JeongEon8/AlgorithmStudyinGumi
Jan 2026 – Feb 2026
2 Months active
Languages Used
JavaMarkdown
Technical Skills
JavaJava programmingalgorithm designalgorithm developmentdata structuresdocumentation
Jhanoo
PROFILE
Jhanoo
JeongEon8 contributed to the JeongEon8/AlgorithmStudyinGumi repository by developing six algorithmic features over two months, focusing on Java and Markdown. Their work included implementing graph algorithms such as Prim’s Minimum Spanning Tree and Union-Find-based cycle detection, as well as dynamic programming solutions for hotel optimization and the RGB distance problem. They also addressed geometric computations using the shoelace formula and applied sliding window techniques for subarray constraints. Emphasizing correctness and documentation, JeongEon8 enhanced input/output handling and modular code structure, resulting in reusable modules that support competitive programming practice and efficient problem-solving without introducing major bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
6
Lines of code
881
Activity Months2
Your Network
12 people
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 — JeongEon8/AlgorithmStudyinGumi: Delivered two core algorithmic capabilities to expand the project toolkit and improve problem-solving efficiency. Key features: 1) DP RGB Distance Solver: dynamic programming solution for painting-cost minimization with the constraint that adjacent houses cannot share a color. 2) Graph Cycle Detection: Union-Find-based approach to determine the first cycle in a graph. These components are implemented with clean interfaces for reuse in teaching scenarios and competitive programming practice. Minor robustness improvements were added to input validation and edge-case handling across the modules. No major bugs were reported this month; stability was improved through targeted fixes and test scaffolding. Business value: accelerates problem-solving workflows, enables rapid prototyping of algorithmic solutions, and provides reusable, well-documented modules for learning and assessment. Technologies/skills demonstrated: dynamic programming, graph algorithms, Union-Find, algorithm design, modular coding, version control discipline, and problem decomposition.
2 Commits • 1 Features
Feb 1, 2026February 2026 — JeongEon8/AlgorithmStudyinGumi: Delivered two core algorithmic capabilities to expand the project toolkit and improve problem-solving efficiency. Key features: 1) DP RGB Distance Solver: dynamic programming solution for painting-cost minimization with the constraint that adjacent houses cannot share a color. 2) Graph Cycle Detection: Union-Find-based approach to determine the first cycle in a graph. These components are implemented with clean interfaces for reuse in teaching scenarios and competitive programming practice. Minor robustness improvements were added to input validation and edge-case handling across the modules. No major bugs were reported this month; stability was improved through targeted fixes and test scaffolding. Business value: accelerates problem-solving workflows, enables rapid prototyping of algorithmic solutions, and provides reusable, well-documented modules for learning and assessment. Technologies/skills demonstrated: dynamic programming, graph algorithms, Union-Find, algorithm design, modular coding, version control discipline, and problem decomposition.
February 2026
January 2026
7 Commits • 5 Features
Jan 1, 2026January 2026 — JeongEon8/AlgorithmStudyinGumi: Delivered a focused set of algorithmic features and documentation improvements, reinforcing learning, benchmarking, and problem-solving capabilities. Key features were implemented across documentation, graph algorithms, dynamic programming with tree queries, geometry, and sliding window techniques. No major bugs reported; emphasis on correctness and code quality. Impact: expanded algorithm coverage, clearer progress tracking, and a stronger foundation for competitive programming practice. Technologies demonstrated include graph algorithms (MST/Prim), DP and tree queries, geometry (shoelace), and two-pointer/sliding window approaches; committed code demonstrates end-to-end problem solving and documentation readiness.
January 2026
7 Commits • 5 Features
Jan 1, 2026January 2026 — JeongEon8/AlgorithmStudyinGumi: Delivered a focused set of algorithmic features and documentation improvements, reinforcing learning, benchmarking, and problem-solving capabilities. Key features were implemented across documentation, graph algorithms, dynamic programming with tree queries, geometry, and sliding window techniques. No major bugs reported; emphasis on correctness and code quality. Impact: expanded algorithm coverage, clearer progress tracking, and a stronger foundation for competitive programming practice. Technologies demonstrated include graph algorithms (MST/Prim), DP and tree queries, geometry (shoelace), and two-pointer/sliding window approaches; committed code demonstrates end-to-end problem solving and documentation readiness.
Activity
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture95.4%
Performance91.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaMarkdown
Technical Skills
JavaJava programmingalgorithm designalgorithm developmentdata structuresdocumentationdynamic programminginput/output handlingproblem solving
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