How Netflix cracked the formula for hit shows

I'm a sucker for comics. Specifically super hero comics. And, after seeing Netflix's Daredevil, I was taken by how accurate and well done it is. It's one of the best adaptations I've seen to a comic series. Fast paced, good action, just blunt and dark enough.

That got me thinking - How the hell did they do that? After "Orange is the new black" and "House of cards", Netflix seem to be churning out great TV, again and again.

The answer is simple. Netflix, which started as a mail rental service (Just because broadband infrastructure wasn't relevant back then) and moved on to online video rental, simply know exactly what we want as viewers.
They know which movies we like.
Which movies we watch until the end, and which movies we "break" in the middle.
They also know which scenes we run over and over again.

By tagging every scene in every content, they have the ability to create the perfect show for each one of their viewers. They can actually make script decisions (Exactly how the story will be built) according to data.

Furthermore - In the future, story would not be linear as we know it, but every subscriber will get his own, personal, customised version of the show. Yoni Bloch's Interlude might actually close this gap from the production end.

That's pretty amazing.

That Magic Factor

Apple came out very strong from yesterday's event.

Though nothing's really new (Macbook model was long due, though it's much more stunning than I've expected), there is one thing I take from this:


The Magic Factor.

Watch wearables, and successful ones, exist for years now. Garmin, Pebble, Android wear - They're all there. But still, none of them generates the excitement that the Apple watch does.

Than I've looked at last year's major Apple releases, and found out that each year, there was a feature released that was either magical or breakthrough - None of the competitors had it, and it worked like magic:

- Retina screen (iPad and than Macbook Pro)
- Macbook air battery life
- iPad mini form factor
- Touch ID
- iMac 5K
(And now) The watch, force touch, HR monitor, research kit...

Apple seems to have a 'game changer' out at least once a year. One thing that creates a buzz. When was the last time you were THAT excited about a product from Microsoft, Samsung or Google?

My Next Laptop

Google, The One Trick Unicorn.

A lot of people like having the discussion of picturing Google as this evil, all seeing dystopian future type conglomerate.
While Google does all but admitting to be one (A monopoly with more power than politicians, buying AI and robotic companies, creating self driving cars etc.), it's still a bit far from reality.

So let's take a step back, and see what Google is. It's the biggest ad tech company in the world. Google makes money from pushing ads.

Not a content company. Not a hardware/ space/ AI company. Not a social network, email provider. Not a hosting company.

A huge ad company.

How big is it?
It's about as big as Microsoft in terms of market capacity, but earns 44% less. And Microsoft does a lot more business than just ads.
Google is almost twice as big as Facebook, but Facebook is leaner and growing like mad these days - And video monetising is yet to come.

But the biggest fear of Google, being a one trick unicorn (I refuse to call Google a pony), is being disrupted. Because people are aching to disrupt them.

So Google is not just trying to take over the world. It MUST do that in order to survive. It needs another meaningful business. Something that IBM, Apple, Microsoft and other tech giants which are here to stay have done.

The Cloud Will Be Disrupted.

The cloud is where it's all at.. A centralised, managed, virtual place, where all the data and logic go. It's comfortable. It's better than in house IT. It's safer than keeping your docs on your PC.

For my 100th blog post, I would like to make a careful prediction about the future of applications.

Like traditional IT before, the cloud also has its drawbacks. The biggest one: It's not really free, and it's not really yours.

The docs you put on Google, mails you send via your free provider, images you post on Facebook, files you put on dropbox - Unless you start paying a monthly tax for these conglomerates, is theirs to use, and sometimes delete.
Some of the big companies built an eco system (Google, Facebook, but also airBNB, Uber and a lot of the shared economy companies), which gets a lot of the population to be dependent on them for their monthly pay check.
Some of these companies have enough cash and political power to bend governments.

The shared economy is best for the customer, however it puts a lot of power in the hands of too few companies. Imagine that all taxi drivers at the states belong to Uber.

Part of the fear of societies and companies from bitcoin is not the currency itself, but the democratic, P2P model of its application - A distributed application, in which logic, as well as data, could be shared by every user, not just by the service provider - Because every user is, essentially, a service provider.

Imagine an airBNB service hosted by every airBNB user, containing all the users in his country - Without the need to pay commission to the airBNB. Or a 'floating' Uber like service, where drivers and customers connect and bid directly.

This will get the prices even lower, and give more to the specific service provider, rather than cut a fat commission to an already fat company.

The infrastructure is already there - In my opinion, we will start see those applications during the coming years. Like P2P shook the media world, decentralised applications will shake and disrupt the cloud.

Your lovely graphs are useless.

Visualisation used to be everything. If you need to make a point, show it with a nice graph.
A lot of times, when we demo our system to decision makers, they are really impressed with our ability to analyse their data and display it in graphs.

However, while increasing credibility and showing our software's lovely abilities, graphs have a learning curve, and sometimes it takes an expert to analyse graphs and conclude the necessary conclusions. While we accompany our clients during the initial use of the system, not too many of them are keen to learn the delicate art of data analysis, even if it looks great.

This is why we're starting to move towards insights. Instead of showing a scatter chart with quadrants and let the customer deduct where each group is located, we can display them a summary, showing each group's grade - This group consumes too much energy in relation to its throughput. this one is more efficient. A link to the graph is attached, for credibility, however once the customer trust your system, he won't even need it.

Build trust with your customer, and then only show him the bottom line instead of confusing him with complicated visuals. He will feel better treated as a decision maker rather than one of your analysts.

Do not bury the Laptop just yet...

Four years ago I had an argument with a peer of mine about Chromebooks.
We got a Chromebook from Google, I tested it and found it slow and useless, both as a work tool and a browser. Even battery life was poor.
Laptops (And PCs) seemed to be a decaying platform back then. Tablets were all the rage, and for consumer (And education) seemed like the go to platform.
My prediction was that ChromeOS, which was parallel to Android, was useless, and sales figures approved this.

But Google, with its limitless cash, kept on pushing.
Web tools got way better, becoming usable (Google docs, Office 365, Dropbox to name a few)

The first hit came from Samsung, which sold a 250$ Macbook air lookalike, with decent screen and battery life. It quickly became the most selling laptop in Amazon.

Finally, after finding out that tablets are a nice play tool, but not as good for education, the educational market also caught on.

I'm pretty sure that decent developer frameworks are on their way as well, though the road is still a bit long.

So now, I'm admitting I was wrong - The Google way of doing things (Throwing pre mature products to the market, and then throwing a lot of $$$ on them until they take off) actually works.

Unless you're Google plus.

From one hosting to another...

This weekend, we moved our cluster from Rackspace to Amazon.

It was a very tense process, as we need to transport GBytes of data seamlessly from one server to another, as well as shift our data writing server from the old database to the new one, while losing as little data as possible.
Eventually, it was done in less than two hours, most of which were exporting data from the old server and transporting it into the new one.

My main tips for performing such a move:
- Sit down before, and write the process step by step. Preparations for the new server, exporting and transporting the data, updating DNS services, testing. Discuss and review with your team.
- Review the process, and estimate risks and contingencies. What happens if it takes too long to transport the data? What happens if you have to roll back? What happens if the new server crashes?
- In case of DNS updates, it would be faster and more reliable to add the new domain ip to /etc/hosts instead of waiting for DNS to refresh, which might take time. Don't forget to jot down the IPs of your old servers, you might need them :)
- In our case, we also had some one premise sensors with no access to DNS, which means we had to use both the old and new stacks active, both working with the new database, until we're able to access those sites and change the destination IP.
- After you've got your plan laid down, do a dry run. There's nothing like a dry run to sort out bugs and add missing steps.
- Be 100% clear about who performs which step and when, but keep one person in charge of the whole process.
- Keep everyone in the loop - Upgrade was performed Friday night, so we decided to do it from home.. We were a team of three, using Google Hangout to communicate while everyone is online.

Eventually, process went quite well, Amazon SSD servers are fast and zippy, and we're ready for our next scale challenge, which would have us scaling to tables larger than 100m records while keeping our high performance standards.