# hoanhan101/algo

101+ coding interview problems in Go

Repository: https://github.com/hoanhan101/algo
Canonical: https://ross.abutalabs.com/products/hoanhan101-algo
Homepage: https://hoanhan.co/posts/101-challenges
Language: Go
License: MIT
License Family: permissive
Last push: 2020-11-03T21:49:13+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": 2601, "days_push": 2129, "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 3685, forks 368 (observed 2026-08-28T04:08:13.858960+00:00)

## What it is
A curated collection of 116+ coding interview problems solved in Go, with detailed thought processes, documented solutions, test cases, and time/space complexity analysis. It serves as a study guide for preparing for technical interviews.

## Use cases
- prepare for coding interviews in Go
- learn algorithms and data structures with worked examples
- practice common interview problems like merge meetings or balanced binary tree
- study time and space complexity analysis with test cases
- find frequently asked interview questions from LeetCode and Interview Cake

## When to choose
- you are interviewing for a role that uses Go
- you want interview problems with documented reasoning and complexity analysis
- you prefer learning from test-driven Go code

## When to avoid
- you need interview prep in a language other than Go
- you want an interactive judge or auto-graded platform
- you need up-to-date content, as the repo has not been updated since 2020

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: education, programming-languages
- platform: go
- tags: coding-interview, data-structures, go, test-cases, complexity-analysis, algorithms

## Member repositories
- hoanhan101/algo (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:13.858960+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 2026-08-29T18:31:10.433667+00:00, confidence not recorded.
  - readme: https://github.com/hoanhan101/algo (fetched 2026-08-28T04:08:13.858960+00:00, sha 78fd0a602ef2)
- Data as of 2026-08-30T08:39:29.467469+00:00.