AtCoder AI Tagged Problems: Instant Topic-Based Problem Discovery
A zero-configuration platform for filtering AtCoder problems by algorithmic topic, difficulty, and contest type.
AtCoder AI Tagged Problems provides structured access to the AtCoder problem set through AI-generated topic annotations and a multi-dimensional filtering interface. The application requires no installation, no user account, and no browser extensions.
Core Capabilities
AI-Generated Topic Annotations: Every problem in the AtCoder archive is annotated with algorithmic topics including Dynamic Programming, Graph Theory, Greedy Algorithms, Mathematics, String Processing, and Computational Geometry. Tags are generated automatically and available immediately for all problems, without dependence on community voting or manual curation.
Multi-Criteria Filtering Interface: Problems can be filtered concurrently by algorithmic topic (single or multiple tags), difficulty rating range (e.g., 1200–1600), contest category (ABC, ARC, AGC, or others), and text search over problem titles and statements.
Optional Progress Tracking: Providing an AtCoder username enables client-side highlighting of previously solved problems and displays personalized metrics including difficulty progression and topic-wise performance distribution. No credentials are stored or transmitted beyond the public submission data fetched from kenkoooo.com.
Responsive, Dependency-Free Interface: The application functions across desktop and mobile browsers without requiring JavaScript extensions, user registration, or third-party cookies. The interface prioritizes English-language accessibility for an international user base.
Dual View Modes: List View (filterable grid optimized for topic-focused practice sessions) and Contest View (problems organized by contest with visual difficulty indicators for simulation-style preparation).
Access Information
- Application: atcoder-ai-tagged-problem.vercel.app
- Source Code: GitHub Repository
- Issue Tracking and Feature Requests: GitHub Issues tab
Acknowledgements
- Thanks to the AtCoder community and maintainers of public data infrastructure, particularly kenkoooo.com, for enabling third-party tool development.
Feedback
Suggestions regarding tag accuracy, additional filter dimensions, API extensions, or interface refinements are welcome via GitHub discussions or comments below.
