My first advice: take all of my advice with a grain of salt.


  • Don’t look things up and don’t ask for help until you’ve really thought about a problem/idea for yourself. The thoughts of others won't always be available, so it’s not worth it in the long run to rely on them. On that note, it's good to predict results before they're told to you (e.g. during lecture).

  • If possible, expose yourself to the material before lecture – perhaps by reading. You'll get more out of lecture/section if they aren't your first time processing the concepts, and a multi-stage process is in general pretty critical for learning.

  • Strive for mastery over "doing better than everyone else." According to CSE 599, according to education studies CS students are better served (ironically even grade-wise) when they prioritize the former.

  • Whenever you're programming in a visual computing context, visualize early and visualize often! In this discipline, "looking at pictures" is a bona fide debugging strategy.

  • Studying for Exams, by Josh Hug


  • There are four main reasons, not exclusive, to pursue an activity. One is that you are good at it. Another is that you can be paid for it. Another is that people need it. And finally there is the possibility that you love it. This last reason is the most important, I think, and is arguably most likely to precipitate each of the others. It is much easier to excel at something if you enjoy it, for example, because then you will be happy to spend time improving. So enjoy – or find a way to feel like you enjoy – what you do!

  • For every good photo you capture, there are a thousand awful ones. Perhaps this will be obvious, but if you keep trying different approaches then eventually you’ll stumble onto something that works. Generally, the more mindful you are about choosing approaches, the less time this process will take. But the key is to try, to think, to do things in order to give yourself the chance to fall into flashes of "brilliance."

  • Drink a ridiculous amount of water.

  • On Productivity, by Philip Guo