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Comment by hwers | Hacker News Reader

Its unfortunate that there’s mode collapse around what the consensus “best way” to use these things are. It’s too bad we didn’t have a period where these things were great teachers but didn’t attempt to write code because in my opinion the ideal way to use them is not by agents mass producing sloppy buggy disorganized code, but to teach you things way faster than the old alternatives, rubber duck, and occasionally write snippets of functions when your brain is too tired or it’s throwaway cli code or some api you’re not familiar with.

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> It’s too bad we didn’t have a period where these things were great teachers but didn’t attempt to write code

The period is now. Just add "be a great teacher but don't attempt to write code" in the prompt.

(yes, it's a teacher who gets things wrong from time to time. You still need to refer to the source and ground truth just like when you're taught by a human teacher.)

No, expectations have shifted. In a lot of companies, managers expect you to use LLMs to produce more features faster.

> to teach you things way faster than the old alternatives

I'm not sure if you ever had a teacher or instructor that you didn't trust, because they were a compulsive liar or addiction or any other issue. I didn't (as least not that I can remember) but I know I would be VERY on guard about it. I imagine I would consequently be quite stressed learning with them, even if they were brilliant, kind, etc.

It would feel a bit like walking on thin ice to get to a beautiful island. Sure, it's not infeasible and if you somehow make it, it might be worth the risk, but honestly wouldn't you prefer a slower boat?

I agree, it can be incredibly frustrating at times. My rule is that if it “compiles” in my brain as an understood idea then i accept it. I also push back a lot (sometimes it points out good errors in my thinking, sometimes it admits it hallucinated). Real humans hallucinate a lot as well or confidently state subtly wrong ideas, it’s a good habit anyway. It’s basically the same approach when presented with a “formula” for something in school. If i dont know how to derive/prove it then i dont accept it as part of my memorized or accepted toolkit/things i use (and try to forget it). If it fits with the rest of my network of understood ideas i do. It’s annoying but still more time efficient than trawling through lecture slides with domain specific language etc

> Real humans hallucinate a lot as well or confidently state subtly wrong ideas, it’s a good habit anyway.

I think that's actually deeply different. If a human keeps on apologizing because they are being caught in a lie, or just a mistake, you distrust them a LOT more. It's not normal to shrug off a problem then REPEAT it.

I imagine the cost of a mistake is exponential, not linear. So when somebody says "oops, you got me there!" I don't mistrust them just marginally more, I distrust them a LOT more and it will take a ton of effort, if even feasible, to get back to the initial level of trust.

I do not think it's at all equivalent to what "Real humans" do. Yes, we do mistake, but the humans you trust and want to partner with are precisely the one who are accountable when they make mistakes.

>Real humans hallucinate..

You seem to have a different understanding of what it means in the context of neural networks.

Real humans will not make up non existent api and implement a solution with it, (unless they do it on purpose).

> My rule is that if it “compiles” in my brain as an understood idea then i accept it.

Unfortunately, individual people are not anywhere as reliable as a compiler for ensuring compliance to reality. We are particularly susceptible to flattery and other emotional manipulation, which LLMs frequently employ. This becomes particularly problematic when you ask for feedback on an idea.

In that case, a useful hack is to frame prompts as if you're an impartial observer and want help evaluating something, not as if the idea under evaluation is your own.

I feel like this is partially a skill issue - You can get direct, cited information from LLMs. There's a level of personal responsibility for over-using the tools and letting them feed you bad/false information, but if you try researching specific abstractions, newer documentation, most LLMS now correctly call and research the tools available, directly citing them.

I think you can build a very easy workflow that reinforces rather than replaces learning, I've used a citation flow to link and put into practice a ton of more advanced programming techniques, that I found incredibly difficult to locate and research before AI.

I'd say the comparison is faulty, it's more akin to swimming to an island (no-ai) vs using a boat. You control the speed and direction of the boat, which also means you have the responsbility of directing it to the correct location.

The analogy was about the unknown thinnest of the ice, not just the fastest way to get there. It's specifically about the lack of reliability of the process.

Yes, I was disagreeing with the premise of the analogy - what would the slow boat in this case be? As my experience, going through software engineering before AI, is that you'd get lost to the ice, with nobody to really help you get out.

If you get lost on the ice and you have someone who confidently tells you the path but is sometimes wrong, is it actually helpful?

PS: sorry if the analogy is a bit wonky but it's quite dear to me as I do ice skating on frozen lakes and it's basically a life or death information "game" that I can relate to. It might not be a great analogy for others.

Haha it's a good analogy, i'm being a little bit argumentative for the sake of it potentially.

I guess in my view - the main alternative you'd have beforehand is just to drown.

For me, AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.

I'd agree there's currently a ton of people that are using these tools to essentially just find the specific route - but i'd argue those people probably shouldn't be skating in the first place, and would've fallen one way or the other.

> I guess in my view - the main alternative you'd have beforehand is just to drown.

Before most who didn't know the ice didn't went out on it, today a lot of people who shouldn't be there go far out on the ice.

Totally true - althuogh I feel like that's been the case since the first coding bootcamps

> AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.

Right, but AFAICT most people just venture over the ice and don't bother to check. In fact a lot of people venture there, do check once or twice, then check less and less frequently. The fact that you do it is great but others seem a lot less careful, until cracks start to show and then it might be too late.

Very true - I won't dispute it!

I'd only argue that people were doing this before AI, slop development was just copy pasting from the first stack overflow issue that matched the question rather than thinking

So i'd argue there's a part of it that is just personal responsibility with how these tools are used

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