Sending an AI response communicates more than just the response itself:
1. "I'm not entirely sure, but this is what it says to save you some time."
2. "You didn't ask the question precisely because you are not an SME, but I reworded it using the jargon that would allow the AI to answer better and here is the response."
3. "This response is AI, but in general my other ones are not"
One of the values of doing your own research is it forces you to speak the "language" of what you're trying to do.
It's like the struggle that we've all had when learning our first programming language. If we weren't forced to wrestle with compilation errors, our brains wouldn't have adapted to the mindset that the computer will do whatever you tell it to do and only that.
There's a place for LLMs in learning, and I feel like it satisfies the same niche as pre-synthesized Medium tutorials. It's no replacement for reading documentation or finding answers for yourself though.
The lack of syncing doesn't bother me, because the purpose of taking notes always falls into one of these categories:
1. I read the code to get an idea of how something works. The code is there to make examples/variable names concrete, but I don't need to know the exact implementation.
If the notes need to sit in the code, usually that's because the answer spans multiple methods (eg "what does an e2e request look like?"). A set of comments on outdated code is always good enough for me.
Otherwise, a lot of times the answer can be summarized in one line (eg "where is the state tracked?" -> in FooBarClass). These can go into personal notes.
2. I need to know the implementation and it is complex and hard to follow.
If I need to know the implementation, either it is because I'm actively working on it, or I need to make [complex idea] more concrete in my head.
If it's the former, usually I'll have memorized it by the time I read through it.
If it's the latter, by the end of it I'll have gotten the main idea and it's fine to forget the implantation details.
> What’s deeply frustrating is that for more than a decade Sal Khan similarly said that the videos on his “Khan Academy” would revolutionize education, and they utterly failed to do so.
How does one determine whether an edtech startup like KA has succeeded or failed? As someone who has found KA useful at times, I don't understand where the author is coming from
I recently heard someone say their favorite cure to writer's block is to read more and do research.
I found something profound about that. Writing is hard when you don't have anything to write about, and maybe there's something analogous to be said for goal setting.
As a fellow overthinker who also struggles to articulate my goals, I find it easier to think about incremental improvements than overarching goals.
For example, I want to become a better systems programmer, but I'm not really sure what to do with that desire. Goals are hard. But when I see C++ code, I know that I tremble at the sight of syntax I'm not familiar with, and that uncertainty is much easier to act on. I still won't "know" C++ in its entirety after an afternoon Googling session, but I definitely made some progress
> He refuses to engage earnestly with the “doomer” arguments. The same type of motivated reasoning could also be attributed to himself and Meta’s financial goals - it’s not a persuasive framing.
Exactly my thoughts too.
I don't agree with the Eliezer doomsday scenario either, but it's hard to be convinced by a scientist who refuses to engage in discussion about the flagged risks and instead panders to the public's fear of fear-mongering and power-seizing.
1. "I'm not entirely sure, but this is what it says to save you some time."
2. "You didn't ask the question precisely because you are not an SME, but I reworded it using the jargon that would allow the AI to answer better and here is the response."
3. "This response is AI, but in general my other ones are not"
4. "I trust the AI's response in this scenario."