Building ruxpy: My Journey from Idea to v1.0.0

Motivation: Why I Started Building Ruxpy

I built Ruxpy with one primary goal in mind: to learn by doing.
Everyone talks about how the best way to master new concepts is to actually build something with them, and I wanted to test that advice for myself. Rather than reading endless tutorials, I decided to dive into a project where the learning curve would be steep but rewarding.

Version control systems immediately stood out as the perfect candidate. They’re at the heart of how developers work, and recreating even a small part of Git felt like tackling “real” software engineering problems. At first, I considered just building a basic git clone, but that felt like reinventing the wheel without adding much value.

To push myself further, I set a few guiding principles: - Rust for speed, performance, and system-level control. - Python for flexibility and high-level abstractions, since I’m still fairly new to Rust and wanted a language I could be productive in. - A unique theme to differentiate my project from being “just another Git clone.” I found that in Star Trek, which inspired the naming of commands and user experience.

This combination made Ruxpy more than just a learning project—it became a hybrid experiment in building fast, practical, and fun developer tools.

Initial Brainstorming and Design Decisions

Once I had the idea, the next step was figuring out how to bring it to life. The first big decision was about language choice. I wanted Ruxpy to balance performance with flexibility, so I leaned into a hybrid approach:

• Rust would handle the heavy lifting—anything performance-sensitive, low-level, or related to speed and reliability. Rust’s memory safety guarantees also gave me confidence as I experimented with systems-level concepts.
• Python would provide the high-level layer, making it easier to script, prototype, and glue components together. Since I’m still fairly new to Rust, Python let me stay productive while I climbed the Rust learning curve.

With the language split decided, I set up the initial project structure:

• src/lib.rs → The Rust core library, handling performance-critical logic.
• ruxpy/ → The Python package, serving as the interface and command layer.

This separation made it clear which parts of the system were optimized for speed and which were designed for developer ergonomics. It also kept the project modular, so I could iterate on either side without breaking the other.

Core Features and Architecture

To give Ruxpy its identity, I mapped familiar Git commands to their Star Trek–themed counterparts:

• git init → ruxpy start
• git config → ruxpy config
• git status → ruxpy scan
• git add → ruxpy beam
• git commit → ruxpy starlog

This mapping provided both a roadmap of essential functionality and a playful twist that made the project fun to work on.

Data Flow Between Rust and Python

The architecture followed a clear split of responsibilities. Rust powered the performance-critical parts—for example, listing files in the current working directory, saving blobs, and reading them back. These formed the backbone of the system, where speed and correctness mattered most.

On top of that, Python provided the user-facing commands. Using PyO3 and maturin, I bridged the two worlds: Python commands like ruxpy beam could directly call into Rust utilities. This setup kept the core efficient while still giving me the flexibility and rapid development speed of Python.

Handling Real-World Problems

As Ruxpy matured, I started facing the kinds of issues that make a version control system feel reliable and practical.

File Ignoring

In the early stages, I handled ignored files with simple, hardcoded checks for a few directories. This was enough to keep the project moving, but it wasn’t scalable or flexible. Much later, I implemented proper .dockignore support, allowing users to define ignore patterns dynamically. This evolution—from quick hacks to a maintainable solution—was a small but important lesson in iterative development.

Robust Error Handling

Initially, Ruxpy would crash or behave unpredictably in edge cases: missing files, empty directories, or invalid input. I focused on catching these errors and providing clear, actionable feedback to users. For example, if a user ran ruxpy starlog with no staged files, the program would now explain why the commit failed and how to fix it.

Dealing with Edge Cases

Version control has many subtle edge cases. Some examples I addressed:

• Empty commits or staging areas.
• Files with unusual characters or deeply nested paths.
• Missing or malformed configuration files.

Handling these gracefully transformed Ruxpy from a prototype into a tool that could actually be used without frustration. It reinforced the importance of thinking beyond the happy path and designing for reliability from day one.

Cross-Platform Distribution

Once Ruxpy’s core features were stable, the next challenge was making it usable across different operating systems. Packaging a hybrid Rust/Python project comes with its own set of challenges, especially when you want seamless installation for macOS, Linux, and Windows users.

Building and Testing Wheels

I used maturin to build Python wheels directly from the Rust code, which simplified distribution. Each wheel included the compiled Rust components, so users didn’t need Rust installed locally. I tested builds on all three major platforms to ensure functionality remained consistent, handling subtle OS differences like file path formats and line endings.

Automated Builds with GitHub Actions

To streamline releases, I set up GitHub Actions. This CI/CD pipeline automatically built and tested wheels for every push and pull request. It also prepared release artifacts whenever I tagged a new version, making it easier to maintain consistent distribution without manual intervention.

Lessons Learned

• Packaging hybrid projects is non-trivial—Rust and Python have different conventions, and aligning them requires careful structure and tooling.
• Automating builds early saves headaches later, especially when targeting multiple platforms.
• Testing on all target OSes is critical, even for a small project; subtle differences can break otherwise functional code.

These steps ensured that Ruxpy wasn’t just a personal experiment—it became a tool that could, in principle, be installed and used reliably by anyone on any major platform.

Challenges and Solutions

Building Ruxpy was full of unexpected challenges, each of which taught me something new about hybrid Rust/Python development and software design.

Major Bugs and Roadblocks

• Rust–Python integration quirks: Early attempts to call Rust functions from Python exposed lifetime and ownership issues. PyO3’s safety guarantees were great, but I had to rethink how I passed data between Rust and Python to avoid panics and memory errors.
• Edge cases in commands: Commands like ruxpy starlog and ruxpy beam initially didn’t handle empty directories or unusual file names correctly, leading to crashes or incorrect indexing.

Decisions Reconsidered

• Hardcoded ignores → .dockignore: Early shortcuts were convenient, but evolving to a proper ignore system made the project scalable.
• Monolithic code → modular Rust/Python split: Initially, some Rust functions tried to do too much. Refactoring them into smaller, testable components made debugging and iteration much easier.
• Manual builds → automated CI/CD: Setting up GitHub Actions for builds and releases saved a lot of repetitive manual work.

Each challenge reinforced a key lesson: building a real tool means thinking beyond the happy path, anticipating failures, and designing systems that are robust and maintainable. These solutions, while sometimes small, collectively transformed Ruxpy from a learning experiment into a functioning VCS.

Reaching v1.0.0

After a few intense days of development, Ruxpy reached a point where I felt comfortable calling it v1.0.0. Reaching this milestone wasn’t about adding every possible feature—it was about creating a stable, usable, and testable tool.

Defining “Done”

For me, “done” meant:

• All core commands (start, config, scan, beam, starlog) were functional and behaved predictably.
• Edge cases were handled gracefully, with meaningful feedback for users.
• The Rust utilities were correctly integrated with Python, without panics or memory issues.

Testing the Final Product

Testing was mostly manual, walking through real-world workflows:

• Initializing a repository, adding files, and committing changes to ensure end-to-end functionality.
• Verifying that Python commands correctly invoked Rust functions and handled errors or unexpected input gracefully.

Preparing for Release

Before tagging v1.0.0, I:

• Wrote basic documentation explaining how to install and use Ruxpy.
• Created a changelog summarizing what had been implemented so far.
• Built the Python wheels using maturin, making it installable with pip directly from GitHub.

Reaching this milestone felt rewarding because it validated all the learning and experimentation packed into just a few days. Ruxpy had quickly evolved from an idea into a usable tool ready for others to try.

What’s Next?

With v1.0.0 released, Ruxpy is functional, but there’s still plenty of room to grow. These are some of the features and improvements I plan to tackle next:

• Unit tests for Rust utilities: Adding proper tests will ensure the core engine is reliable and easier to refactor.
• Additional Git-like commands: Expanding Ruxpy beyond the core five commands to include features like branching, logs, and diffing.
• Improved error reporting and UX: Making messages even clearer and adding helpful hints for common mistakes.
• Better cross-platform support and packaging: Testing on different OSes and refining the build/release process for smoother installation.

Even though I’m currently the only developer, having a reproducible environment with Nix flakes means future contributors can join without friction. Eventually, I hope to open the project up for collaboration, allowing others to experiment with the hybrid Rust/Python architecture and contribute new features.

Conclusion

Building Ruxpy in just a few days was an intense learning experience. I gained hands-on knowledge of Rust/Python integration, handling edge cases, and iterative development. The project reinforced that the best way to learn is by doing, and even small experiments can evolve into fully functional tools. I hope this journey inspires others to start their own open-source projects.