- screenshots of scores will be posted in comments
- screenshots of completed sections will be posted in comments
This article gave me a lot of insight into how a conscious effort to change a community can, over time, open up opportunities to those who would not otherwise have them. I think that the diversity of ideas that comes from a diverse group of people can have an immesurable effect on an industry as dynamic as technology. I feel excited for the progress expressed by the statistics in the article. It is significant to me that there is an effort to reduce the amount of homogeny in the industry because, as a minority member of the community, I grealy benifit from this drive.
This article was a great read, it touches on some important topics related to the philosophy of technology which was a focus of study for me in college. The author's ideas about the lack of empathy in the tech industry and the consequences thereof are interesting but I don't entirely agree. While technology is designed with a specific purpose it is extremely difficult, if not impossible to predict how it will be utilized by people. It is within the nature of technology that it builds upon itself, and while one person may design something that has very strict perameters upon which it can be implemented, we can't say how someone else might use that as a foundation for something unaticipated. I love the idea of technolgy being designed with empathy in mind; however, once it is out there in the hands of the users the effect it has on society is within the control of those using it, not those who created it.
The culture at HubSpot that Dan Lyons described in his article was kind of disturbing. It seemed as if HubSpot was spending as much, if not more, time and effort to convince their employees of the value of their narritive as their customers. They appear to use a lot of smoke and mirrors to distract from the fact that they're not selling anything other than themselves. I can't quite understand how they could be labled as a tech company if the foundation of their buisness model is marketing, not technology. It makes me appreciate the mission of Turing, insofar as its belief that diversity inspires inovation. This article also changed my mind about empathy in technology. Regardless of the effects a particular piece of technology may have in the future, creating it with empathy in mind doesn't seem like such a bad place to start.
Group Member Names: Charlotte, Courtney, Valerie, Casey
When are group members available to work together? What hours can each group member work individually? Are there any personal time commitments that need to be discussed?
How will group members communicate? How often will communication happen, and how will open lines of communication be maintained?
Shell is a command language. Over time people have developed different varities of shell by adding extra features
I used the twitter gem to grab stream data from twitter. Before writing any code I needed to create a developer account and project on twitter to get all the api keys I would need. Once that was done I could install the gem and start an irb session. Then I was able to initialize the client.
client = Twitter::Streaming::Client.new do |config|
config.consumer_key = "YOUR_CONSUMER_KEY"
config.consumer_secret = "YOUR_CONSUMER_SECRET"
config.access_token = "YOUR_ACCESS_TOKEN"
config.access_token_secret = "YOUR_ACCESS_SECRET"
The general concept behind this project is a hashtag generator. I used data taken from twitter to train a Recurrent Neural Network using the OpenNMT-py library. The idea is that if you feed the Neural Network a tweet or sentence, it can suggest appropriate hashtags. I chose the Recurrent Neural Network Sequence to Sequence model because it had the ability to generate novel hashtags, as opposed to another strategy that would have been limited to a set of hashtags/classes. Ultimately, I decided to do this project to explore a novel and potentially entertaining use of a sequence to sequence generator. In the process, I learned about several things including how to consume and clean data from twitter, the training process of Neural Networks, and how to use the OpenNMT-py library.
The model took just over 36 hours to train on a training set of about 2900 examples. I definitely have a greater appreciation for how much computing power is required to