Using Cursor for Open Source Library Maintenance

The maintenance burden of open source software projects is significant. Even for relatively simple libraries, the accumulation of issues, feature requests, dependency updates, and keeping up with language evolution can quickly become overwhelming. As a result, many promising projects fall into neglect, with maintainers unable to dedicate the time required to keep things running smoothly.

I’ve experienced this firsthand with several of my open source projects. While each started with enthusiasm and regular updates, the demands of work, life, and other interests gradually pushed them to the back burner. Sound familiar? If you’re an open source maintainer, you’ve probably been there too.

But here’s the good news: with the emergence of AI-assisted coding tools like Cursor, there’s new hope for maintaining these projects more efficiently. In this post, I’ll share how Cursor has helped breathe new life into three of my neglected Python libraries, and how you might leverage similar tools for your own projects.

The Projects: A Brief Introduction

Before diving into maintenance strategies, let’s (re) meet the three libraries that had been gathering dust on my GitHub profile:

PyGeoHash

PyGeoHash is a lightweight Python library for working with geohashes - a hierarchical spatial data structure that encodes geographic coordinates into short strings. I created it in 2015 as a Python 3 compatible fork of the original geohash library, which was Python 2 only.

The library offers functionality to:

  • Encode latitude/longitude pairs into geohashes of varying precision
  • Decode geohashes back to coordinates
  • Calculate approximate distances between geohashes
  • Find neighboring geohashes
  • Visualize geohashes on maps (added in recent updates)

We used this heavily at Predikto, where we often needed performant approximate geospatial queries in Elasticsearch.

Keeks

Keeks is a specialized library for bankroll allocation algorithms, including the Kelly criterion. It provides tools to help users optimize their betting strategies and manage their bankrolls effectively.

Key features include:

  • Implementation of the Kelly criterion for optimal bet sizing
  • Tools for calculating expected value and variance
  • Simulations to test different bankroll management strategies

Elote

Elote is a Python package that implements various rating systems for comparing entities based on head-to-head matchups. It was inspired by the challenge of ranking college football teams with limited direct comparison data.

The library includes:

  • Elo rating system (used in chess rankings)
  • Glicko rating system (an improvement on Elo)
  • Other rating algorithms
  • Arena and tournament simulation tools

This project was mentioned in my 2017 year-end review as one of my favorite new projects at the time.

The Maintenance Challenge

All three libraries faced similar maintenance challenges that might sound painfully familiar if you maintain open source projects:

  1. Technical debt - Outdated dependencies, deprecated language features, and compatibility issues with newer Python versions.

  2. Documentation gaps - Incomplete or outdated documentation that made the libraries harder for new users to adopt.

  3. Feature requests - A growing backlog of GitHub issues requesting enhancements that I never had time to implement.

  4. Testing inadequacies - Incomplete test coverage and outdated testing frameworks.

  5. Infrastructure issues - Outdated CI/CD pipelines, packaging configurations, and deployment processes.

The result? A collection of projects that still provided value to users but were increasingly difficult to maintain and extend. Taking PyGeoHash as an example, prior to recent updates, it still used a setup.py-based build system rather than modern pyproject.toml, had no formal CI/CD pipeline, and lacked proper test coverage for newer Python versions.

These challenges are not unique to my projects. In fact, they’re common across the open source ecosystem, as I’ve previously discussed in my post about the journey of category_encoders, which grew from a weekend hack to a widely-used tool with millions of downloads.

How Cursor Helps with Maintenance

Cursor is an AI-powered code editor built on top of VS Code that integrates language models like Claude to assist with programming tasks. Here’s how it’s helping me revitalize these neglected projects:

1. Rapid Orientation and Code Understanding

One of the biggest hurdles in returning to a neglected project is re-orienting yourself with the codebase. When issues come in, you often need to quickly understand parts of the code you haven’t looked at in years.

Cursor excels at this task. For instance, when working on Elote recently, I was debugging an issue with inconsistent behavior in the rating adjustment after draws. Rather than spending hours re-reading the code, I simply asked Cursor:

Explain how the Elo rating system handles draws in the current implementation,
and highlight any potential issues.

Cursor immediately provided a detailed explanation, identified that the draw handling wasn’t consistent across all competitor classes, and even suggested a fix that aligned with the mathematical principles of the Elo system. This rapid orientation capability dramatically reduces the barrier to diving back into maintenance tasks.

2. Streamlining Development Environment Setup

Setting up development environments for multiple projects with different dependencies can be tedious. Cursor helps automate this process by generating configuration files and helping navigate dependency conflicts.

For PyGeoHash, I asked Cursor to help modernize the project structure:

Help me convert this package from using setup.py to using pyproject.toml 
with modern Python packaging practices.

Cursor generated a complete pyproject.toml file, explained the changes compared to the old setup, and helped me understand the implications for users and CI/CD pipelines. It also suggested updates to documentation and GitHub Actions workflows to match the new structure.

3. Automating Housekeeping Tasks

Many maintenance tasks are necessary but mundane: updating dependencies, fixing linting errors, adding type hints, or improving documentation.

Cursor excels at these tasks because it can quickly understand the codebase and make targeted changes. For example, when modernizing Keeks, I asked:

Implement ruff for linting in the project and configure it to follow the existing code style guidelines.

Cursor not only configured ruff but also ensured that the existing code style guidelines were preserved. It maintained the current behavior while making the code more maintainable and consistent with the project’s standards.

4. Publishing Rulesfiles for Project Standards

One of Cursor’s most powerful features is the ability to create rulesfiles that capture project standards and knowledge. These rulesfiles help both the maintainer and contributors understand the project’s structure, conventions, and goals.

For PyGeoHash, I’ve published Cursor rulesfiles in the repository that contain:

  • Project overview, including the purpose and key features
  • Testing standards, detailing the required test coverage and testing frameworks
  • Documentation standards, specifying the format and content required for docstrings
  • Python standards, outlining the coding conventions and style guidelines
  • Instructions on how to use the project Makefile, including common tasks and commands

These rulesfiles serve as living documentation that evolves with the project and helps maintain consistency even when I’m not actively working on the code.

Tips for Using Cursor in Library Maintenance

Based on my experience, here are some practical tips for using Cursor effectively:

  1. Document First: Use Cursor to map out the library’s structure. This helps you understand what needs fixing and provides context for future maintenance.

  2. Address Technical Debt: Let Cursor identify outdated code and suggest updates. This simplifies future maintenance and prevents issues from compounding.

  3. Test Thoroughly: Ask Cursor to create test cases for key functions. This ensures changes don’t break existing features and builds confidence in your updates.

  4. Create Rulesfiles: Document project standards in rulesfiles to maintain consistency. Pro tip: add a note that the model should never add test-specific code into the library. I’ve found that AI models often try to “fix” tests by adding if statements that identify when a test is running and return hard-coded results.

  5. Use AI for Issues: Use Cursor to analyze and prioritize new issues. This helps manage user feedback efficiently. A good GitHub MCP will help you triage issues and prioritize them based on impact and difficulty directly in pane.

  6. Use a Makefile: Implement a Makefile to streamline common tasks like running tests and debugging. Cursor’s agent mode is excellent for executing these tasks, but a Makefile prevents the model from repeatedly guessing the correct commands, avoiding potential issues with Python environments. Utilizing UV and a basic Makefile ensures a smooth workflow without unnecessary complications.

Challenges and Limitations

While Cursor has been tremendously helpful, it’s not a silver bullet for all maintenance challenges:

1. Domain Knowledge Still Matters

For specialized libraries like PyGeoHash, domain-specific knowledge about geospatial algorithms remains critical. Cursor can help understand the implementation but may not fully grasp the mathematical or theoretical underpinnings. Its ideas about what to add to a library or how to structure it are often middling. Planning and thought around what to actually build are still valuable human contributions.

2. Community Management Requires Human Touch

Cursor can help with code, but managing community interactions, setting project direction, and making prioritization decisions still requires human judgment and empathy.

3. Tools Can’t Replace Commitment

Even with AI assistance, maintaining open source projects still requires a commitment of time and energy. Tools reduce the friction but don’t eliminate the need for human oversight. At least for now, AI is making the workload manageable but not automating it completely.

The Future of AI-Assisted Maintenance

Looking forward, I see AI tools like Cursor becoming an essential part of the open source maintenance toolkit. They lower the barrier to ongoing maintenance and make it more feasible for maintainers to support projects long-term without burning out.

Conclusion

The maintenance burden of open source projects has always been a significant challenge, leading many valuable libraries to fall into neglect. Tools like Cursor are changing this dynamic by reducing the friction associated with maintenance tasks.

For PyGeoHash, Keeks, and Elote, Cursor has helped breathe new life into projects that had been neglected for years. The ability to quickly understand code, automate routine tasks, and get assistance with complex debugging has made maintenance more manageable and even enjoyable.

If you maintain open source projects, I encourage you to explore how AI-assisted coding tools might help streamline your workflow. And if you’re a user of neglected libraries, consider that the maintainers might be more responsive if they had these tools at their disposal.

Open source software is a community effort, and embracing new tools that make maintenance more accessible benefits everyone. By reducing the burden on maintainers, we can help ensure that valuable libraries continue to thrive rather than languishing in neglect.

Have you used AI tools like Cursor for maintaining your own projects? I’d love to hear about your experiences in the comments or on social media.