A collection of Ruby implementations for common algorithm problems, focused on technical interview preparation.
Algorithms is a repository of Ruby implementations for common algorithmic problems, many of which are frequently encountered in technical interviews. It provides working solutions to help developers understand and practice solving these problems efficiently. The project addresses the need for a clear, executable reference when preparing for coding interviews at major tech companies.
Software engineers and developers preparing for technical interviews, particularly those targeting companies like Google, Facebook, and LinkedIn. It's also useful for Ruby developers looking to improve their algorithmic problem-solving skills.
It offers a curated collection of interview-relevant algorithms with ready-to-run Ruby code, saving time compared to searching for scattered solutions. The focus on real interview questions and executable examples provides practical, hands-on preparation.
algorithms playground for common questions
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Solutions target questions from top tech companies like Google and Facebook, with direct links to LeetCode, making it highly relevant for job prep.
Each file includes runnable code with test samples—for instance, running `ruby brackets_combinations.rb` outputs valid combinations, allowing immediate verification.
Spans sorting, DP, graphs, and more, such as Dijkstra's and KMP algorithms, providing a broad practice set for interview scenarios.
Open to pull requests for enhancements, encouraging contributions to expand or refine solutions based on real-world use.
All code is in Ruby, limiting utility for developers in other ecosystems who need cross-language references or comparisons.
README lacks deep analysis or teaching notes; it primarily lists problems and links, relying on external sources for context.
Solutions are standalone scripts without gem integration or dependency management, making them unsuitable for direct use in larger projects.
Repository activity is sparse, potentially missing recent interview trends or algorithm optimizations seen in active communities.