Day One of Soylent

Photo Aug 02, 6 59 23 PM

Sunday, September 1st, 2013, I placed a $255.00 order for One Month Supply of Soylent, a futuristic powder food that simply requires the addition of water to sustain human life indefinitely on planet earth and beyond. The purchase was an impulse decision governed by two underlying facts:

  1. A good friend from college was experimenting with a homemade DIY version of soylent, which alerted me to its existence.1
  2. My position in life was changing such that I would soon be more directly responsible for my daily bread. Like most Americans, I’m too starved for time to have to worry about other forms of starvation simultaneously.

My understanding was that my Soylent was to arrive in late 2013, once Rosa Labs2 had the recipe finalized. During the interim, I bought about ten food powders from Amazon and made my own version, which I wrote about. DIY soylent was interesting but ultimately non-sustainable as a viable food option. I daresay most who partook in that experiment would concur.

My Soylent did not arrive in late 2013, though the company kept promising that the shipment was just around the corner. In November Rosa Labs promised a January 2014 shipment. In January they hoped “by the end of February” to begin shipment. Each update pushed the shipment date further and further into the new calendar. It became a longstanding joke with my skeptical friends that Soylent was perpetually coming “in a couple of weeks”.

It was not until Saturday, August 2nd, 2014, that I finally received my Soylent. This was 11 months and 1 day after I had pre-ordered it. So the burning question is, was it worth the wait? How good is Soylent—is it a viable alternative to what most humans consider edible food? As I see it, a given food has four distinct questions that it must answer:

  1. Is it healthy?
  2. Is it inexpensive?
  3. Is it easily prepared?
  4. Is it tasty?

We can knock the first 3 questions out of the way fairly quickly, as you can get most of the pertinent information from the Soylent web site. Soylent is healthy, inexpensive, and easily prepared.3-4

Answering the question regarding taste first requires a historic and international context. For most of human existence, the problem of having enough food—let alone food that is nutritious and varied—has been a huge problem. Even in the 21st century, 13% of the world is going to bed hungry according to the World Food Programme. Every 2 in 7 world citizens are affected by vitamin and nutrient deficiencies. Modern Americans have access to virtually every kind fruit, vegetable, grain, meat, and recipe that has graced the tables of fine dining throughout the millennia. In a historic and international context this is abnormal human experience. Millions of citizens in other countries have lived and continue to live on beans and rice three times per day, seven days per week.

A limited diet may sound bad to you until you remember that the United States is the second fattest nation in 2014, second to Mexico.5 Would you rather be obese with an unlimited menu, or fashionably thin with less exciting meals?

Soylent clearly falls in the latter category. In the words of Robert Rinehart, CEO of Rosa Labs, Soylent is “definitely not trying to compete with the experience of your mom’s cooking.” I tend to agree. Unlike DIY soylent, however, it is possible to eat Soylent unchilled and without frozen fruit. Due to the level of carbohydrates—84g per serving—Soylent is slightly sweet and quite agreeable. I plan to add raw fruits in the future for their enzyme benefits but it’s important to note that blending in mixed fruit is not necessary to make Soylent palatable.

What about the flatulence problem, which caused my shipment an additional 1 month delay? Thus far, I am not experiencing abnormal amounts of flatulence.6 In the words of Benjamin Franklin, “It is universally well known that in digesting our common food, there is created or produced in the bowels of human creates a great quantity of wind. … [S]o retained contrary to nature, it not only gives frequently great present pain, but occasions future diseases.”7 So, I will leave this subject for the time being by saying that flatulence was a problem 300 years ago, it will always be a problem, and Soylent does not noticeably magnify the problem in my experience.

In conclusion, I have not yet re-ordered Soylent8 but I plan to soon. If you’re on the fence whether to get any or not, that should be the deciding factor. People eat this stuff.

1 You’ll notice sometimes I write about soylent in title case and other times in lowercase. As Conan Doyle wrote, “There is madness to the method,” but here is the method. The Soylent blog declares, “The soylent community has established the protocol that Soylent refers to the our product, and soylent indicates a DIY or otherwise unofficial version.”

2 Rosa Labs is the company that engineers and produces Soylent. I’m not sure exactly the timeframe that this entity came into existence, but it was sometime before November 7, 2013.

3There was some ambiguity early on about exactly how much Soylent cost per meal. When I received my order confirmation, my one month supply was said to break down to $2.83 per meal. At a total cost of $255.00, this meant Rosa Labs intended to mail 30 days of food. The actual delivery was 4 boxes containing 7 meals each, which rounds to $3.04 per meal. This is still a third of the price of a Beast Burger and soft drink at Northeastern State University’s cafeteria, so I maintain that Soylent is inexpensive.

4The only common complaint about preparation is that the Soylent powder gets everywhere in the kitchen, no matter how hard you try. My experience is similar, though I’m not sure how you can solve this problem with a powder food. Sometimes life is messy.

5Thanks to modern fast food, a third world country can eclipse a first world country in obesity. Fascinating.

6I’ve eaten 2010 calories of Soylent in the past 24 hours.

7Isaacson, Walter. (2004). Benjamin Franklin : An American Life. New York, NY: Simon & Schuster Paperbacks. Pg. 373.

8I have yet to decide how much weight to give to the words, “As a #SoylentPioneer, we’ll give your reorder top priority.” Does that mean I’ll get my reorder in 6 months, as opposed to having to wait another 11?

The Desperate Shortage of Designers in Computer Science

This summer I’m interning at a tech company that is very design centric. When building new software, our first consideration is what will be the best user experience and what will look great, not what is technologically feasible.

How often do you see an app that’s absolutely beautiful and a joy to look at but doesn’t really do anything? That’s an all-too-rare problem to encounter. It is much more common to see applications that have a mediocre (at best) interface.

Computer Scientists Need to be Better Designers

It’s tempting to say that design and computer science are different fields, but that’s really not true.

To implement a design you have to be a good programmer, and to build an interface for humans you need to be a good designer.

It’s not good enough to have a graphic designers build pixel-perfect wireframes and then trust non-designer programmers to implement them. There are a million and one little decisions that must be made when this implementation occurs1, and if these aren’t done by folks who understand design, your originally-beautiful-idea is going to have terrible execution.

I get it—not all computer scientists have design chops, it’s unfair to expect that, and you need people who focus strictly on the backend. I’m not saying all computer scientists need to be excellent at visual design, but the problem is that they’re currently non-existent in proportion.

Here’s a novel idea: build something incredibly stupidly simple, and spend 90% of your development time improving the design and user experience of that incredibly stupidly simple idea.

You’ve probably never done this before and you’ll be shocked at the result. Your software will be so simple that the average user is (1) delighted by the unusually good user experience (2) delighted that your app does so little that it’s actually usable.

We need less functionality and more user experience. This sounds like a terrible idea to 10% of the population, and they voice their opinions very loudly. The other 90% are the quiet bread-and-butter user base who are desperately craving for their digital lives to be simplified. Simplicity liberates them and they pay good money for this. The market has proven this over and over again.

How Can Computer Scientists Become Better Designers?

Artists have many books that talk about design: hues, values, composition, and textures. Graphic designers have books that talk about the intricacies of fonts: families, sizes, line heights, and characters per line. These books should not be confined to just artists and designers. They should be referenced by computer scientists just as heavily as textbooks on backend programming. They should be a core part of postsecondary academia. They should be a major component of discussion for every software project that’s intended for humans.2

In short, computer scientists can become better designers by studying and practice. That’s how they learned how to program in the first place, right?

The Danger of Not Becoming Better Designers

Making visual decisions usually requires more creative thinking than building software that’s functionally matching a specifications memo. You can teach a monkey how to program.3 Design is more stimulating and satisfying. And right now, it’s much more rare.

If you don’t get good at design, you will:

  • Confine yourself to enterprise and legacy backend systems where visual chops don’t matter
  • Never have an opportunity to participate in the startup world, where design is the #1 deciding factor of whether you’ll make it or not4
  • Never experience the sheer delight of users of your software. Laypersons who use your software get excited about an incredible design and UX, not about reducing database queries by .015 seconds
  • Outdate yourself as inexpensive labor and futuristic coding-writing-programs make your job obsolete

If you’re a good designer who is capable of executing your designs, you will never be out of work. You can work on what you want to work on. You can build your own stuff. You can write your own ticket.

Now go design, computer scientist. You know how to code, which means you can implement those designs. That’s dangerous. The world needs you.

1Just trust me. I’m young but I’ve been doing this for 5 years.

2Many programs in enterprise are written to be consumed by other programs and systems instead of humans, which is why I make this distinction.

3Almost.

4Okay, I just made up this statistic, but I’ll die defending it. I firmly believe you can sell ice in Antarctica if your design is good enough. And by “good enough” I’m mean better than 99% of what’s passed today as good design. That’s how starved the computer science community is for design skills. There’s lots of design talent in the world, but this talent doesn’t control the code base generally speaking, and that’s a shame.

The Mathematical Probability of Accuracy for Ecological Validity in a Given Experiment for Behavioral Psychology

In behavioral psychology, there are two ways to observe animal and human behavior: in a controlled laboratory environment, and in a non-controlled real-life environment.

Knowing how many independent variables1 exist in such an experiment is crucial to a diagnosis of the experiment’s accuracy. Ideally, one should have a single IV. When this IV changes and the DV consistently changes with it, one can accurately say that a direct correlation exists. Predictable outcomes, after all, are one of the primary goals of psychology.

In a controlled environment, it is quite easy to boil the IVs down to a single one. The time of day, the light intensity, the amount of food present—all of these things can be more or less kept constant from experiment to experiment. This is why laboratory testing was so popular in the early 1900s. Things were easy to control, and experiments like Little Albert and Pavlov’s dogs were in the books while they were still in the laboratories.

The problem with controlled experiments is that they often do not reflect real life at all. Dogs usually don’t hear bells before given food. Babies usually don’t hear loud noises after touching rats. Real life is much less consistent. Laboratories can give key insights into some aspects of existence, but they often don’t have a helpful takeaway for everyday life. They lack ecological validity.

To remedy this, scientists observe humans and animals in real life. The difficulty with this approach is that real life is much harder to control than a laboratory. There are many moving parts. No two mornings are alike in a non-controlled environment.

All of this is stuff you’ll find in a normal psychology textbook; it’s also intuitive even if you have never studied psychology. But as a person interested in mathematics, I wanted to quantify in numbers just what this looked like: as you introduce new IVs into an experiment, what is the statistical likelihood that the results of that experiment accurately reflect a correlation between what you suspect is the primary IV, and the resulting DV? A mathematical formula seemed appropriate to answer such a question. Sitting at my desk with pen in hand, I began to derive a formula.

First, I started with the obvious: a single IV would lead to a 100% accuracy. Let’s say you knew there are 3 ways to get a rabbit scared: by playing a loud noise, by grabbing it suddenly, or by showing it a predator. In a laboratory environment, you could throw away the last two (it’s a controlled environment, remember) and just experiment with a loud noise. This becomes your single IV. You observe that when you play a loud noise, the rabbit is scared, and when you do not, it is not. Direct correlation, 100% predictability.

Second, I needed to find a pattern before I could begin writing my formula, so I asked, what happens when there are two IVs present? To use our example, what if you played a loud noise and grabbed the rabbit at the same time? Assuming you hadn’t performed the earlier experiment, you would conclude the following: something scared the rabbit, and therefore it was either (1) the loud noise (2) the grabbing (3) or both. You know it is one of those three options, but you’re not sure which. If you had to choose among these options, your accuracy would be 33%.

Just by introducing a second IV, the accuracy drastically dropped from 100% to 33%! This was quite a jump. I needed one more data point before I could really write my formula. So, what happened if you introduced a third IV by also showing a predator to the rabbit while performing experiment #2? Then you would have seven explanations for what caused the scaring: (1) the loud noise (2) the grabbing (3) the predator (4) the loud noise and the grabbing (5) the loud noise and the predator (6) the grabbing and the predator (7) or all three. Seven combinations meant that the likelihood of any of them being the right combo was 1 in 7, or about 14%.

It was around this time that I realized we were working with binary math:

  • 1 in binary is 1 in decimal
  • 11 in binary is 3 in decimal
  • 111 in binary is 7 in decimal

There was our formula! With each additional IV, simply you add a “1” to the binary number like tally marks, which then increases the decimal equivalent, which in turn forms the denominator of the answer for that scenario.2

Armed with my formula in hand, it was time to make a visual out of this.3 Thanks to Xcode 6 and Swift, I was able to code it up fairly quickly and post a gist of it for you to scrutinize. This graph is the nexus of psychology, mathematics, and computer programming. It was a fun project to see to completion.

Screen Shot 2014-07-07 at 10.30.31 PM

As you can see from this graphic, ecological validation is very very difficult to attain with certainty. Real life contains many IVs but after less than half a dozen of them, the accuracy of the DV plummets to near-zero.

This is the reason that, despite their limits, laboratories are still in use in psychology. This is also the reason that Facebook tinkered with users’ feeds for a massive psychology experiment: if you’re going to insist on doing experiments in real life, you have to do them at such a scale that you can offset the huge unlikelihood that the IV you suspect is causing the DV outcome, is really the right IV.

1I assume your knowledge of independent and dependent variables. For the remainder of this article, I use the acronyms IV and DV to denote them.

2I studied mathematical proofs in discrete mathematics. You could get a lot more formal than I have here with a comprehensive proofs, ending in quod erat demonstrandum (QED), or “thus it has been demonstrated.” But I’m really not interested in that, and I didn’t think you were either.

3Unless you want to geek out over the code, you can safely ignore the values on the X axis. It’s the Y axis (percentage of accuracy) and the addition of new data points (each new dot is an additional IV) that you should find particularly interesting and, if you’re a behavioral psychologist, disturbing.

This Nonsense about Twitter Followers to Tweets Ratio

I’m noticing some people are perpetually concerned that they have 5,000 or 20,000 tweets but only a dozen or a hundred followers. They’re making a ratio out of these two numbers and it’s making them feel guilty.

Maybe you’ve not met these people, and if that’s the case then count your blessings. But think about this: what’s the purpose of Twitter, for you? 1% of people on Twitter are influential. The other 99% follow them. It takes both. Twitter is just a natural extension of the social validation rules that occur in all areas of life. The one and the many.

But—and this is equally important—Twitter also exists for people in the 99% to talk to other people in the 99%. In this function of Twitter, it’s a one-to-one communication tool.1 Think free SMS with multiple beautiful apps to choose from (notice how with true SMS you’re limited to a single app that your operating system provides, which may or may not have taste).

The SMS analogy is important because the ratio is transferrable. Over the course of months and years, you send thousands of text messages to 10 or 20 people. Nobody ever felt guilty that they had too few contacts for the amount of texting they were doing.2

And so—long live tens of thousands of tweets and 50 followers. That’s healthy and normal.

1Twitter seems to be more heavily promoting this aspect of communication. In the browser version of Twitter, replies are not visible in the timeline of a user’s profile—even if they are replies to someone you follow. The tweets are still technically public assuming you have a link to them, but otherwise, if you’re not a recipient of the reply, I’m not sure how you would get to them. It will be interesting to see if this shift becomes the standard in native Twitter apps, which still show replies on profiles.

2Well, unless you’re under 20 or a very strange person.