Stuff The Internet Says On Scalability For November 23rd, 2018

Wake up! It's HighScalability time:

Curious how SpaceX's satellite constellation works? Here's some fancy FCC reverse engineering magic. (Delay is Not an Option: Low Latency Routing in Space, Murat)
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- 5: ios 12 jailbreaks; ~43 million: dataset of atomic wikipedia edits; 1.88 exaops: peak throughput Summit supercomputer; 2 million: ESPN subscribers lost to cord cutters; 302: neurons show simple behavior of learning and memory; 224: seconds to train a ResNet-50 on ImageNet to an accuracy of approximately 75%; 120 milliseconds: space shuttle tolerances; $1 billion: Airbnb revenue; 900+ mph: tip of a whip;
- Quotable Quotes:
- @JeffDean: This thread is important. Bringing the best and brightest students from around the world to further their studies in the U.S. has been a key to success in many technological endeavors and to lead in many scientific domains.
- @davidgerard: > crypto has a provably lower carbon footprint than fiat actually, this is trivially false bitcoin: 0.1% of electricity, for 7tps THE WHOLE REST OF CIVILISATION: 99.9% of electricity, for more than 7000tps
- Paris Martineau: Lotti recalls the investor saying that if she wanted Lashify to succeed, quality didn’t matter, nor did customer satisfaction—only influencers. And they didn’t come cheap. She was told to expect to shell out $50,000 to $70,000 per influencer just to make her company’s name known, an insane amount for a new startup. There was no way around it; that’s just how things worked.
- @KevinBankston: Ford's CEO just said on NPR that the future of profitability for the company is all the data from its 100 million vehicles (and the people in them) they'll be able to monetize. Capitalism & surveillance capitalism are becoming increasingly indistinguishable (and frightening).
- Waqas Dhillon: The goal of in-database machine learning is to bring popular machine learning algorithms and advanced analytical functions directly to the data, where it most commonly resides – either in a data warehouse or a data lake. While machine learning is a common mechanism used to develop insights across a variety use cases, the growing volume of data has increased the complexity of building predictive models, since few tools are capable of processing these massive datasets. As a result, most organizations are down-sampling, which can impact the accuracy of machine models and created unnecessary steps to the predictive analytics process.
- Can you eat more after Thanksgiving? Lots of leftovers. Dig in...