| I am! In my experience, you need to keep a human in the loop. This implies that you can't get the technology to scale, but I'm optimistic because LLMs have rapidly gotten better at following directions while I've been using them over the last six months. Summarization is probably the clearest strength of LLMs over a human. With ever-growing context windows, summarizing books in one shot becomes feasible. Most books can be summarized in one sentence, though the most useful, information-dense ones cannot. I had Gemini 1.5 Pro summarize an old book titled Natural Hormonal Enhancement yesterday. Having just read the book, the result was acceptable. https://hawleypeters.com/summary-of-natural-hormonal-enhance... For information-dense books, it seems clear to me that chatting with the book is the way to go. I think there's promise to build a competent agent for this kind of use case. Imagine gathering 15 papers and then chatting about their contents with an agent with queries like: What's the consensus?
Where do these papers diverge in their conclusions?
Please translate this passage into plain English. I haven't done this myself, but I have a hard time imagining such an agent being useless. Perhaps this is a failure of imagination on my part. The brightest spot in my experimentation is [Cursor](https://cursor.sh). It's good for little dev tasks like refactoring a small block of code and chatting about how to use vim. I imagine it'd be able to talk about how to set up various configs, particularly if you @ the documentation, a feature that it supports, including [adding documentation](https://docs.cursor.sh/features/custom-docs). Edit: I think a lot of disappointment comes from these kinds of tools not being AGI, or a replacement for a human that does some repetitive task. They magnify the power of somebody that's already curious and driven. They still empower lazy, disengaged users, but with goals like doing the bare minimum, and avoiding work altogether, these tools cannot help one accomplish much of use. |