Mike and I have made some progress towards our open sourcing of Heupr - we've cleaned up most of the old Issue cards and have reorganized all of the documentation. We're planning on meeting this weekend (hopefully) to finalize some stuff but we're hoping to officially open source the codebase in a few weeks!
Heupr is a machine learning-powered platform to help manage open source software projects on GitHub. It's written entirely in Go and we'd love to collaborate! Our roadmap includes stuff like building more feature models, changing some architecture, and even (potentially) building a Slack bot and dashboard UI (desktop or browser). Reply to this message or shoot us an email at heuprHQ@gmail.com if you're interested!
Just out of curiosity, did you roll your own ML library or did you use an existing one? Judging by the website you have a recommender system and a text classifier, and seeing the Go code for that could be very instructional for quite a few people :)
A little bit of both. We used a few libraries/APIs for some of the core stuff but we hand-built the logic for pre-processing the training/prediction data. We're still learning about it ourselves and we'd love to have more people onboard learning as well!
Just out of curiosity, why did you chose Go for this task? I've done several different NLP over the last 10 or so years, and it's really rather hard to beat Python in regard to available libraries (or even Java with OpenNLP).
Great question! We chose Go for a couple of reasons.
The first is Mike and I wanted to learn and the idea of the relatively young, productive, and powerful language that is Go appealed to us.
With regards to machine learning, you're definitely right - compared to Python the libraries are fewer, but they are there. We tend to believe in the longer-term expectations for Go in machine learning given Google's strong support for the language as well as the Go APIs available for things like TensorFlow and the NLP services; I would bet the trend continues in that direction and libraries will pop up as a result. In our project, we kept things as simple as possible, given we're focusing on applied machine learning, and opted to hand-roll components as necessary.
Any level of experience is welcome! Mike comes from a .NET background and I come from Python but we both love Go; we learned a lot about the language working on this project and you can definitely get involved anywhere you'd like. I'm in the process of writing some initial Issue cards for our codebase rebuild so there will be both bigger and smaller tasks to handle for sure.
Cool, I'm interested! I will send an email.
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