Israeli Social Travel and Software Development: Update 2

Today was the last day of my third week at GoWith, marking the halfway point in my internship period. It’s hard to believe that my time in this country is already more than half over. In the last two weeks, my work partner (another intern) and I have been slowly chipping away at our delegated portion of GoWith’s app. The largest challenge of this has been working with new iOS libraries and APIs in order to get the functionality we need. This is exacerbated by the my (and the rest of the team’s) lack of experience with iOS. Everyone in the company has been learning and helping each other out, though I can’t help but feel like

I’ve also been doing a lot of work with real-time databases (through the service Firebase) in order to collate airport data from online with user data in the app. The other interns (separated into teams) are also independently working on their own features that also use Firebase. We’re starting to identify an impending issue with this, however– we’re all using separate databases for each of our own projects, but once the features are all rolled into a single app, we’ll need to unify these features under one database, which may mean having to reorganize our database structures. I’ve talked with our CEO about this, and he’s planning to tentatively deal with this next week. We’ll still be independently working on our own features, but if everything’s connected to a singular, unified database with agreed-upon structures, stitching these features into an app will be a lot easier down the line.

Development on my portion of the app has been steady but slow. Our feature does its job in the most basic sense, but there are elements that still need to be implemented (regrettably, I can’t be specific, as confidentiality was a condition of my internship). Our next big task will be to add a feature that will be used only by the airport and its administrators. This feature will use natural-language processing (NLP), an area of machine learning, to quantify and analyze guest sentiment based on user-generated text. We’ve made a lot of progress so far, but there’s still a lot to be done!

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