AlgoriGym-study/AlgoriGym
Apr 2025 – Feb 2026
9 Months active
Languages Used
JavaMarkdownSQLPython
Technical Skills
AlgorithmAlgorithm StudyBreadth-First SearchData StructuresGame LogicHashMap
dbscjstk3
PROFILE
Dbscjstk3
Over nine months, dbscjstk3 developed and maintained a diverse suite of algorithmic features and problem-solving utilities for the AlgoriGym-study/AlgoriGym repository. They delivered end-to-end solutions for game simulations, scheduling optimizations, and data analytics, applying Java, Python, and SQL to address challenges such as grid-based pathfinding, dynamic programming, and flexible work schedule reconciliation. Their work included modular code refactoring, robust input handling, and the creation of reusable algorithmic modules, supporting both maintainability and rapid iteration. By integrating data analysis workflows and enhancing problem templates, dbscjstk3 enabled scalable content expansion and improved the platform’s value for learners and contributors alike.
Overall Statistics
Feature vs Bugs
96%Features
Repository Contributions
34Total
Bugs
1
Commits
34
Features
23
Lines of code
2,298
Activity Months9
Your Network
12 people
Work History
February 2026
3 Commits • 3 Features
Feb 1, 2026February 2026 — AlgoriGym (AlgoriGym-study/AlgoriGym). Focused feature delivery across problem-solving, data analytics, and scheduling optimization. Key work delivered for AlgoriGym includes three core features with targeted commits. No major bugs fixed this month; maintenance and refactors were performed to stabilize the release baseline. These efforts collectively improve user value by enabling faster practice cycles, richer analytics, and more efficient scheduling, contributing to better learning outcomes and platform scalability.
3 Commits • 3 Features
Feb 1, 2026February 2026 — AlgoriGym (AlgoriGym-study/AlgoriGym). Focused feature delivery across problem-solving, data analytics, and scheduling optimization. Key work delivered for AlgoriGym includes three core features with targeted commits. No major bugs fixed this month; maintenance and refactors were performed to stabilize the release baseline. These efforts collectively improve user value by enabling faster practice cycles, richer analytics, and more efficient scheduling, contributing to better learning outcomes and platform scalability.
February 2026
January 2026
7 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for AlgoriGym study: Focused on code quality, feature delivery, and data-driven capabilities to improve maintainability, user-facing value, and operational insight. Delivered modular architecture improvements and quantitative analytics support that enable faster iteration and better decision-making.
January 2026
7 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for AlgoriGym study: Focused on code quality, feature delivery, and data-driven capabilities to improve maintainability, user-facing value, and operational insight. Delivered modular architecture improvements and quantitative analytics support that enable faster iteration and better decision-making.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025: Delivered the Flexible Work Schedule Not-Late Calculator feature for AlgoriGym that computes the number of employees not late by reconciling work schedules with time logs. This automated attendance metric supports flex-time policy compliance, enables real-time staffing insights, and reduces manual reconciliation effort. No major bugs were reported; minor improvements focused on data integrity and edge-case handling. Overall impact includes improved accuracy of on-time metrics, faster HR reporting, and data-driven decision-making for resource allocation. Technologies/skills demonstrated include data processing, scheduling logic, time-log reconciliation, algorithmic problem solving, and Git-based collaboration.
1 Commits • 1 Features
Nov 1, 2025November 2025: Delivered the Flexible Work Schedule Not-Late Calculator feature for AlgoriGym that computes the number of employees not late by reconciling work schedules with time logs. This automated attendance metric supports flex-time policy compliance, enables real-time staffing insights, and reduces manual reconciliation effort. No major bugs were reported; minor improvements focused on data integrity and edge-case handling. Overall impact includes improved accuracy of on-time metrics, faster HR reporting, and data-driven decision-making for resource allocation. Technologies/skills demonstrated include data processing, scheduling logic, time-log reconciliation, algorithmic problem solving, and Git-based collaboration.
November 2025
September 2025
4 Commits • 4 Features
Sep 1, 2025September 2025: Key feature deliveries in AlgoriGym include (1) Algorithm Challenge Set Update: removed Baby Shark and Green Clothes; added YHS0042 and YHS0043. (2) Pathfinding on Grid with One-Wall Break (BFS): implemented BFS-based pathfinding with one-wall break; includes Node class and 3D visited state tracking. (3) Daily Algorithm Study Problems: Middle Eight Average and Maximum. (4) YHS0048: Min-Max from Space-Separated Numbers. These updates were accompanied by targeted commits to ensure traceability (see below). No major bugs were recorded this month; minor maintenance and refactoring occurred as part of feature work. Overall impact includes expanded learning content, enhanced problem-solving capabilities, and improved alignment with curriculum goals. Technologies and skills demonstrated include BFS pathfinding with constraint (one-wall break), custom Node class design, 3D visited state management, sorting-based statistics, and robust string parsing for min-max tasks. This work directly supports business value by increasing learner engagement, improving progression metrics, and enabling scalable problem-set curation.
September 2025
4 Commits • 4 Features
Sep 1, 2025September 2025: Key feature deliveries in AlgoriGym include (1) Algorithm Challenge Set Update: removed Baby Shark and Green Clothes; added YHS0042 and YHS0043. (2) Pathfinding on Grid with One-Wall Break (BFS): implemented BFS-based pathfinding with one-wall break; includes Node class and 3D visited state tracking. (3) Daily Algorithm Study Problems: Middle Eight Average and Maximum. (4) YHS0048: Min-Max from Space-Separated Numbers. These updates were accompanied by targeted commits to ensure traceability (see below). No major bugs were recorded this month; minor maintenance and refactoring occurred as part of feature work. Overall impact includes expanded learning content, enhanced problem-solving capabilities, and improved alignment with curriculum goals. Technologies and skills demonstrated include BFS pathfinding with constraint (one-wall break), custom Node class design, 3D visited state management, sorting-based statistics, and robust string parsing for min-max tasks. This work directly supports business value by increasing learner engagement, improving progression metrics, and enabling scalable problem-set curation.
August 2025
2 Commits • 2 Features
Aug 1, 20252025-08 monthly summary for AlgoriGym (AlgoriGym-study/AlgoriGym). Delivered two new feature sets and enhanced platform content, with a focus on business value and technical excellence. Key outcomes include the addition of Zelda's Labyrinth as a fully specified problem with input/output formats and sample tests, plus end-to-end solutions for three complementary problems, expanding practice catalog and problem-solving diversity.
2 Commits • 2 Features
Aug 1, 20252025-08 monthly summary for AlgoriGym (AlgoriGym-study/AlgoriGym). Delivered two new feature sets and enhanced platform content, with a focus on business value and technical excellence. Key outcomes include the addition of Zelda's Labyrinth as a fully specified problem with input/output formats and sample tests, plus end-to-end solutions for three complementary problems, expanding practice catalog and problem-solving diversity.
August 2025
July 2025
6 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for AlgoriGym-study/AlgoriGym: Delivered expansion of problem solutions with refactors for core problems (0030/0031) and added new topics including median finding in a stream, maximum teams based on relationships, priority queue printing order, graph-based DFS for friends/enemies, and sensor placement distance optimization. Also fixed a package declaration typo in PJE0029.java to improve build recognition and file organization. These efforts broaden problem coverage, improve maintainability, and reduce onboarding friction for contributors, enabling faster practice iterations and more robust examples for learners.
July 2025
6 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for AlgoriGym-study/AlgoriGym: Delivered expansion of problem solutions with refactors for core problems (0030/0031) and added new topics including median finding in a stream, maximum teams based on relationships, priority queue printing order, graph-based DFS for friends/enemies, and sensor placement distance optimization. Also fixed a package declaration typo in PJE0029.java to improve build recognition and file organization. These efforts broaden problem coverage, improve maintainability, and reduce onboarding friction for contributors, enabling faster practice iterations and more robust examples for learners.
June 2025
3 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for AlgoriGym-study/AlgoriGym: Delivered three feature-driven improvements focused on algorithm visualization, optimization, and problem-solving utilities. No major bugs documented for this period. Impact: enhanced user engagement through visual spiral pattern generation, data-driven discount experiments increasing potential sign-ups and purchases, and a reusable recursive solver for combinatorial problems. Demonstrated skills include algorithm design and implementation, recursion/backtracking, combinatorial optimization, input handling, and Git-based collaboration. Commits: f61458b86a9b4832b9abd574f21c49ddc691425b; ffe9467388af75003ffd05d5a720b4a906f5bc54; ff850b68a37f8209476550ece4cf71ea929ef9c0.
3 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for AlgoriGym-study/AlgoriGym: Delivered three feature-driven improvements focused on algorithm visualization, optimization, and problem-solving utilities. No major bugs documented for this period. Impact: enhanced user engagement through visual spiral pattern generation, data-driven discount experiments increasing potential sign-ups and purchases, and a reusable recursive solver for combinatorial problems. Demonstrated skills include algorithm design and implementation, recursion/backtracking, combinatorial optimization, input handling, and Git-based collaboration. Commits: f61458b86a9b4832b9abd574f21c49ddc691425b; ffe9467388af75003ffd05d5a720b4a906f5bc54; ff850b68a37f8209476550ece4cf71ea929ef9c0.
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05 for AlgoriGym-study/AlgoriGym: Focused on codebase maintainability and clarity with a targeted refactor. Key features delivered: Codebase Refactor: Package and Class Rename in the Java code to better reflect problem context; core logic unchanged. Major bugs fixed: none documented this month. Overall impact: improved code organization, easier onboarding, and smoother future refactors without risking regressions. Technologies/skills demonstrated: Java, package/class renaming, careful change management with version control best practices and a structured commit history.
May 2025
1 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05 for AlgoriGym-study/AlgoriGym: Focused on codebase maintainability and clarity with a targeted refactor. Key features delivered: Codebase Refactor: Package and Class Rename in the Java code to better reflect problem context; core logic unchanged. Major bugs fixed: none documented this month. Overall impact: improved code organization, easier onboarding, and smoother future refactors without risking regressions. Technologies/skills demonstrated: Java, package/class renaming, careful change management with version control best practices and a structured commit history.
April 2025
7 Commits • 4 Features
Apr 1, 2025Monthly work summary focusing on key accomplishments for 2025-04 (AlgoriGym-study/AlgoriGym). Delivered core game simulation features, practical algorithmic solutions, and a targeted codebase refactor, driving product capability and maintainability.
7 Commits • 4 Features
Apr 1, 2025Monthly work summary focusing on key accomplishments for 2025-04 (AlgoriGym-study/AlgoriGym). Delivered core game simulation features, practical algorithmic solutions, and a targeted codebase refactor, driving product capability and maintainability.
April 2025
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Quality Metrics
Correctness94.4%
Maintainability90.0%
Architecture89.4%
Performance88.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaMarkdownPythonSQL
Technical Skills
AlgorithmAlgorithm StudyAlgorithmsBacktrackingBreadth-First SearchCode RefactoringData StructuresDepth First SearchDequeDynamic ProgrammingGame LogicHashMapHeapJavaJava Development
Repositories Contributed To
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