A Positive Disaster

14 Apr 2026 in Writing

As a part of a two-semester undergraduate Order of Magnitude (OoM) astronomy course, the second semester culminated in a group project where we created our own OoM problems. Our group created a problem titled “A Positive Disaster”, a problem about ripping electrons off the moon!
_{An LLM was used to create this cover image.}

Imagine an alien race has come to our solar system with an “electron capture” magnet. They position the magnet at the moon and start ripping away electrons from the moon, making it positively charged, and they conveniently don’t repel each other or attract other positive charges when doing so. As the moon continues to get more positively charged, it will eventually “explode” (become unbound).

(a) Calculate the number of electrons you have to remove before the moon explodes/becomes unbound.
(b) Is this a sensible amount of electrons?

In this post I talk about the problem itself and the writing process.

A Comparison of tf.data.Dataset with Manual Image Loading

20 Aug 2025 in Programming

Loading data for AI models can be RAM and time-intensive. For a larger project (HackAI 2025) with a big dataset, I ran an optimization test on a manual loader and a Tensorflow pipeline to see the variations in time and RAM. As expected, Tensorflow’s model is much more efficient.

While working on our HackAI 2025 project, we found that our original way of loading the image data for our multi-headed regression was incredibly slow, and was crashing some of our computers due to RAM usage. Eventually, we converted our pipeline over to one that utilizes the more-optimized tf.data.Dataset pipeline. For our own entertainment, we made plots comparing the old method with the new method.