My thoughts on hacking, ai, faith, and more.
© 2024 rez0.
There’s been a lot of discussion lately about how AI struggles with long-running tasks. And it makes sense when you think about it. These large language models can generate a ton of text in a few seconds. But then what? They’ve put out all these words or code and don’t really have a clear direction on what to do next.
OpenAI just made a big move in the AI space with the release of GPT-4o (“o” stands for “omni”). This new model is crazy because it is a single model that can process not just text, but also audio and images. And it’s going to be accessible to free users (or at least the text version).
As I’ve been discussing AI agent authentication with some brilliant people in San Fran this week, it’s become clear to me that there will likely be various solutions based on different use cases and risk tolerance levels. By imagining what a mature AI agent ecosystem might look like, I’ve come to the conclusion that the industry will essentially be divided into three main categories.
My wife got me the new Meta AI Ray-Bans as an early birthday present, and I have to say, they are seriously impressive. I’ve messed with some of the other AI devices out there, but these are on a whole different level. I’ll explain why by elaborating on each use case:
Pitching the assumptions_made metadata field for AI Agents
