If you've ever contributed to OSS, you're familiar with the challenge: diving into a new codebase, aligning with maintainers' expectations, and making meaningful contributions can be daunting. For maintainers, managing a flood of contributions and providing guidance while maintaining quality is overwhelming. As a contributor to PyTorch & an OSS author, I’ve been on both sides over the years. This challenged me to become a better engineer & team player, seeking ways to facilitate smooth collaborative software development.
This is where Quack steps in, as your AI companion for software team alignment. For developers, it's like having a seasoned mentor guiding you through the intricacies of a new codebase, offering live in-line hints based on the project’s guidelines to craft high-quality pull requests. For maintainers, it’s a practical toolset to identify and address workflow inefficiencies and align contributions effectively with project objectives.
As code generation gets commoditized, aligning these diverse efforts becomes critical if you wish to convert individual productivity boosts into team velocity. Quack AI is here to solve this alignment, and make collaborative software development scale without losing any efficiency.
We're committed to keeping the service accessible and free for OSS communities while we plan to generate revenue from the enterprise suite. The platform (React, Next JS), the IDE extension (VSCode) and the backend API (Python, FastAPI) are licensed under Apache 2.0. You can find a short demo here [1] and the GitHub project here [2].
Our roadmap includes:
* offering autocompletion and code chat in the IDE, making the contribution process even more intuitive and seamless;
* finalizing the transition of the community version to hostable OSS models;
* identifying ambiguities or unspecified aspects of a given project’s guidelines;
* developing a Fitbit-like feature for your software development productivity to identify bottlenecks in your workflow.
This is still the early days, but we've seen how sharing a "public design doc" with the community can significantly improve the outcome! How was your experience of managing inbound as maintainers? What were your personal hacks to mitigate those challenges? We'd love to hear about how it has impacted your developer life or if you have any feedback about the above.
Cheers!
[1] Demo video: https://dub.sh/quack-demo
[2] Open Source repo: https://github.com/quack-ai/companion
Our documentation: https://docs.quackai.com/
