Copasetic Flow

 With all the work that's been put into making agents "correct" by construction, I gotta say, sometimes I need an LLM agent to take a chance at just being wrong. I'm working on a book project called The Gladych Files . While the book is narrative nonfiction about the history of general relativity research, it explores the liminal space inhabited by very rich fringe scientist speculators of the 1950s who funded mainstream general relativity advances, (more or less on accident.) In those spaces, you'll find Tesla, the architect of the FBI building, Timothy Leary's LSD explorations and many,  many other things, institutions, and people.  I've accumulated hundreds of pages of historical documents from various archives, and I'm using orchestrated agentic AI, (in the form of Gastown), to review those documents. So far, the analysis has gone well, but last week I saw something that made me look up. I'd accidentally input the same archive page twice, so i...

 I took time to play with a new Dolt enabled app example called Quorum last night. Quorum sets 13 LLM agents with different defined personas  loose on a users question. The agents come up with solutions to the question and then discuss their individual solutions with each other to arrive at a consensus. There's much more detail in this blog post that accompanies the app. Quorum is cool. It is not, however, what I wanted to talk aobut here. Instead, I'm going to focus on the blog post for the app. In short, I'm very excited to see ideas that I've used to manage verification processes for years get codified into tools for LLM agents. Here's one of the important parts " I can shut down the app, lose the server, or disappear entirely — and the deliberation history remains, publicly accessible and cryptographically verified. " Imagine what an engineer can do to work back through their debug hypothesis tree with that sort of infrastructure! As the article'...