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.