DaleStudy/leetcode-study
Jun 2026 – Jul 2026
2 Months active
Languages Used
No languages
Technical Skills
AlgorithmsData StructuresJavaScriptBit ManipulationDynamic ProgrammingRegular Expressions
Juntae Hwang
PROFILE
Juntae Hwang
Contributed to the DaleStudy/leetcode-study repository by developing a suite of JavaScript solutions for LeetCode algorithmic problems over a two-month period. Focused on implementing features that addressed a range of topics including dynamic programming, bit manipulation, tree traversal, and regular expressions, each solution was organized as a standalone function within dedicated directories to promote modularity and code reuse. Emphasized clean interfaces, testability, and descriptive commit practices, while maintaining a consistent delivery cadence. No major bugs were reported or fixed, allowing attention to remain on code quality, documentation scaffolding, and establishing a scalable foundation for future automated evaluation and benchmarking.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
184
Activity Months2
Your Network
310 peopleWork History
July 2026
2 Commits • 1 Features
Jul 1, 2026July 2026 monthly performance summary for DaleStudy/leetcode-study. Delivered a curated set of LeetCode algorithm solutions in JavaScript, organized as standalone functions within dedicated problem directories. Implemented 5 solutions spanning arrays, dynamic programming, tree traversal, bit manipulation, and string processing. Work was tracked in weekly commits (Week02 and Week03). This refines the learning workflow, improves code reuse, and establishes a scalable foundation for future problem coverage, benchmarking, and automated evaluation. No high-severity bugs documented; focus remained on feature delivery and code quality improvements. Technologies demonstrated: JavaScript, modular design, directory-based problem organization, clean commit discipline.
2 Commits • 1 Features
Jul 1, 2026July 2026 monthly performance summary for DaleStudy/leetcode-study. Delivered a curated set of LeetCode algorithm solutions in JavaScript, organized as standalone functions within dedicated problem directories. Implemented 5 solutions spanning arrays, dynamic programming, tree traversal, bit manipulation, and string processing. Work was tracked in weekly commits (Week02 and Week03). This refines the learning workflow, improves code reuse, and establishes a scalable foundation for future problem coverage, benchmarking, and automated evaluation. No high-severity bugs documented; focus remained on feature delivery and code quality improvements. Technologies demonstrated: JavaScript, modular design, directory-based problem organization, clean commit discipline.
July 2026
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for DaleStudy/leetcode-study: Delivered five JavaScript LeetCode algorithm implementations, covering hash maps, dynamic programming, and set-based approaches, organized as individual files. The deliverables are five solutions: contains-duplicate, two-sum, top-k-frequent-elements, longest-consecutive-sequence, and house-robber, committed in a single changeset (97fa4170481d9e40603692dbabc049a8a758c9a2) with descriptive messages. No major bugs were reported this month. The work strengthens the repository as a practical reference for algorithm study and interview prep, improves code readability and reuse, and lays groundwork for automated testing and documentation.
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for DaleStudy/leetcode-study: Delivered five JavaScript LeetCode algorithm implementations, covering hash maps, dynamic programming, and set-based approaches, organized as individual files. The deliverables are five solutions: contains-duplicate, two-sum, top-k-frequent-elements, longest-consecutive-sequence, and house-robber, committed in a single changeset (97fa4170481d9e40603692dbabc049a8a758c9a2) with descriptive messages. No major bugs were reported this month. The work strengthens the repository as a practical reference for algorithm study and interview prep, improves code readability and reuse, and lays groundwork for automated testing and documentation.
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture80.0%
Performance86.6%
AI Usage60.0%
Skills & Technologies
Programming Languages
No languages yet
Technical Skills
AlgorithmsBit ManipulationData StructuresDynamic ProgrammingJavaScriptRegular ExpressionsTree Traversal
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