23 Daily — The open-source project astral-sh/ruff gained significant attention on November 7, 2026, by appearing on the GitHub Trending list as an extremely fast and feature-rich Python code linter and formatter. Written entirely in Rust, this tool significantly improves the efficiency of Python developers in checking and formatting code, according to the project's description.

High-speed and All-in-One Python Code Quality Tool

Ruff claims to be 10 to 100 times faster than existing linters and formatters like Flake8 and Black. It supports Python 3.15, is installable via pip, and configurable through pyproject.toml files. The tool includes over 900 built-in rules covering Flake8 and many popular plugins, with automated fixing capabilities such as removing unused imports. It also features built-in caching to avoid re-analyzing unchanged files, further boosting performance.

Streamlined Workflow and Improved Developer Productivity

Designed to replace Flake8 and dozens of its plugins, pydocstyle, pyupgrade, autoflake, and more, Ruff consolidates multiple Python code quality and formatting tools under a single interface. It integrates with popular editors like VS Code, supports GitHub Actions, and can be used as a pre-commit hook. Additionally, it offers monorepo-friendly hierarchical and cascading configurations, making it suitable for large-scale multi-project repositories.

Active Community and Practical Adoption

Ruff is actively used in major open-source projects and backed by the Astral team. Core contributors such as Sebastián Ramírez, Nick Schrock, and Bryan Van de Ven praise its exceptional speed and practicality. For example, Ruff analyzes the entire Dagster codebase (250k lines) in just 0.4 seconds, compared to several minutes with other tools, marking it as an important innovation in Python code quality management.

Editor's take: Opportunities and Validation for Startup Teams

By significantly accelerating Python code linting and integrating multiple tool functionalities, Ruff presents an opportunity for startups to automate development workflows and maintain high code quality. As Python projects grow, traditional tools may become inefficient; Ruff offers a feasible and real-time improvement. Startups can validate its value and cost-effectiveness by introducing Ruff into existing codebases and measuring its impact on code quality and continuous integration efficiency.