[altmanaltman]

A few counterpoints:

1. If you don't care about code and only care about the "thing that it does when it's done", how do you solve problems in a way that is satisfying? Because you are not really solving any problem but just using the AI to do it. Is prompting more satisfying than actually solving?

2. You claim you're done "learning about coding and stretching my personal knowledge in the area" but don't you think that's super dangerous? Like how can you just be done with learning when tech is constantly changing and new things come up everyday. In that sense, don't you think AI use is actually making you learn less and you're just justifying it with the whole "I love solving problems, not code" thing?

3. If you don't care about the code, do the people who hire you for it do? And if they do, then how can you claim you don't care about the code when you'll have to go through a review process and at least check the code meaning you have to care about the code itself, right?

[keeda]

Note I'm not saying one is better than the other, but my takes:

1. The problem solving is in figuring out what to prompt, which includes correctly defining the problem, identifying a potential solution, designing an architecture, decomposing it into smaller tasks, and so on.

Giving it a generic prompt like "build a fitness tracker" will result in a fully working product but it will be bland as it would be the average of everything in its training data, and won't provide any new value. Instead, you probably want to build something that nobody else has, because that's where the value is. This will require you to get pretty deep into the problem domain, even if the code itself is abstracted away from you.

Personally, once the shape of the solution and the code is crystallized in my head typing it out is a chore. I'd rather get it out ASAP, get the dopamine hit from seeing it work, and move on to the next task. These days I spend most of my time exploring the problem domain rather than writing code.

2. Learning still exists but at a different level; in fact it will be the only thing we will eventually be doing. E.g. I'm doing stuff today that I had negligible prior background in when I began. Without AI, I would probably require an advanced course to just get upto speed. But now I'm learning by doing while solving new problems, which is a brand new way of learning! Only I'm learning the problem domain rather than the intricacies of code.

3. Statistically speaking, the people who hire us don't really care about the code, they just want business results. (See: the difficulty of funding tech debt cleanup projects!)

Personally, I still care about the code and review everything, whether written by me or the AI. But I can see how even that is rapidly becoming optional.

I will say this: AI is rapidly revolutionizing our field and we need to adapt just as quickly.

[skydhash]

> The problem solving is in figuring out what to prompt, which includes correctly defining the problem, identifying a potential solution, designing an architecture, decomposing it into smaller tasks, and so on

Coding is just a formal specification, one that is suited to be automatically executed by a dumb machine. The nice trick is that the basic semantics units from a programming language are versatile enough to give you very powerful abstractions that can fit nicely with the solution your are designing.

> Personally, once the shape of the solution and the code is crystallized in my head typing it out is a chore

I truly believe that everyone that says that typing is a chore once they've got the shape of a solution get frustrated by the amount of bad assumptions they've made. That ranges from not having a good design in place to not learning the tools they're using and fighting it during the implementation (Like using React in an imperative manner). You may have something as extensive as a network protocol RFC, and still got hit by conflict between the specs and what works.

[RHSeeger]

> I truly believe that everyone that says that typing is a chore once they've got the shape of a solution get frustrated by the amount of bad assumptions they've made.

To a lot of people (clearly not yourself included), the most interesting part of software development is the problem solving part; the puzzle. Once you know _how_ to solve the puzzle, it's not all that interesting actually doing it.

That being said, you may be using the word "shape" in a much more vague sense than I am. When I know the shape of the solution, I know pretty much everything it takes to actually implement it. That also means I've very bad at generating LOEs because I need to dig into the code and try things out, to know what works... before I can be sure I have a viable solution plan.

[skydhash]

I understand your point. But what you should be saying is that you have an idea of the correct solution. But the only correct solution is code or a formal proof that it is in fact correct. It’s all wishes and dreams otherwise. If not, we wouldn’t have all of those buffer overflow, one by off errors, and xss vulnerabilities.

[RHSeeger]

All we _ever_ have is an idea of the correct solution. There's no point at which we can ever say "this is the correct solution", at least not for any moderately sized software problem.

That being said, we can say

- Given the implementation options we've found, this solution/direction is what we think is the best

- We have enough information now that it is unlikely anything we find out is going to change the solution

- We know enough about the solution that it is extremely unlikely that there are any more real "problems/puzzles" to be solved

At that point, we can consider the solution "found" and actually implementing it is no more a part of solving it. Could the implemented solution wind up having to deal with an off-by-one error that we need to fix? Sure... but that's not "puzzle solving". And, for a lot of people, it's just not the interesting part.

[keeda]

I think you would be surprised by how much these AIs can "fill in the blanks" based on the surrounding code and high-level context! Here is an example I posted a few months ago (which is coincidentally, related to the reply I just gave the sibling comment): https://news.ycombinator.com/item?id=44892576

Look at the length of my prompt and the length of the code. And that's not even including the tests I had it generate. It made all the right assumptions, including specifying tunable optional parameters set to reasonable defaults and (redacted) integrating with some proprietary functions at the right places. It's like it read my mind!

Would you really think writing all that code by hand would have been comparable to writing the prompt?

[skydhash]

I’m not surprised. It would be like being suprised by the favt that computers can generate a human portrait (which has been been a thing before LLMs), but people are still using 3d software because while it takes more time, they have more control over the final result.

[keeda]

We still have complete control over the code, because after the AI generates it, it's right there to tweak as we want!

But the point is, there were no assumptions or tooling or bad designs that had to be fought. Just an informal, high-level prompt that generated the exact code I wanted in a fraction of the time. At least to me that was pretty surprising -- even if it'd become routine for a while by then -- because I'd expect that level of wavelength-match between colleagues who had been working on the same team for a while.

[PaulDavisThe1st]

> Coding is just a formal specification

If you really believe this, I'd never want to hire you. I mean, it's not wrong, it's just ... well, it's not even wrong.

[snovv_crash]

I'd still hire them, in fact I see that level of understanding as a green flag.

Your response and depth of reasoning about why you wouldn't hire them is a red flag though. Not for a manager role and certainly not as an IC.

[PaulDavisThe1st]

I provided zero depth of reasoning.

Coding is as much a method of investigating and learning about a problem as it is any sort of specification. It is as much play as it is description. Somebody who views code as nothing more than a formal specification that tells a computer what to do is inhibiting their ability to play imaginatively with the problem space, and in the work that I do, that is absolutely critical.

[snovv_crash]

> zero reasoning

Yes.

> inhibiting play

Strongly disagree. The more abstraction layers you can see across, the bigger your toolbox and the more innovative your solutions to problems can be.

[altmanaltman]

Honestly, I fundamentally disagree with this. Figuring out "what to prompt" is not problem-solving in a true sense imo. And if you're really going too deep into the problem domain, what is the point of having the code abstracted?

My comment was based on you saying you don't care about the code and only what it does. But now you're saying you care about the code and review everything so I'm not sure what to make out of it. And again, I fundamentally disagree that reviewing code will become optional or rather should become optional. But that's my personal take.

[sho]

> Figuring out "what to prompt" is not problem-solving in a true sense

This just sounds like "no true scotsman" to me. You have a problem and a toolkit. If you successfully solve the problem, and the solution is good enough, then you are a problem solver by any definition worth a damn.

The magic and the satisfaction of good prompting is getting to that "good enough", especially architecturally. But when you get good at it - boy, you can code rings around other people or even entire teams. Tell me how that wouldn't be satisfying!

[keeda]

> My comment was based on you saying you don't care about the code and only what it does. But now you're saying you care about the code and review everything so I'm not sure what to make out of it.

I'm not the person you originally replied to, so my take is different, which explains your confusion :-)

However I do increasingly get the niggling sense I'm reviewing code out of habit rather than any specific benefit because I so rarely find something to change...

> And if you're really going too deep into the problem domain, what is the point of having the code abstracted?

Let's take my current work as an example: I'm doing stuff with computer vision (good old-fashioned OpenCV, because ML would be overkill for my case.) So the problem domain is now images and perception and retrieval, which is what I am learning and exploring. The actual code itself does not matter as much the high-level approach and the component algorithms and data structures -- none of which are individually novel BTW, but I believe I'm the only one combining them this way.

As an example, I give a high-level prompt like "Write a method that accepts a list of bounding boxes, find all overlapping ones, choose the ones with substantial overlap and consolidate them into a single box, and return all consolidated boxes. Write tests for this method." The AI runs off and generates dozens of lines of code -- including a tunable parameter to control "substantial overlap", set to a reasonable default -- the tests pass, and when I plug in the method, 99.9% of the times the code works as expected. And because this is vision-based I can immediately verify by sight if the approach works!

To me, the valuable part was coming up with that whole approach based on bounding boxes, which led to that prompt. The actual code in itself is not interesting because it is not a difficult problem, just a cumbersome one to handcode.

To solve the overall problem I have to combine a large number of such sub-problems, so the leverage that AI gives me is enormous.

[skydhash]

What people are wary of is not solving the problem in the first pass. They are wary of technical debt and unmaintainable code. The cost of change can be enormous. Software engineering is mostly about solving current problems and laying the foundation to adapt for future ones at the same time. Your approach's only focus is current problems which is pretty much the same as people that copypaste from StackOverflow without understanding.

[keeda]

Technical debt and understanding is exactly why I still review the code.

But as I said, it's getting rare that I need to change anything the AI generates. That's partly because I decompose the problem into small, self-contained tasks that are largely orthogonal and easily tested -- mostly a functional programming style. There's very little that can go wrong because there is little ambiguity in the requirements, which is why a 3 line prompt can reliably turn into dozens of lines of working, tested code.

The main code I deal with manually is the glue that composes these units to solve the larger computer vision problem. Ironically, THAT is where the tech debt is, primarily because I'm experimenting with combinations of dozens of different techniques and tweaks to see what works best. If I knew what was going to work, I'd just prompt the AI to write it for me! ;-)

[pdntspa]

Why can't both things be true? You can care about the code even if you don't write it. You can continue learning things by reading said code. And you can very rigidly enforce code quality guidelines and require the AI adhere to them.

[altmanaltman]

I mean if you're reading it and "rigidly" enforcing code quality guidelines, then you do care about the code, right? But the parent comment said they don't care about the code but what it does. Both of them cannot be true at the same time, since in your example, you do care about the code enough to read it and refactor it based on guidelines and not just "what the code" does.

[danielmarkbruce]

are you really solving the problem, or is the compiler doing it?

[altmanaltman]

is the compiler really solving the problem or the electricity flowing through the machine?

[ukuina]

Is it the electricity, or is it quantum entanglement with Roko's Basilisk?

[hellouruguay]

Is it the Basilisk, or just a bit flip in the parent simulation?

[danielmarkbruce]

the parent simulation wouldn't use something so crude as a "bit"...

[danielmarkbruce]

exactly.....