Ongoing work on differentiable Swift

[Finagolfin]

@Brad_Larson

We've written a few posts about differentiable Swift itself and why it's neat , but we definitely could elaborate on the specific use cases that we find so exciting. I go into this in one of my posts above, but differentiable Swift really shines for machine learning and optimization applications that are not well served by existing frameworks.

I'm only seeing your post now, but I'd like to second @sspringer's suggestion that you submit a post to the Swift blog. I think a lot of people wrote off differentiable Swift after Lattner left google, and a post by you would let people know that is not the case.

At the very least, you should submit your current posts to common tech aggregators, I see no indication they were ever submitted to HN, for example. While I have no interest in these ML use cases, many do these days, so you are missing out by not spending a bit more time to publicize your work.

We really need someone with the Swift blog who will solicit these posts on a regular basis, rather than my sporadic attempts in this forum.

[Finagolfin]

@Troy_Harvey, I just saw your linkedin post from last month on how differentiable Swift is benchmarking much better than the competition, which generated some discussion on the r/MachineLearning subreddit.

Would you or @Brad_Larson be interested in submitting a post about your work to the Swift blog? Given the popularity of ML these days, I think a lot of people would be interested.

@alexandersandberg, what do you think?

[Brad_Larson]

Thanks for the interest! Regarding a blog post, we may have more to say about that soon. We absolutely will be talking more about this in the near future.

At the very least, I'm working to get the source code for these benchmarks or equivalents hosted in a public repository for others to try out. I know this was a popular request in the wake of those published numbers, but we got a little busy with our launch event. We're getting back to this now.