AlgoriGym-study/AlgoriGym
Jun 2025 – Oct 2025
5 Months active
Languages Used
JavaMarkdown
Technical Skills
AlgorithmAlgorithm DesignAlgorithmsBacktrackingData StructuresDijkstra's Algorithm
marinrim23
PROFILE
Marinrim23
Marin Rim developed a robust suite of algorithmic solutions and simulations for the AlgoriGym-study/AlgoriGym repository, focusing on competitive programming and educational modules. Over five months, Marin implemented features such as grid-based simulations, dynamic programming, and graph traversal, using Java and Markdown for both code and documentation. The work included end-to-end solutions for problems like Snake Game simulation, Baby Shark grid navigation, and optimization challenges involving Dijkstra’s algorithm and greedy strategies. Marin emphasized code quality through modular design, clear documentation, and incremental commits, resulting in a maintainable, extensible codebase that accelerates onboarding and supports rapid prototyping of algorithmic challenges.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
34Total
Bugs
2
Commits
34
Features
20
Lines of code
2,413
Activity Months5
Your Network
12 people
Work History
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 performance summary for AlgoriGym: Delivered two Java-based algorithmic solutions expanding the repository’s competitive programming capabilities. Key features delivered include a DFS-based DP solution for Baekjoon 1005 ACM Craft to compute the minimum construction time by modeling dependencies as a graph and identifying the critical path, and a sorting-based greedy solution for Baekjoon 1202 Jewelry Thief to maximize value carried via a priority-queue approach. No major bugs reported this month; focus was on robust implementation, code quality, and maintainability. The work enhances problem-solving coverage, provides reusable algorithm patterns, and supports faster onboarding for similar tasks. Technologies demonstrated include Java, graph modeling, DFS-based dynamic programming, sorting, and priority queue-driven greedy algorithms. These contributions deliver business value by enabling faster, reliable solutions for optimization problems and strengthening the codebase for future extensions.
2 Commits • 2 Features
Oct 1, 2025October 2025 performance summary for AlgoriGym: Delivered two Java-based algorithmic solutions expanding the repository’s competitive programming capabilities. Key features delivered include a DFS-based DP solution for Baekjoon 1005 ACM Craft to compute the minimum construction time by modeling dependencies as a graph and identifying the critical path, and a sorting-based greedy solution for Baekjoon 1202 Jewelry Thief to maximize value carried via a priority-queue approach. No major bugs reported this month; focus was on robust implementation, code quality, and maintainability. The work enhances problem-solving coverage, provides reusable algorithm patterns, and supports faster onboarding for similar tasks. Technologies demonstrated include Java, graph modeling, DFS-based dynamic programming, sorting, and priority queue-driven greedy algorithms. These contributions deliver business value by enabling faster, reliable solutions for optimization problems and strengthening the codebase for future extensions.
October 2025
September 2025
16 Commits • 13 Features
Sep 1, 20252025-09 Monthly Summary — AlgoriGym-study/AlgoriGym Key focus: deliver end-to-end algorithmic capabilities, improve code quality, and document complex problems to accelerate onboarding and future contribution. The month combined feature implementations, graph/DSP planning, grid-based simulations, and thorough documentation, with an emphasis on reliable, reusable components and measurable business value. Key features delivered - Baby Shark grid simulation (shark growth and movement): grid navigation, edible fish consumption, growth logic, and total time calculation. Commit: 7a22e5cf9727cb6dfdf65401ebb8033646baeee5. - Dijkstra pathfinding for Green-Clad Kid problem (Baekjoon 4485): minimum cost path on a grid supporting multiple test cases until input 0. Commit: ebb3910a61593f79f7b2fb11433ffccd7097e1df. - Largest number after removing digits (sliding window): generate the largest possible number by selecting digits within a sliding window while removing k digits. Commit: 48a3f17359faa250d7eb997de7faa284366f351f. - Snake game simulation with turns: grid-based apples, growth, turns, and termination conditions. Commit: b744715bd17836e89e921eab4ac23b6d41cbe934. - Break Down Walls and Move documentation: Markdown documentation detailing input/output formats and examples for the Break Down Walls algorithm. Commit: 59600dbfaee9c79f6a12401d50fc1d4133d2893d. - DFS for maximum unique alphabets (initial and enhanced): exploring grid to maximize unique alphabets with backtracking. Commits: 0277ec4ed63d46455dc1ba0eccccc3c3624d937c; 885fbd4061416af0417da3489375095dc58d96b5; ca9bba84d85db9f8c2ee268cb2aee2c3057cfb31. - ACM Craft (DP and DFS) — dynamic programming with dependencies to minimize construction time. Commit: 31b363b25634c51c65cb7e0ade206df634015ce9. - Sugar Delivery (BOJ 2839) — DP-based minimization of bags for a given weight. Commit: 7c7e4886e9fef953885303265dc8739cf4a59db8. - 1,2,3 Add DP solution (BOJ 9095) — counting ways to sum to N. Commit: 66f4614146c1ea51c39a24fcb20566696665c7b7. - 2xN tiling DP solution (BOJ 11726) — DP approach for tiling. Commit: d1a14b261b43eedf052e62f4e9a8a570c110b301. - KMR0045_2 — binary search for time gap to minimize processing time within constraints. Commit: cabcbbc8a34392fe4657cc09e07147c9fb059b94. - KMR0045.java — added explanatory comments to clarify tricky logic for readability. Commit: fb3daf33aff57a03a619907aa13c816f2ba63b41. - Break Down Walls and Move — additional documentation and problem breakdown associated with problem-solving library expansion. Major bugs fixed - Removed all existing code from KMR0044.java to reset the file and avoid broken references. Commit: bd5a7eabff325dbea72a9d730043d3b0ac7c1745. - Cleanup of blank lines in KMR0040_2 and KMR0044 to remove non-functional whitespace and improve readability. Commit: 9a727faae28aa8f7833a4c9a4f12fdc05f097c29. Overall impact and accomplishments - Expanded a versatile algorithm library across DFS, DP, graph algorithms, and grid-based simulations, enabling faster solution framing for competitive problems and real-world grid problems. - Improved code quality, readability, and maintainability through targeted refactors, comments, and documentation, reducing onboarding time for new contributors. - Delivered end-to-end problem-solving artifacts, including working implementations, testable code paths, and markdown docs that clarify input/output formats and edge cases for future reuse. - Demonstrated end-to-end development discipline: feature work, bug remediation, code cleanup, and documentation within a single monthly cycle. Technologies, skills demonstrated - Java-based implementations with algorithmic patterns: DFS (with backtracking), DP (multi-problem patterns), Dijkstra’s algorithm, sliding window techniques, and grid simulations. - Problem decomposition, incremental delivery, and code documentation practices (markdown docs and inline comments). - Emphasis on maintainability, scalability, and reuse for a growing problem-solving library.
September 2025
16 Commits • 13 Features
Sep 1, 20252025-09 Monthly Summary — AlgoriGym-study/AlgoriGym Key focus: deliver end-to-end algorithmic capabilities, improve code quality, and document complex problems to accelerate onboarding and future contribution. The month combined feature implementations, graph/DSP planning, grid-based simulations, and thorough documentation, with an emphasis on reliable, reusable components and measurable business value. Key features delivered - Baby Shark grid simulation (shark growth and movement): grid navigation, edible fish consumption, growth logic, and total time calculation. Commit: 7a22e5cf9727cb6dfdf65401ebb8033646baeee5. - Dijkstra pathfinding for Green-Clad Kid problem (Baekjoon 4485): minimum cost path on a grid supporting multiple test cases until input 0. Commit: ebb3910a61593f79f7b2fb11433ffccd7097e1df. - Largest number after removing digits (sliding window): generate the largest possible number by selecting digits within a sliding window while removing k digits. Commit: 48a3f17359faa250d7eb997de7faa284366f351f. - Snake game simulation with turns: grid-based apples, growth, turns, and termination conditions. Commit: b744715bd17836e89e921eab4ac23b6d41cbe934. - Break Down Walls and Move documentation: Markdown documentation detailing input/output formats and examples for the Break Down Walls algorithm. Commit: 59600dbfaee9c79f6a12401d50fc1d4133d2893d. - DFS for maximum unique alphabets (initial and enhanced): exploring grid to maximize unique alphabets with backtracking. Commits: 0277ec4ed63d46455dc1ba0eccccc3c3624d937c; 885fbd4061416af0417da3489375095dc58d96b5; ca9bba84d85db9f8c2ee268cb2aee2c3057cfb31. - ACM Craft (DP and DFS) — dynamic programming with dependencies to minimize construction time. Commit: 31b363b25634c51c65cb7e0ade206df634015ce9. - Sugar Delivery (BOJ 2839) — DP-based minimization of bags for a given weight. Commit: 7c7e4886e9fef953885303265dc8739cf4a59db8. - 1,2,3 Add DP solution (BOJ 9095) — counting ways to sum to N. Commit: 66f4614146c1ea51c39a24fcb20566696665c7b7. - 2xN tiling DP solution (BOJ 11726) — DP approach for tiling. Commit: d1a14b261b43eedf052e62f4e9a8a570c110b301. - KMR0045_2 — binary search for time gap to minimize processing time within constraints. Commit: cabcbbc8a34392fe4657cc09e07147c9fb059b94. - KMR0045.java — added explanatory comments to clarify tricky logic for readability. Commit: fb3daf33aff57a03a619907aa13c816f2ba63b41. - Break Down Walls and Move — additional documentation and problem breakdown associated with problem-solving library expansion. Major bugs fixed - Removed all existing code from KMR0044.java to reset the file and avoid broken references. Commit: bd5a7eabff325dbea72a9d730043d3b0ac7c1745. - Cleanup of blank lines in KMR0040_2 and KMR0044 to remove non-functional whitespace and improve readability. Commit: 9a727faae28aa8f7833a4c9a4f12fdc05f097c29. Overall impact and accomplishments - Expanded a versatile algorithm library across DFS, DP, graph algorithms, and grid-based simulations, enabling faster solution framing for competitive problems and real-world grid problems. - Improved code quality, readability, and maintainability through targeted refactors, comments, and documentation, reducing onboarding time for new contributors. - Delivered end-to-end problem-solving artifacts, including working implementations, testable code paths, and markdown docs that clarify input/output formats and edge cases for future reuse. - Demonstrated end-to-end development discipline: feature work, bug remediation, code cleanup, and documentation within a single monthly cycle. Technologies, skills demonstrated - Java-based implementations with algorithmic patterns: DFS (with backtracking), DP (multi-problem patterns), Dijkstra’s algorithm, sliding window techniques, and grid simulations. - Problem decomposition, incremental delivery, and code documentation practices (markdown docs and inline comments). - Emphasis on maintainability, scalability, and reuse for a growing problem-solving library.
August 2025
2 Commits • 1 Features
Aug 1, 2025Monthly Summary for 2025-08: Delivered a fully functional Snake Game Simulation feature for AlgoriGym on an N x N grid, including apples, snake movement, boundary checks, and self-collision. The feature is configurable via user input for board size, number of apples, and turn directions, enabling flexible experimentation and validation of core mechanics. Commits documenting the work: 457e250fc86db594c20a77d5faa2c38fede64cff and 87be3bad8ba50a9fffc43c35f23f6e9d44ef6429. No major bugs fixed this month. Overall impact is an interactive, educational module that demonstrates grid-based state management, input handling, and collision logic, with clear path for future enhancements (scoring, AI, multi-snake scenarios). Technologies/skills demonstrated: grid-based state management, boundary handling, collision detection, and incremental, traceable commits.
2 Commits • 1 Features
Aug 1, 2025Monthly Summary for 2025-08: Delivered a fully functional Snake Game Simulation feature for AlgoriGym on an N x N grid, including apples, snake movement, boundary checks, and self-collision. The feature is configurable via user input for board size, number of apples, and turn directions, enabling flexible experimentation and validation of core mechanics. Commits documenting the work: 457e250fc86db594c20a77d5faa2c38fede64cff and 87be3bad8ba50a9fffc43c35f23f6e9d44ef6429. No major bugs fixed this month. Overall impact is an interactive, educational module that demonstrates grid-based state management, input handling, and collision logic, with clear path for future enhancements (scoring, AI, multi-snake scenarios). Technologies/skills demonstrated: grid-based state management, boundary handling, collision detection, and incremental, traceable commits.
August 2025
July 2025
10 Commits • 1 Features
Jul 1, 2025July 2025 monthly performance summary for AlgoriGym-study/AlgoriGym. Focused on feature delivery and codebase maturation to enable faster prototyping of algorithmic challenges and maintainable growth. No high-severity bugs reported this month; primary efforts centered on feature consolidation, refactoring, and knowledge consolidation to improve reuse and onboarding.
July 2025
10 Commits • 1 Features
Jul 1, 2025July 2025 monthly performance summary for AlgoriGym-study/AlgoriGym. Focused on feature delivery and codebase maturation to enable faster prototyping of algorithmic challenges and maintainable growth. No high-severity bugs reported this month; primary efforts centered on feature consolidation, refactoring, and knowledge consolidation to improve reuse and onboarding.
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly highlights for AlgoriGym study repository. Key features delivered across KMR algorithm study: three new problem solutions (KMR0026, KMR0027, KMR0029) covering linked-list manipulation, Dijkstra-based Hide and Seek, and a dynamic programming approach to maximize expression value; a recursive solution for a secret code decryption problem; and an emoticon discount rate optimization solution to maximize user sign-ups and total sales. All work is tracked in AlgoriGym-study/AlgoriGym with clear commit history for traceability.
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly highlights for AlgoriGym study repository. Key features delivered across KMR algorithm study: three new problem solutions (KMR0026, KMR0027, KMR0029) covering linked-list manipulation, Dijkstra-based Hide and Seek, and a dynamic programming approach to maximize expression value; a recursive solution for a secret code decryption problem; and an emoticon discount rate optimization solution to maximize user sign-ups and total sales. All work is tracked in AlgoriGym-study/AlgoriGym with clear commit history for traceability.
June 2025
Activity
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Quality Metrics
Correctness90.2%
Maintainability83.0%
Architecture81.2%
Performance81.2%
AI Usage20.6%
Skills & Technologies
Programming Languages
JavaMarkdown
Technical Skills
AlgorithmAlgorithm AnalysisAlgorithm DesignAlgorithm ImplementationAlgorithm StudyAlgorithmsBacktrackingBinary SearchBreadth-First Search (BFS)Code CleanupCode DocumentationCombinatoricsCompetitive ProgrammingData StructuresDepth First Search
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