The podcasts are grating to listen to and usually only contain very surface information I could gain from a paper’s abstract.
It’s a wildly impressive technical achievement though.
My issue with AI hype is exactly this. Everything is “imagine if this was just better enough to be useful”
“Imagine if we had an everything machine”
“Image everyone having a personal assistant/artist/tutor/programmer”
“Imagine a world where finance is decentralized and we all truly own our digital stuff”
<rant>
I’m not much of a visionary, admittedly, but it’s exhausting being told to imagine products that only half exist now.
Having worked with LLMs in the autonomous agent space, I think we’re very far away from agents actually doing useful work consistently.
There are still so many problems to be solved around the nature of statistical models. And they’re hard problems where the solution, at least at the product level, boils down to “wait for a better model to come out”
I’m just tired of people imagining a future instead of building useful things today
<\rant>
Some instructions that worked for me:
- Specifics instead of high level
- Approach from non-critical perspective
- Dont be philosophical
- Use direct quotes often
- Focus on the details. Provide a lesson, not reflections
- Provide a 'sparknotes' style thorough understanding of the subject
