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NYC Lower East Side Cheap Eats Tour

This past weekend I decided, last-minute (8 days out), to take a weekend trip up to New York for food touring. I went with my roommate Matt (from the Ecuador trip) and Yelp Elite friend, Kana. Both share a love for food.

How’d we get there? 

With two people, the best way to get to NYC (at least from Philly) is the Chinatown bus. The $20 roundtrip fare is cheaper than tolls/gas. The bus removes the hassle of parking in New York, which can be its own adventure. We left from Chinatown Philly and arrived in the heart of Manhattan at 120 E Broadway. The bus isn’t less comfortable than Greyhound.

Armed with our tickets and light backpacks, we stood in line in Philly at 9am for the bus. A weary traveler, a girl of maybe 23 years, ambled to the back of the line behind us. She wore a multicolored Adidas tank top and messy bleached blond hair. She’s a recent grad from the University of Virginia. She quit her job in investment banking and was on a mission of self-discovery in NYC, no return ticket booked. Our conversation on the way up helped pass the time on the Jersey Turnpike, between light naps. We talked about Excel, the meaning of work, and Charlottesville.

You can check the Chinatown bus schedule here.

The Food

Upon disembarking the bus and leaving the wafting diesel fumes behind, we scurried with packs in hand down the grungy trash-scattered streets of Chinatown in New York. We were in a hurry to find our food tour companion. I met Kana at a Yelp event in Philadelphia this past March. We shared food tour battle stories and argued over the merits of Yelp. Kana lives up in NYC and was a natural companion for the excursion this past weekend. She’s an insurance analyst by day, food aficionado by night. She might be the first person I’ve met that has a deeper passion than I for restaurants, chefs, and food. She has impressive pedigree, having eaten at nearly every restaurant I know of.  She’s level-headed, fun, and passionate about food. I’m grateful she joined us.

Our first stop was Lam Zhou. Lam Zhou is a barebones dumpling eatery in Chinatown. There are no decorations, signs, or artwork. The only interesting thing about Lam Zhou are the dumplings, and the noodles, and the women who labor in the back tirelessly pressing dumplings all day. The shop is compact, oddly yellow, and a dirty. The floor is lined with cheap wooden folding tables and chairs where diners share common space. The dumplings are excellent, crispy and chewy, filled with scallions, fish and soy sauce, and ground pork. You dip these pockets of love in a little vinegar, and you’re mouth fills with sweet, savoury, and acidic juices and meats. 8 of them for $3 is hard to beat in New York.


The food tour was created by grabbing 450 cheap eats of NYC into a spreadsheet. They were filtered by rating and number of reviews. I eliminated restaurants with less than 25 reviews (this turned out to be too low of a cutoff) and cut off the super popular places with 1000+ reviews. These places have big, touristy lines.  I imported the remaining places  into Google MyMaps, which plots the locations on a map.

With this map, I plotted a tour route. Using Mapquest’s Route Optimizer, I picked 8 places close to each other. The app optimize d the shortest path between them.

The Food (2)

From Lam Zhou, we walked our way up the east side of Manhattan.

Clinton Square Pizza

I enjoyed both the NY Style thin crust and the Sicilian pizza. The Sicilian was the winner. It had thicker cheese and soft, buttery crumb. The sauce was too sweet on it, however.

Ni Japanese

One man runs this Japanese stall in the Essex Market. I found the salmon slightly overcooked and the rice too hearty. This place felt too much like an intentionally healthy meal to enjoy.

Gaia Italian Café

Despite the creepy mythological nomenclature, we gave this place a try because it shows up on cheap eats lists across the web.

We ordered the chicken “Milanese” panino and mushroom truffle ravioli.

The panino was 3.5/5 stars. The chicken cutlets were small though well-executed: juicy, well seasoned, crisp on the bite of the crust. The bread was thick, soft, and light crisp on the exterior. Unfortunately the bitter arugula dominated the flavor profile and made the pesto and tomatoes at most an afterthought.

The ravioli on the mushroom truffle ravioli was handmade, lightly chewy, and the right floury/doughy taste. The mushrooms inside were savoury, well-seasoned, and juicy. These were complemented by earthy romano cheese shavings on top. The sauce was a medium-bodied bechamel with hints of truffle oil. Light sprinkling of parsley balanced the cream and fat of the sauce with a fresh and green flavor and mouthfeel.

The ravioli was good enough to redeem the panino’s mediocrity, but not enough to make the experience 5 stars.

Xe May

Xe May is a hip and popular bahn mi joint. The sandwiches aren’t Saigon street prices at $9. But, they are tasty, a nice balance of fresh bread, sweet bbq savoury pork, and vegetables.

Fat Cat Kitchen

Close to Union Square. Their breakfast sandwich had a fluffy potato roll and well executed eggs. The bean spread on the sandwich was savoury and nicely seasoned.


I enjoyed this place that came recommended by my Yelp friend. The chocolate cannoli was crunchy and buttery on the shell. The inside was sugary, but not grainy/powdery in texture. The milk chocolate added complexity.

Ise Kitchen

I’m so glad Yelp and Opentable pointed us to Ise in the East Village. The Hexagon meal was a no-brainer choice. It is five courses served on stackable trays. It’s unique. It’s well executed. Everything about the meal is efficient and thoughtful.

The starter layer of the hexagon is a few appetizers. There were a few preparations of Tofu along with salad-like app.

The sushi layer contained various types of nigiri including Tuna, Salmon, and Salmon roe along with a tuna roll and two other cuts of white fish. The fish was fresh and creamy as it ought to be. The rice was well executed. The next layer contained house-pulled Soba noodles. These were firm, spongy, fresh, and held onto their dipping sauce very well.

The final course was a chicken consomme mixed with the remaining soba dipping sauce. This was so cool. Very savoury and well seasoned.

I finished my meal with vanilla ice cream dusted in soy powder. The ice cream was silky and creamy. The sauce tasted like maple syrup and peanut butter. Amazing.

This is a good value for a meal of its caliber in NYC.

Blue and Gold Tavern

My roommate introduced us to this place. It’s a dark and grungy dive bar. But the drink prices were the lowest I’ve seen in Manhattan. $4 and $5 for a beer!

Crif Dogs

At the end of the night on Saturday many people end up at Crif Dogs. They have good hotdogs at a reasonable price. The dogs are all beef, have a nice snap, and can be topped with a tasty chili.


TheInfatuation.com led us to the Emmy burger in Brooklyn which it touted as the “best burger in NYC.” While the burger was delicious and came together with a unique emphasis on carmelized onions and charred cheddar, I don’t believe it deserves the top spot. The search for NYC’s best burger continues. This one is overpriced at $27.

Sheep’s Meadow

It’s become a tradition of mine to spend time lounging on Sheep Meadow in Central Park. There’s a massive green space, an excellent view of the skyline, and hundreds of interesting subjects for people watching.


The cheapest non-Chinatown dump motel I found was the International Student Residence in Williamsburg, Brooklyn. While cheap, the accommodations were Spartan. The room was nothing more than wooden stall with a bunk bed and a trash can. There was no ceiling. The place was clean, but noisy and very tight. It’s hard to justify spend 3x as much for a hotel room in Manhattan, but I’d be tempted.

New York has a diversity of thought, activity, and resources unlike anywhere I’ve ever been. It’s a privilege living 2 hours away. During my food planning, I found there are over 20,000 restaurants in the NYC area, with options consistently changing. Because the options are nearly limitless, choosing one place to eat is a problem in discipline and information science.  New York presents the perfect opportunity to analyze restaurant ratings in a rigorous manner. It gives one access to some of the best restaurant options in the world, within walking distance.


Optimize your closet, music library, and schedule

When should you stop looking for a better deal on a house? How much new music should you listen to vs. your classics? What’s the most efficient way to organize your closet? Which chores should you do first on Saturday?

These practical questions lack an intuitive answer. We experiment over time and act accordingly. There doesn’t seem to be a correct answer to any of them. Computer scientists have proved this wrong. In writing algorithms dictating computers’ actions, computer scientists have determined the most efficient ways to explore new concepts, sort large quantities of data, and organize a to-do list.

In their recent book, Algorithm’s to Live By, Brian Christian and Tom Griffiths explore these solutions and present them in a manner accessible to non-geeks. Find below a distillation of a few key points from their book. It’s worth exploring if you’re interested in this organizing your life.

Optimal Stopping

There is a best time to look for something and a best time to stop looking. Consider looking for a house in a fast-paced housing market. The most efficient strategy is to determine the total number of houses it is reasonable to consider in your search. Say 100.
The optimal stopping point is 37% of the # of houses you are willing to consider. Computer science has proven the optimal strategy is to pick anything better than the first 37% of houses you consider. This will prevent you from falling into the trap of stopping too early or late. Look at the first 37 houses with no commitments, then on the 38th house take anything that is better than the first 37.

This is a simplified rule. The book details how adding new parameters, like the ability to backtrack to previous considerations, will modify the optimal strategy. If you have quantifiable parameter (such as average home price per sq. ft in your market), you should utilize this data to bolster your dataset and set an average price/sq.ft threshold for choosing instead of strictly following the 37% rule.

All things beings equal and choices disappearing after you pass over them, picking something from a wide variety of unknowns, set the number of considerations you can reasonably make, look at 37% of them without choosing, then pick anything better than those first 37%. I’ll give one more example to illustrate. If you’re at your work holiday party and they have 100 unwrapped holiday gifts being pulled from a bag and you get the first choice whether to take what’s pulled or let it go forever to your coworkers…your optimal choice is to pass over the first 37 items in that bag and pick the first gift better than those first 37.

Attempting to explore why this is the case would be futile in a blog post. Suffice to say, computers often have to pick a way to solve a computational problem with limited resources. This is the best way for a computer to do this.

Explore vs Exploit

Life is always a balance of the novel and the familiar. We want to find new and exciting things while savoring those we love. Computer science also has insights here. There is no perfect answer as to the balance of your Pandora station (new “recommended tracks vs your library) but there is an optimal strategy: minimize regret.

To develop your perfect library with minimal regrets, use the Gittins Index (see red line in image). Starting on Pandora, or any new area of life, everything is new. After about 50 songs, you should listen to about 50% new songs. After 100 you should listen to 25% new to old songs. As you approach 1000 songs, listen to 3% new to minimize your regret level.

Listening to only new music will expose you to many songs you don’t enjoy. While listening solely to your library will prevent you from discovering songs you’ll love even more. The optimal balance to reduce regret is listed in the chart above. As your library builds, you’ll experience more variety and novelty within your library and thus be less bored by the familiar. The optimal music library size may be around 500, where marginal gains in new music level off. At that point, you can be 90% confident your music is better than the next random song in your “recommended” track list. Listen to a new song every 10 songs to minimize new-song regret and you’ll be on your way to building the optimal music library. This doesn’t account for boredom with your current library. It assumes every song in your library is of a constant enjoyment value to you. You could, however, factor in a certain enjoyment decay factor to account for this.

Sorting your DVD’s A-Z isn’t necessary. But there is a best way to do it. Don’t take all your DVD’s off the shelf and put one back, then place one more before or after, then one more in it’s alphabetized place. This is inefficient because every new sort task needs to be compared against the whole library.

The optimal solution is called merge sort. You break the DVDs into several large groups, then merge those groups together.

But, when in doubt, err on the side of messiness in ordering/sorting problems. Take the DVD home library for example. Unless you have a massive collection, it will almost always be less effort to search through your collection than to intentionally sort it. There is a better third option to random organization and alphabetical: Caching.

Caching is choosing a small portion of your library to be quickly accessible. Computers use caches to make memoryfaster. For this a good example is your clothes closet. The tendency may be to sort articles of clothing by type. This is not the best way. In order to have the highest likelihood of accessing the right piece of clothing, you should place everything back in the order of most recent use in a prominent place. Because in almost all aspects of life, what is used most recently is most likely to be used again. This also applies to papers on your desk. You shouldn’t try to organize them into a “logical” order. Placing the most recently used papers on top is the best, assuming these are papers that belong on your desk and aren’t for archiving.

Another note: Keep things where you use them.

For scheduling tasks, make your goals explicit. Prefer tasks with soonest due date, weighted by importance, with roadblocks of any size as a priority. If a new task comes that is due sooner than your current task switch to it if the switch in context seems merited. When checking your email, do not sort it by importance level before opening. Going at random will prevent the waste of time in the “metatasking” of email inbox organization. It’s almost always better to batch a bunch of tasks together to get cognitive momentum and remove distractions from your working schedule in at least 90-minute chunks and avoiding “metatasks” such as spending lots of time choosing what to do first. Have a system for prioritizing and stick to it. However, some jobs and situations won’t allow this and you’ll need to be cognizant of that.

Optimal Auctions
The optimal auction is called the Vickrey auction. Everyone bids once what they believe an item is worth. Then the winner pays the #2 bid. This prevents unneeded competition and potential misevaluation due to false trends in bids. An example of a pitfall in standard bidding wars: Consider five companies bidding on a bridge construction project. Consider: Company one bids $6 million instead of $8 million because of a surveyor’s error. Company 2 would have bid $6.25 million but is undercut by Company one so now bids $6 million as well even though they knew the project was worth $8 million. Now companies 3, 4, and 5 see the bids and are convinced the value of the project is somewhere between $6 and $6.25 million. The Vickrey auction would have avoided this by preventing comparative sequential bidding that leads to a battle over information.

Computational Kindness
It’s a common theme amongst my friends when asked “where do you want to eat for dinner” to say, “I don’t care, anywhere you pick is fine.” While feigning kindness this is a burden being placed on your friends. Without any information and parameters, choosing becomes more cognitively complex. In reality, it’s almost never true that anything is fine.

Computer Science and Mathematics can help us build a practical structure to life. Machines need to adapt to environmental changes and human demands like us. Observing how mathematicians use theory to design machines can be invaluable in implementing better systems in our own lives.


Two Websites for Finding Super Cheap Flights

Two essential 2016 resources for finding cheap flights

In the past 6 months, I’ve changed my strategy for finding cheap flights. I’ve booked some pretty incredible deals and skipped out on a few more. I’m now confident to share it publicly as a reliable and effective system for finding cheap flights to anywhere.

The strategic principles of airline savings from 2015 still hold:

Be flexible in your dates and destinations. Fly on Saturdays. Travel light.

Tools I’m using in 2016

Flight Deal

The Flight Deal is a Facebook page that curates the cheapest flight deals offered around the country each day. They use geographic targeting on Facebook to only show flights relevant to your local airports. I booked an upcoming flight from NYC to Tokyo for $398 using this method. Beware that the flights only last for a limited time (3-5 hours) You’ll have to be able to pull the trigger quickly. They will show you how to book your flights through the respective carriers. For Tokyo, this was a quick checkout on Air China’s website.

Other deals I’ve seen are $525 roundtrip for Philadelphia to Hawaii and $149 roundtrip to Belgrade, Serbia. The idea of a $149 flight to central Europe is unbelievable. To sign up for The Flight Deal visit their Facebook page and “Like” them. 


Skyscanner is a Scotland-based website dedicated to generating a searchable list of flight deals around the world. Unlike The Flight Deal, Skyscanner offers search functionality and far more flights. It allows searching by country, cheapest month, and flexible locations. This makes it much more useful for people with time constraints on travel.

With Skyscanner, I booked a $90 roundtrip flight from Miami to Ecuador. Search for all flights departing the entire US. From there you can view all international flight from the USA, in the cheapest month available, sorted by price. Skyscanner allows you to make your destinationEverywhere” which allows you to compare flight prices from 50+ countries departing from the US. At the time of posting the best deal I can find is NYC to Paris for $331 roundtrip in January with a possible stopover in Iceland.

Skyscanner’s database is not perfect. Some flights you find may be sold out.
These two websites are now my two most popular. For the spur of the moment trips, The Flight Deal offers some insanely low prices. If you’re interested in being a bit more selective with your destinations and dates, I recommend Skyscanner. If you need very specific times I still believe Google Flights and Airfare Watchdog are the way to go.