CS 373 Spring 2021 Week 2: Pranay Kalagara

Pranay R Kalagara
2 min readJan 31, 2021

1.) What did you do this past week?

This past week, I tried going over some basic python tutorials and read the Collatz project page in order to understand what’s going to be expected of us.

2.) What is in your way?

One thing that is in my way is trying to familiarize myself with Docker. I’ve never used the tool before so it’s all new to me. I also need to do a little catch up on python since I’ve never done a whole lot of coding in the language.

3.) What will you do next week?

Next week, I’ll start working on the first project and hopefully finish it before the week ends. I want to get ahead so that way I can free up my weekend to do other things.

4.) If you read it, what did you think of the makefile?

I found the makefile to be pretty interesting. Seeing what “make” is actually running after using it for so long was cool and I hope to keep learning about it.

5.) What was your experience of Docker?

I’ve never used docker before so it was all kind of new to me. I have yet to try it on my local machine but that will be something I’ll tackle this upcoming week.

6.) What was your experience of assertions?

I never really used assertions a whole lot in my personal code so seeing their usefulness and practical applications was informative. I’m looking forward to seeing how it will help my code.

7.) What was your experience of unit tests?

I thought the unit testing was helpful in ensuring that each piece of code works. This is important so it’s easier to find and fix bugs before the code gets bogged down by dependencies.

8.) What made you happy this week?

I moved into my apartment in Austin so I’m glad to be back.

9.) What’s your pick-of-the-week or tip-of-the-week?

My pick-of-the-week is Kafka Streams which I’ve been messing around with on a website. It’s a real time streaming application that allows you to gain insights and data from web services as they happen! This proves very useful for creating insights and identifying trends or anomalies in streaming data.

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