[Folcon]

I'm going to try and be honest with you because I'm where you were at 3 months ago

I honestly don't think there's anything I can say to convince you because from my perspective that's a fools errand and the reason for that has nothing to do with the kind of person either of us are, but what kind of work we're doing and what we're trying to accomplish

The value I've personally been getting which I've been valuing is that it improves my productivity in the specific areas where it's average quality of response as one shot output is better than what I would do myself because it is equivalent to me Googling an answer, reading 2 to 20 posts, consolidating that information together and synthesising an output

And that's not to say that the output is good, that's to say that the cost of trying things as a result is much cheaper

It's still my job to refine, reflect, define and correct the problem, the approach etc

I can say this because it's painfully evident to me when I try and do something in areas where it really is weak and I honestly doubt that the foundation model creators presently know how to improve it

My personal evidence for this is that after several years of tilting those windmills, I'm successfully creating things that I have on and off spent the last decade trying to create successfully and have had difficulty with not because I couldn't do it, but because the cost of change and iteration was so high that after trying a few things and failing, I invariably move to simplifying the problem because solving it is too expensive, I'm now solving a category of those problems now, this for me is different and I really feel it because that sting of persistent failure and dread of trying is absent now

That's my personal perspective on it, sorry it's so anecdotal :)

[bigfishrunning]

>The value I've personally been getting which I've been valuing is that it improves my productivity in the specific areas where it's average quality of response as one shot output is better than what I would do myself because it is equivalent to me Googling an answer, reading 2 to 20 posts, consolidating that information together and synthesising an output

>And that's not to say that the output is good, that's to say that the cost of trying things as a result is much cheaper

But there's a hidden cost here -- by not doing the reading and reasoning out the result, you have learned nothing and your value has not increased. Perhaps you extended a bit less energy producing this output, but you've taken one more step down the road to atrophy.

[rectang]

Seeing the code that the LLM generates and occasionally asking it to explain has been an effective way to improve my understanding. It's better in some ways than reading documentation or doing tutorials because I'm working on a practical project I'm highly motivated by.

I agree that there is benefit in doing research and reasoning, but in my experience skill acquisition through supervising an LLM has been more efficient because my learning is more focused. The LLM is a weird meld of domain expert/sycophant/scatterbrain but the explanations it gives about the code that it generates are quite educational.

[ben_w]

I think there's a potential unstated assumption here, though forgive me if it was made explicit elsewhere and/or I missed it.

LLM-assisted can be with or without code review. The original meaning of "vibe coding" was without, and I absolutely totally agree this rapidly leads to a massive pile of technical debt, having tried this with some left-over credit on a free trial specifically to see what the result would be. Sure, it works, but it's a hell of a mess that will make future development fragile (unless the LLMs improve much faster than I'm expecting) for no good reason.

Before doing that, I used Claude Code the other way, with me doing code reviews to make sure it was still aligned with my ideas of best practices. I'm not going to claim it was perfect, because it did a python backend and web front end for a webcam in one case and simultaneously on a second project a browser-based game engine and example game for that engine and on a third simultaneous project a joke programming language, and I'm not a "real" python dev or "real" web dev or any kind of compiler engineer (last time I touched Yacc before this joke language was 20 years earlier at university). But it produced code I was satisfied I could follow, understand, wasn't terrible, had tests.

I wouldn't let a junior commit blindly without code review and tests because I know what junior code looks like from all the times I've worked with juniors (or gone back to 20 year old projects of my own), but even if I was happy to blindly accept a junior's code, or even if the LLM was senior-quality or lead quality, the reason you're giving here means code review before acceptance is helpful for professional development even when all the devs are at the top of their games.

[Aeolun]

> By not doing the reading and reasoning out the result, you have learned nothing and your value has not increased

AI helps at the margins.

It’s like adding anti-piracy. Some people would simply never have bought the game unless they can pirate it.

There’s a large volume of simple tools, or experimental software that I would simply never had the time to build the traditional way.

[Folcon]

I mean you're not wrong

I suppose the way I approach this is, I use libraries which solve problems that I have, that in principle understand, because I know and understand the theory, but in practice I don't know the specific details, because I've not implemented the solution myself

And honestly, it's not my job to solve everything, I've just got to build something useful or that serves my goals

I basically put LLM's into that category, I'm not much of a NIH kinda person, I'm happy to use libraries, including alpha ones on projects if they've been vetted over the range of inputs that I care about, and I'm not going to go into how to do that here, because honestly it's not that exciting, but there's very standard boring ways to produce good guarantees about it's behaviour, so as long as I've done that, I'm pretty happy

So I suppose what I'm saying is that isn't a hidden cost to me, it's a pragmatic decision I made that I was happy with the trade off :)

When I want to learn, and believe me I do now and again, I'll focus on that there :)

[brianwawok]

Example for me: I am primarily a web dev today. I needed some kuberntes stuff setup. Usually that’s 4 hours of google and guess and check. Claude did it better in 15 minutes.

Even if all it does is speed up the stuff i suck at, that’s plenty. Oh boy docker builds, saves my bacon there too.

[Draiken]

And you learned nothing and have no clue if what it spit out is good or not.

How can you even assume what it did is "better" if you have no knowledge of kubernetes in the first place? It's mere hope.

Sure it gets you somewhere but you learned nothing in the way and now depend on the LLM to maintain it forever given you don't want to learn the skill.

I use LLMs to help verify my work and it can sometimes spot something I missed (more often it doesn't but it's at least something). I also automate some boring stuff like creating more variations of some tests, but even then I almost always have to read its output line by line to make sure the tests aren't completely bogus. Thinking about it now it's likely better if I just ask for what scenarios could be missing, because when they write it, they screw it up in subtle ways.

It does save me some time in certain tasks like writing some Ansible, but I have to know/understand Ansible to be confident in any of it.

These "speedups" are mostly short term gains in sacrifice for long term gains. Maybe you don't care about the long term and that's fine. But if you do, you'll regret it sooner or later.

My theory is that AI is so popular because mediocrity is good enough to make money. You see the kind of crap that's built these days (even before LLMs) and it's mostly shit anyways, so whether it's shit built by people or machines, who cares, right?

Unfortunately I do, and I rather we improve the world we live in instead of making it worse for a quick buck.

IDK how or why learning and growing became so unpopular.

[misja111]

No I agree with you, there are area's where AI is helping amazingly. Every now and then it helps me with some issue as well, which would have cost me hours earlier and now it's done in minutes. E.g. some framework that I'm not that familiar with, or doing the scaffolding for some unit test.

However this is only a small portion of my daily dev work. For most of my work, AI helps me little or not at all. E.g. adding a new feature to a large codebase: forget it. Debugging some production issue: maybe it helps me a little bit to find some code, but that's about it.

And this is what my post was referring to: not that AI doesn't help at all, but to the crazy claims (10x speedup in daily work) that you see all over social media.

[newsoftheday]

[flagged]

[econ]

It's great in unfamiliar terrain.

[lawlessone]

you haven't contributed much to GitHub since 2022?

*edit unless your commits are elsewhere?