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Comment by garbage_stain | Hacker News Reader

Number of stars on Github is not a great metric of the health of a project. TensorFlow, in particular, has received a lot of attention, so a lot of people have starred it, but this does not measure the number of non-Google contributors or what would happen to the project if Google abandoned it.

Note that I'm not saying TensorFlow is going to fail in the future; I'm saying that the measures used in this article are dubious at best.

Does nobody remember Elefant, Alex Smola's machine learning library that had a lot of hype then suddenly died after he left NICTA? The machine learning world is littered with tons of dead projects, and many of these died without a lot of warning. I'm concerned that this effect is only going to get worse now that machine learning libraries are often company-controlled instead of academic.

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Hi there, I wrote the article. We also looked at data on things like number of forks and contributors on GitHub as well as massive rise in questions on StackOverflow (thanks to Delip Rao's great post here http://deliprao.com/archives/168 and invaluable ecosystem analysis from Francois Chollet). These other things were cut for purposes of length and because we felt a mainstream reader (which is more Bloomberg's audience) would be able to understand the 'stars' figure most easily of all metrics.

Obviously I agree with you that this measure isn't definitive, but we felt it was relatively easy to understand. And the rest of the reporting we did for this story bore out the idea that TF has gathered an unusual level of both enthusiasm and commitment in a short amount of time.

Thanks for reading!

good points, though I would argue that tensorflow fundamentally is a way to define transformations. Think of it like assembly language. There are higher languages that expose higher level constructs (see keras)

Agreed it's not a great metric, but it is a good proxy especially if you suplement it with another source, such as the number of HN front page articles discussing TensorFlow. Given those two signals, I imagine Tensorflow will continue to gain momentum, at least in the short term.

However, like all SDKs (is that still a thing?), it can be easily be supplanted when something that is easier to use, fixes the flaws of, and is better documented comes out. This is doubly true given that the AI space is still (imo) in its infancy, wrt to developer tooling.

Politicians quote the number of "Likes" they get on social media, too. Stars are no different. It's a measure of interest, where the error bars are quite significant.

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