[ori_b]

It's deeper. We used to mock architects that stepped back and stopped coding, because they generated trash.

There's a cycle that is needed for good system design. Start with a problem and an approach, and write some code. As you write the code, you reify the design and flesh out the edge cases, learning where you got the details wrong. As you learn the details, you go back to the drawing board and shuffle the puzzle pieces, and try again.

Polished, effective systems don't just fall out of an engineers head. They're learned as you shape them.

Good engineers won't continue to be good when vibe-coding, because the thing that made them good was the learning loop. They may be able to coast for a while, at best.

[beeandapenguin]

Reminds me of Gall’s Law from his book Systemantics.

A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over with a working simple system.

https://en.wikipedia.org/wiki/John_Gall_(author)#Gall's_law

[ori_b]

I find that the learning and iteration tends to lead to a simplified system, if you're willing to look hard enough at the shapes needed.

When there's a lot of complexity, it's often repetitive translation layers, and not something fundamental to the problem being solved.

[sothatsit]

You don’t need to write code by hand to learn from iterations and experiments. I run more experiments and try out more different solutions than I ever could before, and that leads to better decisions. I still read all the code that gets shipped, and don’t want to give that up, but the idea that all craft and learning is lost when you don’t is a bit silly. The craft/learning just moves.

[ori_b]

How much calculus do you think you could pick up skimming a textbook without doing exercises?

We mocked these "architects" from experience. We knew that if you weren't feeling the friction yourself, you wouldn't learn enough to do good design.

Maybe you don't care about engineering great systems. Most companies don't. It's good for profit. This isn't new, though AI enables less care.

[sothatsit]

The entire mistake you are making is comparing using AI to skimming textbooks, or taking shortcuts. Your entire premise is wrong.

People who care about craft will care about the quality of what they produce whether they use AI or not.

The code I ship now is better tested and better thought through now than before I used AI because I can do a lot more. That extra time goes into additional experiments, jumping down more rabbit holes, and trying out ideas I previously couldn’t due to time constraints. It’s freeing to be able to spend more time to improve quality because the ROI on time spent experimenting has gone up dramatically.

[zarkov99]

I care deeply about craft, but:

a) I cannot effectively review more than 2000 lines of code a day. The LLMs can produce much more than that. b) Even if I accepted my reading throughput limitations as the cost of being in the loop, reading is not enough to keep cognitive debt in check: my skills will atrophy if I do not participate in the writing ("What I cannot create I cannot understand").

So, to me, it seems like we, humans, either have to come up with higher (and deterministic) abstractions than code to communicate with LLMs or resign ourselves to letting the LLM guess what we want from English and then banging on the output to see if it sort of works. This later state of affairs seems to be what the current trend is and I find that absolutely revolting.

[sothatsit]

I think the distinction is that for experiments and prototypes the behaviour of the final system is what we are trying to design. We can experiment and see the tradeoffs and explore the design space before committing to a direction. And then we can sit down and produce the final code to a quality we are happy with. If you are serious about this process, there is no way you are producing 1000s of lines of code a day, unless it is trivial boilerplate.

In terms of higher-level abstractions, I agree this is one particularly treacherous rung on the ladder of abstractions. Previous abstractions like compilers or garbage collectors have at least had more structure/rules to rely upon. I don't know exactly how that will look but I don't think we will solely be relying on banging on the output, we will also be spot-checking the source code, using profilers or other tools to inspect the behaviour of systems, and asking the agent to explain the architectural decisions made. I'm not sure exactly how this will look, but I do believe that people who care will still find ways to do good work.

[ori_b]

You can keep telling yourself that. I have seen the results from others making the same arguments. The result is invariably trash.

[codebolt]

My agentic workflow probably differs somewhat from the majority of others here, but I can positively guarantee you that both the quality and quantity of my output is significantly higher than it has ever been, in my 20-something years of writing code. And at least 90÷ of the code I've written this year was output by an LLM. You can keep sticking your head in the sand, in the end it will only be to your own detriment.

[sothatsit]

Well you have obviously already made up your mind, so have fun with your confirmation bias. We'll all be over here having a good time, getting more work done. Feel free to come over when you put down your grudge.

[torginus]

Imo the biggest issue with these no-code architects has been that you could become one without ever having coded at any noteworthy level of skill (which meant most of them were like this).

In my experience, in a lot of organizations, a lot of people either lacked the ability or the willingness to achieve any level of technical competence.

Many of these people played the management game, and even if they started out as devs (very mediocre ones at best), they quickly transitioned out from the trenches and started producing vague technical guidance that usually did nothing to address the problems at hand, but could be endlessly recycled to any scenario.

[necovek]

This is an unpopular take, but when I was in undergrad maths in an old-school two-semester courses with one exam (exercises + oral) to cover it at the end, I was able to get to 60-80% score on exercises when I did just theory as prep.

I couldn't get exercises done where there were tricks/shortcuts which are learned by doing a lot of exercises, but for many, these are still the same tricks/shortcuts used in proofs.

This was indeed rare among students, but let's not discount that there are people who _can_ learn from well systemized material and then apply that in practice. Everyone does this to an extent or everyone would have to learn from the basics.

The problem with SW design is that it is not well systemized, and we still have at least two strong opposing currents (agile/iterative vs waterfall/pre-designed).