I don't quite understand the "censorship" argument. Of course governments and public health officials are going to push the most effective tools we have to save as many lives as possible.
What is censorship in this case? Not spreading debunked or poorly-supported claims?
True censorship would've been something like keeping hush the rare blood-clotting incidents with J&J. But there are so many studies across so many countries that are freely available to read and be informed by.
Even the claims of twitter/other social media censoring don't hold water for me. Extraordinary claims should require extraordinary proof imo
> This is also the last major release of multi-backend Keras. Going forward, we recommend that users consider switching their Keras code to tf.keras in TensorFlow 2.0
What many of the comments seem to be missing is that you don't need to enable javascript on all google sites, only the sign-in page. With that in mind, I don't think there needs to be as much concern as we're seeing here.
I might not be understanding correctly, but it doesn't seem like you're being paid for the content you're hosting on PeerTube.
The model feels different than a webserver where you are either hosting your own content (and paying for that) or charging others to host their content on your server. Or am I misunderstanding?
Seems like a great idea, but not much activity (from what I can see). How is this model sustainable? Do I have any incentive to setup a PeerTube server?
2FA security aside, it really is remarkable how Jared was able to talk the hacker down. We seem to really undervalue those sorts of social skills. Jared's one conversation could have saved hundreds of thousands of dollars (for himself and others).
It's my understanding that the neural engine API wasn't actually exposed to Core ML until the iPhone XS. The GPU is the important factor for performance of those models
Syncing smaller databases between devices seems to be the best use case of CouchDB.
However, Couch is extremely difficult to manage at scale, especially when managing indexes on top of documents. The index generation was extremely slow and wasn't able to take advantage of beefier hardware at all. Deploying updates to indexes had to be done extremely carefully as it will rebuild the entire index and lock reads from that index in the meantime.
Iterating through GB of documents takes a wildly long amount of time, especially if other operations such as indexing updates are happening.
Furthermore it had scary failure modes. There were many times that a couch instance would die with documents cached in memory but not yet written to disk, even though couch had returned a successful document write. We found that documents we thought existed were lost forever during this blip.