So, You Wanna Make A Pokemon Go Clone?

I told you not to do it.

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But suddenly my 2013 blog post about displaying maps in Unity3D is now my top page of the month. There are lots of Pokemon Go clones being built right now.

Well, if you absolutely insist, here’s how I’d go about it.

Step 1: Raise tons of money

You’re going to need it. And it’s not just for user acquisition. You’ll need a lot of dry powder for scaling costs in the unlikely event this game is as successful as you’ve claimed to your investors. For small apps, accessing something like the Foursquare API may be free–but it will require an expensive licensing deal to use it at the scale you’re thinking of and without restrictions.

Step 2: Buy every single location based game you can

Just having access to a places API such as Foursquare or Factual isn’t enough. You need location data relevant to a game–such as granular details about places inside of larger locations that are of interest to players. Pokemon Go has this from years of Ingress players submitting and verifying locations around the world.

Nearly 10 years ago, there was a frenzy of investment in location based games. The App Store is now littered with dead husks of old LBS games and ones that are on life support. With that pile of money you raised, it should be easy to go on a shopping spree and buy up these games. Not for their users, or even the technology, but for the data. Most of these games may have been fallow for years, making their location data stale. Yet, it may be possible with machine learning or old fashioned elbow grease to work that data into a layer of interesting sub-locations for your game to be designed around.

Step 3: Plan for Database Hell

Designing for scale at the start is a classic mistake for any startup. You’re effectively building a football stadium for a carload of people. That doesn’t mean you shouldn’t entertain the idea of scaling up a service once it’s successful.

Full disclosure, I’ve never built an app at the scale of Pokemon Go. Few people have. I suspect many of the server issues are related to scaling a geospatial database with that many users. It’s much harder to optimize your data around location than other usage patterns. Don’t take my word for it, check out this analysis.

It’s been years since I’ve looked at geospatial databases. Despite some announcements, it doesn’t look like a lot has changed. A cursory search suggests PostGIS is still a solid choice. Plus, there are a lot of Postgres experts out there that can help with scaling issues. MongoDB’s relatively new spatial features may also be an option.

As for fancier alternatives–Google App Engine is an easy way to “magically” scale an app. They have also started releasing really interesting new geospatial services. Not to mention some great support for mobile apps that may make integrating with Unity3D a bit easier. However, GAE  is very expensive at scale, and the location features are still in alpha. Choosing Google App Engine is a risky decision, but also may be an easy way to get started.

To avoid vendor lock-in, have a migration strategy in mind. One of which may be using your pile of money to recruit backend people from startups with large amounts of users.

Step 4: Get Ready for the Disappointing State of Mobile AR

Pokemon Go has sparked a lot of renewed interest in AR. Much like geospatial databases, not much has changed in the past 5 years as far as what your average smartphone can do. Sure, beefier processors and higher res cameras can get away with some limited SLAM functionality. But, these features are very finicky. Your best bet is to keep AR to a minimum, as Pokemon Go smartly did. Placing virtual objects on real world surfaces in precise locations, especially outdoors, is the realm of next generation hardware.

Step 5: ??????

Ok, this isn’t a precise recipe for a Pokemon Go clone. But hey, if you’ve completed step one, maybe you should contact me for more details?

Social Recruiting: Google App Engine Experiments 3

My latest experiment is a “social hiring” tool built on top of Google App Engine: zenthousand.

We don’t have a tech talent crisis in America, we have a hiring crisis. Between clueless recruiters and the oblivious companies that rely on them, it’s no wonder nobody can hire effectively.

The thinking exhibited by a lot of employers is if you actually respond to a job post or are actively looking for a position, you must be a total loser. The race is on to hire people who don’t want a job. So, I decided to build a site that pulled in data from Github, Meetup, Stackoverflow, Behance, and other social networks developers gather on to allow people to search for so-called passive candidates. There are countless startups doing the exact same thing under the banner of social recruiting.

This didn’t deter me because last time I tried to build a startup it was something nobody else had done before. It turns out, doing something that’s been done half a dozen times is preferable–at least to investors. Plus, it really didn’t seem that hard to make. How is it that so many companies have raised rounds to do something seemingly so trivial? I wanted to find out.

It took about 3 weeks, but the end result is zenthousand. It’s rough, buggy, and features a useless and sparsely populated database. Still, it gets the point across. Sign up for an account and start searching for candidates based on location and skill set. Bookmark the ones you like so you can browse and annotate them later. Oh, and the paid options use Stripe for billing, but just use the free demo account to test it out. I don’t expect anyone to actually pay for this in its current state.

Building a service like this is like being a detective. Think about where developers hang out and what footprints they may leave. Then write an algorithm using publicly available APIs to extract this information and organize it in a searchable way.

The location search isn’t very accurate–try using no location for more results. I’m using Google’s beta search API which seems to have some bugs in it. Also, not a lot of Github profiles have location attached to them, so I have to spend a bit more time trying to sniff out where candidates are from. I have yet to run the Meetup and Stackoverflow crawlers either, so most profiles only have a Github profile attached.

Most social recruiting startups claim to use big data analysis to detect how skilled a candidate is in specific technology. This is absolute nonsense. No algorithm can overcome Dunning-Kruger. There is no substitute for an interview facilitated by someone with expertise in the area you are hiring for. That’s not to say big data algorithms aren’t useful for filtering candidates by what’s relevant to the user.

Zenthousand was a 3 week crash course in Google App Engine with Python, social network APIs, and finessing an interface with Twitter Bootstrap. As much as I’m infatuated with GAE as of late, I think a big data service like this is too expensive to run on it. If I take this project further, I’ll most likely re-write the GAE-specific parts, migrate to MongoDB, and move the whole thing to Google Compute Engine or a colo.

Donut Vision: Google App Engine Experiments 2

Some (well, very few) of you may remember my previous post on Google App Engine. Developing a GAE app using JSP was a trip down memory lane, using a technology that has seemingly been left unchanged since 2001.

I recently began a project that involves using Vine and Twitter to sort through video clips. I decided to build on Google App Engine again. This time I’m using Python. My initial hacking has resulted in Donut Vision–a search portal for donut videos on Vine. Hey, don’t laugh. These guys are trying to build an actual business off of the same type of sites–Presumably with cokehead money.

Using Python (GAE’s original language) has been an absolute pleasure. On GAE, it really does seem much faster than using Java. GAE’s built in webapp2 framework and Django templates make building sites and APIs a breeze. I swear not having to type brackets has given me some kind of minor productivity boost–Or not. But placebo is a real thing.

My general “get off my lawn” nitpicks with Python are mostly due to it being a weird hybrid of a dynamic language, yet strongly typed. This gives PyDev in Eclipse a problem performing autocomplete since it really doesn’t know what type you’re referring to in most cases. PyDev and Eclipse is a decent combination due to the convenience of deploying to GAE within the IDE. I’d switch to something else with better autocomplete support, though.

As for the details of how this works, it’s really pretty simple. There’s no Vine API yet, so I simply use the Twitter API to search for Vines with relevant hashtags and pull the URLs out of them. Originally I was using Vine’s new embed code to display videos, but I eventually resorted to grabbing the URL of the MP4 file in the S3 bucket it’s stored in to have more control over the video when playing it with video-js. I expect Vine to shut down this method since I’m just running up their AWS bill with no benefit to them–not even a link back to the Vine app. Hey, if Vine provides a proper API, I’d use it.

Oh also, in my earlier post I stated that Google App Engine is not available in China. This is only partially true. The default appspot domain is indeed blocked in China. Yet, when putting my custom domain, donuts.pw, through GreatFirewallOfChina.org I get nothing but green status. Yes, I’m boldly sparking a democratic revolution one French Cruller at a time. So, if you want to serve Chinese customers via GAE, just map a custom domain to it.

I’m seriously considering using Google App Engine as a backend for a new game. The only problem is cost estimation. I have constant paranoia of real-world usage patterns running up my bill. Especially with improperly indexed datastores, you can rack up charges pretty fast. Still, simply writing an app and uploading it to Google’s cloud is significantly easier than fiddling with Amazon Web Services and Beanstalk. If you haven’t checked it out since the early days, GAE is worth another look.

Oh, also the latest version of GAE has sockets support. It’s still experimental, but this may lead to GAE being suitable for real-time applications such as multiplayer game servers.

Google App Engine Experiments

For a while I’ve been complaining about the fact that sites such as AppAnnie and AppFigures don’t send daily summary emails of not just your apps, but the top apps in the App Store. I want to know what’s trending and topping the charts every day.

I could have made something in PHP to do this in a matter of hours, but I like to use side projects to learn something new. As an excuse to learn Google App Engine I built UpTopR: a site that emails a daily summary of the top 10 apps for iOS and iPad. It’s slow and ugly, but does what I need it to.

I used the Java API since I couldn’t find a way to deploy Python projects to GAE as easily as the Google plug-in for Eclipse does. I only had to learn how to use Google’s NoSQL App Engine Datastore and caching APIs. Otherwise, getting up and running on GAE is as easy, if not easier, than deploying a servlet on Tomcat. The whole process of learning GAE and finishing the app took about 4 days.

I’m big on PaaS now. Writing an application that magically scales inside Google’s environment is much easier than managing a cluster of EC2 instances as virtual infrastructure. Of course, writing a giant scaling servlet isn’t appropriate for a lot of tasks–but for the back-end of an asynchronous mobile game it makes a lot of sense.

Although last year’s pricing changes caused a revolt with long time GAE users, low traffic applications fall under the free usage quotas. Noodling around on GAE costs you nothing. This is great for prototyping.

Unfortunately, Google App Engine doesn’t work in China. The vast majority of IAPs in China are fraudulent, but China is kind of a big deal. Also, as useful as Google’s Datastore is, it still can’t search using geolocation without some suspect hacks. Amazon Web Services is available in China, and I can attach any kind of database I want to Amazon’s GAE equivalent, Beanstalk. This includes the geohash-supporting MongoDB. For these reasons I’m most likely going to use Amazon’s Beanstalk as a GAE alternative on future projects.

PROTIP: I had this problem for a while when trying to use uptopr.com as the domain for the app. Here’s what you have to know about using custom domains for GAE apps:

  • Only domain aliases for your main domain your App Engine Account is hosted on can be used with Google App Engine apps.

  • For mysterious reasons, naked domains can’t be used. You have to use a subdomain such as www.uptopr.com and use a URL redirect to point the naked domain at the subdomain.

  • Once your domain alias is registered with Google Apps, you have to type in the main active domain the alias is for on the Domain settings page for the GAE app. Then it will direct you to your Google Apps administration panel where you will be able select the alias from a dropdown.

I wish I knew this earlier! It took a few days of banging my head against a wall to figure out how to host my App Engine app on a custom domain.