Comment by sureglymop | Hacker News Reader

Comment by sureglymop | Hacker News Reader

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I do also believe that those who are often looked at or referred to as 10x engineers will maybe only see a marginal productivity increase.

The smartest programmer I know is so impressive mainly for two reasons: first, he seems to have just an otherworldly memory and seems to kind of have absolutely every little feature and detail of the programming languages he uses memorized. Second, his real power is really in cognitive ability, or the ability to always quickly and creatively come up with the smartest and most efficient yet elegant and clean solution to any given problem. Of course somewhat opinionated but in a good way. Funnily he often wouldn't know the academic/common name for some algorithm he arrived at but it just happened to be what made sense to him and he arrived at it independently. Like a talented musician with perfect pitch who can't read notation or doesn't know theory yet is 10x more talented than someone who has studied it all.

When I pair program with him, it's evident that the current iteration of AI tools is not as quick or as sharp. You could arrive at similar solutions but you would have to iterate for a very long time. It would actually slow that person down significantly.

However, there is such a big spectrum of ability in this field that I could actually see this increasing for example my productivity by 10x. My background/profession is not in software engineering but when I do it in my free time the perfectionist tendencies make me work very slowly. So for me these AI tools are actually cool for generating the first crappy proof of concepts for my side projects/ideas, just to get something working quickly.

I like the quip that AI raises the floor not the ceiling. I think it helps the bottom 20% perform more like the middle 50% but doesn't do much for people at the top.

I add to that analogy. AI raises the floor but some of the floor tiles fall away, unpredictably.

maybe, but I find that it makes it much faster to do things that _I already know how to do_, and can only slowly, ploddingly get me to places that I don't already have a strong mental model for, as I have to discover mistakes the hard way

I've only used Copilot, but this is just about exactly right. (I've only used it for Python.)

If I'm writing a series of very similar test cases, it's great for spamming them out quickly, but I still need to make sure they're actually right. This is easier to spot errors because I didn't type them out.

It's also decent for writing various bits of boilerplate for list / dict comprehensions, log messages (although they're usually half wrong, but close enough to what I was thinking), time formatting, that kind of thing. All very standard stuff that I've done a million times but I may be a little rusty on. Basically StackOverflow question fodder.

But for anything complex and domain-specific, it's more wrong than it's right.

things backed by Claude Sonnet can get a little further out than Copilot can, and when it’s in agent mode _sometimes_ it will do things like read the library source code to understand the API, or google for the docs

but the principle is the same: if the human isn’t doing theory-building, then no one is

Exactly. I'm in a situation right now where I've inherited a bunch of systems without enough documentation, and nobody knows how some things work. Sure, we've got features to build - but one of the most important things I can possibly do is make sure someone knows how stuff works, and write it down.

Maybe to get an impression that they'd be performing like them - but not actually performing.

It helps me being lazy because I have a rough expectation of what the outcome should be - and I can directly spot any corner cases or other issues the AI proposed solution has, and can either prompt it to fix that, or (more often) fix those parts myself.

The bottom 20% may not have enough skill to spot that, and they'll produce superficially working code that'll then break in interesting ways. If you're in an organization that tolerates copy and pasting from stack overflow that might be good enough - otherwise the result is not only useless, but as it provides the illusion of providing complete solution you're also closing the path of training junior developers.

Pretty much all AI attributed firings were doing just that: Get rid of the juniors. That'll catch up with us in a decade or so. I shouldn't complain, though - that's probably a nice earning boost just before retirement for me.

I randomly stumbled across Tekwetu who've made a pretty good step-by-step example of coding with Claude Code, using MCPs, etc.[1]. None of the upsell or gushing. It's a pretty simple app with a backend, with a slightly complicated storage mechanism.

I was watching to learn how other devs are using Claude Code, as my first attempt I pretty quickly ran into a huge mess and was specifically looking for how to debug better with MCP.

The most striking thing is she keeps on having to stop it doing really stupid things. She slightly glosses over those points a little bit by saying things like "I roughly know what this should look like, and that's not quite right" or "I know that's the old way of installing TailwindCSS, I'll just show you how to install Context7", etc.

But in each 10 minute episodes (which have time skips while CC thinks) it happens at least twice. She has to bring her senior dev skills in, and it's only due to her skill that she can spot the problem in seconds flat.

And after watching much of it, though I skipped a few episodes at the end, I'm pretty certain I could have coded the same app quicker than she did without agentic AI, just using the old chat window AIs to bash out the React boilerplate and help me quickly scan the documentation for getting offline. The initial estimate of 18 days the AI came up with in the plan phase would only hold truye if you had to do it "properly".

I'm also certain she could have too.

[1] https://www.youtube.com/watch?v=erKHnjVQD1k

It's worth a watch if you're not doing agentic coding yet. There were points I was impressed with what she got it to do. The TDD section was quite impressive in many ways, though it immediately tried to cheat and she had to tell it to do it properly.

Personally I find MCP a bit limiting - I'm using Emacs bindings, and then provide LLMs elisp functions to call.

I posted a demo here a while ago where I try to have it draw turtle graphics:

https://news.ycombinator.com/item?id=44013939

Since then I've also provided enough glue that it can interact with the Arch Linux installer in a VM (or actual hardware, via serial port) - with sometimes hilarious results, but at least some LLMS do manage to install Arch with some guidance:

https://github.com/aard-fi/arch-installer

Somewhat amusingly, some LLMs have a tendency to just go on with it (even when it fails), with rare hallucinations - while other directly start lying and only pretend they logged in.

I think its more effective at lowering the floor. The amount of people that can't code at all but can now slap something together makes it a huge step forward. Albeit one that mostly steps on a pile of dogshit after it hits any sort of production reality.

Its like Wordpress all over again but with people even less able to code. There's going to be vast amounts of opportunities for people to get into the industry via this route but its not going to be a very nice route for many of them. Lots of people who understand software even less than c-suite holding the purse-strings.

AI is strong in different places, and if it keeps on being strong in certain ways then people very soon won't be able to keep up. For example, extreme horizontal knowledge and the ability to digest new information almost instantly. That's not something anyone can do. We don't try to compete against computers on raw calculation, and soon we won't compete on this one either. We simply won't even think to compare.

People keep focusing on general intelligence style capabilities but that is the golden grail. The world could go through multiple revolutions before finding that golden grail, but even before then everything would have changed beyond recognition.

So write an integration over the API docs I just copy-pasted.

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