Hey, it's HighScalability time:

 

Slovenia. A gorgeous place to break your leg. Highly recommended.

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• 294: slides in Internet trends 2018 deck; 110 TB: Hubble Space Telescope data; $124 million: daily App store purchases; 10 billion: monthly Siri requests; 1000 billion: yearly photos taken on iOS; one exabyte: Backblaze storage by year end; 837 million: spam taken down by Facebook in Q1; 86%: of passwords are terrible; 10 Million: US patents; 72: Transceiver Radar Chip; C: most energy efficient language; $138 Billion: global games market; $50 billion: 2017 Angry Birds revenue; 50 million: cesium atomic clock time source; 1.3 Million: vCPU grid on AWS at $30,000 per hour; $296 million: Fortnite April revenue; 4000: Siri requests per second; 


• Quotable Quotes:
  • @adriancolyer: Microsoft's ServiceFabric runs across over 160K machines and 2.5M cores, powering core Azure services as well as end-user applications. 
  • @StabbyCutyou: To summarize: Tech is not easy, but people are always harder. Until the robots come to replace us all, you won't be able to avoid dealing with people.
    Focus on the above skills, and "technology harder" as a way to increase the scope of where you can be useful to other engineers.
  • Mani Doraisamy: I would rather optimize my code than fundraise.
  • Backblaze: The failure rates of all of the larger drives (8-, 10- and 12 TB) are very good, 1.2% AFR (Annualized Failure Rate) or less. The overall failure rate of 1.84% is the lowest we have ever achieved, besting the previous low of 2.00% from the end of 2017.
  • @rbranson: Things I once held as sincere beliefs: * Databases are bad queue backends * Static typing is a waste of time * Monoliths are always worse * Strong consistency isn't worth fighting for * Using the "right" tool always trumps using the one you know * The JVM sucks
  • @aleksbu: Since I switched back to SSR for my product, productivity went through the roof. SPAs has been a fun journey for me but all this added complexity makes it super expensive. My product doesn’t need any of the benefits that SPAs bring at the moment so switching to SSR was natural.
  • @ajaynairthinks: Real car convo with my 5YO: "V:Papa What's a Lambda" [after hearing my call] Me: Well its a way to run code without servers V: What's code Me: Its like algorithms [from a game he plays] V: Ohh so Lambda is cool because I know what code is and because I dunno what a server is Me: [Yes!]
  • @OpenAI: AI and Compute: Our analysis showing that the amount of compute used in the largest AI training runs has had a doubling period of 3.5 months since 2012 (net increase of 300,000x)
  • @JeffDean: TPUv3!  So hot it needs help with cooling: first liquid cooled devices in our data centers. A TPUv3 pod is 8X as powerful as the TPUv2 pod announced at IO last year, offering more than 100 petaflops of ML compute, allowing us to tackle bigger problems & build better products.
  • Platformonomics: As I keep repeating, CAPEX is both a prerequisite to play in the big boy cloud and confirmation of customer success. Both IBM and Oracle are tens of billions of dollars in cloud infrastructure CAPEX behind Amazon, Google, and Microsoft.
  • Eugene Wei: I believe the core experience of Twitter has reached most everyone in the world who likes it. Let's examine the core attributes of Twitter the product 
  • Harrison Jacobs: The joke used to be that Chinese people like to live near good public schools, Liyan Chen, the manager of international corporate affairs at Alibaba, told Business Insider. “The joke now in China is that they want to live where the Hemas are, because then they can get everything delivered to them really easily.”...The technological advancements Alibaba has brought to Hema — easy in-app ordering, ultrafast delivery, price matching, facial-recognition payment, tailored stocking based on spending habits, etc. — Amazon could easily bring to Whole Foods. And in my opinion, given Amazon's obsession with efficiency, it's a matter of not if, but when.
  • @lizthegrey: Some uses of ML today in Google production: predicting user clicks on ads, prefetching next memory/file accesses, scheduling jobs and capacity planning, speech recognition, fraud detections, smart responses, and machine vision. #SREcon
  • @lizthegrey: What's ML look like in prod? "Don't worry, it's just another data pipeline" 10% of the effort is offline training -- transforming/training prod data using TPUs, validating, and producing a trained model. We then push the trained model to prod. #SREcon
  • Joab Jackson: [Facebook] developed a tool, called Packager, which uses machine learning to automate the process of deciding which files to bundle into a package for a specific end user. It relies heavily on statistical analysis: Which files will the users need right away? Which will they need eventually? Which files have been updated? Some files get updated constantly; others not so much so.
  • It has been a while so you know there's a lot more...

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