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Comment by misja111

21 hours ago

A while ago someone posted a claim like that on LinkedIn again. And of course there was the usual herd of LinkedIn sheep who were full of compliments and wows about the claim he was making: a 10x speedup of his daily work.

The difference with the zillion others who did the same, is that he attached a link to a live stream where he was going to show his 10x speedup on a real life problem. Credits to him for doing that! So I decided to go have a look.

What I then saw was him struggling for one hour with some simple extension to his project. He didn't manage to finish in the hour what he was planning to. And when I had some thought about how much time it would have cost me by hand, I found it would have taken me just as long.

So I answered him in his LinkedIn thread and asked where the 10x speed up was. What followed was complete denial. It had just been a hick up. Or he could have done other things in parallel while waiting 30 seconds for the AI to answer. Etc etc.

I admit I was sceptic at the start but I honestly had been hoping that my scepticism would be proven wrong. But not.

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 :)

  • >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.

    • 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.

    • 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.

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    • > 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.

    • 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 :)

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  • 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.

  • 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.

    • 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.

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I think people get into a dopamine hit loop with agents and are so high on dopamine because its giving them output that simulates progress that they don't see reality about where they are at. It is SO DAMN GOOD AT OUTPUT. Agents love to output, it is very easy to think its inventing physics.

Obviously my subjective experience

  • Ironic that I’m going to give another anecdotal experience here, but I’ve noticed this myself too. I catch myself trying to keep on prompting after an llm has not been able to solve some problem in a specific way. While I can probably do it faster at that point if I switch to doing it fully myself. Maybe because the llm output feels like its ‘almost there’, or some sunken cost fallacy.

    • Not saying this is you, but another way to look at it is that engaging in that process is training you (again, not you, the user) -- the way you get results is by asking the chat bot, so that's what you try first. You don't need sunk cost or gambling mechanics, it's just simple conditioning.

      Press lever --> pellet.

      Want pellet? --> press lever.

      Pressed lever but no pellet? --> press lever.

  • > I think people get into a dopamine hit loop

    I also think that's the case, but I'm open to the idea that there are people that are really really good at this and maybe they are indeed 10x.

    My experience is that for SOME tasks LLMs help a lot, but overall nowhere near 10x.

    Consistently it's probably.... ~1X.

    The difference is I procrastinate a lot and LLMs actually help me not procrastinate BECAUSE of that dopamine kick and I'm confident I will figure it out with an LLM.

    I'm sure there are many people who got to a conclusion on their to-do projects with the help of LLMs and without them, because of procrastination or whatever, they would not have had a chance to.

    It doesn't mean they're now rich, because most projects won't make you rich or make you any money regardless if you finish them or not

  • You nailed it - like posting on social media and getting dopamine hits as you get likes and comments. Maybe that's what has got all these vibe coders hooked.

> What I then saw was him struggling for one hour with some simple extension to his project. He didn't manage to finish in the hour what he was planning to. And when I had some thought about how much time it would have cost me by hand, I found it would have taken me just as long.

For all who are doing that, what is the experience of coding in a livestream? It is something I never attempted, the simple idea makes me feel uncomfortable. A good portion of my coding would be rather cringe, like spending way too long on a stupid copy-paste or sign error that my audience would have noticed right away. On the other hand, sometimes, I am really fast because everything is in my head, but then I would probably lose everyone. I am impressed when looking at live coders by how fluid it looks compared to my own work, maybe there is a rubber duck effect at work here.

All this to say that I don't know how working solo compares to a livestream. It is more or less efficient, maybe it doesn't matter that much when you get used to it.

  • Have done it, never enough of an audience to be totally humiliated. It's never going to be more efficient.

    But as for your cringe issue that the audience noticed, one could see that to be a benefit -- prefer to have someone say e.g. "you typed `Normalise` (with an 's') again, C++ is written in U.S. English, don't you know / learn to spell, you slime" upfront than waiting for compiler to tell you that `Normalise` doesn't exist, maybe?

  • I suspect livestream coding, like programming competition coding and whiteboard coding for interviews, is a separate skill that's fairly well correlated with being able to solve useful problems, but it is not the same thing. You can be an excellent problem solver without being good at doing so while being watched, under time pressure.

I feel like I've been incredibly productive with AI assisted programming over the past few weeks, but it's hard to know what folks' baselines are. So in the interest of transparency, I pushed it all up to sourcehut and added Co-Authored-By footers to the AI-assisted commits (almost all of them).

Everything is out there to inspect, including the facts that I:

- was going 12-18 hours per day

- stayed up way too late some nights

- churned a lot (+91,034 -39,257 lines)

- made a lot of code (30,637 code lines, 11,072 comment lines, plus 4,997 lines of markdown)

- ended up with (IMO) pretty good quality Ruby (and unknown quality Rust).

This is all just from the first commit to v0.8.0. https://git.sr.ht/~kerrick/ratatui_ruby/tree/v0.8.0

What do you think: is this fast, or am I just as silly as the live-streamer?

P.S. - I had an edge here because it was a green-field project and it was not for my job, so I had complete latitude to make decisions.

There were such people also here.

Copy-pasting the code would have been faster than their work, and there were several problems with their results. But they were so convinced that their work is quick and flawless, that they post a video recording of it.

  • Hackernews is dominated by these people

    LLM marketers have succeeded at inducing collective delusion

    • > LLM marketers have succeeded at inducing collective delusion

      That's the real trick & one I desperately wish I knew how to copy.

      I know there's a connection to Dunning Kruger & I know that there's a dopamine effect of having a responsive artificial minion & there seems to be some of that "secret knowledge" sauce that makes cults & conspiracies so popular (there's also the promise of less effort for the same or greater productivity).

      Add the list grows, I see the popularity, but I doubt I could easily apply all these qualities to anything else.

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> So I answered him in his LinkedIn thread and asked where the 10x speed up was. What followed was complete denial. It had just been a hick up. Or he could have done other things in parallel while waiting 30 seconds for the AI to answer. Etc etc.

So I’ve been playing with LLMs for coding recently, and my experience is that for some things, they are drastically faster. And for some other things, they will just never solve the problem.

Yesterday I had an LLM code up a new feature with comprehensive tests. It wasn’t an extremely complicated feature. It would’ve taken me a day with coding and testing. The LLM did the job in maybe 10 minutes. And then I spent another 45 minutes or so deeply reviewing it, getting it to tweak a few things, update some test comments, etc. So about an hour total. Not quite a 10x speed up, but very significant.

But then I had to integrate this change into another repository to ensure it worked for the real world use case and that ended up being a mess, mostly because I am not an expert in the package management and I was trying to subvert it to use an unpublished package. Debugging this took the better part of the day. For this case, the LLM may be saved me maybe 20% because it did have a couple of tricks that I didn’t know about. But it was certainly not a massive speed up.

So far, I am skeptical that LLM’s will make someone 10x as efficient overall. But that’s largely because not everything is actually coding. Subverting the package management system to do what I want isn’t really coding. Participating in design meetings and writing specs and sending emails and dealing with red tape and approvals is definitely not coding.

But for the actual coding specifically, I wouldn’t be surprised if lots of people are seeing close to 10x for a bunch of their work.

I suspect there's also a good amount of astroturfing happening here as well, making it harder to find the real success stories.

I've noticed a similar trend. There seems to be a lot of babysitting and hand holding involved with vibe-coding. Maybe it can be a game changer for "non-technical founders" stumbling their way through to a product, but if you're capable of writing the code yourself, vibe coding seems like a lot of wasted energy.

Shopify's CEO just posted the other day that he's super productive using the newest AI models and many of the supportive comments responding to his claim were from CEOs of AI startups.

Theres too much money, time and infrastructure committed for this to be anything but successful.

Its tougher than a space race or the nuclear bomb race because there are fewer hard tangibles as evidence of success.

  • I think there is also some FOMO involved. Once people started saying how AI was helping them be more productive, a lot of folks felt that if they didn't do the same, they were lagging behind.

You're supposed to believe in his burgeoning synergy so that one day you may collaborate to push industry leading solutions