# shomali11/go-interview

Collection of Technical Interview Questions solved with Go

Repository: https://github.com/shomali11/go-interview
Canonical: https://ross.abutalabs.com/products/go-interview
Language: Go
License: MIT
License Family: permissive
Topics: interview, question, answer, golang, go
Last push: 2023-07-10T21:02:37+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": 2349, "days_push": 1150, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4683, forks 480 (observed 2026-08-28T04:08:56.921251+00:00)

## What it is
A curated collection of technical interview questions solved in Go, covering algorithms, data structures, and common evaluation problems. It serves as a study reference and example codebase for Go developers preparing for coding interviews.

## Use cases
- prepare for a Go coding interview
- learn data structures implemented in Go
- study example solutions to algorithm questions
- review linked list and tree problems in Go
- practice technical interview questions with Go code
- find reference implementations of LRU cache or priority queue in Go

## When to choose
- you are preparing for technical interviews using Go
- you want readable Go implementations of classic data structures and algorithms
- you need example code to study rather than a production library

## When to avoid
- you need production-grade, battle-tested data structure libraries
- you work in a language other than Go
- you need comprehensive algorithm tutorials with explanations rather than code

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: education, programming-languages, tutorials
- platform: go, cross-platform
- tags: interview-preparation, data-structures, coding-interview, golang, algorithms, example-code

## Member repositories
- shomali11/go-interview (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:56.921251+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 2026-08-29T18:19:18.888900+00:00, confidence not recorded.
  - readme: https://github.com/shomali11/go-interview (fetched 2026-08-28T04:08:56.921251+00:00, sha 52dd01118789)
- Data as of 2026-08-30T08:39:29.467469+00:00.