wrong tool

You are finite. Zathras is finite. This is wrong tool.

Towards a new application stack

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I stumbled upon this post about the bitcoin application stack.

And what got me intrigued was that the stack described is very similar to the way network database works.

A world where the database is not owned by anyone creates applications that must co-ordinate using open distributed protocols where bad-actors are expected. In the network world these kinds of protocols take years to evolve and develop and that constrains the speed with which new services can be deployed …

What could be very interesting is if the bitcoin transaction technology can be evolved to be consumed by non-experts in cryptography to be able to share and store data and the applications that are built on top of that technology don’t require up-front protocol collaboration …

I worry that I am in fuzzy brave new world thinking here… Maybe the speculation is too great. There is something here, though. Something meaningful.

It is encouraging that people are thinking about this.

 

The duality of my life

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On the one hand I read this fascinating article on AI and the implications of how we think and discuss AI.

And on the other hand I see this:

Nude programmers or smart people thinking about hard problems – one is the field I joined the other — well — not so much.

 

 

Hardware not Software is Eating the World: Len was right.

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In 1996, Len Widra, a Principal Engineer at SGI, and I got into a heated argument over the importance of software vs. hardware.

I was a kid out of school with an ego to match. Of course, I thought I knew everything. The crux of our debate was whether the software was a relevant technology or important technology.

Len’s observation was that software was irrelevant or something like that. Hardware, he observed, was the important technology.

As a software engineer, this was infuriating. As a computer scientist, this wasn’t very kind. How dare he say that a bunch of silicon was more important than my code?

It’s been almost 18 years, and I’ve learned more.

What I have learned is that new software rarely, if ever, displaces old software unless some new hardware shows up. New hardware shows up, and that new hardware makes the old software irrelevant or obsolete.

There is one interesting caveat. Some software applications are really dependent on the quality of the algorithms, and as the algorithms improve, the software gets obsoleted regardless of the underlying hardware changes. In many cases, the emergence of new algorithms creates new hardware that helps obsolete the old software.

For the vast majority of software systems, however, that’s not the case.

When you are looking for a new opportunity in the technology space, what you need to look for is where new hardware is emerging. If it is sufficiently different, that new hardware will obsolete the old software that was tied to the new hardware creating new opportunities for new software.

A mouthful.

A few examples:

(1) the emergence of x86 servers created the opening for Linux. Before x86 servers were a reality, the UNIX vendors owned the entire software and hardware stack. When x86 became good enough, a new software stack could win because the software used new hardware.

(2) Flash in the storage industry has truly created a massive disruption, enabling many different kinds of software stacks.

(3) Merchant (aka Broadcomm) Silicon is disrupting the networking space that reminds me of the x86 disruption.

(4) ARM processors made mobile computing plausible.

Maybe my favorite example is this picture from TIOBE Software that measures the popularity of programming languages. TIOBE measures popularity – not use or lines of code – and has been doing that analysis for many years:

2014-11-28_0849

You look at the chart, and you realize how slowly programming language popularity changes except for one programming language: Objective-C. The popularity of a single programming language changed dramatically not because it was good or bad but because of a single new hardware platform that enabled new software.

The hardware disrupts because it enables software that was impossible before. The carefully calibrated trade-offs that are baked into a system are tossed into the sea with new hardware. When you want to look for disruptions to your business, never look at software; software is irrelevant; look at the hardware …

 

Tablets are just lighter laptops

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In 2012 I argued with anyone who would listen that the tablet was a smaller lighter laptop and not a new computer. Because it was a lighter laptop Microsoft’s strategy of a unified OS strategy although poorly executed was the right one.

The reason this is important is if you believe tablets are a new growth platform.

The slow down in tablet sales makes my point.

Tablets are a replacement for laptops that are lighter weight. And as laptops get lighter and cell phones get bigger their advantages dissipate ….

x-platform mobile technologies

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The folks at Google wrote about their new toolkit  for their new mail app. An app, by the way, that is actually great.

I was convinced – from using the app – that they had a lot of platform specific code. Instead, being great engineers, they cracked a hard nut – how do you build a UI rich application without writing most of the code twice that I didn’t think was going to get cracked anytime soon.

The challenge with UI rich products is that they must interact with the native software interface of the device. And the native interface is very different and is written in very different programming langues.

What Google has done is very interesting. My stock recommendation for UI rich applications  is that you have a core that is in C++ and a bunch of platform specific code for each device, the approach Google has taken may indicate a new third way. The Prezi guys did this to great effect. And many of my former Zynga colleagues are doing the same.

I must dig in some more…

 

The Universal Translator and Thank You Google

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startrek-universal-translator

 

An old friend and his family came to stay at my sister’s house in Santorni

That you can stay at too and should!

What was really amazing was that my friend and his children only speak English and my cousins only speak Greek and some broken English. And yet they were able to communicate and become friends. Because when they couldn’t explain things to each other, when the limits of their shared understanding came to fore they relied on the 21st century universal translator:

2014-11-23_0929

 

The world is becoming a smaller place because some companies are trying to change the world…

 

Things are getting better with Data Science and our Data

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Yesterday I wrote about how the failure of companies to respect the privacy and happiness of their customers posed an existential threat to the entirety of services that relied on big data.

Some folks on twitter remarked that my Data Scientist Hippocratic Oath is what their companies live and breathe.

And that’s great. I think that protecting user-data aligns with being a great company… And I think a great company sometimes may need to be explicit about how it thinks about user data.

Juliet asked how does this apply when the customer isn’t a person? I guess we need to refine the oath to be a little bit more specific – instead of customers we should talk about people.

  1. I will do no intentional harm. I will not knowingly manipulate people to be unhappy or sad or miserable without their explicit clear and obvious consent
  2. I will never use our data in ways that are not aligned with the customer needs of the person whose data this is.
  3. The company is not the customer, and if I must choose the customer needs p person whose data this is over the company I will always choose the person. My job is to protect the user’s data not the company’s survival

And while I called out uber and facebook in my post, it’s only fair to share with folks that Facebook has been working to create a better code for it’s data science efforts described here and that the folks at Uber have hired an outside team to look at their approach to privacy.

I believe the self-interest of companies with vast amounts of data that we want to be kept private will ensure that the data is private because if it isn’t we will have encryption deployed everywhere. And I am delighted to see that happening.

And we’re seeing that. We, as users, need to demand that kind of data protection. The alternative is what happens in medicine where data is so regulated that our ability to fight diseases is being impaired.

 

A modest proposal for a Hippocratic Oath for Data Scientists

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Over the last two years, two troubling incidents from large companies have demonstrated the challenge facing modern companies that rely on large amounts of aggregated data to provide a compelling service.

The first was Facebook’s usage of data to test how people behaved when they were made upset.

The second was Uber’s threat to use information about what we are doing with the service for less than admirable reasons.

In both cases, customers had – unknowingly – handed over vast amounts of private information to a service provider that could use that data in ways that didn’t align with our best interests.

Although it’s easy to demonize Facebook and Uber, let’s not. Big Data is an industry that has exploded, and we are all collectively learning how to behave. Every industry has gone through a maturation phase. And my goal is to suggest a path for good because I believe in the value of these services.

The danger for service providers is twofold.

The first is that customers will begin to distrust the service providers and stop using the service. As a very private person about certain very personal topics, Facebook’s confusing and changing approach to privacy has made it difficult to use the service for topics where privacy matters. This switch from early on in my life with FB, where its commitment to my privacy was why I used the product.

The second is that customers will demand or gravitate to services that provide privacy. As a result, the ability to use the data to deliver better and more personalized services will be hurt. This is more likely. And what is more likely is that incumbents will provide that security. And almost as if to prove my point about this, WhatsApp now has a fully encrypted messaging channel. With fully encrypted messaging channels – SPAM becomes a much harder problem to solve, and ad-supported free mail services become much harder to monetize – this is not necessarily a better world for consumers…

What needs to be done?

I believe that people are decent. The world works not because we police it but because decent people know what they should do. We have morals and standards, and decent human beings gravitate to them.

With Big Data and the power to know more about your customers and manipulate your customers in ways that are not always aligned with their wants and desires, the danger for unintended evil is great.

Much like some medical experiments are not done because we think they are wrong, some data uses are wrong.

I don’t think regulation is necessary at this time. I think regulation will hurt the industry. What I do think is needed is a Hippocratic Oath for Data Scientists. If we agree on what is acceptable behavior, then most decent people will behave in the right way, and then the bad ones will be easily identified as bad actors.

More laws won’t protect us from bad people; decent people knowing what is right will protect us.

In that spirit, let me offer an oath.

  1. I will do no intentional harm. I will not knowingly manipulate people to be unhappy or sad or miserable without their explicit, clear, and obvious consent.
  2. I will never use our data in ways that are not aligned with the customer’s needs.
  3. The company is not the customer, and if I must choose the customer’s needs over the company, I will always do so. My job is to protect the user’s data, not the company’s survival.

I suspect if those three rules existed and we relied on the basic decency of human beings, the recent justifiable outrage would be significantly more muted because the things that did happen would not have.

We are a new industry, and as a new industry, we are figuring things out, and figuring things out means making mistakes.

Let’s take this opportunity where we made mistakes to make things better.

 

 

Some performance benchmarks of browser languages

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Read this really interesting performance analysis of various browser programming languages

Two things that come out of this.

The first is the power of meta-programming languages like Haxe. Using a better programming language and a typeless goop as your target machine-language can deliver great performance. Not entirely surprising – of course – because hardware is un-typed 🙂

I first encountered Haxe four years ago as an answer to my prayers around the mess of Php and Javascript inflicting typeless chaos on the world of web programming.

We actually used Haxe at Zynga to write a renderer if memory serves me write and got better performance than we could have if we had written it in entirely in Flash.

The second is how underwhelming the performance of emscrimpten is compared to Haxe when you don’t have asm.js. There was a time before the mobile web died that I though that C++ to JS would be important. Now that it’s clear that the mobile web is deader than a door knob, the value of Javascript as a front-end mobile programming language is trending to zero fast.

 

 

The pursuit of the 10x engineer

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I had the good fortune of reading an article about how we’re getting better at getting better in the New Yorker.  And it got me thinking about the pursuit of the 10x engineer and the lack of on-the-job training at most tech companies.

One of my beliefs is that as the size of a company expands the engineering population becomes average through sheer force of numbers. You can fight this problem indefinitely like Google and NetFlix do, or you can ask yourself – what the hell am I supposed do about this?

The general approach most companies take is to lather process on top of everything in attempt to prevent anything bad from happening. In effect, acknowledging that the average employee is no longer good enough to do their job, you rely on a small number of gatekeepers to act as fulcrums on your product quality. Hence the emergence of architecture reviews and design reviews and gatekeepers of quality.

We look at small high-talent density teams and wonder why can’t the entire company be like those teams? Heck we fantasize about the small high-quality teams and their productivity.

We rarely ask the question of how do we systematically improve our teams. We rarely approach making engineers better a core part of our core values. We make our value hire only the best, not make the best better.

The wasteful approach we take to talent, and to people will get disrupted by a company that realizes that it’s better to systematically train hundreds of engineers than to keep trying to find the best…

Our industry’s approach to talent is wasteful in the extreme – we discard 50% of the human race, we don’t train people, we love youth at the expense of the old, we think that insane hours that burn people out are okay and we’re able to do that because there is this almost endless supply of new kids coming in from every corner of the world dreaming of getting a piece of the action.

This approach reminds me of a company that never asks the question of what happened to the OLD users because of all of the NEW users that are joining every day.

I hate to leave a post with only a problem. What are some things that need to change?

Two things that need to happen:

  1. Change the review process. Right now we are loathe to evaluate and improve people’s work because it ties so intimately with salary and promotion discussions. We need to find a way to continuously evaluate people’s work not to punish or reward but to improve. Evaluations all feel punitive because we don’t use them to specifically target ways to improve just to ding people.
  2. Invest in teaching. Too much of training is done on the job through osmosis and practice because that’s all that’s available. And if someone is teaching the guys doing the teaching are very bad at it because we don’t train them on how to teach. We ask people who suck at teaching to create training materials and are surprised at how little value anyone gets in them.

This space feels like there is a lot of opportunity for innovation because of how little is currently being done. And if you believe that companies success is a function of the people, then a deep and sophisticated training program may be a strategic differentiator.

Something to ponder.