5 ms·

> That sounds great until you realize that "hey Claude, read this Jira ticket and implement the feature in this code base" isn't really worth $200K/year. Nor e

• enraged_camel 2mo ago
  FWIW: The highest paid devs I know excel at 2, and aren't even that good at 1.
  • finaard 2mo ago
    I guess that somewhat describes me - and guess what, that also transfers to "should we even throw AI at that specific problem, or are there other solutions?". Early curiousity in figuring out the limits of LLMs now is turning into revenue by customers with problems that didn't exist a few years ago at all - which currently is quite an exciting field to work in as a lot of things just don't have pre-established solutions yet. It's a similar level of excitement as when we started doing devops stuff before devops even was a thing (which has been one of our main things for a very long time now).
  • wnmurphy 2mo ago
    Complement: I know many devs who are exceptional at 1 (writing code), and 2 (contextual thinking) never even occurs to them. They will happily implement whatever they are told, without ever thinking to ask "why am I being asked to implement this?" or "is this actually solving the problem?"
• tayo42 2mo ago
  Ai can work through all of those questions in number 2 and design around it. I was just doing that yesterday designing a game demo lol
  • matsemann 2mo ago
    Works for a demo, but quickly stagnates. Future models may of course push the limits further, but right now it quickly goes sideways without someone knowledgeable at the helm.
    • tayo42 2mo ago
      You don't design things with never ending details though. If your developing a feature there's a limited scope to it. If your designing the direction of a large project you're working with higher level concepts. The point is that AI is aware of constraints and can manage a round it. You've got to keep in mind the size of a typical software project and plan. Most people aren't writing a kernel. They're writing some backend tool and need a project that fits into a couple of quarters with a handful of people at most involved.
      • dan_ggggg 2mo ago
        [flagged]
        • deleted 2mo ago
          [deleted]
      • addandsubtract 2mo ago
        > The point is that AI is aware of constraints and can manage a round it. Only if you instruct it with constraints. Otherwise, it's happy to implement whatever workaround it needs. But it still takes a dev to know what those constraints are and why they're needed.
  • nevertoolate 2mo ago
    Are you an AI?
• matsemann 2mo ago
  Before, if I would start working / thinking on a problem and discover it was harder than anticipated, that often was a signal that the feature may not be worth it. Because implementing it doesn't fit the current model of how things work or similar. Then it was back to the drawing table and find a better way of solving the underlying issue. But an LLM just happily chugs along and does it, no one feels the friction, which means you never stop up to think if you're solving the right problem, or in the right way. I'm already seeing this bite us in the ass, as you get hacks upon hacks.
  • kypro 2mo ago
    I first brought this up late last year – that there used to exist a kind of selection pressure against both developers and against features which would prevent certain functionality from ever being shipped, specifically: - Functionality which exceed the technical ability or knowledge of the developer which built it; and - Functionality which would require an excessive number of changes that time/cost became a constraint (likely because it wasn't an incremental change but a significant rework, or just a bad fit with the existing product). Sometimes I had the displeasure of joining a company where you could see developers hit these blockers and tried to fight through them (likely under pressure from management) rather than avoid them. And in the process they created a mess of buggy, half-functional spaghetti code which someone else later had to resolve. Today however, people can use agents to smash through these blockers and ship an incomprehensible amount of crap. And what's worse is they're celebrated by management who don't know any better and see this all as a productivity win with no downside. I suppose there are two ways to look at this – some would argue that engineers don't need to understand what they're building in detail anymore so non of this matters. Instead they can always use agents to explain what's going on and prompt them to fix any issues that come up. Then there's another camp which might argue that agents will fundamentally have all the same limitations as humans, and that at some point a codebase will grow too complex that it exceeds even the limits of an agent's knowledge retention or intelligence. Or a codebase may just grow so large that it costs 1 million tokens to make even a simple change. Unless agents start saying, "there's no way I'm shipping that" like humans used to I don't really see how we avoid the latter scenario... Complexity simply must have limits even if agents allow the bounds of those limits to grow. At some point models will have to take control or we risk hitting these limits in irrecoverable ways since complexity far exceed that of what a human can reason about well. If this wasn't true then "build me all the software" would be a reasonable prompt. Because why wouldn't we just get an agent to build everything we could ever possibly need? It's obvious that in the limit there will be limits in knowledge, intelligence and cost. Whether it's humans or agents, someone needs to manage complexity. That is the most important thing a good SWE used to do. It's why technology selection mattered, it's why good architecture mattered, it's why clean code mattered.
• warmedcookie 2mo ago
  I feel like there is also the "Nerd Factor" to consider which is this unrelenting passion to type away at a screen all day, whether that is yesterday's code or tomorrow's prompt. Few non-technical people have the attention span to suffer such monotony. (Even most devs don't have that to varying extents)
  • skydhash 2mo ago
    It's only monotone from someone looking, not from someone doing. If you look at digital painting tutorial, you'll see a lot of timelapse, because it's a very slow process where you spend the majority of time correcting stuff. But if you're the one doing it, each individual brush stroke is the result of a conscious decision.
    • orangedog 2mo ago
      I'm not sure; I find the job pretty boring.
      • emodendroket 2mo ago
        Working on a passion project or working through the hairy details of a complex algorithm or whatever can be fun. But that's not really what the job is like most of the time.
      • skydhash 2mo ago
        All the boring parts I've encountered have soon been automated out of my workflow. There's the waiting part of some process, but that's why I have HN for.
  • ben_w 2mo ago
    Aye. This is true for many professions, I think. I'm trying to write a novel, have been for a while now; one thing I hear from professional writers is that one should only do this if you are prepared to re-read whole thing six times before you even send it to the editor, and when you do send it to the editor, you and they will likely spend several months repeatedly re-reading the whole thing as you respond to a long list of changes they give you.
• roncesvalles 2mo ago
  >My actual job also included non-coding work: Does this attempted solution even work at all? Is this solving the right problem? Even if it's a valid solution to the right problem, is it the best solution given the time constraints? This is not non-coding work. This is coding.
  • polotics 2mo ago
    this is software development
    • bhavansig 2mo ago
      There's a reason it's called Software Engineering
      • ben_w 2mo ago
        Coding < Development < Engineering. My claim is just that LLMs are basically fine now with the "coding" part. Not so much with development, definitely not with engineering.
        • ryeights 2mo ago
          Outside of the most niche domains like frontier AI development, are there software engineering questions that Sol 5.6/Fable 5 can't answer better than the median software engineer if explicitly asked? It strikes me that models already have superhuman software architecture capabilities, they are simply underelicited. Furthermore the AI companies are spending gobs of money and compute to address this—harness development, orchestration/agent coordination, computer use are all designed to lower the skill level required to get the best results out of a given model. Soon the models themselves will be more persistent, more observant, and more skilled than any of us at driving models to obtain the "right" solution in a given SWE context.
          • cameronh90 2mo ago
            In terms of getting a the consensus answer to a specific question, they're likely better than a median software engineer at most questions. Where they still fall massively short, even compared to a straight out of college junior developer, is figuring out what the correct question to ask is. This has led to situations like Fable 5 spending six days debugging a deadlock because it didn't think to add any observability to detect when the job stalls, and would wait until the job timed out each time. Any junior developer would have fixed it in a few mins, but I left Fable running out of curiosity to see how long it'd take.
• deleted 2mo ago
  [deleted]
• bigstrat2003 2mo ago
  > The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to write. That is certainly not true. LLMs can't write code worth a damn still, and you have to babysit them to make sure they aren't doing stupid stuff. A human is still by far the best choice for doing programming, and when trend-chasing companies realize the damage they've done to their businesses they will stop pushing LLMs so hard.
  • fsloth 2mo ago
    "LLMs can't write code worth a damn" I think it depends on the - ahem - context. In my experience they usually can, sometimes they can't. But generally their sense of software architecture remains abysmal, so even if their writing is ok, you need to have architectural authority the very least to guide them.
    • izzydata 2mo ago
      The fact that they sometimes can't means you can't have someone who is not a software engineer. Otherwise there is no way to verify whether the LLM wrote correct code or not.
      • wek 2mo ago
        Exactly. Expertise is frequently required to steer, correct, rework, know what is good and what isn't. This happens 500 times a day on both the micro level and the architecture level.
      • CM30 2mo ago
        I feel like this depends on the kind of project/work to some degree. Lots of software engineering time is spent on very simple websites, apps and other projects that LLMs have been well trained on. I suspect if you want a static site with a contact form or a CMS theme/plugin, you could probably get a usable result by having some non-technical person plug some prompts into an AI. They wouldn't be able to verify if the code is correct, but based on my testing, if the work is simple enough, it probably wouldn't matter much. There are a lot of companies whose modus operandi is basically "build the same basic site 300 times, except with a different colour scheme and logo".
• hintymad 2mo ago
  > The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to write. Remember we used to spend enormous amount of time in school and in our spare time studying computer science? Algorithms, operating systems, compilers, and etc. All kinds of insights. All kinds of fun. All kinds of hard engineering. Yet, how much time do we really need to spend in our day-to-day work implementing or using the algorithms and etc that we have learned? Engineers have done amazing work of abstracting away the hard algorithms and data structures. In the meantime, there has been little progress or few new fields in the past 10 years or so in business that ask for implementation of new algorithms. In contrast, getting LLM to work is a new field, so it requires tons of new implementations: KV caches, speculative decoding, all kinds of variants of attention like FlashAttention, all kinds of parallel processing techniques, RL pipelines, post-training pipelines, and etc. It's just that the field is so concentrated that only luck few get to work on them. So, maybe it's not LLM per se that removes the need of writing code. It is the maturity of the software engineering that has done so. It's just that LLM fills the last gap: making knowledge transfer so much faster and cheaper - if all that's left for most of us is slicing and dicing of what has been already been implemented, then LLM can reliably take over.
  • hirvi74 2mo ago
    > Remember we used to spend enormous amount of time in school and in our spare time studying computer science? Algorithms, operating systems, compilers, and etc. All kinds of insights. All kinds of fun. All kinds of hard engineering. Yet, how much time do we really need to spend in our day-to-day work implementing or using the algorithms and etc that we have learned? Because great insight and expertise stems from foundational knowledge. I am a huge hockey fan. In the NHL, the players do not stop practicing the fundamentals once they make it to the NHL. They practice the fundamentals even more. Many practice the same drills as youth leagues -- stick handling, passing back and forth, shooting, edgework, shooting, rebound control, etc.. The best of the NHL might not hit the hardest, have the most accurate shot, or skate the fastest. What separates them from the rest of the lot is that they are fundamentally better than everyone else.
    • whattheheckheck 2mo ago
• yieldcrv 2mo ago
  staying within free tiers has led to some really great optimizations for me took complex pub/sub event architectures down to the events postgres can do on its own, and a system architecture that works with that payload. am aware of the limits with this approach and stress potential on db
• keybored 2mo ago
  This I solemnly swear. To take my paycheck with gratitude, even if my employer makes three times as much based on my output. To take whatever technological disruption that graces my path as an immaterial and impersonal force. To politely and discreetely bow out when the statistics soup trained on my fellow colleagues makes me unemployable and worthless. Amen.
• nunez 2mo ago
  > 1) The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to write. Horrible if you got into this industry because you loved to code. Amazing if you only ever saw the code as a means to an end.
  • yuye 2mo ago
    I think this can be generalized even more: Horrible if you got into engineering because you wanted to solve problems. Amazing if you only ever wanted to see the problem solved.
• thelastgallon 2mo ago
  > 2) My actual job also included non-coding work: Does this attempted solution even work at all? Is this solving the right problem? Even if it's a valid solution to the right problem, is it the best solution given the time constraints? Unfortunately, this won't be captured by the metrics, because its not easily observable or instrumentable. There will be relentless pressure to use AI and more and more and prove that it is adding value. It won't add value, but whats available to executives is to lower wages and employment. There will be 'success' stories by most VPs that they've been using AI and here is proof (low wages + employment) and trillions of lines of code. LOC has always been a useless metric, but nobody cares. > At least, I hope this "is a place where my experience is still useful"; I may just be telling myself a nice story, same as all the other people through history who have found themselves obsoleted when the automation came for them. I don't think there will be more automation (at least not in a productive, helpful way), but a lot more enshittification and success stories of AI. Nobody cares about consumer experience or who is actually using the product. Almost all CEOs/C-Suite/VPs are far removed from reality, they will do whatever is being hyped right now. Every Corp has had massive DEI initiatives in the previous administration and now anti-DEI in the current administration. Corps were doing blockchain nonsense and now they do AI.