You shouldn't try to assess what people care about from what you see in the media, including social media. Focused thought and gigantic resources go into shaping the public perception of reality through media.

Pay every Nth month, or M months per year :-) Use the free tier features throughout.

> My finance makes fun of me

Your financial situation is very interesting!

From my experience it has been largely solved for one significant use case which is chatting to frontier models about the reality as described by public knowledge. 2 years ago models would rely on their training data, today they go out of their way trying to look it up on the Internet and verify thoroughly. I have not had a problem for a very long time.

When working off of limited, private/unverifiable context, LLMs still hallucinate, but again, much less than 2 years ago, and more within a "getting confused where a human would easily get confused" range, rather than "outrageously making things up" range.

What are some viable architectures for such an agent that are token-efficient, effective and convenient?

I am not familiar with the Linux contributor ecosystem. But I am not sure it is valid to compare a human contributor to Linux to an AI agent. By "I am not sure" I really mean I am not sure; maybe it is valid nowadays? Successful Linux maintainers are the cream of the crop; can even the modern LLMs compare at all? OTOH, Linux is so popular that it attracted lots of slop even before LLM explosion. And LLMs are so much easier to reason with than humans (I do believe that).I don't know... food for thought.

First you have to know that "it" exists and is possible. A cookbook like this introduces readers to concepts and features they didn't know were there in the first place.