Talks @ GA

It occurred to me that I failed to mention any of the speakers who came to talk to us during the WDI program in my weekly blog posts. Not quite sure how I managed that because we saw some awesome people with really interesting advice and experiences to share, so here's a post to make up for it.

The first person to come and visit was Jack Danger, a software engineer at Square. As an engineer also from a non-CS background, he talked a lot about the things you tend to feel particularly as a new engineer, to do with thinking everyone else is smarter and/or more qualified than you. Impostor Syndrome is definitely something I'm susceptible to, so some of the things he said helped to put things in perspective. For example, he talked about how when you meet someone that knows just one thing you don't, they seem like the smartest person in the world in comparison. And conversely, someone who knows one thing less can seem like the most stupid, when in fact, they're probably actually just a few days away from being where you are in terms of knowledge.

Next was Erik Michaels-Ober, a previous Code for America fellow now working at SoundCloud in Berlin. One of his interesting projects that was part of Code for America was Adopt-a-Hydrant, a Rails app people in Boston could use to claim responsibility for shoveling out a fire hydrant after it snows. He also has a pretty impressive 365+ day streak on GitHub.

Then Jennifer Dewalt, the woman behind 180 websites in 180 days came in to talk to us about learning to code. This was brilliant as I'd stumbled across her project a while ago and thought it was amazing, but she was super down to earth and shared many of the same struggles we were experiencing as beginner coders.

Although not connected to GA, another highlight for me was an Airbnb tech talk by Dan Hill about predictive price technology. I went along expecting this to mostly be way over my head, but actually it was a lot more human than the title sounds. The talk was about how they'd solved a really interesting problem: building the technology to suggest the optimal price for an Airbnb listing. When you think about it, it's really hard to get people to put a value on staying in their home. And pitch drastically under or over, and there are a lot of negative implications. So he talked about the various things they'd tried, what worked and what didn't, and the UX considerations they'd put into trying to get people to select Airbnb's recommended price.