So while I’ve been recommending the resort to people verbally, I wanted to take a moment to write down what I learned vacationing there, and what people might want to know before they take my advice and book their own trip:

• Having a nanny while you are on vacation, as I mentioned on Twitter, is amazing. Compared to my week in Puerto Rico last year with the baby, this was total relaxation. I had exactly as much toddler time as I wanted. And I was lying in the hammock, and Patrick wanted to play basketball, Carlene our nanny was happy to take him, and I could go with them or catch up later. I was able to do my own things, like trying out scuba diving, without having to negotiate childcare with my wife.
• The resort is wonderful, for what it is. It is locally owned, and definitely not maintained to Disney standards. They stay on top of maintenance, but there is still chipped paint to be found, overgrown plants in places, and parts of the pool deck collect water. But the price is good, the staff members are wonderful, and I didn’t have any facility problems that actually impacted my vacation.
• If you want to spend time with your kids, you have to be really clear with the nannies. Their default mode is to whisk the kids off into kid-oriented parts of the resort, where there are lots of activities, lots of other kids, and other nannies to socialize and share duties with. Our son was a bit sick, and tended to be clingy. But whenever he wanted to head off to the playground, our nanny Carlene was happy to take him. By day three we realized we were missing him. But it took a couple of conversations to get to the right balance of Patrick-directed, Carlene-directed, and parent-directed activities.
• “Gratuity included” is only technically true. While the prices and the organizations is gratuity included, many of your fellow resort-goers, particularly the repeat customers, will be ignoring this rule. Tips are not large, mostly one dollar US or one hundred Jamaican (which is about $1.20), but they help to get you priority service at the bar and at breakfast. If the resort had been more crowded, they might have been more necessary.
• Nannies are happy to stay late, but you won’t get a lot of guidance from the hotel management. Based on conversations with our nanny and others at the resort, a large fraction of their income comes from overtime when you hire them after 5pm for additional childcare at $6/hour. We converged on having Carlene take a break and come back at 6:30 for a few hours while we went to dinner. Tips are also big for them, it seemed standard to tip the nanny $100 for the week if you were happy.

The resort wasn’t without a few opportunities for improvement:

• The food isn’t very good. This may be par for mid-range all-inclusive resorts. The menu gets a bit repetitive, and in particular the deserts were downright poorly executed. Also, presumably because it was a light week, only 2 of the 3 restaurants were open on any given night. On the bright side, this probably helped me avoid gaining weight, and the breakfasts were reasonably good.
• Some parts of the facility aren’t as child-safe as I would like. Part of this is Jamaica’s more relaxed building codes, and part of it is that kids are pretty much always being watched, so they don’t have much opportunity to get into trouble. But, for example, the railing between the pier and the ocean had big gapes, definitely not up to US code. And the cribs they provided were not SIDS safe. If you are a parent who is especially concerned about these things, it could be quite frustrating.
• It could be crowded. We were there the slowest week of the year. If all the rooms were full, I expect the deck chairs around the pool and other facilities could be more taxed. It’s something I’ll keep an eye on when booking our next trip.
• They are not helpful with arranging off-property trips unless the trip is run by one of their partners. This means that if you are interested in walking ten minutes down the road to the town center in Runaway Bay, and want advice for a place to eat, you won’t get any answers from the staff. Of course, Jamaica is a high crime island, so maybe you are best off staying on the resort. But if you want to catch a cab or their shuttle bus to do some shopping at a tourist-friendly Jamaican mall, expect to be taken advantage of unless you haggle the price and set things up in advance. Just because the resort booked your trip doesn’t mean you will get a good price or good service.

Of course I’m still very positive about the resort, and if you have young kids and are looking for a relaxing vacation I highly recommend it.

I hope you find what I wrote here useful. Especially if you are booking a trip to the FDR, leave a comment to let me know. Thanks.

The metaphor that keeps coming to mind, particularly when watching the destructive testing of my belongings, is that a toddler is a video gamer playing with a new engine. They get their bearings by testing out the physics model: how far can I throw a grenade? Do they bounce? If I shoot up the wall and leave the room, does the damage persist or does the state reset?

Patrick is learning the physics engine of his world. To do it, he is doing a large number of observations. And try to make sense of these observations, which is quite difficult.

“He needs to figure out if he is a universe governed by Newtonian mechanics, or just the Quake engine.”

“Aristotle never got past the Quake engine.”

Of course, humans don’t make use of nearly all the data that is available to them. If they did, they would learn a whole lot more a whole lot more quickly, as we can learn from Eliezer Yudkowsky’s fable That Alien Message. But we do receive a whole lot of data, and somehow filter through it to learn to interact with a complex universe, to empathize and communicate with other humans, to learn and to create new ideas. The amount of data consumed by a child before he learns to speak or throw a ball is staggering.

Computer Science is just starting to learn how to manage and analyze data sets at this scale. Today’s Big Data systems come in many flavors, from Google and Facebook to Walmart and eBay. There is some debate about what big data means, with Curt Monash and Dan Abadi having recent posts on the topic.

Regardless of how you define it, there is lots of data available that computers are still ignoring. What passes for big data in artificial intelligence is only starting to approach what passes for big data in biological systems. As big data gets truly big, interesting things will happen. It may be that previous generations of AI weren’t bad ideas. They were just data starved.

Startups and the Death of VC

A lot of the links in my bookmarks are about startups and venture capital. A popular topic in 2010 is the death of VC, or debates about angels versus VCs. Also, discussions about whether Boston or New York are the number two startup hub to Silicon Valley.

• Twilight of Venture Capital: Back to the Future? How about Forward to the Past! and It IS Broke!
• Every Time an Engineer Joins Google, a Startup Dies
• Warren Buffet on the hidden risks of investing in rapidly growing industries
• It’s not East Coast versus West Coast, it’s about making more places like the valley
• IPO Anxiety East Coast Version
• Canada Now Somewhat Less Anti-Startup

Startup Visa

The notion of a startup visa also caught my attention. Here are just a few links.

• Maine Business is Shut Without a Renewed Visa
• Startup Visa and Startup Visa
• Tech Entrepreneurs and US Immigration: A Documentary

Startups Without Software

At the same time, everyone is excited about the prospects of tech startups that don’t have any technology risk, because they don’t actually require new technology. Unfortunately, the best examples of these startups are fairly small businesses

• How I set up an sold a product using Unbounce, Wufoo, and Chargify
• How I Made $6k With My eBook

Old Ideas are New Again

Another theme was all the old ideas that are new again, demonstrating that timing and execution can both make all the difference.

• Party Like It’s 1999: 10 Old Tech Ideas That Are New Again
• The 10 Most Tempting Software Startup Categories

The Maker Movement

I’m not sure if it is old or new, but the “Maker Movement” is getting a lot more press attention, and a lot more bookmarks from me.

• Adafruit’s successful sponsorship of Kinect driver hacking, which got mainstream attention.
• Not just tech, there is a lot of food blogging with a maker feel, from Unwholesome Food’s Turdunkin to My Jello Americans artistic jello shots.
• Sometimes tech and food go together: Microwave Vacuum Dehydration: failed experiment “What I hadn’t thought of was that under partial vacuum, microwave ovens really, really want to make balls of plasma.”

iPad

The iPad was the biggest new consumer technology of the year

• What iPads did to My Family
• The iPad: a Near-Miracle for My Son With Autism
• Seth Godin asked for the iPad App of his Dreams
  • Set up a wiki about it
  • And someone built it

• Hyperbole and a Half
  • God of Cake
  • My Boyfriend Doesn’t Have Ebola Probably
• In The Pipeline
  • How Not To Do It
  • Things I Won’t Work With

Memes

Far be it from me to catalog all the memes, but here are two

• Some of the strangest stuff to come out of America’s two wars: Navy I’m on a Boat also Afganistan Army Telephone.
• Old, but apparently I didn’t see it until 2010: Hitler Orders a DMCA Takedown

Next Generation Marketing:

Though speaking of memes, I also have a bunch of bookmarks around next generation marketing.

• The Insane Clown Posse have possibly the most clever marketing ever around their Miracles video and How Magnets Work.“I know this sounds crazy, but I’m being as honest as I can: We planned all this out.” Or maybe he’s lying.
• For a different technique: Groupon CEO Andrew Mason: “I Decided To Set The Bar Very Low And Make Up Lies About Myself That Make Me Sound Lame”
  
• Or, more historically: How Viagra and related drugs were introduced to the scientific community through edgy marketing: How (not) to communicate new scientific information: a memoir of the famous Brindley lecture
• A big change in 2010 was increased optimization of headlines for social media. I saw evidence of this constantly. Here is a how-two: Blogging by Numbers: How to Create Headlines That Get Retweeted
• Of course, those tweetable headlines make for questionable journalism. As the Columbia Journalism Review found: Business Insider and Financial Press Sensationalism

Data and Journalism

The use of data to drive new journalism was a big trend.

• Databases will soon be eligible for the top prize in journalism: Pulitzer board announces changes for 2011 competition
• NYTimes Netflix Culture Map
• Mapping America
• Guardian publishes their data for public access
• Chicagocrime.org is now EveryBlock
• Analytic X has set up a prize for social predictions
• Very Small Array: The Streets of Park Slope, Brooklyn has a clever take on mapping culture
• The OkCupid blog does a lot with data about online dating: Don’t Be Ugly By Accident and How Races and Religions Match in Online Dating and The Four Big Myths of Profile Pictures

Visualization

If you are going to do journalism with data, visualization matters a great deal:

• A Tour Through the Visualization Zoo
• Unfortunately visualization oriented startup Verifiable had to shutdown because customers were not interested in paying for the product.
• But you can get Sparklines in Microsoft Excel
• And IBM Research’s Many Eyes
• Quantitative Brokers has a great visualization of market microstructure on the CME

NoSQL

• MapReduce and Parallel DBMSs: Friends or Foes?
• NoSQL v. SQL is the worst holy war ever
• MongoDB is Web Scale
  
• Visual Guide to NoSQL Systems
• Using MySQL as a NoSQL: a story for exceeding 750,000 qps on a commodity server

Software

• PyPy JIT for Regular Expressions
• God’s Number is 20: http://www.cube20.org/
• Putting It All Together: Using Socio-Technical Networks to Predict Failures

Who Programs and Who Doesn’t

• If You Were Hacking Since Age 8 It Means You Were Privileged
• Why Johnny Can’t Program
• Separating Programming Sheep from Non-Programming Goats
• How does biology explain the low numbers of women in computer science? Hint: It doesn’t.
• Why Diversity Matters
• Female Teachers Transmit Math Anxiety to Female Students

Video Games

• Video Games: The Addiction
• Minecraft
  • Penny Arcade on Minecraft Part 1 and Part 2
  • How to Survive your First Night
  • 16-bit ALU in Minecraft
• Nethack: The Greatest Game You Will Ever Play

Prohibition

• Wayne B Wheeler: The Man Who Turns Off the Taps A history of prohibition, women’s suffrage, and the origins of single-issue politics
• Who’s Got the Acid? These days, almost nobody. and This Is Your Country On Drugs by Ryan Grim

Birth Control

It was the 50th anniversary of the birth control pill, inspiring a range of articles, including two contrarian pieces:

• Why I Hate The Pill
• Waking Up From The Pill

The Beatles

It was also the 30th anniversary of John Lennon’s death

• I really enjoyed reading Scenes From An Alternate Universe Where The Beatles Accepted Lorne Michaels’ Generous Offer
• The Future History of the Beatles is very clever
• I had never read 1980 Playboy Interview with John Lennon and Yoko Ono and it clarified a lot of things about Lennon for me.

Keeping Your Eyes Open

Finally, two links about keeping your eyes open for the surreal and the wonderful things around you every day:

• Entrances to Hell In and Around the UK
• There is a Horse in the Apple Store

Normal Accidents (or as wikipedia calls them “System Accidents“) are major failures caused by unintended and unexpected interactions of many small failures. The term was coined by Yale professor Charles Perrow in his book Normal Accidents: Living with High-Risk Technologies. Complex systems fail for complex reasons. In systems engineered for safety and redundancy the failures that do happen require many contributing factors. Perrow’s focus was on large industrial systems, such as power plants, chemical plants, aircraft, shipping, and military operations. TIme and again we see complex failures in places like Three Mile Island, the Challenger shuttle, or BP’s current oil spill.

In a normal accident, the contributing factors come from many areas and often many organizations. Errors result from poor regulation, lack of training, operator error, specification errors, mechanical failures, lax maintenance, poor morale, organizational structures, economic incentives, and many other areas. Because systems are tightly coupled, many of these factors are able to mutually reinforce one another to lead to systemic failure. The resulting cascade of failures can look like a Rube-Goldberg machine in it’s complexity.

In these tightly coupled systems, potential-normal-accidents are happening all the time. Systems are too complex to be entirely without failures. However, in the common case these partial failures are caught and resolved quietly. In fact, these near misses are an opportunity to understand the unintended failure modes of the system. Rather than build once and deploy, safety must be a continuous process of improvement and understanding. Systems aren’t stable and they are not deployed in a vacuum. As they evolve, failures and near misses must be examined and used to drive improvements.

Software, especially modern networked software, dramatically increases the incidence of normal accidents. As anyone who has ever created, deployed, and debugged software knows, it is common for individual software bugs to have all the characteristics of a normal accident all by themselves. Add together software written by multiple different organizations connecting over a network and it’s a wonder anything works at all.

Getting back to the events of May 6th, the “Flash Crash“, they are best explained as a system accident. People have tried to blame one cause or another, from a fat fingered trader or a faulty brokerage system, to investor agitation over Greek debt and high frequency trading firms going wild, to the NYSE hybrid market system, bugs in other members of the national market system, and outdated circuit breaker regulations. Without going into detail about all these potential causes, I’d like to suggest that the most likely explanation is that all of these causes, together, are what created the exceptional failure of market prices, broken trades, and finger pointing. No one cause is really more precipitating than any other, and apportioning blame is much less important than understanding in detail what happened.

It is impossible to eliminate normal accidents as we increase the complexity of our systems. The best we can do is to learn from accidents, and from near misses, to introduce the kind of slack in our systems that will protect us from the worst accidents. Learning requires transparency. But in systems which cross organizational and regulatory boundaries, with billions of dollars and reputations at stake, transparency is going to be a challenge.

PS: If you’re interest in learning more, I suggest this NASA powerpoint on normal accidents.

I started out reading ATWB because of the Computer Science implications. Design patterns, a popular notion in software development, are based on the notions of pattern language developed in ATWB by Christopher Alexander. Alexander’s book is about the architecture of buildings and towns, rather than of computer programs. But I wanted to get back to the source to understand where design patterns came from. More on that later.

As a book about architecture, being read by a layman (me), A Timeless Way of Building is fabulous. It lays out the general notion of patterns, and helps you begin to understand why some buildings work while others do not. Beyond design principles for good buildings, ATWB lays out the societal drivers for bad buildings in our culture. Good buildings are defined by patterns, patterns that work together to form a pattern language. Bad buildings generally result from failing to understand the pattern it is trying to follow, or from not having a pattern at all.

A pattern is a way of build something, a functional unit of building. For example, a parlor at the front of the house, or a front porch, or a farmhouse kitchen. Some patterns may nest inside other patterns, for example an eating alcove might be part of a farmhouse kitchen. A pattern language combines a set of interdependent and self-sustaining patterns to form an ecosystem of buildings that work well together. For example, a pattern language might describe everything from the town square to the livestock pens of a rural French farming village.

Alexander’s patterns are based around human behavior. The pattern only comes about because of how the users interact with the building, and often how the culture of the users constructs that behavior. A front porch isn’t really a front porch until you sit on it in the evening, and neighbors out for a stroll stop and say hello. A farmhouse kitchen isn’t just a big room with lots of work surface, plumbing, and appliances; the work that goes on there defines the pattern of the space, and makes it fit into the pattern language around it.

In Alexander’s mind, modern construction and architecture is blind to pattern languages because we have separated the concerns of the users from the builders.

Traditionally when farmers built a cow barn (or when the Amish build a cow barn today) the people building the barn were experts in its use. They were cow farmers themselves, from the local community. The barn was built, with only small variations, in the same way all the other cow barns were built, because that worked. And if some variation in the construction process interfered with cow farming, the builders would be capable of identifying it and correcting it.

Today, when computer scientists decide to build a research center, the job is put out to bid by university committees composed of administrators and researchers. An architect is selected, a building is designed, and building firms are contracted. Through the process new ideas are developed and handed down the chain to be implemented. But at the end of the day, the workers pouring the concrete know nothing about the work that will be done in the new building. And neither do the foremen, the draftsmen, or anyone else. The architect might know a little, but is unlikely to have ever gotten hands on with the work. And the computer scientists, who understand their work, don’t feel they have standing to participate in the building process.

The result can be a woefully inadequate building. The two cases I’m familiar with, are the MIT Media Lab and the Stata Center. Both are cutting edge buildings, and very nice in some ways. But both also have many problems which have been chronicled by their residents as well as independent authorities. The Media Lab features prominently as a failure in How Buildings Learn. The Stata Center is the cover story of Architecture of the Absurd. The latter in particular blames architects and the procurement process. But from the perspective of pattern languages the problems are more systemic.

Getting back to single family houses, they do not suffer from the university procurement problem. But they do suffer from a lack of understanding by builders of how the buildings will be used.

One might expect that any individual would know how a single family home is to be used. This might be true, but what we all lack is a shared pattern language that helps our homes and our neighborhoods work together with our lifestyles and our culture. And that’s a tall order. Our lifestyles and culture are changing dramatically from one generation to the next, faster than we replace our housing stock. It’s not surprising that a house built for young families in 1950 doesn’t match a young family in 2010. Or a three-decker built for middle class professionals in 1910 doesn’t precisely fit the needs of nine grad students in 2010.

But even if we look at new construction it is hard to identify clear patterns. There are some features people like, such as granite counters, big closets, multi-car garages, and open floorplans. But these don’t come together to form a pattern language. They don’t say how large the family will be, how it will take meals, or how social entertaining will be organized. New houses built on spec are designed to sell, with curb appeal and attractive luxury features prioritized over usability. People buy what they’ve seen on TV, even though most of the houses they see on TV only have three walls.

Reading a book on capes, I learned that the cape house was designed to be built over time as your family grew. You’d start with a fireplace and two rooms, the door on one side. When you had children, you would build the other side of the house. They were easy to extend, adding breezeways, outbuildings, workshops. Or add dormers upstairs and sleep on the second floor. The evolution of the house mirrored the lifestages of the family.

In 2010, few families if any build their house in stages this way. They buy complete houses, and either move or have custom renovations done by professionals when the house no longer meets their needs. And families don’t follow a single path through the stages of life. Some families live multigenerationally, others do not. Some families entertain, or have formal meals, or cook, or don’t cook. Families have 0, 1, 2, or more children. All these choices and more mean that your neighbors probably do not live like you.

And that, more than the discipline of modern architecture, is why we have lost our pattern language for single family homes. Pattern languages evolve slowly, as new structures are built. But houses last 60 years or more. 60 years ago there was no birth control, no pizza delivery, no internet, no supermarkets, and no thermal glass. Most families had one car and milk was delivered daily.

Our culture is changing too fast for any evolved pattern language to keep up. We’re stuck with buildings created through intelligent design. Unfortunately, intelligent design isn’t very good.

In the sphere of financial derivatives, our civilization has recently come to understand that there are a whole class of financial products which look attractive, and perform reasonably well some of the time, but which eventually fail. The most obvious of these are the credit default swaps of the latest crisis. But other examples include portfolio insurance, made famous in the 1987 market crash. The problem with these products is that they are designed to protect the buyer against losses in all circumstances, even when the market is behaving badly. But when the market is behaving badly, it can behave very badly. These products reduce to end of the world insurance. When the world is ending, who is left to pay out the insurance?

I think there is a useful parallel to the halting problem. If your new financial product can be used as end of the world insurance, it probably will be. And since end of the world insurance is fundamentally flawed, it should raise questions about what your product is really accomplishing.

To quote a friend of mine, when asked how we much we should bid on the house:

In a first-price auction, there’s no dominant strategy. However, by the Law of Revenue Equivalence, the seller will, on average, get the second-highest valuation of all the bidders. So, if you think you value the house more than everyone else, all you have to do is guess what the next-best buyer would be willing to pay for it, and bid slightly more than that to win.

Yes, well, that is entirely correct, but unfortunately unhelpful. We are dealing with a non-repeating negotation (so “on average” doesn’t apply). And it’s remarkably unclear that the auction will be run according to any rules at all. Predicting the behavior of the seller and the sellers agent is quite challenging. On Saturday at brunch a different friend recommended The Strategy of Conflict by Thomas Schelling, from the era of game theory research that brought us such gems as “mutually assured destruction.” Unfortunately there wasn’t time to read it.

Obviously (obvious to anyone who hangs around certain kinds of mathematicians) the right thing is to have a Vickrey auction, where the top bidder pays the price of the second highest bid. But this is far from obvious to realtors. They treat making an offer as a very expensive operation. And counteroffers from the seller seem to be very uncommon. There is a lot of concern about “offending” or missing out on an offer. I’m not sure why. If someone wants to buy your house on Monday for $x, they probably still want to buy it on Wednesday. Unless maybe their Realtor is reading something into the delay.

One is left wondering if the world would converge on more optimal auction structure without real estate agents muddying the waters. Or if, as eBay has demonstrated, people would rather keep working in an easy to understand system over an optimal one. Unlike eBay, the current system is quite complicated, with many conventions and few rules. In the age of Redfin, Realtors have an obvious interest in preserving the status quo, since understanding the rules is the only thing they have left. In the past, they at least had priority access to listings and historical sale data. Now they are left understanding the negotiating process.

In the end we offered $410k for the house. There were 5 offers, three of them at a similar price point to ours, $10k over asking. There was one offer at $430k, so the sellers decided to accept that offer, rather than encouraging further bidding. Case closed, transaction completed, Realtor happy. Money might have been left on the table, but getting it would have required both work and risk on the part of the agent.

It started with a quest. On our way to Arecibo we had seen this strange structure off in the distance, and been unable to identify what it might be. Even when we got up close, it was unclear what was going on. The possibly-associated giant stone cross only increased our curiosity.

Looking at it in Google maps we were able to identify it as part of Space Rocket Plaza, which turns out to be part of Parque de las Ciencias. Some kind of science museum in suburban San Juan? That sounds like a good combination of local culture and nerd tourism, just right for Aletta and I. And maybe there will be baby oriented things for Patrick.

When we got there, the entire facility was bizarrely deserted. We arrived 45 minutes after the park opened, and as far as we could tell we were the only guests. The parking lot was empty and there was no one around. The first three facilities we went to (planetarium, railroads, and something else) were closed for repairs. The only people we saw were involved in cleaning and maintenance of the park, mostly powerwash. As we walked further and further in we became more convinced that the park was closed or somehow shut down.

The zoo didn’t seem to be entirely empty, but was kind of strange. I don’t know what kind of zoo puts deer, peacock, and chickens in the same cage. The selection of animals was at best strange. Little did we know that it was only getting started.

The first indoor museum that was actually open turned out to be the collection of some local big game hunter. Like the zoo, there was little to no information about what we were looking at.

The most bizarre part of the park was what initially appeared to be a model town. However, it was really unclear what it was trying to model. To make the puzzle more challenging, many of the buildings had evidently had their artifacts removed.

Others contained the most bizarre displays:

Eventually we realized this “town” was a kind of museum of classic Puerto Rican television comedy. Coming in with no cultural awareness, it was definitely quite weird. I would have been interested to learn more, but of course there was no signage in English or Spanish.

The pinnacle of our Parque de las Ciencias experience was the Museo del Telefono

This museum seems to be the result of a compulsive collector who was forced to clear out his garage after some kind of family intervention. There are piles of telephone equipment, from every vintage and at every level of repair.

None of it carries any information. For example, here is a lovely pile of mobile phones. The only sign? “No Tocar” (do not touch).

Imagine the thrill of finding the first descriptive sign in English in the whole park, here in the telephone museum!

Unfortunately the sign was sitting on top of this machine, which I can only hope is so much more than a touch tone phone:

Really, that was the telephone museum. And the whole park went like this. The transportation museum, which was a barn full of old cars (and one steam engine and one lunar lander). The archeological museum, which was a random assortment of mostly undocumented bones and bits of pottery. The aero/astro museum, which was a collection of posters from space and telescope magazine, an assortment of NASA paraphernalia, and a collection of model spaceships, some real, some fictional. There are photos, but you get the idea. Actually, there is one I have to share, from a sizable anex to the air and space museum:

Yes, completing the experience of a whole series of museums that could have been eclectic personal collections, here we have the section of the air and space museum dedicated to stamp collecting.

One bright spot was the art museum. The signage and interpretation was still nonexistant, but the museum contained some quality art by mostly Puerto Rican artists, and it was nicely put together. Of course, no comparison to Museo de Arte de Puerto Rico, which we visited later in the trip.

As for Space Rocket Plaza, well, they did have some legitimate space rockets. Looks like leftovers from early NASA. Of course, in keeping with the rest of the facility, no signs or details to let me know the history of these pieces. But hey, they are rockets.

Of course the flight simulator is out of service, as it apparently has been for quite a while according to internet reviews. And the observation deck that originally attracted us to the park? Well, the road up to it from inside the park was closed. That left the elevator, on the other side of the parking lot. On our way towards its base we encountered a motley crew of park employees. None of the uniformed ticket takers or guides would admit to understanding our question about access, but one of the workmen with them offered to interpret. The question and it’s reponse was more challenging than I would have expected. Eventually we got “It’s closed” … “It’s closed, like for a wedding.” Alright, I suppose. Though 10am on a Wednesday seemed somewhat unlikely. Will it be open tomorrow, we asked. This lead to another spate of discussion, where our interpreter seemed as confused as we. “No, it is not open to public, permanently,” he eventually related.

Project Euler is a neat website that presents a series of computer science problems suitable for students and non-students to practice the skill of writing algorithms to solve numerical puzzles. It’s a great way to learn a new programming language, or just to kill time in a mentally engaging way.

Two of the peculiarities of the MIT Mystery Hunt are that the puzzles are drawn from a wide range of domains, and that there are no restrictions on what resources can be brought to bear on a puzzle. The result is both puzzles that are explicitly about software (e.g. perl program cryptograms) as well as puzzles that are best solved by writing some kind of program. Many of my favorite puzzles over the years have come from this category. Some hunts have few, some hunts have many, but every year there is a call to write some kind of software solution to a puzzle.

The programming skills required to write puzzle solving programs in high pressure situations are different from those normally required to write software, and so it can be useful to practice them. I have collected and categorized here all of the software puzzles since the 2000 hunt. In most cases, I recommend attempting to solve the puzzle, knowing that software is likely the best way. But because the solutions are available, if you are stumped it can still be interesting to read the solution and attempt to duplicate it in software. In most cases the solution is linked from the puzzle page; where it is not I have provided a link. The categories here are presented from least elegant to most elegant, in my opinion. Feel free to jump around and do puzzles which you find attractive.

Just Decompile It

A useful trick is to decompile any java applet or flash thing that you are given in a puzzle. The answer might just be in a string constant, though more likely it will just give you a different puzzle to solve.

• Maze (2005)
• String Theory (2003)
• Gnireenigne Lab (2003) This reverse engineering puzzle is pretty challenging. Only one team solved it during the longest hunt on record.
• Cubology (2002) Unfortunately decompiling this one doesn’t really help, but what the hey. (answer)
• Twisty Little Passages (2008) Not quite decompilation, but pulling the whole maze down to your local machine is the best way to handle this puzzle.

MIT Computing

• Some Trolleys Named Lust (2006) The title clues Moira, the computer system that does configuration management for MIT Athena, which has command line utilities named after characters from A Streetcar Named Desire. This puzzle contains components which are no longer online, so you are limited to reading through the solution.

Mathematical Manipulation and Brute Force Enumeration

Many search problems can be solves on modern hardware with brute force enumeration, or maybe slightly smart enumeration.

• Bars of Soap (2005) Unfortunately the puzzle isn’t available, only the solution, though it still provides a useful programming exercise.
• Ginormous (2005) Prime factorization is always a fun thing to do with numbers.
• The Road Signs Of Unspeakable Chaos (2003)
• Mathophobia (2001) (answer)

Text Manipulation

Sometimes a program is the easiest way to do a bulk text manipulation. Particularly when you need to experiment with various transforms, or might need to do them repeatedly. This includes basically all cryptograms, which I am not including here, though software often comes in handy solving them.

• Blather (2007)
• Decode This (2006)
• Long Division (2006) Not so much text manipulation as diagram manipulation, but you can get the diagram as postscript, which makes it easy to extract the data. Doesn’t help you figuring out the algorithm, but it does make trying out variations easier.
• Shotgun Wedding (2005) Gene sequencing also comes up occasionally. There are some pretty simple text matching algorithms that turn out to be critical parts of that work.
• Lost (2004)
• Reminders World (2004)
• Whoa — I have a Migraine! (2003)
• Famous First Words (2002) (answer)
• Lions and Tigers and Bears (2000) My first attempt to solve a puzzle with software, this one wasn’t very successful, partly because of the challenge of getting the grid and the source data accurately input, and partly because of a slow algorithm. The fact that computers were notably slower in 2000 might have also been relevant. I think the most powerful machine available to me was an Ultra 10. In the end people doing it by hand finished first. (answer)

File Identification and Manipulation

Many puzzles require you to identify files, or to do interesting manipulations to entire files. Often both at the same time. Sometimes the files are programs written in other languages.

• Hyperextensions (2009)
• Surgical Files (2009) This puzzle triggered a programming language race, between Perl and Haskell. The race ended in a tie, but I learned some cool Haskell tricks from a teammate in the process.
• White Noise (2006) Being able to manipulate audio files with programs or tools is also important.
• Noise in the Air (2004) Knowing your off the shelf cryptography tools (ie, openssl) is sometimes helpful
• Two-Timer (2004)
• A Problem With Printing (2003) This is probably my favorite mystery hunt software puzzle. It’s written in the postscript language, which is a great language for puzzlers to know, and makes use of the peculiarities of that language.

Programming Language Identification and Cryptograms

Many puzzles come down to identifying obscure or not very obscure programming languages. One thinng to be on the lookout for is knitting notation, which can often look like a programming language. Of course, writing programs is often still more efficient than knitting an actual object. Adding complexity, often the programs you are given are cryptograms, or otherwise obfuscated.

• Tragedy (2008)
• Badness 10000 (2006) Unfortunately no longer online, but I like the structure. You can read the solution.
• Square Mess (2005) presents you with a machine language and a set of constraints on the notation used for a program in it.
• Who’s There (2004)
• Sixty Degrees of Separation (2004)
• What Do You Do With A Genteel Sailor (2004)
• Whoa — I Know Knitting! (2003)
• A Nugget of Wisdom (2002) (answer)
• Use of Gothicism Considered Harmful (2002) (answer)

Honorable Mention

Blue Steel (2006) You can try to solve, but you probably just want to read the solution. It will remind you not to think so hard sometimes.

[Edited 2010-01-11 to add Twisty Little Passages (2008). Feel free to add other missing puzzles in the comments.]