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How I Became the Most Hated Person in San Francisco, for a Day

How I Became the Most Hated Person in San Francisco, for a Day

This morning I put the finishing touches on, and launched,, a site where I’m selling reservations I booked up at hot SF restaurants this Fourth of July weekend and beyond.


I built it over the weekend after waiting at Off the Grid for 30 minutes for a burrito from Señor Sisig, and realized that there’s got to be a market for the time people spend waiting for tables at our finest city dining establishments.  Turns out I’m not the first person to think it, as there are two startups doing this very thing in New York City (here and here).

It’s a simple site with a simpler backend. I book reservations under assumed names, list them on ReservationHop, and price them according to the cost of the restaurant and how far in advance they need to be booked up. I don’t use OpenTable; I call the restaurants directly. And I have a policy of calling and canceling reservations that don’t get snapped up, because I don’t want to hurt the restaurants (the assumption being that on-demand restaurants with high walk-in traffic won’t have trouble filling those tables).

I anticipated some mild interest when I launched this morning, emailing the 20 or so potential customers I had interviewed at Off the Grid and some friends. I expected maybe having to make somewhat of an effort in order to get people to discover what I’m doing.  I never expected a maelstrom of internet hate.

Not all of the responses have been negative, but an overwhelming number of them has been.

I totally understand the frustration people have with SF’s particular brand of “innovation.” And it seems that everywhere you look cherished public resources are being claimed by startups, whether it’s Google laying claim to bus stops or parking apps laying claim to, well parking spaces. I’d half expect someone to come along one day and put picnic blankets down in Dolores park and sell them at $25 apiece.

I also understand that this represents, as one Tweeter put it, “a caricature of SF tech bro shithead.” And as someone who spends a lot of time complaining to my friends about how much of an insular bubble San Francisco has become, what with apps built by the 0.1% for the 0.1%, I completely agree. In fact, I would have much preferred the media raised this much a fuss about Drillbit or The Creative Action Network or any of my other startups over the years.

But there’s something peculiar about SF, in that our media seems to love hating on stuff like this, so I guess I’m not surprised that I got Valleywagged almost immediately, followed by a post from The Next Web. I responded to an interview request from TechCrunch so it’s written up there too.

Meanwhile, traffic has gone through the roof. Here’s my actual Google Analytics graph from today.

Screen Shot 2014-07-03 at 5.50.59 PM

I guess you can say that any press is good press.

But let’s talk about the questions/criticisms everyone has. What was I thinking! How dare I sell something that’s free! Is this even legal? Is it ethical? Restaurants are going to hate this!

To be honest, I haven’t spent a lot of time thinking through these questions. I built this site as an experiment in consumer demand for a particular product, and the jury’s still out on whether it will work. But I can tell you what I have thought through.

The initial criticism has been about the fact that restaurant reservations are free, and I shouldn’t be selling them. First off, reservations aren’t free. Restaurant tables are limited, in high demand and people wait a good long time as walk-ins to get them. Reservations take time and planning to make and the restaurant assumes an opportunity cost from booking them. My friend joked that it took me less time to build this site than most people spend hunting for OpenTable reservations in a given year.

What about ethics? We are talking about an asset that most people don’t think about having a value. That doesn’t necessarily mean that it doesn’t have a value, or that people wouldn’t be willing to pay for it. For instance, no one would have thought that taking a cab during rush hour should cost more than a normal ride, until Uber launched surge pricing and we realized that people are willing to pay for it. Clearly, the service of booking a reservation in advance has value to patrons. This is evidenced by the startups doing this right now in New York City.

If someone does pay for it willingly, is it really unethical? The consumer has made a choice, the reservation stands, the restaurant gets a table filled as planned, and I have made money for providing the service. That seems perfectly ethical to me. I am aware that the ethical conundrum is around the “what if” question: If I book a table and no one buys it, the restaurant loses business, doesn’t it? I don’t know if that’s true yet, and I’m also working at a volume so low that it probably won’t matter.  I’m canceling the reservations 4 hours before if they don’t get bought, and certainly a restaurant that’s booked weeks in advance won’t have trouble filling a table with their high walk-in traffic, or someone who gets lucky and snaps up the reservation for free on OpenTable.

But more importantly, I think that a paid reservation lets customers get skin in the game, and that means that restaurants might even reduce no-shows if paid reservations become a thing. When Alinea introduced ticketing (pre-paid reservations), they dropped their rate of no-shows by 75%. That’s a pretty good deal in an industry with razor-thin margins.  I’m just speculating on whether this might provide value for restaurants; I can’t speak for them and need to parse this out over the next couple days.

So, back to becoming the most hated person in SF. I learned a lot today about how media, culture and technology in this city interact, and I have to say that overall, I think that the people who have sent me violent threats via email and Twitter, while excessive, may have a point. So in the interest of ethics and fairness, I want to talk to restaurants about working with them directly on a better reservation system. I’ve heard that OpenTable is loathed by many restaurants who don’t want to pay to fill tables. There may be a ticketing solution to high-demand restaurants. If you’re a restaurant, please drop me a line.

And if you’re a regular Jane or Joe, and you missed an opportunity to get a reservation at a hot SF restaurant for your first wedding anniversary this weekend, check to see if there are any reservations available for you at

UPDATE: We have made a “soft pivot” to address feedback from the restaurant and tech industries. Read more here.

July 3, 2014132 commentsRead More
Being a Productive Animal

Being a Productive Animal

We all feel unproductive at times, some of us more than others.

For me, personally, I have high expectations of myself and what I’m able to do, so I feel unproductive frequently. It’s very rare that I put in a solid 10- or 12-hour day of work and feel good about it. Sometimes it’s because I don’t work solidly and I know it. Other times it’s because I work solidly but don’t feel like I accomplished anything.

I’ve been through long periods of employment and “funemployment” and it’s easy to feel unproductive in both cases. When I’m employed, it’s easy to work very hard and look back and not have anything significant to show for it. It’s not always clear if it’s the sort of work that adds up and pays off at the end or if the entire project, concept or enterprise is on the wrong track from the start. At other times you have a bad manager or conflicting goals from the top that make productive work in one direction difficult. In either case it’s easy to feel unproductive even if you’re spending all your time working.

When not employed, there’s even less of a way to tell whether your work is productive or not. If you’re spending your time looking for a job, a similar type of busy work can be had sending out hundreds of applications. It’s easy to be unproductive whilst seeming productive, especially when looking for a job.

If you’re spending your funemployment trying to start a startup or build a project, you face a similar crisis of confidence. You can spend all day building a website or a MVP only to take it to your first customer and be told it’s useless. Or you can scrape a list of all the hospitals and clinics in the world from a directory site only to decide that the market testing you spent $500 for came up a dud. Or you can do nothing, which sometimes feels more productive than working in circles.

I feel most productive when I feel like I’m building something, even if it’s something no one wants or will use (or read). But the sense of accomplishment is fleeting; then there is another task ahead, another project to tentatively poke and see if it’s got some life in it.

I don’t like committing wholeheartedly to a project unless I can see how it’s worth it. But that often means not doing enough work on it to reach the right conclusion. That’s a trap to avoid.

This is what I think the cult of “failing fast” is all about. Not simply doing something and failing, but doing something seemingly productive and then realizing it was all for naught. To then be able to pick up again and feel productive about something potentially useless is a rare gift.

I felt productive writing this post. I will hopefully feel productive later today when I’m done cranking out the finishing design touches to my new site so that people will actually buy what I’m selling. I think that’s probably the easiest way to tell if what you’re working on is productive: not whether you feel productive working on it, but if before you work on it, you can define why you’re doing it and how it will help your project succeed.

June 30, 2014Comments are DisabledRead More
Announcing Drillbit: Who’s on your Mailing List?

Announcing Drillbit: Who’s on your Mailing List?

The genesis of this idea was a couple weeks ago when my cofounder said: “Would it be possible to see what percent of our email list was female or male based on their names alone?” Thus Drillbit was born.

Screen Shot 2013-06-20 at 6.01.30 PMIn the last couple weeks I have been pouring over data sets and trying different formulas to find the best way to break down a list of seemingly random name data into digestible information. The resulting app allows anyone to upload their mailing lists and see who’s in them, and in perhaps the coolest feature, they can segment their list as well.

The Project

Drillbit uses publicly available datasets to create a likely demographic profile of mailing lists based on first and last names. Upload your mailing, customer or user list with first and last names, and based on that information we will create an age, gender and demographic profile of your list.

The Datasets

Listed here are the foundational datasets of this project, including for analysis tools that haven’t yet been released.


The essential principle behind Drillbit is that an individual’s first and last names betray a lot of information about his or her background, origins, language, gender, and even income and ideology. Names can be both varied in their originality and popularity as well as conservative in their staying power. A surname can be passed down for generations, whereas first names have a tendancy to be cyclical.

As an example, take the name “Max.” It is a common name, or common enough it would seem, that one could find out very little information from the name alone. But as it turns out, “Max” only may seem common to us given its surge in popularity in the late 80’s and early 90’s–the birth years of the rapidly matriculating Generation Y. In 1974, only 400 Max’s were born nationwide!

Of course, baby name popularity is not a new idea. But the variance is astounding, and not just in terms of popularity. In 2012, the two most popular baby names for boys and girls were “Jacob” and “Sophia.” Unlike “Max,” both of these popular names seem to have spent the last 100 years on the up-and-coming list.

With this amount of unique variance in names–some names jump and others sink, some names are like fads and others never really take off–it isn’t surprising that, in the aggregate, it is possible to take a list of people and determine how old they are likely to be.

So that’s what I did. Using the above datasets on name popularity, I was able to come up with some pretty convincing initial results, benchmarking against existing lists I knew well.

The first step is to condense the data I had into a table which compared year of birth, and gender, with the % likelihood that any random “Michael” born in the last century was actually born in that year. For example, if 10,000 Michaels were born between 1900 and 2000, and 1000 Michaels were born in 1950, then 1950-M-MICHAEL has a 10% likelihood; i.e., given a random Michael, there is a 10% chance he was born in 1950.

With the charts above, you can see how this would play out. If you were to use Drillbit to upload a list of 5000 Jacobs, you would see the age match pattern roughly cohere to the above chart. The more Jacobs there are, the higher confidence we would have in the result.

There are some obvious complications with this model. The first is that although 10% of all Michaels might have been born in 1950, they would be over 60 now, and their chance of being around is much smaller than that of a Michael born in 2000. That’s where actuarial data comes in. Using the above actuarial table divided by gender, I was able to normalize the distribution based on likelihood of survival in each age cohort. No matter how many Max’s were born in the 1910’s, there aren’t a lot left today.

The second problem is that names are not unisex; in fact, most names in the database aren’t 100% unisex, Michael included. It became clear that age data had to be done on the basis of gender, and not on totals. Names that are popular with one gender are not necessarily popular with the other at the same time.

To compensate for this, age data was tabulated separately, all the way down to the actuarial normalization. Female names were rated and graded against each other, male names were separately, and only at the end were they normalized against each other.

Compared to Age, Gender and Race were quite easier. Gender analysis was a simpler form of the age analysis–likely names were divided by gender and then normalized by age. Race/ethnicity data was also quite simple based on surnames–the data was already organized by the Census, albeit 13 years ago, so getting it into a searchable database wasn’t tricky.


There are some obvious limitations to my method. The first is in the nature of large numbers, or small numbers as the case may be. If you were to put a list of 2 names into Drillbit, it would spit out a similar looking demographic profile running the gammut of all ages and perhaps some different races as well. There are few names that are reliably “Black” names or “Over 65” names (although, there are a few names with a 100% incidence within one to five years–challenge you to find them). Like with any aggregate data project, the larger the list, the more reliable Drillbit will be.

The other limitation is in any sort of list that comes with existing biases. Say, a list of NBA players (heavily 25-35 and black) or a list of sitting US Congresspeople (heavily male, white, and 35-55). These inherent biases will be reflected in the anlaysis, but probably not to the extent that they could be. This is the House of Representatives, according to Drillbit:

Obviously 18-24 year old congresspeople would be impossible. And yet, even with a small list of 435 names, the trends in age in reality poke through.

In short, you shouldn’t use Drillbit to analyze a list whose composition is already known to you to skew heavily in favor of one or two demographics. However, it’s worth nothing that Congress is 18.3% female, and Drillbit predicted 20.5% based on names alone. Not shabby.

The final inherent bias that’s worth mentioning is in skewing toward younger ages. Since younger people are overwhelmingly more likely to be alive, post-normalized numbers skew younger. In addition, in development, I had a category be “Under25” but it became apparent that although my database could detect age variability all the way to Age 0, babies aren’t going to be on mailing lists, and they were throwing off all the results. So to compensate for the younger skew, I made a judgment call to make a cutoff at 18, and not track any younger cohorts, even though some websites may have 13-18 year olds as users.

Now that you know more about how I did it, upload a list and try it out!

June 21, 2013Comments are DisabledRead More
Why Live Video is So Cool

Why Live Video is So Cool

People broadcast their Nintendo sessions. Their Jägermeister. A low angle on their TV set as they prop the phone up on their couch. Someone is broadcasting his daughter’s basketball game. And another is broadcasting her baby’s bathtime. I know this because as product manager for Broadcast for Friends I have the keys to the castle–that is, I can see the public broadcasts made by our users flicker across my screen and I can drop in on them from time to time.

What amazes me still is how cool most people–that is, people who don’t live in a world of technology–find what Ustream can do with live video. People laugh with delight when they find out they are live on air. People are happy to broadcast the most mundane details of their lives–one man has been broadcasting his hummingbird feeder for three hours–if only to reach obscure corners of the world. The magic is only compounded by the immediate sociability of the feed. It’s not only live, it’s on Facebook, and freely available to any one of your friends with an internet connection.

Why is live video so cool? What is it about the sharing of peoples’ lives half a world away in Germany or Korea so interesting? Surely we can do the same thing by turning on the television news. But the rawness of mobile broadcasts, the shaking of the phone, the looks on peoples’ faces when they find themselves on live TV at the whim of a consumer device–these experiences are brand new in the history of the universe. It’s not just the shareability of video, which itself has created its own niche market (think Viddy and SocialCam). It’s the fact that this video is live, realtime, untampered with. Seeing that “Live” label on the viewer confirms for the audience the video’s authenticity, and broadcasters, in turn, experience acute self-awareness at the immutability of their broadcast. “We are live on Facebook,” someone said. “Don’t screw it up!”

August 25, 2012Comments are DisabledRead More