# ByteByteGoHq/coding-interview-patterns

Repository: https://github.com/ByteByteGoHq/coding-interview-patterns
Canonical: https://ross.abutalabs.com/products/coding-interview-patterns
Language: Java
License: NOASSERTION
License Family: other
Last push: 2026-01-26T05:37:12+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 64, release rhythm 35, longevity 47
- inputs: {"age_days": 658, "days_push": 219, "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 1262, forks 304 (observed 2026-08-28T04:04:10.274839+00:00)

## What it is
A companion repository for the book 'Coding Interview Patterns' containing solutions to 101 coding interview problems in Java, Python, C++, and Kotlin. It covers algorithm and data structure patterns like two pointers, sliding windows, dynamic programming, and graphs.

## Use cases
- prepare for coding interviews
- learn algorithm patterns like sliding window and dynamic programming
- practice data structure problems with solutions
- study 101 interview problems with explanations
- compare solutions across Java Python C++ and Kotlin

## When to choose
- you are preparing for technical coding interviews
- you want structured pattern-based algorithm practice
- you want reference solutions in multiple languages

## When to avoid
- you need a runnable software tool or library
- you want a general algorithms textbook rather than interview prep
- you need problems beyond the 101 covered in the book

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools
- domain: education, tutorials
- platform: cross-platform
- tags: coding-interview, data-structures, algorithms, interview-preparation, java, python, cpp, kotlin, book-companion

## Member repositories
- ByteByteGoHq/coding-interview-patterns (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:10.274839+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 2026-08-30T05:04:12.436565+00:00, confidence not recorded.
  - readme: https://github.com/ByteByteGoHq/coding-interview-patterns (fetched 2026-08-28T04:04:10.274839+00:00, sha 776a8ff5143e)
- Data as of 2026-08-30T08:39:29.467469+00:00.