# sagivo/algorithms

algorithms playground for common questions

Repository: https://github.com/sagivo/algorithms
Canonical: https://ross.abutalabs.com/products/sagivo-algorithms
Language: Ruby
License Family: other
Topics: algorithm, ruby, computer-science, interview-questions
Last push: 2022-08-18T11:55:51+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4315, "days_push": 1476, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3244, forks 352 (observed 2026-08-28T04:07:50.622206+00:00)

## What it is
A Ruby-based collection of solutions to common algorithm and data structure interview questions, sourced from LeetCode-style problems. It includes interview tips from major tech companies like Google, Facebook, and LinkedIn.

## Use cases
- prepare for coding interviews at tech companies
- study common algorithm problems with Ruby solutions
- practice dynamic programming and data structure questions
- review interview tips from Google and Facebook
- learn algorithms through worked examples

## When to choose
- you are preparing for technical interviews and want reference solutions
- you prefer learning algorithms in Ruby syntax
- you want curated solutions to classic LeetCode-style problems

## When to avoid
- you need a production-ready algorithm library for your application
- you work in a language other than Ruby
- you need comprehensive coverage of advanced algorithms beyond interview staples

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: education, programming-languages
- platform: ruby
- tags: interview-preparation, leetcode, data-structures, computer-science, ruby-examples, algorithms

## Member repositories
- sagivo/algorithms (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:50.622206+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:24:29.309975+00:00, confidence not recorded.
  - readme: https://github.com/sagivo/algorithms (fetched 2026-08-28T04:07:50.622206+00:00, sha 211f8b64c078)
- Data as of 2026-08-30T08:39:29.467469+00:00.