This year I decided to dress up as the Maneki Neko, or the Lucky Cat. These creatures actually come in many different colors; white represents happiness, purity, and positivity. It was a lot of fun to DIY my costume (and weird out my sister, who shares my Amazon Prime account, by buying a large bell and cat ears many weeks before Halloween).
Trees — they’re all around us in real life, providing us with fresh oxygen, giving us comfortable places to sit under and read, and acting as cool backgrounds to pose by.
(Okay, maybe that last one’s a me thing.)
As it turns out, trees are also all around us in the digital world. They’re the underlying data structures behind most file systems, library catalogs, 3D video games, network routers, music compression algorithms, and more. We just can’t see them, so we often don’t know that they’re there.
Although (usually) subtle, in-store music helps to set the tone of your shopping experience.
Sam is the Music Intern for Urban Outfitters. He helps curate the songs you hear in store (I totally don’t have a playlist consisting of remixes and covers of this UO song or anything…). We talked about personal tastes in music and aesthetic, and how a prospective UO intern can stand out.
It’s that time of year again — a time when college students eagerly send in their resumes, cover letters, and application materials to companies of their choice in order to land a coveted summer internship.
A lot of people have messaged me since I started posting interviews with the URBN Class of 2017 interns, asking me for more tips, tricks, and experiences I had while I was with the company.
I’m really nostalgic for my time there. It’s where I met my best friend, and turned twenty-one, and learned how to be a halfway-decent software engineer. I had such a good time that I spent the entire first week back at school incredibly homesick. Even now, the Navy Yard, University City, SEPTA, Old City, and Fishtown have a special place in my cold, dead heart.
I was looking through my stuff from the summer earlier and realized that I hadn’t posted all of the interviews I’d held. I’m going to publish them — along with the interviews that are already on here — as the URBN Class of 2017 Intern-Views (yes, pun intended) blog series. As you’ll see, URBN is such a big place that literally every single intern experience is different, and that’s what makes it so much fun! I was an engineering intern with the IT department, but learned a lot from other interns who were doing completely separate things.
Through this series, you’ll get a better idea of what it’s like to work there (and get some tips and tricks for the application process, too!). #MyURBNSummer was the coming-of-age story I didn’t know I needed — hopefully you’ll have a similar experience.
As usual, feel free to reach out with any questions, comments, or concerns. Enjoy!
When was the last time that you sorted something?
Maybe you accidentally dropped your freshly printed Parisian Art History final paper all over the floor and had to put it back in order according to the page numbers on the upper right-hand corners. Maybe you were making door signs for all the members of your sorority, and decided to put them in alphabetical order. The last time I sorted something by hand, I was organizing all of the dresses in my closet by color.
Take a step back and recall your last sorting memory. How would you explain your process to an intelligent alien? (“Umm, I just started picking things up and putting them in order” won’t cut it, sorry not sorry).
When you really take the time to think about it, sorting becomes pretty complex and hard to put into words. And there are so many ways to do it.
Welcome to the world of sorting algorithms.
When I first started studying algorithms, I had an incredibly hard time because of two things.
One, I didn’t have a very strong mathematical foundation, and would struggle to understand the reasoning behind certain algorithms. Two, I was just plain bored. “Implement insertion sort and time your algorithm input on arrays of size 10,000…” Ugh! I approached my assignments with the same sort of dread that one normally reserved for seeing disagreeable relatives during the holidays: Yeah, sure, I’ll bang out this code as quickly as possible and retreat to my room to watch Rick and Morty the first chance I get.
Because I had such an approach, it was painful to study algorithms. Even worse, the knowledge didn’t stick. I couldn’t remember a damn thing after I’d handed in an assignment. Insertion sort? How was that different from Merge Sort, or Selection Sort, or, God forbid, BogoSort?
I know what you’re thinking –“Big-O Time Complexity” sounds like the punchline of some nerdy that’s-what-she-said joke.
(I’m That Kid who snickered loudly at the term “Big-O notation” when it was first introduced sophomore year, so I won’t judge if you do the same.)
Big-O is actually one way to measure the amount of resources needed for an algorithm to run (also known as an algorithm’s complexity). “Resources” usually refers to time or space in memory. For simplicity’s sake, I’m going to be writing about time complexity only, so for the duration of this post, you can think of Big-O as a way to measure the amount of time an algorithm needs to run, depending on the size of its input.
If this all sounds hella confusing but also slightly interesting, good! Keep reading. Information sticks way better when you’re curious.
When I walked into Algorithms class earlier this semester, my first thought was “Oh God.”
I had not been a fan of Discrete Mathematics, the precursor to the course, and the thought of having to learn these data structures and algorithms — and implement them programmatically — made me want to hide under a large rock and never come back out again.
Then I dove into the class, and realized that algorithms are really cool, and understood everything right away, and got a great job in Silicon Valley, and saved up money over the years and bought a nice house on the water where I lived happily ever after with my 50 cats.
I wear a lot of dresses.
Most of them are the result of me spending an obscene amount of time on online secondhand-shopping apps such as Vinted, Poshmark, Ebay, and Mercari throughout my college career.
I first became hooked on Vinted because it advertised that users could trade clothing if they didn’t feel like spending money. Freshman-year me was strapped for cash but still a shopaholic, and during my first semester of college I both sent and received so many packages that the mail staff knew me by name by the end of the first week. #GoodTimes
There’s a special feeling that comes when you trade clothing with someone. Because there’s no actual money involved, it’s a more accessible form of acquiring a new wardrobe; the only factor is a mutual consensus from all parties involved. You also make instant connections with others through your mutual love of clothing — yeah, that dress is really pretty! I’ll give you this shirt for it.
It got me thinking. Although I’ve made many cool trades on these established platforms, selling for money is still the primary method of transaction. What if there were an app whose main focus was on trading, and trading only?