Delphi, is offering a messaging platform to better keep friends engaged in each other’s lives, because we’re all getting out of touch with friends as we age. Existing communication solutions lack the prioritization of friends you haven’t talked to in a while, and it can be hard to come up with conversation starters for distant friends. Delphi keeps conversation frequent and fluid by suggesting relevant conversation starters like articles and memes based on your unique conversation histories. Log on today and reignite your meaningful relationships.
Maintaining friendships with busy lives is challenging, especially with long-distance friends. Unlike instant messaging applications where the user actively engages with their friends, Delphi’s mission is to nudge users to engage with their long-distance friends by recommending conversation starters based on keywords of their chat history. For the purpose of our application, Delphi recommends basketball related content because of the basketball related messages in the chat history. Here is how the app works… The user logs-into the application with his Facebook account then chooses to chat with Benjamin, the user then engages either by sending a text message or a recommended article or meme. Delphi spurs users to actively engage with their long-distance friends by intelligently recommending content related to their chat history.
I learned that working in a team of 2 can be challenging in terms all the requirements of each assignment, especially with the coding parts of the project. On the other hand, being only 2 people, helped our team in terms of scheduling meetings and emphasized our ability to prioritize and complete work efficiently. If I had to do it all over again, we would have like to worked more on the algorithm for recommending content based on the chat history. Although, that seems a more advanced and arduous time, our team would have liked to spend more time on that. Being a team of 2, we focused more on conveying and displaying our idea into a prototype, rather than generating recommender algorithms. Our team has been mostly successful when planning and prioritizing tasks demonstrated by the good scores in most of our assignments. The only mistakes were when we started coding the application and we ran into bugs during the assignment due date. Starting a couple of days earlier could have mitigated this issue.
I learnt that seeking help early from teaching assistants especially during the coding phase of the project was helpful to debug and solve issues about the project. Starting earlier on the assignment would have also been beneficial in avoiding last minute stress, especially when encountered with bugs in the project. I also learnt that planning early will save time later, especially during midterm weeks when time is limited and a lot of work has to be completed. If I had to do it all over again, I would have chosen an idea that has a greater chance of innovation. I think that instant messengers are saturated in development. I think our team’s mistake was thinking that we would have made an artificial algorithm for the project and that was ultimately out the scope of the class. Successful planning and execution occurred when other events spurred our team to work earlier.