One of my standard agent uses is to create “mini-books” on historical topics, such as countries I visit. I have an app that allows me to name a topic or place. This feeds into a pre-written prompt that generates a syllabus written by 5.5 Pro, which is itself composed of prompts for each topic. These prompts are then fed into 5.5 Thinking, and the outputs are put into a nicely formatted website. The full output is usually the length of a short book. All I have to do is write the topic and press enter, and the whole thing appears a handful of minutes later. This is a very vanilla use of coding agents and LLMs, but it’s a remarkable capability that continues to blow my mind. I have never felt more joy in using computers than I do these days.
Dean W. Ball, Hyperdimensional Substack author, uses a 5.5 Pro and 5.5 Thinking agent workflow to generate customized mini-books
Each generated book consumes 4 million to 20 million tokens
Users criticize AI agent systems for generating short books that lack sustained arguments and structural depth, while highlighting risks of unchecked hallucinations and difficult debugging in multi-agent book publishing.
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tweeting about this caused me to think about the structure of this project, which was one of the first things I did when coding agents crossed the good enough threshold circa October/November. as currently conceived, the project is very much rooted in a 2024/5 conception of ai: decentralized api calls to llms. chatbots.
but after tweeting this, I realized you can now plausibly just create a digital organization filled with teams of agents, since the agents are getting so good at orchestrating teams of agents. if you're willing to spend the tokens, that is.
so what I am attempting is a kind of digital publisher, with 'submitter' agents who pitch book proposals, editors who pick them, research agents, writing agents, fact-checking agents, editors, and similar--hundreds in total. the token usage will be outrageous. we will see how much better the quality is.
One of my standard agent uses is to create “mini-books” on historical topics, such as countries I visit. I have an app that allows me to name a topic or place. This feeds into a pre-written prompt that generates a syllabus written by 5.5 Pro, which is itself composed of prompts for each topic. These prompts are then fed into 5.5 Thinking, and the outputs are put into a nicely formatted website. The full output is usually the length of a short book. All I have to do is write the topic and press enter, and the whole thing appears a handful of minutes later. This is a very vanilla use of coding agents and LLMs, but it’s a remarkable capability that continues to blow my mind. I have never felt more joy in using computers than I do these days.
Of course they are not books in the sense of carrying out a sustained argument, or building structural metaphors/analogies, over hundreds of pages. Models cannot really do that still ime, which is ironic given how much their analysis of writing is obsessed with structure.
One of my standard agent uses is to create “mini-books” on historical topics, such as countries I visit. I have an app that allows me to name a topic or place. This feeds into a pre-written prompt that generates a syllabus written by 5.5 Pro, which is itself composed of prompts for each topic. These prompts are then fed into 5.5 Thinking, and the outputs are put into a nicely formatted website. The full output is usually the length of a short book. All I have to do is write the topic and press enter, and the whole thing appears a handful of minutes later. This is a very vanilla use of coding agents and LLMs, but it’s a remarkable capability that continues to blow my mind. I have never felt more joy in using computers than I do these days.
@HaydnBelfield @rustbeltjacobin I want to try this with the new approach I'm using which is maybe 50x more token intensive
@deanwball @rustbeltjacobin Couldn't an agent pull out the text to a separate website pretty easily?
Loads of folks would be interested to have a gander at one of these minibooks
@deanwball This is basically the structure of Co-Scientist!
https://deepmind.google/blog/co-scientist-a-multi-agent-ai-partner-to-accelerate-research/
tweeting about this caused me to think about the structure of this project, which was one of the first things I did when coding agents crossed the good enough threshold circa October/November. as currently conceived, the project is very much rooted in a 2024/5 conception of ai: decentralized api calls to llms. chatbots.
but after tweeting this, I realized you can now plausibly just create a digital organization filled with teams of agents, since the agents are getting so good at orchestrating teams of agents. if you're willing to spend the tokens, that is.
so what I am attempting is a kind of digital publisher, with 'submitter' agents who pitch book proposals, editors who pick them, research agents, writing agents, fact-checking agents, editors, and similar--hundreds in total. the token usage will be outrageous. we will see how much better the quality is.

@deanwball I'm doing something my similar to study languages. Here's the content part
"I'm a widely read PhD, know all type. Pls suggest 10 pieces on subject X that I'm likely to not know"
Then I select.
@deanwball @rustbeltjacobin Interesting to think how in/efficient it is compared to eg interview transcripts, taking notes and writing drafts for a traditional book
Eg 4m-20m tokens for 10-100k words book
@HaydnBelfield @rustbeltjacobin yeah I just blasted 4m tokens on a book but I think there’s substantial room to run. trying a new iteration of the agent org structure that may well get me closer to 20m.

@deanwball debugging this must be a nightmare. what happens when research hallucinates and the fact-checker just agrees