on core data structures and problem-solving patterns. Dale implemented eight algorithm solutions in Python, covering arrays, strings, dynamic programming, and binary trees, with each solution committed individually for traceability. The approach emphasized modular design and reusable templates, enabling efficient onboarding and benchmarking for future contributors. By establishing a scalable workflow for adding new problems, Dale ensured the codebase remains maintainable and extensible. The work demonstrated depth in algorithm design and development, providing a robust foundation for interview preparation and ongoing algorithmic practice.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
1
Lines of code
109
Activity Months1
Your Network
1376 peopleWork History
March 2026
8 Commits • 1 Features
Mar 1, 2026March 2026 summary for DaleStudy/leetcode-study: Focused on delivering a robust Algorithm Practice Library and establishing a scalable workflow for adding new problems. No major bugs were reported this month; all work centered on feature delivery and code quality. Delivered eight algorithm solutions across arrays, strings, dynamic programming, and trees, with individual commits for each problem: stock profit, two-sum, contains-duplicate, max subarray, climbing stairs, valid-anagram, product of array except self, and BST validation. These implementations provide a reusable foundation for interview prep and accelerate future onboarding and benchmarking.
8 Commits • 1 Features
Mar 1, 2026March 2026 summary for DaleStudy/leetcode-study: Focused on delivering a robust Algorithm Practice Library and establishing a scalable workflow for adding new problems. No major bugs were reported this month; all work centered on feature delivery and code quality. Delivered eight algorithm solutions across arrays, strings, dynamic programming, and trees, with individual commits for each problem: stock profit, two-sum, contains-duplicate, max subarray, climbing stairs, valid-anagram, product of array except self, and BST validation. These implementations provide a reusable foundation for interview prep and accelerate future onboarding and benchmarking.
March 2026
Activity
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Quality Metrics
Correctness97.6%
Maintainability97.6%
Architecture97.6%
Performance95.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PythonPython programmingalgorithm designalgorithm developmentbinary treesdata structuresdepth-first searchdynamic programmingproblem solving
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