SSAFYnity/Job-Preparation-Challenge-4th
Jan 2025 – Jan 2025
1 Month active
Languages Used
Java
Technical Skills
AlgorithmBacktrackingCachingData StructuresDepth First SearchGreedy Algorithm
김인호
PROFILE
김인호
During January 2025, Linos developed the Coding Challenge Solutions Suite for the SSAFYnity/Job-Preparation-Challenge-4th repository, consolidating four user-facing algorithmic solutions into a single, reusable codebase. Working in Java, Linos applied skills in data structures, depth-first search, and caching to address problems such as LRU cache simulation, minimum-cost optimization, and maximum-sum grid traversal. Each solution was delivered with explicit performance benchmarks and traceable commits, ensuring maintainability and transparency. The suite was designed for demonstration and candidate evaluation, reflecting a focus on correctness, efficiency, and future reuse. This work demonstrates depth in both problem-solving and software engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
265
Activity Months1
Your Network
1012 people
Work History
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 — Delivered the Coding Challenge Solutions Suite for SSAFYnity/Job-Preparation-Challenge-4th, consolidating four user-facing algorithmic solutions: Cookie's Body Measurement, 1차캐시 LRU cache simulation, Meat Shop minimum-cost purchase optimization, and Tetromino maximum-sum search using DFS. Achievements include successful feature delivery across all problems with traceable commits, and explicit performance metrics per problem (Cookie: 164ms 20876kb; 1차캐시: 40.70ms 108mb; Meat Shop: 416ms 42732mb; Tetromino: 792ms 35844kb). This work demonstrates correctness, efficiency, and maintainability, enabling effective demonstrations to stakeholders and potential reuse in future evaluation pipelines.
4 Commits • 1 Features
Jan 1, 2025January 2025 — Delivered the Coding Challenge Solutions Suite for SSAFYnity/Job-Preparation-Challenge-4th, consolidating four user-facing algorithmic solutions: Cookie's Body Measurement, 1차캐시 LRU cache simulation, Meat Shop minimum-cost purchase optimization, and Tetromino maximum-sum search using DFS. Achievements include successful feature delivery across all problems with traceable commits, and explicit performance metrics per problem (Cookie: 164ms 20876kb; 1차캐시: 40.70ms 108mb; Meat Shop: 416ms 42732mb; Tetromino: 792ms 35844kb). This work demonstrates correctness, efficiency, and maintainability, enabling effective demonstrations to stakeholders and potential reuse in future evaluation pipelines.
January 2025
Activity
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Quality Metrics
Correctness97.6%
Maintainability85.0%
Architecture85.0%
Performance87.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
AlgorithmBacktrackingCachingData StructuresDepth First SearchGreedy AlgorithmGrid TraversalProblem SolvingSorting
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