> AI agents are just software, after all. I wasn’t going to understand them by reading prompt guides. I had to use them for real work, see where they failed, change the prompts, tools, or environment, and try again.
AI agents are just software, so the only way to understand them is trial and error?
Tried and true approach. Sometimes the only way to proceed if you are dealing with - embedded devices, protocols, long and deep stacktraces or freecad.
I think that's similarly in line with software; you don't really know the limitations of a library until you use it, and see that it fails for X reason at 10Hz, or 1000Hz.
Some people like doing DIY plumbing and related tasks around their own homes. Work might not be "fun" in the traditional sense, but it can be satisfying to build and fix things.
I gotta be honest, I don't really think these immigrant groups see or care what Trump/RFK are saying. If anything they see them as an "enemy" who they should not trust.. This is an incredibly left-leaning city where the majority of public health information (or any other messaging) being disseminated is counter to what the current federal admin is saying (as a rule, it seems).
So to answer your question, I don't personally think it helps or hurts in any significant amount relative to other cultural drivers.
I don't think it was the roll-out or anything along those lines, so much as the lying. Saying people won't get sick or won't catch COVID if they get the vaccines [1] was just false and very myopic. Obviously the intent was to try to get people to take the shots, but they completely destroyed trust in the process. Broader skepticism predictably follows from there.
I think in order to notice and take offense to those tropes, you already have to be prejudiced against them. And you probably also have the pretense that what you're reading was thought important enough by another person to write. So when you realize that this isn't the case you become disappointed.
But some people just don't care, and are just in it for novel rearrangements of the narrative structure. I'll give you that I don't notice this so much with literary content, but I have seen people being entertained by AI generated music and videos quite a bit, especially children and those who in general are uncritical of what they consume.
For example, the other day I was at a restaurant where they were playing some AI abomination of Frank Sinatra called "Take me to the Moon", and nobody really seemed to notice or care while I was becoming nauseous to my stomach. You have to already be biased to notice it, let alone to even be bothered by it.
Most popular fiction is formula entertainment like Tom Clancy novels. Tom Clancy novels are their own industry now. I have zero doubt that AI tools can generate countless variations of good guy US special OPs going after bad guy terrorist books. The "AI tropes" are over sold on HN. With the correct prompting and iteration today's AI tools can make nuanced fiction which avoids the low effort tropes. BTW "high effort" work with AI tools is but a small fraction of what of the effort using traditional means.
This is certainly overstating the difference between compiled code and the source code. If I write an if/else statement, I understand what the computer is going to do when it's evaluated even if I don't know or see the literal machine code.
Only if the compiler is perfectly naive. With the state of today's optimizing compilers, the output might be very different from what you'd expect. But it also doesn't matter because the business goal is the results from executing the program, not the static code. Hence why, as the earlier commenter pointed out, efforts go into validation rather than understanding the generated code. 99% of the time the code a compiler spits out simply doesn't matter. The same is now proving true as we move up a level of abstraction. The code spit out from an LLM doesn't matter any more than the code spit out from a compiler, only validation that it functions as expected matters.
Yes, the difference between a compiler and an LLM is that a compiler's output correctness is binary, whereas an LLM's output correctness is probabilistic.
That may be an interesting distinction in a vacuum, but given that the input to both is words put together randomly with a mathematical probability a compiler's binary correctness isn't actually the useful property that it may first seem. In the real world, to overcome the probabilistic input, you have to validate the input with respect to the output. And it turns out once you have systems in place for that, you gain the same assurances with a probabilistic output as you do with binary output.
So, while there are some technical differences, there is no practical difference.
The datacenter antipathy is odd, but then again so are the AI corps' marketing strategy of making everyone afraid for their jobs and the future of the species.
What else would they advertise with? "Our model constantly makes mistakes so you still have to hire expensive humans to proof-read everything"? The entire point of the AI industry is to automate human jobs. There is nothing else to advertise with.
AI agents are just software, so the only way to understand them is trial and error?
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