Snowball

Never underestimate the bandwidth of a station wagon full of tapes hurtling down the highway. [Andy Tanenbaum, 1989]

As  someone who did a lot of computing before The Cloud or Dropbox was a thing, I have a little box of hard drives tucked away in my living room. A bunch of these drives will be paperweights by now, the ball bearings frozen-up or platters otherwise unreadable, but I would happily pay for the salvageable data to be thrown up on Amazon for  posterity and my own nostalgia. I tried trickle-copying the data over our Sonic DSL connection, but things were happening at a geologic time scale. Enter Snowball, Amazon’s big data transfer service. You sign up and the service piggy-backs on your usual Amazon Web Services (AWS) billing & credentials. Then they ship you a physical computer, a 50 pound honking plastic thing that arrives on your doorstep via two-day UPS:

AWS Snowball Enclosure

The first thing I noticed was a cleverly-embedded Kindle that serves as both shipping label and user interface:

AWS Snowball, Shipping Label

The plastic enclosure itself opens DeLorean-style to reveal a handful of spooled cables:

AWS Snowball, Cable Spool

You plug the Snowball into your normal 120V AC mains power, and boot the thing:

AWS Snowball, Bootup

 

Next you install some AWS software on another machine on your network, and then use that software to copy data over the network to the Snowball itself:

AWS Snowball, CLI Client

Tucked away inside is a serious amount of disk storage, 50 terabytes in the case of the Snowball I tried. The device itself is an intimidating “engineering sample,” whatever that means:

AWS Snowball, Engineering Sample

This is where I noted the first serious snag in my plans: The Snowball relies upon your own (home) network for data transfer, which puts a bandwidth bottleneck at your router. My suddenly-beleaguered Netgear thing was tapped-out within moments, and installing Linux on the router (WW-DRT) would not have gotten me further than a 2x speedup.

Also the Snowball client runs on another machine on your network, which is not much of a limitation when used in an institution. However I was copying data from an external hard drive sitting in a SATA IDE to USB 3.0 adapter thing, which put another bottleneck and layer of complexity at the USB port.

Why not just interface my external hard drives directly to the Snowball? Or maybe even install the hard drives as, temporary, internal disks within the enclosure? The enclosure is almost hermetically sealed (“rugged enough to withstand a 6 G jolt“), and exposes only Cat 5 and fiber network ports.

Here is me telling the Snowball via its command-line client that it is ready to be returned to AWS in Oregon:

AWS Snowball, Return Label

So! I found the Snowball to be a relatively sophisticated and honest approach to the realities of the Internet bandwidth vs. storage size growth curve. However it is not a good solution for those of us wanting to upload a bunch of rotting hard drives to The Cloud. Amazon has a legacy service that accepted shipped disk drives directly, but I believe it has gone away. On the other hand, I expect Snowball to be a very efficient and slick solution for most organizations. But for the guy sitting on some dusty hard disks, it did not get the ball rolling.

Happy Birthday, Hipsteraunt

Last month was the two year anniversary of the website Hipsteraunt, which I built with my friend Lance Arthur. He did the design, I did the random menu generation. It is a quirky bit of AI and NLP under-the-hood, so a user gets menus featuring free-range suspended chicken feet, truffled shisito pepper with achiote, and marshmallow crudo, at a place with an ampersand in its name. The inspiration had been a particular dinner out in San Francisco, at an immensely overrated restaurant. But it could have been Brooklyn or the West Loop. I am a quant & machine learning researcher by happy vocation, but also a chef by training. (Le Cordon Bleu with honors, thank you.) So the term “foodie” has always struck me as what privileged folks call themselves when they like to eat fancy food, but would not be caught dead hanging out with a line cook.

Hipsteraunt remains a tender satire of a certain sort of fetishized dining out. It was meant to be an acerbic call to check-your-privilege, together with a reminder that nothing in food is new. No combination of ingredients or flavors has not been tried a thousand times before. Even offal and the Asian flavors everyone loves to exoticize. (Awkward…) We lived through the fusion cuisine of the 1980s, remember? In hindsight, it might have cut a bit too close to the bone. The site garnered plenty of attention, but less heady pokes like the fake Guy Fieri menu and the brilliant Jacques le Merde have been far more successful. An annoying bug with making menu URLs permanent snagged things up the first couple weeks, too. Nonetheless on Hipsteraunt’s second birthday, I celebrate by raising an artisanal cocktail (a lemongrass aviation, perhaps) and toasting the addition of a few new ingredients: Keep an eye out for those trendy signifiers of faux-edgy cuisine we all love, like burrata and purslane, za’atar and togarashi. Goodbye ginger, goodbye almond milk. But it looks like bacon is still there.

 

On Breath Catalogue

Breath Catalogue is a collaborative work by artist/scholars Megan Nicely and Kate Elswit, and data scientist/interaction designer Ben Gimpert, together with composer Daniel Thomas Davis and violist Stephanie Griffin. The project combines choreographic methods with medical technology to externalize breath as experience. Dance artists link breathing and movement patterns in both creation and performance. In Breath Catalogue, the goal is to expand the intrinsic dance connection between breath and gesture by visualizing and making audible the data obtained from the mover’s breath, and inserting this into the choreographic process to make the breath perceptible to the spectator. To do so, they are working with prototypes of breath monitors from the San Francisco-based startup Spire. Following the San Francisco premiere, Katharine Hawthorne interviewed Ben Gimpert to understand the inner workings of the technology interaction.


Katharine Hawthorne: What is the output of the breath sensor (what does it “measure”), and how does this get manipulated or translated into the visualizations?

Ben Gimpert: The sensor measures four things: the diaphragmatic or chest pressure placed on the device, as well as three dimensions of acceleration. These four numbers are sampled about thirty times per second, and then sent over Bluetooth radio to a laptop.

Is there latency in the sensor, in other words, how quickly is information transmitted and processed?

There is very little latency between sampling and receiving the data via Bluetooth on the computer. However, there are lot of complications. First the Bluetooth transmitter in the breath sensor can be easily disrupted or interfered-with by other radio frequency devices. Ironically, a dancer’s body can also block the radio transmitter in the device.

There is also an important but nuanced frame-of-reference problem when using this sort of sensor in performance: The breath sensor does not know the Euclidean origin of the space, what acceleration might occur at point (0, 0). It similarly does not know what is the beginning or end of a breath’s pressure. For this reason, the different breath visualizations avoid working with much memory of a breath. They always work from the difference between this moment’s breath pressure, and the last moment one thirtieth of a second ago. For the mathematically inclined, the viz uses plenty of moving averages and variance statistics. These moving averages give an intentional sort of latency, as Kate or Megan’s movement eases into the visuals.

I am curious about how you chose the specific graphics and visuals used in the piece (the lines and the other projected images).

The famous Joy Division album cover. Smoky particles at a rave in the nineties. The dancers wanting their breath to leave an almost-real residue in the space.

In each case the breath is not visualized literally, because that would be boring. If the pressure sensor has a low reading, suggesting that Kate or Megan is at an inhale, the code might move the frequency blanket imagery in a snapped wave upward. Or invert the breath by sending the neon bars outwards.

Relatedly, how much did you collaborate with the lighting designer on integrating the data visualizations into the overall visual landscape of the performance?

Alan [Willner] was great. He designed the lighting based on videos we sent him of the piece and the visualizations ahead-of-time.

Who is driving the collaboration? Did the dancers/choreographers suggest modes of interaction and then the visuals develop to suit the choreography? Or did the possible visualizations shape the movement landscape?

I have seen a lot of contemporary dance where an often-male technologist projects his video onto usually-female dancers. This is both sloppy politics, and pretty lazy. I wanted there to be a genuine feedback loop between what my code would project in the space, and how Kate and Megan move. So I was in the dance studio with the dancers throughout the creation of the piece.

Can you provide an example of a section where the “movement” led the development and/or a section where the “tech” led? I want to understand this feedback loop better. How was this process different than a traditional dance/tech collaboration?

The tech side of a typical tech/dance collaboration starts with an existing piece of software like MaxMSP or Isadora. The tech person puts together a couple cool looking visualizations, and then brings these along to the studio. In rehearsal, the visualizations are typically put on in the background while the dancers “interpret” or literalize the visualization with their bodies. This produces a lot of great looking stuff, but there is very little feedback going either direction. In Breath Catalogue, we developed a custom piece of software specifically for the piece. This custom approach with a hardware prototype like the sensor and avoided a proprietary (commercial) software dependency. In a very practice-as-research sense, I would often make live changes to the code while in the studio. The Breath Catalogue visualizations run in a web-browser, so it was easy for Kate and Megan to run them outside of the studio. at home. We are planning to release the Breath Catalogue software under an open source license, to support the community. (Some utility is already released on Github.)

A few specific examples of tech/dance collaboration in Breath Catalogue: At one point I was dragging the virtual 3D camera around the frequency blanket visualization (i.e. Joy Division). Kate and Megan asked me to hold at the point when the viz was like a roof above their heads. They developed some movement vocabulary based on this metaphor, and then later I made modifications to the JavaScript code so the roof looked more naturally lit. Another time, on a whim, Kate and Megan noticed that the breath sensor does a heightened job of tracking breath when the dancer is physically against a wall. That was the genesis of the “wall pant” section. My aesthetics run toward grand gestures and the baroque. In general, contemporary dance tends to minimalism and the referential, which nudged the visualizations toward abstract shapes and muted colors.

How much communication occurs between you and the performers throughout the performance?

Quite a bit. The breath sensor was an unpredictable aspect of the performance, but we three did not want to fake it. So we decided to err on adaptivity instead of pre-recording everything, and this meant a lot of thumbs-up & down cues during the transitions which Hope Mohr noticed for her review. Some of our music was cued off of Kate or Megan taking a certain shape, while at other points the dancers were waiting on the sensor’s connection.

There’s a moment in the piece when the Megan takes off the sensor and transfers it to Kate. Is their breath data significantly different? Also, has this moment ever caused any technical difficulties? Does the sensor have to recalibrate to a different body?

Yes, Kate and Megan each have a distinct style of breathing. If you are adventurous, this can be teased out of the breath data we posted online. In this piece, Megan’s breath is usually more staccato and Kate’s sustained. The sensor reconnects at several points, which is technically challenging. In the next iteration of Breath Catalogue, we will be using multiple sensors worn by one or more dancers. The visualization software that I built already supports this, but it is trickier from a hardware standpoint.

In your experience, how much of the data visualizations translate to the audience? How easy is it for an untrained eye to “get” what is going on and understand the connection between the performer’s breathing and the images?

It turns out to be quite difficult. We added a silent and dance-less moment at the beginning of the piece so the audience could understand the dancer’s breath’s direct effect on the viz. Yet, even with that, the most common question I have been asked about my work with Breath Catalogue was about the literal representation of the breath. As contemporary dance audiences, we are accustomed to referential and metaphorical movement. However I think visualizations are still expected to be literal, like an ECG. Or just decorative.

What is your favorite part of the piece?

In the next-to-last scene, the wireless pocket projector was reading live sensor data from the dancer via the attached mobile phone. Which was pretty fucking tough from a technical standpoint. Also the whimsical moment when Kate watches and adjusts her breath according to the baseline of that Police song. And when Megan grabs the pocket project for the film noir, and then bolts.

If you had the time to rework or extend any section, which would it be?

In one scene we remix the live breath data with data from earlier in that evening’s show. I would have made this more obvious to the audience, because it could be a pretty powerful way to connect breath and time passing.

Philanthropy Picks

The great Dinah Sanders does an annual blog post with her election picks, which is incredibly useful for navigating California’s referendum system. In this vein, here is a list of the philanthropies and charities where we donated this December 31st:

In previous years some of our favorites were Electronic Frontier Foundation (EFF), Planned Parenthood, and the American Civil Liberties Union (ACLU).

Just Put the Bird in the Fucking Oven

Every year there seems to be some elaborate new Thanksgiving turkey preparation technique. For a while we were all deep-frying the poor things, and our parents once tried putting a can of soda in the cavity. To baste from within, or something. By 2014 we have probably reached peak turducken, but nesting poultry is still a thing. Other tricks like butterflying (spatchcocking) and brining will have their day. These techniques have one thing in common. There is always the one anecdote of success, and a quiet majority that knows turducken was still pretty bland.

Yes the turkey is the focus of the Thanksgiving meal, but that does not mean it should be the focus of our cooking efforts. Look — turkeys are incredibly lean birds. They lack duck’s self-basting fattiness, or a chicken’s mild but distinctive flavor. Instead of endangering your porch or driveway with a dubious single-purpose deep-fryer, just put the bird in the fucking oven. Turkey is always dry, and you should accept the zen of this statement. Focus on your vegetable sides and gravy, and you will have a much better dinner.

Here is how to do Thanksgiving turkey right:

  1. Order an organic, hormone-free, all-natural, free-range, beer-fed, daily-massaged, Wagyu, Angus turkey from a farm in Portland. Or do whatever is your closest approximation. Preferably he’s named Colin. (Yes, all eating turkeys are male, because the females lay eggs. Duh.) This might be the only decision that will actually matter for the bird’s taste and juiciness. Avoid a bird that has been frozen. Order about a pound of bird per person at your dinner, adjusting for kids and vegetarians.
  2. Preheat your over to 450 degrees fahrenheit, or whatever that is in Europe.
  3. Prepare a little bowl of seasoning. I like kosher salt, lots of cracked black pepper, minty and citrusy dried herbs like marjoram, and a pinch of sugar. You want several tablespoons of seasoning mix.
  4. Wash the bird inside and out, removing the giblets (offal) inside. Yes, washing poultry may get food-borne nasties like Salmonella all over your kitchen. That is why you have paper towels and a disinfectant handy. Also make sure the bird is fully plucked. An old pair of dull tweezers can help. The more hippie your bird (see #1 above), the more likely it is to have some lingering feathers.
  5. Dry the bird with paper towels. Brush him with melted butter, and then sprinkle all over with your seasoning mix.
  6. Turn the bird upside down on your roasting pan. This bastes the dry breasts with the meagre fat that is in the bird. Oh, and the butter. Butcher-tie the legs and wings close to the body, if you are feeling fancy.
  7. Just put the bird in the fucking oven.
  8. After about a half-hour, or whenever the bird gets brown, turn your oven down to 325 degrees. Then after about three hours more, check the temperature inside a thigh. You want at least 165 degrees fahrenheit, but remember the bird will continue to carry-over cook a bit after you pull it out of the oven. Do not baste the bird, since this loses the heat in the oven and does not help much anyway. Do not open the oven to peek and smell and fret every ten minutes, even if your guests have arrived. Do not cover just the right breast with aluminum foil, and do not stuff the bird. It will all work out, I promise.

The Gravy to End All Gravies

I have been proposed marriage by men and women both, for my gravy. Get some chicken stock and a glassful of sherry boiling in a sauce pain. Add the turkey giblets. Turkey kidneys for the win! If you have some mirepoix chopped-up (onions, carrots and celery), toss them in the pan. Simmer for 45 minutes-or-so.

Since you have been smart and not bothered basting the turkey (right?), your roasting pan probably has a bunch of browned juices and fat. This is fond, the nectar of the gods. Strain your simmering stock right into the roasting pan. Scrape all that lovely fond up into the liquid, with a wooden spoon. (If you do not have wooden cooking spoons, you are a bad person and will always be a failure as a cook.) Return the liquid to your sauce pan, and simmer for about 20 more minutes, then strain again into a new saucepan.

Make a roux in a non-stick pan on the side. I use bread flour and whole, unsalted butter in approximately equal portions by volume. (Don’t overthink this.) Stir the roux as your butter melts. If you want to feel southern, let the roux brown a little bit. Whisk the roux into your simmering stock, and boil for ten minutes to thicken. Add some lemon juice and a ton of salt. If the gravy does not taste right, add more salt. If it still does not taste right, add more salt.

I can hear you asking about the cornstarch… Remember that part about making the best gravy ever? This requires butter, as all good things do. Compared to the glory that is roux, cornstarch is weak sauce.

Employee Founding

Am I a cofounder or an employee?

There is prestige to having been a cofounder of a startup, someone who was there from the beginning taking the lifestyle risk in return for the possibility of striking gold and changing the world. Now with that breathless sentence out of the way, how do you know if you are a founder or an employee? To me there are four key questions to answer:

  • Is the startup funded externally, from an outside entity like a venture or seed fund? This would be someone without huge sunk costs choosing to hand over money, in exchange debt or equity and upside in the startup’s future.
  • Is the startup selling to businesses (“enterprise”), and does the venture have a paying client-or-two outside of the Silicon Valley scene? Consulting for your buddy’s startup does not count.
  • Is the startup selling to consumers, and have consumers written checks or swiped their credit cards for actual money? Tons of freemium traction does not count.
  • Are you working part-time on something else simultaneously? If you spend every Tuesday and Thursday working as a barista to pay the bills, you are not full-time.

If the answer to any of the three is “yes,” then you are probably an employee and not a founder or cofounder, de facto or otherwise.

Outside Ukulele

A model for the POU, probability -of- ukulele.

The Outside Lands 2014 lineup looks to be one of the best in years, and as usual it will be difficult to decide which stage to watch over the weekend. To help, I wrote an NLP model that measures the degree to which a band is likely to lapse into entitlement and self-parody. So think of it as a musical spectrum, from Kanye West to Death Cab for Cutie.

  1. Kanye West
  2. Flume
  3. Paolo Nutini
  4. Ben Howard
  5. Watsky
  6. Ray LaMontagne
  7. Duck Sauce
  8. Jonathan Wilson
  9. Run the Jewels
  10. Jagwar Ma
  11. Tiësto
  12. Big Freedia
  13. Lykke Li
  14. The Brothers Comatose
  15. Kacey Musgraves
  16. Valerie June
  17. Atmosphere
  18. Tycho
  19. Macklemore & Ryan Lewis
  20. Tom Petty & the Heartbreakers
  21. Tegan & Sara
  22. Haim
  23. Bleachers
  24. Holy Ghost!
  25. Christopher Owens
  26. Dum Dum Girls
  27. Lucius
  28. Gold Panda
  29. Courtney Barnett
  30. Vance Joy
  31. Bear Hands
  32. RayLand Baxter
  33. Gardens & Villa
  34. Imelda May
  35. Mikal Cronin
  36. Finish Ticket
  37. Tumbleweed Wanderers
  38. Boys Noize
  39. SBTRKT
  40. The Kooks
  41. The Flaming Lips
  42. The Killers
  43. Grouplove
  44. Warpaint
  45. Arctic Monkeys
  46. Chromeo
  47. Typhoon
  48. Chvrches
  49. Capital Cities
  50. Local Natives
  51. John Butler Trio
  52. Deer Tick
  53. Greensky Bluegrass
  54. Woods
  55. Tedeschi Trucks Band
  56. The Soul Rebels
  57. The Districts
  58. Nicki Bluhm and The Gramblers
  59. Spoon
  60. Jenny Lewis
  61. Phosphorescent
  62. Cut Copy
  63. Night Terrors of 1927
  64. Givers
  65. Disclosure
  66. Death Cab For Cutie

I Program in Whatever

A friend just asked me how to get better at JavaScript, the programming language du jour for Silicon Valley gigs. Or more generally, whether “practice” is the way to overcome learning barriers in programming.

The short answer is, indeed, you just need to practice. The great Peter Norvig says becoming a good coder takes ten years. Bright people heeding good advice can slash these ten thousand required hours quite a bit.

Types of Language
Though if I were you, I would start with separating the learning of a particular programming language from becoming a good programmer. This is one of the trickiest concepts for people coming into programming from another field. The doing of computer science and software engineering has very little in common with the syntax or standard library of a particular programming language, JavaScript or otherwise. Sapir-Whorf be damned, but a programming language is just a tool while a (real) language is a way to communicate. Would you say becoming a radiologist is the same thing as learning to use an x-ray machine? Are statistics and Excel the same thing?

The infamous ThoughtWorks interview process for software engineers that I went through ages ago had almost no questions about standard libraries or syntax. (“How do you close a socket in C?”) No one cares, because you can always look that up in a book. Instead most of the questions were about abstraction, with a few here & there about algorithms. (“How would you cleanup the coupling between this infovis module and the database?”)

Good programmers learn new languages trivially, because they all have the same underpinnings. I find it helpful to think of three schools of programming language now-a-days. The first are the aspiring or popular languages like JavaScript, Go, Ruby, Python, Java, C#, C++ and C. These languages all have their imperative syntactic roots in ALGOL from the 1950’s. The languages are heavy on the syntax, and try to stop programmers from shooting themselves in the foot.

The next school of programming languages are the lower-case-el lisps like Scheme and Clojure. The most important distinction of a lisp is its homoiconicity, a cumbersome term that means you write code in a data structure the programming language is good at manipulating. Paul Graham of Y Combinator is a famous proponent of coding in lisps. They are more powerful and expressive than the popular languages, so it is easier for a good Scheme programmer to pick up Python than the opposite. Even Go’s statically-linked by default killer feature was common in the Lisp and Smalltalk communities thirty years ago.

My third class of programming languages are the functional languages providing different degrees of type safety, like Haskell, OCaml and Erlang. These languages discourage state and side-effects, and by doing so help code run across many CPUs or machines. Functional languages are also about code that is provably correct. This school of programming language is (arguably) more expressive and powerful than even the lisps, so a Haskell hacker should be able to pick up Clojure more easily than the Clojure programmer could learn OCaml.

I intentionally avoid classifying programming languages according to their object oriented-ness, since OOP is just another way to generalize and abstract the coupling between different parts of a software system. You can do object-oriented programming in any language, but languages like Java and Ruby force the issue. (Yes, you can write object-oriented systems in old school C.) Don’t bother with domain-specific languages like SQL, Matlab or R, since they encourage bad habits and are easy to learn later. Nothing is scarier than a R or Python programmer who has never written any lisp.

If you are trying to become a better programmer, the best thing you can do is learn the underlying history and structure of all three schools of programming language. “All of this has happened before, and all of this will happen again.” However the closer your learning language is to the third school, the quicker you will start to understand the core of the matter.

A Little More Advice
What side projects are you helping code? If the answer is “I just program at work” or “I just read a lot of code on Github,” then you will never be a great coder. The advice Hilary Mason got on Twitter a while back was iffy in this regard.

Have you worked through the amazing SICP book yet? There is a reason it was MIT’s main textbook for a zillion years. The book seems to be a cultural signal or marker of good coders. Others think the Van Roy & Haridi book is better than SICP, but the writing style is really dry.

New Sentiment Dataset

The good folks in Stanford’s Natural Language Processing Group have built a powerful new dataset for a paper being presented at the EMNLP conference in Seattle next month. The underlying foundation of the dataset is not particularly exciting, being yet another corpus of labeled movie reviews: The review sentence “Stealing Harvard doesn’t care about cleverness, wit or any other kind of intelligent humor” is provided along with its negative sentiment label, for example. What is more interesting is the corpus providing sentiment labels at every level of composition. So for the same sentence, the dataset also provides a distinct sentiment label for the sub-phrase “any other kind of intelligent humor” which is actually positive. Hence the dataset is a treebank, not just your typical corpus. A lot of Mechanical Turk wrangling went into this! This compositional and recursive labeling is a great resource for training contextual models, especially ones that go beyond the bag-of-words legacy.

Here at Trending we are experimenting with an online, regularized, high-dimensional linear approximation to the Stanford paper’s tensor RNN model, one that lets us use the whole Vowpal Wabbit stack. Next month they plan to release some (Matlab) code to parse the treebank, but have already released the data itself. Therefore I put together a simple Ruby module to parse the treebank, for your own statistical NLP, sentiment and machine learning projects. It includes a bit of Graphviz logic to render phrase trees and their sentiment as SVG:

The module is hosted on Github at “http://github.com/someben/treebank/” under a nice Free license.

Separated by the Same Language

Some snarky, some important advice about America and England.

About ten years ago, I moved from Chicago to London for grad school. I intended to spend a few years in the United Kingdom, but my best laid plans saw me there for about five years. This is a much longer span of time than the typical study abroad or a backpacker’s tour. This summer I returned to England for an extended visit and observation. Time has clarified some non-intuitive quirks I didn’t know I had learned while living here. So a list for future expats, tourists and the curious:

  • London dominates English culture, far more than New York or Los Angeles dominates American. It is the largest city in the European Union, sprawling bigger than Paris or Rome, and probably the most diverse. On the ground, London fashion leads New York and Los Angeles by a few years. Yes, even New York City. Really.
  • The most bureaucratic aspect of a very bureaucratic country is consumer banking. Everything about English checking accounts, ATMs and credits cards is mind-boggleingly difficult, inefficient and wasteful. Things are still mostly done on paper, with proofs of residence, reference letters and other signs of class being the necessity. Plan to spend literally ten times the amount of effort screwing around with English banks as you would in America.
  • The opposite is true of The Internet. When it comes to healthy competition among mobile phone providers and ISPs, England is incredibly high-tech. This is probably because England is geographically small and wealthy. So pay-as-you-go plans with dumb phones are convenient and dirt cheap, and getting fiber optic broadband to your flat is trivial.
  • The English are far more sensitive to class than Americans, especially around verbal accents. People in England can be extremely wealthy but still “low class,” and vice-versa. Differentiating wealth from class is probably the most alien aspect of English culture, for Americans. My favorite breakfast place in Bristol has a reputation for being posh (a.k.a. high class), but is actually less expensive than most supposedly bohemian hipsteraunts in the city. The English are more likely to “unlearn” a low-class accent, and Americans mistakenly think splashing a lot of cash guarantees privilege.
  • Restaurant servers in England rely less on tips for their income, which makes the service either atrocious, or more honest — depending on your politics. American-style tipping is becoming more common in England, but still the exception. Go with 10% atop the bill if you had good service, otherwise keep the change. You always have the right to dispute any gratuity automatically included in a bill. Do not tip if you pick up a round of drinks at the bar.
  • Speaking of which, English drinkers take turns buying full rounds of drinks for the group. This is good etiquette, and something Americans should take up. The English will notice if you never happen to run for a round, and you will get a bad reputation. Americans think of themselves as heavy drinkers, but we are actually more teetotaling than the English.
  • “In America a hundred years is a long time, and in England a hundred miles is a long way.” Because English culture is so old and the country so densely populated, there is a lot of diversity even between neighboring towns. Driving a couple hours for a visit is nothing to an American, but can baffle an English person.
  • Most English do a good job of differentiating American politics from the American people, even if we do elect those goofballs in DC. Politically speaking, our country is seen as an isolationist and violent bully. But culturally, everyone loves our hip hop and big-budget movies.
  • The English are as likely to think of themselves as European as not, so membership in the EU is a constant point of political tension here. The English are a bridge between the New and Old Worlds. The snarky newspaper headline is “Fog in the English Channel: Europe Cut-off!”
  • Being invited into an English home for a meal, tea or supper is a big deal, more so than in America. Take it as flattery and bring a bottle of wine.
  • The English can hate their (elected) government, but still love their country. This is one surprising upside of still having a monarch. Americans who hate their elected leaders are more likely to be seen as “unpatriotic.”
  • Taxes in the UK are actually not that much higher than in the US, despite what American politicians imply. My nominal tax rate as an evil banker in London was only a few percent more than it was working in Chicago. The English love to hate on the National Health Service (NHS), but it does a decent job of providing widely-accessible health care. There is a parallel private health care system for the wealthy, which is much more American in style. Most English see health care as a civil right like suffrage, unlike Americans who usually see health care as an expense.
  • That said, the English are not necessarily more healthy than Americans, but they are definitely thinner. You can usually spot the American tourist by their weight and the fact that they do not smoke.
  • The geography is confusing but easy to memorize. Britain or Great Britain is the large island off the coast of Europe. It contains the countries of England, Wales and Scotland. So the Scottish are British, but definitely not English! However the United Kingdom includes Northern Ireland, which is not (Great-) British. Sometimes the UK is represented as a whole (i.e. at The Olympics), while at other times the individual countries in the UK matter (i.e. soccer). The UK flag (the Union Jack) is an overlay of the English, Scottish and old Irish flags. The English flag is about St. George the dragon slayer, and looks like a red cross on white.
  • The English are a pretty secular people. They are not necessarily atheists, but religion is just not that big of a deal.
  • Beer is the only inexpensive thing in England. Well, maybe eggs and milk in the grocery store also. The best and most traditional beer is the hand-pulled sort you find at a pub. Start with these bitters, and then try the bright, alcohol-heavy and bubbly lagers. Timothy Taylor’s Landlord is a fine example. (Most Americans only ever drink lager or the occasional stout like Guinness.) Yes English beer is served warmer than American, but the English weather is cooler too. Cocktails in England usually mean carefully measured 25ml shots, leading an English friend to flatter America as the “land of the free-pour.”
  • The best fish & chips is not found in pubs, but in dedicated shops called chippies. To find a chippy, look for counter service, paper-wrapped fries and a small menu. Good fish & chips -fish has a tasty, crispy batter around surprisingly delicate fish. Greasy fish inside is not good fish & chips -fish. Examples are the Fryer’s Delight on Theobald’s Road in Bloomsbury in London, and Fish Lovers on Whiteladies Road [sic] in Bristol.
  • The solution to late-night, drunk munchies in England is your Middle Eastern kebab shop. Mayonnaise-heavy garlic sauce on your chips is a must, especially after a few pints.
  • Talk is of “the pub” as if there is only one, but this is just a quirk of language. There is not a place called The Pub, or ever just one pub in an area. You just say “meet me at the pub.” Similarly, English folks will refer to “my local [pub].”
  • The weather in England is grey and wet, but actually very mild. This is because of the North Sea jet stream, even though the island is on latitude with Scandinavia. Despite the Dickens novels, snow is rare here. And compared with America, there are very few bugs and insects. There have been people living in every part of England “forever,” so there is very little actual wilderness even though the countryside is green and pretty. The high latitude also means very dark winters, and long summer days. There is nothing like leaving the pub at nine o’clock in August while there is still plenty of sunshine.
  • Americans are terrible with European and British geography, but the English are just as bad with ours. When I mention my hometown of Chicago to many English, they presume it is near the East Coast because of movies with skyscrapers and organized crime. Explaining that Chicago is a seventeen hour drive from New York City usually stuns the table… Two friends from Barcelona and the Black Forest in Germany actually grew up closer to each other than my wife and I, from Manhattan and Chicagoland.
  • Traditional businesses in England have flaky and frustrating hours, especially as an American used to working from nine to five, and running errands outside of this window. While I lived in England, pubs were granted more flexible hours (2005) and smoking was banned (2007). So thankfully pubs are no longer required to close early and go lock-in.