Problem-solve-study/code-store
Apr 2025 – Aug 2025
4 Months active
Languages Used
Java
Technical Skills
AlgorithmAlgorithmsCompetitive ProgrammingData StructuresHashMapPriorityQueue
kimyevin17
PROFILE
Kimyevin17
Over four months, Yevin Kim developed and maintained a suite of algorithmic solutions in the Problem-solve-study/code-store repository, focusing on efficient problem-solving modules for competitive programming and interview preparation. He implemented features such as dynamic programming for path feasibility, greedy scheduling algorithms, and grid-based simulations, using Java and core data structures like HashMap and PriorityQueue. His work addressed challenges including resource allocation, interval scheduling, and stateful simulations, emphasizing correctness and maintainability. By designing input-driven interfaces and modular code, Yevin enabled rapid prototyping and scalable testing, demonstrating depth in algorithm implementation and a strong grasp of both simulation and optimization techniques.
Overall Statistics
Feature vs Bugs
95%Features
Repository Contributions
22Total
Bugs
1
Commits
22
Features
20
Lines of code
793
Activity Months4
Your Network
15 people
Work History
August 2025
3 Commits • 3 Features
Aug 1, 2025August 2025 — Problem-solve-study/code-store: Deliverables focused on core algorithms and a grid-based simulation to enable scalable problem-solving workflows. Implementations emphasize correctness, efficiency, and traceability with input-driven interfaces and clean code structure. Key features delivered: - BOJ 1817 - Box distribution into boxes: capacity-constrained assignment algorithm that computes the minimum number of boxes for a given book count; commits include fa99098d228136bad49db602c9b90cd3c895cf49. - BOJ 14469 - Interval scheduling to minimize completion time: interval-based scheduling via sorting by start times and greedy progression to achieve earliest finish; commits include 6ea28b985131a68cb3a1a5f07b18a8eafd5caec6. - Virus spread simulation on a grid: modeled interactions and transformations of two virus types, tracking remaining uninfected area and collision zones; commit 8a2993813d9318dc1a4e96c80de4cfcddf9e9243. Notable outcomes and impact: - Improved problem-solving capabilities within the code-store, enabling faster prototyping of packaging optimization, scheduling strategies, and simulation scenarios. - Enhanced code quality and maintainability through modular design and clear input-driven interfaces. Technologies/skills demonstrated: - Algorithm design (greedy, scheduling), sorting - Grid-based simulation and state tracking - Robust input handling and traceable commits Bugs fixed in this period: - No explicit bug fixes logged; work focused on feature delivery and correctness improvements within the algorithms (box distribution, interval scheduling, grid simulation).
3 Commits • 3 Features
Aug 1, 2025August 2025 — Problem-solve-study/code-store: Deliverables focused on core algorithms and a grid-based simulation to enable scalable problem-solving workflows. Implementations emphasize correctness, efficiency, and traceability with input-driven interfaces and clean code structure. Key features delivered: - BOJ 1817 - Box distribution into boxes: capacity-constrained assignment algorithm that computes the minimum number of boxes for a given book count; commits include fa99098d228136bad49db602c9b90cd3c895cf49. - BOJ 14469 - Interval scheduling to minimize completion time: interval-based scheduling via sorting by start times and greedy progression to achieve earliest finish; commits include 6ea28b985131a68cb3a1a5f07b18a8eafd5caec6. - Virus spread simulation on a grid: modeled interactions and transformations of two virus types, tracking remaining uninfected area and collision zones; commit 8a2993813d9318dc1a4e96c80de4cfcddf9e9243. Notable outcomes and impact: - Improved problem-solving capabilities within the code-store, enabling faster prototyping of packaging optimization, scheduling strategies, and simulation scenarios. - Enhanced code quality and maintainability through modular design and clear input-driven interfaces. Technologies/skills demonstrated: - Algorithm design (greedy, scheduling), sorting - Grid-based simulation and state tracking - Robust input handling and traceable commits Bugs fixed in this period: - No explicit bug fixes logged; work focused on feature delivery and correctness improvements within the algorithms (box distribution, interval scheduling, grid simulation).
August 2025
July 2025
8 Commits • 6 Features
Jul 1, 2025July 2025 focused delivery in Problem-solve-study/code-store with high-impact algorithmic features and essential maintenance, elevating problem-solving capabilities and code quality. Key features delivered introduced robust dynamic programming, combinatorial optimization, and bit-manipulation techniques across multiple BOJ problems, alongside targeted documentation improvements to enhance maintainability and clarity. This month established reusable patterns for energy-based path feasibility, star-placement optimization, bit-level computations, and subsequence optimization, while clarifying stack vs. queue behavior in core code.
July 2025
8 Commits • 6 Features
Jul 1, 2025July 2025 focused delivery in Problem-solve-study/code-store with high-impact algorithmic features and essential maintenance, elevating problem-solving capabilities and code quality. Key features delivered introduced robust dynamic programming, combinatorial optimization, and bit-manipulation techniques across multiple BOJ problems, alongside targeted documentation improvements to enhance maintainability and clarity. This month established reusable patterns for energy-based path feasibility, star-placement optimization, bit-level computations, and subsequence optimization, while clarifying stack vs. queue behavior in core code.
May 2025
7 Commits • 7 Features
May 1, 2025May 2025 monthly summary for Problem-solve-study/code-store: Delivered 7 algorithmic features across problem-solving modules with tangible business value and performance improvements. Highlights include a ticket-exchange simulator, DFS-based parentheses generator, bitwise rounds calculator, Plum Harvest DP, and a department-interview time aggregator. No explicit bug fixes reported in this month; focus remained on delivering robust, reusable problem-solving solutions that facilitate faster iteration and scalable testing. Technologies demonstrated span DFS/backtracking, dynamic programming, bitwise optimization, hash-set filtering, and sorting-based scheduling, reinforcing our capability to build efficient, maintainable algorithms for competitive programming and interview preparation.
7 Commits • 7 Features
May 1, 2025May 2025 monthly summary for Problem-solve-study/code-store: Delivered 7 algorithmic features across problem-solving modules with tangible business value and performance improvements. Highlights include a ticket-exchange simulator, DFS-based parentheses generator, bitwise rounds calculator, Plum Harvest DP, and a department-interview time aggregator. No explicit bug fixes reported in this month; focus remained on delivering robust, reusable problem-solving solutions that facilitate faster iteration and scalable testing. Technologies demonstrated span DFS/backtracking, dynamic programming, bitwise optimization, hash-set filtering, and sorting-based scheduling, reinforcing our capability to build efficient, maintainable algorithms for competitive programming and interview preparation.
May 2025
April 2025
4 Commits • 4 Features
Apr 1, 2025April 2025 – Problem-solve-study/code-store: Delivered four feature enhancements with a focus on efficient data processing, scalable analytics, and validation logic. Achievements were achieved through practical data structures and algorithms, resulting in faster queries, robust aggregations, and improved decision-making support for product features.
April 2025
4 Commits • 4 Features
Apr 1, 2025April 2025 – Problem-solve-study/code-store: Delivered four feature enhancements with a focus on efficient data processing, scalable analytics, and validation logic. Achievements were achieved through practical data structures and algorithms, resulting in faster queries, robust aggregations, and improved decision-making support for product features.
Activity
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Quality Metrics
Correctness98.6%
Maintainability81.8%
Architecture76.4%
Performance90.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
AlgorithmAlgorithm ImplementationAlgorithmsBacktrackingBit ManipulationBrute ForceCompetitive ProgrammingDFSData StructuresDynamic ProgrammingHashMapJavaPriorityQueueProblem SolvingQueue
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