This is a bundle of somewhat unstructured notes from a Twitter Spaces event about value investing. The Spaces event was put on by a good friend of mine, Jason Wong, almost a year ago (May 21st, 2022). While going through my notes recently, I realized that these bullets might be worth publishing if I could turn them into a slightly more readable document. This is about the best I could do.
The killer use case for large language models (LLMs) is clearly summarization. At least today, in my limited experience, LLMs are incapable of generating unique insights. While LLMs are good at writing creatively regurgitated text based on certain inputs or writing generally about a topic, they’re unlikely to “think” something unique. However, LLMs appear to be quite good at knowing what they do and don’t know, and this is especially true when they are provided with a clear chunk of information or text to summarize.
Begin.
I’m going to experiment with free writing for the first time in a long time. I’m in the back of an Uber right now and it’s not that I don’t want to talk to the driver, it’s just that I’m not in a huge talking mood right now. For that reason, I’m going to experiment with free writing in this altered state of consciousness… We’ll see how it goes.
Some women are… you know, they’re fine. They’re gorgeous and beautiful and smart and they’ve got everything. But they don’t have it. And it’s just… well, I have no idea what it is. It’s just there. And I know it. And she knows it. And she has it. And it’s something there, something under the surface, something sparkling, something that dances around like little crystalline packets of icy snow when they bounce off the ice and skitter all around and shine a thousand twinkling light beams through the aether.