We make it super easy to do ML data annotations. We are online platform for teams to collaboratively build ML datasets.

Using this you can enlist your team/colleagues to help out in annotations, we provide good data visualization to help get insights from your data and tracking on which of the team members if helping the most.

Eagerly looking forward to HN communities feedback. :)

We have done a similar thing and build a tool to just do this in house. We have a team of human moderators who look at each review and do moderation. Took a few weeks for a dev to build a hacky solution. Would have liked to see it then, may be we could have given you a try.

Curious, How is this similar to GitHub ?

Yes that's what we are actually trying to fix. What we have seen from personal experience and from talking to people in other places, almost everywhere they build a hacky solution and takes up a full time dev's time to build it and maintain it.

We realised we could build a good solution around this and do a really good job at that so that others can simply use us.

Its similar to github as in you collaborate with your team on a central repo w.r.t datasets.

We are currently looking at feedback from folks on what features we can add more ?

One feedback, please do not let peoples comments influence you on what to build, have your vision and follow your heart. As the famous Steve jobs saying

"In your heart you already know what you want to do".

All the best.

Thanks for the feedback, but this seems pretty opposite advice to what I have seen some ppl saying, "to ask what ppl want and then build that"?

"In your heart you already know what you want to do".

>> Mind doesn't know that.

Get ready for some serious follow ups guys, this looks like something that we have been waiting for.

Looks quite useful for the solution that we are developing. Any discount for hackerNews users? :P

Nice way to collaborate for ML models. More people collaborating, more data = better model

Will it be available for audio and video datasets as well in coming time?

These are the use cases we are actively building out. Should be there in a few weeks.

I invite you to give it a try and provide feedback on how can this be useful for you/your team?

So we provide music streaming services to our users. What we are looking at is that we can tag our music files and track what kind of songs user is listening. So that we can provide recommendations based on that data.

Quite a useful platform