"Silicon Valley" Finished Its Homework And Now It Gets To Have Some Fun

"Silicon Valley" Finished Its Homework And Now It Gets To Have Some Fun

FilmMagic for HBO

On Tuesday night, HBO hosted the premiere party for the fourth season of Silicon Valley at the Letterman Digital Arts Center in San Francisco, a campus-like collection of buildings that houses a few George Lucas-related film and special effects companies, as well as a Yoda fountain and life-size replicas of Darth Vader and Boba Fett. A lot of the guests used the latter as selfie backdrops.

After the screening, the audience full of CEOs, venture capitalists, Twitter-famous engineers, and tech bloggers watched Recode’s Kara Swisher grill the cast and crew. The question she returned to again and again was the same one the cast and creators got pelted with in the press junket beforehand: With the tech world in the crosshairs, how political is Season 4 gonna get?

“Everyone thinks [tech executives are] coastal elites, that some of the reasons for the election were because these people are stealing jobs, becoming wealthy, and leaving behind everyone,” Swisher said. “How do you reflect that in this season?”

Executive producer Alec Berg had a ready answer. “The tricky thing with the show is that we write [it] months before we shoot it, and we shoot it months before it airs, so it’s hard to be topical,” he explained onstage. The inspiration that writers draw from has to stay relevant, “so we can’t really chase trends.”

“You’ll see our United episode in a year and a half,” said Kumail Nanjiani, the actor who plays the perpetual striver Dinesh Chugtai, cutting in.

Later, Swisher tried asking the question a different way: “Do you want the show to get more political or is it just &;let&039;s make fun of the idiots of Silicon Valley&039; kind of thing?”

This time, actor Zach Woods responded. “It’s a tricky thing. [The writers] make fun of the let’s-make-the-world-a-better-place people all the time,” he said, but “then if you get a show that’s too shrill or sanctimonious then you become the person you’re parodying.”

The fourth season may not wade into the internal meltdown currently underway at Uber’s headquarters, but the first episode does kick off with a fake Uber driver. Pied Piper, the data compression startup at the center of the show, has pivoted away from its prized algorithm in favor of PiperChat, a more practical video-messaging app. The company is racking up users, but it can’t afford the server costs, so Richard Hendricks, the spiny, graceless genius behind the code, masquerades as an Uber driver. The plan is to temporarily kidnap a venture capitalist and entice him into investing while he’s sitting captive in the backseat.

The investor quickly realizes that he’s being chauffeured around by the most toxic founder on the peninsula. Please, Richard begs him, we really need the cash. “Really? Is it hard to become a billionaire? Welcome to the Valley, assholes,” the VC replies, demanding to be let out — that is, once Richard can figure out the child locks. A few seconds later, the irate investor pops back in to hand Richard his business card: Look, if PiperChat can actually get to a million users, give him a call. “Then everyone in town will be trying to kidnap you,” the VC says, making it clear that the right numbers can absolve all kinds of sins.

The scene is pretty restrained for a series that leans so heavily on sitcom-style punchlines, but the message still comes through: In Silicon Valley, the FOMO flows both ways. The Uber scenario also sneaks in a subtle point about what constitutes desperation in an industry where three commas in your bank balance is a real possibility. Richard is driving an Uber so he can try to pick up some spare millions for his startup, not because he needs to make ends meet.

Richard&039;s discomfort hard-selling the app sets up the tension of the fourth season. Until this point, the biggest threats to our hapless band of beta males has come from the world outside Erlich Bachman’s living room. Now they’re in danger of sinking under the weight of their own ambition, obliviousness, and poor interpersonal skills.

The decision to satirize personalities instead of ripping the plot from TechCrunch headlines has paid off. Four years in, Silicon Valley is playing to its strong suit and gliding past limitations that critics have latched onto in the past. From the get-go, the show has been more interested in pleasing Reddit with its obsessive technical accuracy than in sending a progressive message. The creators didn’t just do their homework, they waved it all around to make sure you could see the A+ at the top of the page. Year after year after year they were told that the tech industry’s backward-ass attitude toward gender and race is just begging for a comedic takedown, but they choose to go the academic route instead.

Now that its makers have proven themselves, however, there&039;s a buoyancy in the air. Season 4 looks ready to take its learnings — as Gavin Belson (the demented egomaniac running Goog…er, Hooli…played with panache by Matt Ross) might say — for a spin. Like Girls, Silicon Valley seems to be serving a keener sense of pathos now that the pressure is off. The inward turn helps, the dick jokes do not. but because the shifting fortunes and jockeying egos are rendered so breezily, it&039;s easy to forget that the show barely glances outside its bubble.

Perhaps that avoidance is deliberate. In the first episode, Belson is being asked about a Hooli factory in Malaysia but can only think about how another executive forced his private jet to stop in Jackson Hole first, even though Mountain View was closer. It’s a succinct way to show viewers how your world-changing sausage gets made: CEOs may be too consumed with petty concerns to pay much mind to just how far their power can reach.

All told, though, both episodes were a nice reminder that Silicon Valley is responsible for bringing so much of the vocabulary and imagery of this subculture to the mainstream — if audiences wanted to picture Google’s campus before 2014, they had to rely on flights of fancy like The Circle. Or take the return of iconic jackass-like investor Russ Hanneman, he of three comma fame, who shows up with the doors of his orange McLaren raised at full mast. Of course, the show has always been more “funny chortle” than “funny haha.” As one engineer told BuzzFeed News after, when she’s watching home alone she doesn’t laugh.

The season premiere even had a couple echoes back to seasons past. Instead of “Big Head” Bighetti failing upward until he reaches the Hooli roof to “rest and vest,” we see another exec in an elevator that sinks down to the sub-level where he meets the ponytailed server dweller last seen in Season 3.

In Trumpian times, the low-stakes antics are a welcome breather, especially when they involve characters the audience has grown to love and pity. This season Nanjiani’s character, Dinesh, steps into more of a leadership role, complete with a costume change: from casual coder to douchebag pitchman with a one-button blazer, a mess of product in his hair, and a smarmy grin.

“Who were you trying to be?” asked Swisher. “I met about 15 people like that recently.”

During the Q&A, co-creator Mike Judge promised that there would be some female characters this season, including an actor who plays an influential role. She appears for “more than one episode, more than one line — she has a whole arc,” Judge said. A few seats over, Amanda Crew, who plays Monica, the young female investor, was unmissable wearing a Pepto-Bismol–pink suit in a row of seven men. Although Crew joins the cast for press junkets, no one wants to point out that her character doesn’t have as many lines, isn’t as fully developed, and isn’t as integral to the plot. How many viewers know Monica&039;s last name?

She only gets a couple lines in the first two episodes, but they include one of the most poignant. Richard comes to her for advice about dropping PiperChat for something even more ambitious. “Richard, I know people who have spent their entire careers chasing after an app with this kind of growth rate and never hit it,” she says. The scene quickly moves on to a sight gag about Monica being demoted to an office with a view of the urinal, but passing lines like that gesture at how many Richards there are driving around Palo Alto, hoping that millions might fall in their lap.

Quelle: <a href=""Silicon Valley" Finished Its Homework And Now It Gets To Have Some Fun“>BuzzFeed

DevOps: What your application management is missing

DevOps and Application Performance Management (APM) go hand in hand. I want to take you through a simple journey which shows why APM is such a key part of DevOps today. Let’s take a look at typical types of metrics that need to be tracked and measured, as well as the key features needed in APM to help in the DevOps environment.
When we talk about DevOps today, we often also mean cloud, microservices, and cloud level availability, like 99.999 precent or 26.3 seconds per month of downtime. Microservice behavior is critical to DevOps success. In a DevOps environment, microservices must be able to report the following about themselves:

Am I healthy?
What is my latency?
How many times do I connect to my dependent systems?
What is the latency of each of those dependent connections?
How many of those dependent connections succeed and fail?
Am I doing the work I am supposed to do?
How many customers do I have?
Am I gaining new customers?

Microservices need to be built carefully so that these types of metrics are available for each of the microservice instances. Why? If you want to hit that availability figure of less than 26.3 seconds of downtime per month, these metrics will help you to restore service faster. Some of these are easier to measure. But capturing “am I doing the work I am supposed to do” may need some development depending on what your microservice does.
Let’s now talk about two key features that a APM solution must have.
First, developers tend to create some pointers in the application log, like how many times a certain kind of error occurred. This can be problematic because logs have a higher latency than metrics in reaching the server.  And at these demanding levels, every second counts. Therefore, a better practice is to be able to measure latency at the microservice code level and push it to the APM. Then, have the APM system accept these custom metrics and transport it to the server. That way, latency can be analyzed and visualized just like the regular APM metrics.
Secondly, if something fails in the cloud, the standard response is to restart the component in an automated fashion. However, there is one set of problems that is very nasty and cannot be solved by restarts. This happens when the latency of your microservice suddenly goes south. And this is where the APM tool makes a difference, capturing a broad range of metrics like the ones mentioned above. APM can show metrics from different microservices and help users isolate the faulty microservice or other kinks in the process.
If you are in this business for serious production deployments, the development team has already embedded monitoring into the process. If not yet done, better get it done soon. Without APM it is much more difficult to guarantee 99.999 percent service levels.
Want to dig deeper? Check out this blog from my colleague, Mike Mallo, who explores how to drive DevOps transformation when developers own application monitoring. And read APM and DevOps: A Winning Combination to learn more.
 
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Quelle: Thoughts on Cloud

Overloaded? Digital assistants to the rescue

Data is empowering us like never before. But there’s a flip side to having access to so much valuable data: information overload. That’s when data causes more pain than gain. It looks something like this:

It all feels like a bit too much.
The catch-22 of “knowledge work”
The term “knowledge workers” generally describes anyone with a desk job. That’s tens of millions of people across the globe. Each day, these workers must process, analyze and manage information to solve problems and innovate. Already, information overload is hurting workplace productivity.
IDC reports that digital data will surge to a trillion gigabytes by 2025. That’s 10 times the 16.1 zettabytes of data generated in 2016. On the other hand, the number of knowledge workers is shrinking. McKinsey Global Institute predicts a shortage of 80 million knowledge workers worldwide. So while workloads are increasing, the workforce is decreasing.
What’s the solution to this dilemma? Intelligent digital assistants.

Bring on the robots
Meet the next disruptor: the intelligent digital assistant. Enabled by artificial intelligence, these digital helpers can automate complex data work, helping employees do higher-value work. Sifting through mounds of data, prioritizing projects and managing tedious tasks are just a few of the many activities digital assistants can do.
Digital assistants in action: A usage scenario
How might a digital assistant make knowledge work easier? Let’s walk through a scenario.
Imagine Rob, a software account rep with more than 40 accounts, is having trouble staying on top of them. He&;s overloaded with information and tools, including Salesforce, Gmail, Slack, Google Sheets, and LeadLander, among others. Rob needs to constantly check these disparate systems and synthesize information to get the insights he needs to effectively serve his customers.
A digital assistant could do a lot of this work for him. For example, Rob could have his assistant monitor product usage and Salesforce to detect customers who are up for renewals in the next three months, but haven&8217;t been actively using the product. The assistant can send Rob notifications, enabling Rob to resolve any issues that may be occurring and increase the customer’s chances of renewing.
He could also have his assistant watch for new job postings from his clients on Indeed.com and LinkedIn. If the assistant finds a job ad from one of his clients, it can notify Rob that the customer may need additional software licenses for the new employees. The digital assistant can even proactively recommend additional tasks to offload to the assistant, enabling him to be more proactive. Collectively, these actions could add hours back to Rob’s work week while helping Rob better meet his goals.
Rob frequently works with Alice in customer support, who could also benefit from a digital assistant. To provide proactive service to customers, she could train her assistant to monitor new support tickets. If three or more customers report the same problem with the same product within a week, the assistant can automatically send the engineering team a high-priority ticket that includes a summary of the related tickets. The assistant can also send ongoing status updates to management, saving the support team significant time.
As these scenarios illustrate, there are three key capabilities that can make or break the effectiveness of a digital assistant app:
Usability
Many digital assistants require IT intervention because of complexity. That’s not exactly a motivator for adoption. Workers should look for a digital assistant that’s intuitive to set up and use—allowing them to easily create their own complex situations to detect and actions to take. The technology should also include a catalog of pre-built skills that can be personalized—to help users get started quickly.
Customization
Employees should look for a digital assistant that can work with the systems they use and accommodate their unique key performance indicators (KPIs) and work processes. In other words, the digital assistant should be as useful as a human assistant that they might train.
 
Intelligence
Detecting situations that matter, delivering context-driven notifications at the right times and automating actions are all intelligent capabilities. But after a while, the digital assistant should be able to learn from how workers use it, and make proactive recommendations. That’s what users would expect their human assistants to do. So employees should look for a digital assistant that they don’t have to micromanage.
To see how you can start optimizing your productivity with help from an intelligent digital assistant, check out this video.
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Quelle: Thoughts on Cloud