# egonSchiele/grokking_algorithms

Code for the book Grokking Algorithms (https://www.amazon.com/dp/1633438538)

Repository: https://github.com/egonSchiele/grokking_algorithms
Canonical: https://ross.abutalabs.com/products/grokking_algorithms
Language: JavaScript
License: NOASSERTION
License Family: other
Last push: 2026-04-12T21:10:55+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 77, release rhythm 35, longevity 100
- inputs: {"age_days": 3836, "days_push": 143, "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 13641, forks 4094 (observed 2026-08-28T04:11:04.461772+00:00)

## What it is
Companion code repository for the book Grokking Algorithms, containing easy-to-read example implementations of algorithms in multiple languages. It also includes high-resolution images from the book available for non-commercial use.

## Use cases
- learn algorithms with simple code examples
- find companion code for the Grokking Algorithms book
- get illustrations from Grokking Algorithms for teaching
- study data structures and algorithms as a beginner
- see algorithm examples in different programming languages

## When to choose
- you are reading Grokking Algorithms and want runnable code
- you want beginner-friendly, readable algorithm examples
- you need book images for non-commercial teaching materials

## When to avoid
- you need production-ready or optimized algorithm implementations
- you want a comprehensive algorithms reference beyond the book's scope
- you need a library to import into your project

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools
- domain: tutorials, education
- platform: cross-platform, python
- tags: algorithms, book-companion, code-examples, computer-science, data-structures, javascript

## Member repositories
- egonSchiele/grokking_algorithms (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:04.461772+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 2026-08-29T17:13:08.693027+00:00, confidence not recorded.
  - readme: https://github.com/egonSchiele/grokking_algorithms (fetched 2026-08-28T04:11:04.461772+00:00, sha 5b7d75b43483)
- Data as of 2026-08-30T08:39:29.467469+00:00.