13 ms·

Anecdote here, but when I was in grad school, I was talking to a PhD student i respected a lot. Whenever he read a paper, he would try to write the code out and

• vonneumannstan 1y ago
  >I think the "just tweak the prompts bro" people are missing out on learning. Alternatively they're just learning/building intuition for something else. The level of abstraction is moving upwards. I don't know why people don't seem to grok that the level of the current models is the floor, not the ceiling. Despite the naysayers like Gary Marcus, there is in fact no sign of scaling or progress slowing down at all on AI capabilities. So it might be that if there is any value in human labor left in the future it will be in being able to get AI models to do what you want correctly.
  • Brian_K_White 1y ago
    Wishful, self-serving, and beside the point. The primary argument here is not about the capability of the ai. I think the same effect has been around forever in the form of every boss/manager/ceo/rando-divorcee-or-child-with-money using employees to do their thinking as a current information-handling worker or student using an ai to do their thinking.
    • vonneumannstan 1y ago
      >Wishful, self-serving, and beside the point. The primary argument here is not about the capability of the ai. "Alternatively they're just learning/building intuition for something else." Reading comprehension is hard.
  • benterix 1y ago
    That would be true if several conditions were fulfilled, starting with LLMs being actually to do their tasks properly, which they still very much struggle with which basically defeats the premise of moving up an abstraction layer if you have to constantly check and correct the lower layer.
    • vonneumannstan 1y ago
      [flagged]
  • pigpag 1y ago
    [dead]
  • fatata123 1y ago
    LLMs are plateauing, and you’re in denial.
    • vonneumannstan 1y ago
      Show me one metric they are plateauing on.
  • jimkri 1y ago
    I don't think Gary Marcus is necessarily a naysayer; I take it that he is trying to get people to be mindful of the current AI tooling and its capabilities, and that there is more to do before we say it is what it is being marketed as. Like, GPT5 seems to be an additional feature layer of game theory examples. Check LinkedIn for how people think it behaves, and you can see patterns. But they market it as much more.
    • vonneumannstan 1y ago
      >I don't think Gary Marcus is necessarily a naysayer Oh come on. He is by far the most well known AI poo-poo'er and it's not even close. He built his entire brand on it once he realized his own research was totally irrelevant.
  • KoolKat23 1y ago
    Agree with this. I mean the guy assembling a thingymajig in the factory, after a few years, can put it together with his hands 10x faster than the actual thingymajig designer. He'll tell you apply some more glue here and less glue there (it's probably slightly better, but immaterial really). However, he probably couldn't tell you what the fault tolerance of the item is, the designer can do that. We still outsource manufacturing to the guy in the factory regardless. We just have to get better at identifying risks with using the LLMs doing the grunt work and get better in mitigating them. As you say, abstracted.
  • codyb 1y ago
    Really? No signs of slowing down? A year or two ago when LLMs popped on the scene my coworkers would say "Look at how great this is, I can generate test cases". Now my coworkers are saying "I can still generate test cases! And if I'm _really pacificcccc_, I can get it to generate small functions too!". It seems to have slowed down considerably, but maybe that's just me.
    • lazide 1y ago
      At the beginning, it’s easy to extrapolate ‘magic’ to ‘can do everything’. Eventually, it stops being magic and the thinking changes - and we start to see the pros and cons, and see the gaps. A lot of people are still in the ‘magic’ phase.
    • tuesdaynight 1y ago
      Sorry for the bluntness, but you sound like you have a lot of opinions about LLM performance for someone who says that doesn't use them. It's okay if you are against them, but if have used them 3 years ago, you have no idea if there were improvements or not.
      • vonneumannstan 1y ago
        "It can't do 9.9-9.11 or count the number of r's in strawberry!" lol
        • Nevermark 1y ago
          Since models are given tokens, not letters, to process, the famous issues with counting letters is not indicative of incompetence. They are just sub-sensory for the model. None of us can reliably count the e’s as someone talks to us, either.
          • hatefulmoron 1y ago
            It does say something that the models simultaneously: a) "know" that they're not able to do it for the reason you've outlined (as in, you can ask about the limitations of LLMs for counting letters in words) b) still blindly engage with the query and get the wrong answer, with no disclaimer or commentary. If you asked me how many atoms there are in a chair, I wouldn't just give you a large natural number with no commentary.
  • asveikau 1y ago
    This reads to me as extremely defensive.
    • vonneumannstan 1y ago
      It's not but ok. Just responding to another version of "This generation is screwed" that has been happening literally since Socrates.
      • jplusequalt 1y ago
        There has been a growing amount of evidence for years that modern technology is not without it's side effects (mental health issues due to social media use, destruction of attention spans among the youth due to cell phone use, erosion of societal discourse and straight up political manipulation, and now we're seeing impacts to cognitive ability from LLMs).
      • thegrim33 1y ago
        So .. people in the past were supposedly wrong about the next generation being "screwed", and therefore we all know that makes it completely impossible for any new generation at any point in history to ever be in any way worse or more screwed than previous generations. Because some people in the past were supposedly incorrect with similar assertions.
  • lazide 1y ago
    I remember this exact discussion (and exact situation) with WYSIWYG UI design tools. They were still useful, and did solve a significant portion of user problems. They also created even more problems, and no one really went out of work long term because of them.
• benterix 1y ago
  We are literally witnessing the skills split right in front of our eyes: (1) people who are able to understand the concepts deeply, build a mental model of it and implement them in code at any level, and (2) people who outsource it to a machine and slowly, slowly lose that capability. For now the difference between these two populations is not that pronounced yet but give it a couple of years.
  • mrits 1y ago
    I suppose the question is if we need to understand the concepts deeply. I'm not sure many of us did to begin with and we have shipped a lot of code.
    • tmcb 1y ago
      Well, cargo cult programming is definitely a thing, and has been for a long time. It may “deliver value”, but it is not guaranteed. I believe entrepreneurs have an easier time having AI do the work for them because their value assessment framework is decoupled from code generation proper.
    • MobiusHorizons 1y ago
      I see a lot of junior engineers, or more senior engineers who are outside their areas of expertise try to prioritize making progress without taking the time to understand. They will copy examples very closely, following best practices blindly, or get someone else to make the critical design decisions. This can get you surprisingly far, but it’s also dangerous because the further past your understanding you are operating, the more you might have to learn all at once in order to fix something when it breaks. Debugging challenges all your assumptions, and if you don’t have models of what the pieces are and how they interact, it’s incredibly hard to start building them when something is already broken. Even then, some engineers don’t learn the models when debugging and resort to defensive and superstitious behaviors based on whatever solution they stumbled on last time. This is a pretty normal part of the learning process, but some engineers don’t seem to get past this stage. Some don’t even want to.
  • CuriouslyC 1y ago
• geye1234 1y ago
  Interesting, thanks. Do you mean he would write the code out by hand on pen and paper? That has often struck me as a very good way of understanding things (granted, I don't code for my job). Similar thing in the historian's profession (which I also don't do for my job but have some knowledge of). Historians who spend all day immersed in physical archives tend, over time, to be great at synthesizing ideas and building up an intuition about their subject. But those who just Google for quotes and documents on whatever they want to write about tend to have more a static and crude view of their topic; they are less likely to consider things from different angles, or see how one things affects another, or see the same phenomenon arising in different ways; they are more likely to become monomaniacal (exaggerated word but it gets the point across) about their own thesis.
  • martingalex2 1y ago
    Assuming this observation applies generally, give one point to the embodiment crowd.
• zingababba 1y ago
  I just decided to take a break from LLMs for coding assistance a couple days ago. Feels really good. It's funny how fast I am when I just understand the code myself instead of not understanding it and proooompting.
  • marcofloriano 1y ago
    Same here, i just finished my subscription at ChatGPT, i feel free again.
• crinkly 1y ago
  Good to hear. I will add that not everyone who writes a paper expects anyone to read a paper with that level of diligence and it can lead to some interesting outcomes for the paper authors over time. Keep up the good work is all I can say!
• giancarlostoro 1y ago
  > These days, I'm fairly senior and don't touch code much anymore but I find it really really instructive to get my hands dirty and struggle through new code and ideas. I think the "just tweak the prompts bro" people are missing out on learning. If you just use prompts and don't actually read the output, and figure out why it worked, and why it works, you will never get better. But if you take the time to understand why it works, you will be better for it, and might not even bother asking next time. I've said it before, but when I first started using Firefox w/ autocorrect in like 2005, I made it a point to learn to spell from it, so that over time I would make less typos. English is my second language, so its always been an uphill battle for me despite having a native American English accent. Autocorrect on Firefox helped me tremendously. I can use LLMs to plunge into things I'm afraid of trying out due to impostor syndrome and get more done sooner and learn on the way there. I think the key thing is to use tools correctly. AI is like the limitless drug to a degree, you have an insane fountain of knowledge at your fingertips, you just need to use it wisely and learn from it.
  • marcofloriano 1y ago
    It's different to read and understand a prompt and actually produce the code. It's a different level of cognition.
  • amlib 1y ago
    If that held true, than reading lots of source code from random FOSS projects would make you into an amazing coder. But clearly that's not enough to internalize and learn all that knowledge, you need to experiment with it, see it running, debug it, write some, extend it, fix bugs and so on. Just reading it with very little context for the rest of the project is a recipe for mediocrity.
• theptip 1y ago
  Just beware the “real programmers hand-write assembly” fallacy. It was said that compilers would produce a generation of programmers unable to understand the workings of their programs. In some sense, this is true! But, almost nobody thinks it really matters for the actual project of building things. If you stop thinking, then of course you will learn less. If instead you think about the next level of abstraction up, then perhaps the details don’t always matter. The whole problem with college is that there is no “next level up”, it’s a hand-curated sequence of ideas that have been demonstrated to induce some knowledge transfer. It’s not the same as starting a company and trying to build something, where freeing up your time will let you tackle bigger problems. And of course this might not work for all PhDs; maybe learning the details is what matters in some fields - though with how specialized we’ve become, I could easily see this being a net win.
  • daemin 1y ago
    I would agree with the statement that you don't need to know or write in assembly to build programs, but what you end up with is usually slow and inefficient. Having curiosity to examine the platform that your software is running on and taking a look into what the compilers generate is a skill worth having. Even if you never write raw assembly yourself, being able to see what the compiler generated and how data is laid out does matter. This then helps you make better decisions about what patterns of code to use in your higher level language.
  • Jensson 1y ago
    > Just beware the “real programmers hand-write assembly” fallacy All previous programming abstractions kept correctness, a python program produce no less reliable results than a C program running the same algorithm, it just took more time. LLM doesn't keep correctness, I can write a correct prompt and get incorrect results. Then you are no longer programming, you are a manager over a senior programmer suffering from extreme dementia so they forget what they were doing a few minutes ago and you try to convince him to write what you want before he forgets about that as well and restart the argument.
    • invalidptr 1y ago
• bdelmas 1y ago
  Yes in data science there is say: “there is no free lunch”. With ChatGPT and others becoming so prevalent even at PhD level people that will work hard and avoid to use these tools will be more and more seen as magicians. I already see this in coding where people can’t code medium to hard things and their intuitions like you said is wacky. It’s not the imposter syndrome anymore it’s people not being able to get their job done without some AI involved. What I do personally is for every subject that matters to me I take the time to first think about it. To explore ideas, concepts, etc… and answer questions that would ask to ChatGPT. Only once I get a good idea I start to ask chapgpt about it.
• thisisit 1y ago
  This seems like the age old discussion of how does new technology changes our lives and makes us "lazy" or "lack of learning". Before the advent of smartphones people needed to remember phone numbers of their loved ones and maybe do some small calculations on the fly. Now people sometimes don't even remember their own numbers and have it saved on their phones. Now some might want to debate how smartphones are different from LLMs and it is not the same. But we have to remember for better or worse LLM adoption has been fast and it has become consumer technology. That is the area being discussed in the article. People using it to write essays. And those who might be using the label of "prompt bros" might be missing the full picture. There are people, however small, being helped by LLMs as there were people helped by smartphones. This is by no means a defense for using LLMs for learning tasks. If you write code by yourself, you learn coding. If you write your essays yourself, you learn how to make a solid points.
  • marcofloriano 1y ago
    It's not the same with LLMs. What the study finds out is actually much more serious. When you use a phone or a calculator, you don't lose cognitive faculties. But when you delegate the thinking process to an LLM, your Brain gets phisically changed, witch leads to a cognitive damage. It's a completely different league.
    • fragmede 1y ago
      > When you use a phone or a calculator, you don't lose cognitive faculties. Of course you do. I used to be able to multiply two two-digit numbers in my head. Now, my brain freezes and I reach for a calculator.
• scarface_74 1y ago
  I work in cloud consulting specializing in application development. But most of the time when an assignment is to produce code instead of leading a project or doing strategy assessments, it’s to turn around a quick proof of concept that requires a broad set of skills - infrastructure, “DevOps”, backend development and ETL type jobs where the goal is to teach the client or to get them to sign off on a larger project where we will need to bring in a team. For my last two projects, I didn’t write a single line of code by hand. But I refuse to use agents and I build up an implementation piece by piece via prompting to make sure I have the abstractions I want and reusable libraries. I take no joy in coding anymore and I’ve been doing it for fourty years. I like building systems and solving business problems. I’m not however disagreeing with you that LLMs will make your development skill atrophy, I’m seeing it in real time at 51. But between my customer facing work and supporting sales and cat herding, I don’t have time to sit around and write for loops and I’m damn sure not going to do side projects outside of work. Besides, companies aren’t willing to pay my company’s bill rates for me as a staff consultant to spend a lot of time coding. I hopefully can take solace in the fact that studies also show that learning a second language strengthens the brain and I’m learning Spanish and my wife and I plan to spend a couple of months in the winter every year in a Central American Spanish speaking country. We have already done the digital nomad thing across the US for a year until late 2023 so we are experienced with it and spent a month in Mexico.
• hintymad 1y ago
  This is in a way like doing math. I can read a math book all day and even appreciate the ideas in the book, but I'd practically learn little if I don't actually attempt to work out examples for the definitions, the theorems, and some exercises in the book.
  • TheNewsIsHere 1y ago
    I fall into this trap more than I’d care to admit. I love learning by reading, to the point that I’ll read the available documentation for something before I decide to use it. This consumes a lot of time, and there’s a tradeoff. Eventually if I do use the thing, I’m well suited to learning it quickly because I know where to go when I get stuck. But by the same token I read a lot of documentation I never again need to use. Sometimes it’s useful for learning about how others have done things.
    • Cthulhu_ 1y ago
      I'm the same, but thing is, 99% of the things I read about (on e.g. HN) are just... not important. I don't use them in my daily life. But I do have a very large knowledge base of small tidbits of information, so if I do need to ever go in-depth, I know where/how to find it. ...not that I do of course, I struggle with my long term attention span, I can't read documentation front to back and for twenty odd years now have just googled for the tidbit I needed and skipped the rest.
• WalterBright 1y ago
  I learned this in college long before AI. If I didn't do the work to solve the homework problems, I didn't learn the material, no matter how much I imagined I understood it.
• r_singh 1y ago
  I doubt anyone who cannot get their hands dirty with code can gain any value from prompt engineering or using LLMs... Code with LLMs gets large pretty quickly and would have anyone who isn't practiced spinning their head pretty soon, don't you think?