labuladong/fucking-algorithm

Crack LeetCode, not only how, but also why.

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Summary Information

Updated 19 minutes ago
Added to GitGenius on August 30th, 2026
Created on February 19th, 2020
Open Issues & Pull Requests: 1 (+0)
Number of forks: 23,570
Total Stargazers: 135,520 (+0)
Total Subscribers: 2,264 (+0)

Repository Insights (GitGenius)

Median issue/PR response: N/A
Mean response time: 34.0 hours
90th percentile: 0.0 hours
Tracked items: 154

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 14% of issues opened in the past year have been closed. Three people close 90% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 1
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 1 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • vscode-extension-bug (41)
  • algo-websie-bug (33)
  • chrome-extension-bug (29)
  • jb-plugin-bug (28)
  • feature-request (12)
  • in progress (5)
  • algo-visualize-bug (4)
  • need-more-info (2)

Most active issues this week

Detailed Description

fucking-algorithm is a collection of algorithm learning materials that teaches problem-solving approaches for LeetCode questions.

The project addresses the gap between memorizing code solutions and developing algorithmic thinking. Rather than providing isolated answers, it explains the reasoning and mental frameworks behind solutions to common algorithm problems. The material covers data structures, dynamic programming, string matching, and other core topics, organized around patterns and techniques that apply across multiple problems. The approach emphasizes understanding why a solution works and how to recognize when to apply it, rather than copying one-liners from comment sections.

Someone considering this material should know it targets developers preparing for technical interviews who want to build genuine problem-solving intuition rather than accumulate solved problems. It suits learners who prefer conceptual frameworks over isolated code snippets and who have time to study explanations alongside practice. The project explicitly positions itself against approaches that prioritize brevity or mathematical rigor at the expense of clarity, arguing that accessible explanations serve job-interview preparation better than advanced algorithmic theory.

The project maintains an active learning ecosystem beyond the core repository. It provides an online website where articles link directly to LeetCode problems for integrated study, a Chrome extension that surfaces solutions and connections while browsing LeetCode, and a VS Code plugin for the same purpose. The material includes algorithm visualization tools integrated across these platforms to help learners understand data structure behavior and recursion visually. The author continues creating new content and actively maintains the ecosystem of tools around the core teaching material.