I was actually comparing aviation HW+SW vs. consumer software...and making the point that an old C++ invoices processing application, while being way less reliable than aviation HW or SW, is still orders of magnitude more reliable than LLMs. The LLMs don't always miss, true...but they miss far too often for the "hit" part to be relevant at all.
They miss but can self correct, this is the paradigm shift. You need a harness to unlock the potential and the harness is usually very buildable by LLMs, too.
Hm, that is a a lot of generic talk - but very little concrete data and examples.
As someone having watched AI systems being good enough to replace jobs like content creation on CMS, this is being in denial. Yes software developer are still going to be need, except much fewer of us, exactly like fully automated factories still need a few humans around, to control and build the factory in first place.
I concur with your sentiments. Am puzzled why so many on HN cannot see this. I guess most users on HN are employed? Your employers - let me tell you - are positively salivating at the prospect of firing you. The better LLM's get the fewer of you will be needed.
Denial, like those factory workers at the first visit from the automation company, each one hoping they are the ones elected to stay and overwatch the robots.
I have seen projects where translator teams got reduced, asset creation teams, devops head count, support teams on phone lines,...
It is all about how to do more with less, now with AI help as well.
> a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)? ??? Cars replaced horses, not people. In this scenario you are the horse.
Well no, you'd be the horse driver who becomes a car driver
> Well no, you'd be the horse driver who becomes a car driver
Well, that's the crux of the argument. The pro-AI devs are making the claim that devs are the horse-drivers, the anti-AI is making the claim that devs are the horses themselves.
There is no objective way to verify who is right in this case, we just have to see it play out.
+1 - I wish at least one of these AI boosters had shown us a real commercialised product they've built.
I'm sure you're interacting with a ton of tools built via agents, ironically even in software engineering people are trying to human-wash AI code due to anti-AI bias by people who should know better (if you think 100% of LLM outputs are "slop" with no quality consideration factored in, you're hopelessly biased). The commercialized seems like an arbitrary and pointless bar, I've seen some hot garbage that's "commercia…
> I'm sure you're interacting with a ton of tools built via agents, ironically even in software engineering people are trying to human-wash AI code due to anti-AI bias
Please just for fun - reach out to for example Klarna support via their website and tell me how much of your experience can be attributed to an anti-AI bias and how much to the fact that the LLMs are a complete shit for any important production use cases.
> LLMs produce results on par with what I would expect out of a solid junior developer This is a common take but it hasn't been my experience. LLMs produce results that vary from expert all the way to slightly better than markov chains. The average result might be equal to a junior developer, and the worst case doesn't happen that often, but the fact that it happens from time to time makes it completely unreliable fo…
I think any further discussion about quality just needs to have the following metadata: - Language - Total LOC - Subject matter expertise required - Total dependency chain - Subjective score (audited randomly) And we can start doing some analysis. Otherwise we're pissing into ten kinds of winds. My own subjective experience is earth shattering at webapps in html and css (because I'm terrible and slow at it), and anno…
I agree in that these discussions (this whole hn thread tbh) are seriously lacking in concrete examples to be more than holy wars 3.0.
Besides one point: junior developers can learn from their egregious mistakes, llms can't no matter how strongly worded you are in their system prompt.
In a functional work environment, you will build trust with your coworkers little by little. The pale equivalent in LLMs is improving system prompts and writing more and more ai directives that might or might not be followed.
> How would you know whether he is an expert on the topic of software engineering or not? Because of their views on the effectiveness of AI agents for generating code.
Considering those views are shared by a number of high profile, skilled engineers, this is obviously no basis for doubting someone's expertise.
> Considering those views are shared by a number of high profile, skilled engineers, this is obviously no basis for doubting someone's expertise
Again, a lot of fluff, a lot of of "a number ofs", "highly this, highly that". But very little concrete information. What happened to the pocket PhDs promised for this past summer? Where are the single-dude billion dollar companies built with AI tools ? Or even a multiple-dudes billion dollar companies ? What are you talking about?
LLMs generate the most likely code given the problem they're presented and everything they've been trained on, they don't actually understand how (or even if) it works. I only ever get away with that when I'm writing a parser.
> they don't actually understand how but if it empirically works, does it matter if the "intelligence" doesn't "understand" it? Does a chess engine "understand" the moves it makes?
It matters if AGI is the goal. If it remains a tool to make workers more productive, then it doesn't need to truly understand, since the humans using the tools understand. I'm of the opinion AI should have stood for Augmented (Human) Intelligence outside of science fiction. I believe that's what early pioneers like Douglas Engalbert thought. Clearly that's what Steve Jobs and Alan Kay thought computing was for.
They miss but can self correct, this is the paradigm shift. You need a harness to unlock the potential and the harness is usually very buildable by LLMs, too.
Hm, that is a a lot of generic talk - but very little concrete data and examples.
Concrete examples are in your code just as they're in my employer's which I'm not at the liberty to share - but every little bit counts, starting from the simplest lints, typechecks, tests and going to more esoteric methods like model checkers. You're trying to get the probability of miss down with the initial context; then you want to minimize the probability of not catching a miss, then you want to maximize the probability of the model being able to fix a miss itself. Due to the multiplicative nature of the process the effect is that the pipeline rapidly jumps from 'doesn't work' to 'works well most of the time' and that is perceived as a step function by outsiders. Concrete examples are all over the place, they're just being laughed at (yesterday's post about 100% coverage was spot on even if it was an ad).
No it doesn’t read like shilling and advertisement, it’s tiring hearing people continually dismiss coding agents as if they have not massively improved and are driving real value despite limitations and they are only just getting started. I’ve done things with Claude I never thought possible for myself to do, and I’ve done things where Claude made the whole effort take twice as long and 3x more of my time. It’s not l…
The paradigm shift hit the world like a wall. I know entire teams where the manager thinks AI is bullshit and the entire team is not allowed to use AI. I love coding. But reality is reality and these fools just aren’t keeping pace with how fast the world is changing.
Or we're in another hype cycle and billions of dollars are being pumped in to sustain the current bubble with a lot of promises about how fast the world is changing. Doesn't mean AI can't be a useful tool.
When coal powered engines became more efficient, demand for coal went UP. It went up because vastly more things could now be cost effectively be coal-powered. I can see a future where software development goes the same way. My wife works in science and I see all kinds of things in a casual observation of her work that could be made more efficient with good software support. But not by enough to pay six-figures per ye…
Many HN users may point to Jevons paradox, I would like to point out that it may very well work up until the point that it doesn't. After all a chicken has always seen the farmer as benevolent provider of food, shelter and safety, that is until of course THAT day when he decides he doesn't.
It is certainly possible that AI is the one great disruptor that we can’t adapt to. History over millenia has me taking the other side of that bet, seeing the disruptions and adaptations from factory farming, internal combustion engines, moving assembly lines, electrification, the transistor, ICs, wired then wireless telecommunications, the internet, personal computing, and countless other major disruptions.