• Croquette@sh.itjust.works
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      10 months ago

      The US add engineer to everything to sound most prestigious than they are. If you sell your service as a AI prompt writer, you get paid peanuts. If you sell the same service as AI prompt engineer, the C-Suites cream their pants.

      • TrickDacy@lemmy.world
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        10 months ago

        So you’re telling me that people advertise themselves as AI programmers? That does not seem like something to brag about in such a manner

        • SpaceCowboy@lemmy.ca
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          10 months ago

          Yeah right?

          I’ve found it helpful in learning things about languages I’m unfamiliar with, but it seems like saying “I’m an AI programmer” means “I don’t really know what I’m doing in this language, I’m still learning.” Which I suppose shows a willingness to learn, but that’s about it.

        • Croquette@sh.itjust.works
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          10 months ago

          Lots of people think that computers are magic box. And now a diffuse entity in the cloud talk to them? Big heads will gobble that shit up.

  • FIST_FILLET@lemmy.ml
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    10 months ago

    “prompt engineering” in itself is such an embarrassing term for the act of saying “computer uhhh show me epic boobies!!”

    like that joke about calling dishwashing “submerged porcelain technician” but unironically

  • Mango@lemmy.world
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    10 months ago

    Making middle management do everything is not ‘running a business’.

  • doggle@lemmy.dbzer0.com
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    10 months ago

    It’s not engineering either. Or art. It’s only barely writing, in an overly literal sense.

    • frezik@midwest.social
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      10 months ago

      Yes, it is. Mostly because “real engineering” isn’t the high bar it’s made out to be. From that blog:

      Nobody I read in these arguments, not one single person, ever worked as a “real” engineer. At best they had some classical training in the classroom, but we all know that looks nothing like reality. Nobody in this debate had anything more than stereotypes to work with. The difference between the engineering in our heads and in reality has been noticed by others before, most visibly by Glenn Vanderburg. He read books on engineering to figure out the difference. But I wanted to go further.

      Software has developed in an area where the cost of failure is relatively low. We might make million dollar mistakes, but it’s not likely anybody dies from it. In areas where somebody could die from bad software, techniques like formal verification come into play. Those tend to make everything take 10 times longer, and there’s no compelling reason for the industry at large to do that.

      If anything, we should lean into this as an advantage. How fast can we make the cycle of change to deployment?

      • Alexstarfire@lemmy.world
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        10 months ago

        I help make Healthcare software. Mistakes can easily lead to death. Not most, but it’s something we always have to worry about.

      • ChickenLadyLovesLife@lemmy.world
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        10 months ago

        We might make million dollar mistakes, but it’s not likely anybody dies from it.

        I had a coworker who got a gig writing PDA software for a remote-controlled baseball machine. He was to this day the most incompetent programmer I’ve ever met personally; his biggest mistake on this project was firing a 120 mph knuckleball (a pitch with no spin so its flight path is incredibly erratic) a foot over a 12-year-old kid’s head. This was the only time in my 25-year career that I had to physically restrain someone (the client, in this case) to prevent a fist fight. I replaced my coworker on the project after this and you can bet I took testing a little bit more seriously than he did.

    • DontRedditMyLemmy@lemmy.world
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      10 months ago

      In many cases this is accurate. Programming alone doesn’t amount to engineering. Lotta low quality lines of code being churned out these days because standards have dropped.

    • okamiueru@lemmy.world
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      10 months ago

      By how some teams operate, and some developers think, there is certainly cases where the “engineering” aspect is hard to find.

  • TropicalDingdong@lemmy.world
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    10 months ago

    Bro if you could get there just by prompting, it would be.

    There are no models good enough to just ask for something to be done and it gets done.

    There will be someday though.

    • Scrubbles@poptalk.scrubbles.tech
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      10 months ago

      Build an entire ecosystem, with multiple frontends, apps, databases, admin portals. It needs to work with my industry. Make it run cheap on the cloud. Also make sure it’s pretty.

      The prompts are getting so large we may need to make some sort of… Structured language to pipe into… a device that would… compile it all…

      • TropicalDingdong@lemmy.world
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        10 months ago

        I mean it can start much smaller.

        Here is access to a jira board. Here are unit tests. Do stuff until it works.

        • Scrubbles@poptalk.scrubbles.tech
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          10 months ago

          Perfect! We’ll just write out the definition of the product completely in Jira, in a specific way, so the application can understand it - tweak until it’s perfect, write unit tests around our Jira to make sure those all work - maybe we write a structured way to describe each item aaand we’ve reinvented programming.

          I see where you’re going, but I’ve worked with AI models for the last year in depth, and there’s some really cool stuff they can do. However, truly learning about them means learning their hard pitfalls, and LLMs as written would not be able to build an entire application. They can help speed up parts of it, but the more context means more VRAM exponentially, and eventually larger models, and that’s just to get code spit out. Not to mention there is nuance in English that’s hard to express, that requirements are never perfect, that LLMs can iterate for very long before they run out of VRAM, that they can’t do devops or hook into running apps - the list goes on.

          AI has been overhyped by business because they’re frothing at the mouth to automate everyone away - which is too bad because what it does do well it does great at - with limitations. This is my… 3rd or 4th cycle where business has assumed they can automate away engineers, and each time it just ends up generating new problems that need to be solved. Our jobs will evolve, sure, but we’re not going away.

          • TropicalDingdong@lemmy.world
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            10 months ago

            I mean, I had beta access to ChatGPT and have gotten excellent results from clever use, so I don’t appreciate the appeal to authority.

            No, the jobs are going away and you are delusional if you think otherwise. ChatGPT is the DeepBlue of these kinds of models, and a global effort is being made to get to the AlphaGo level of these models. It will happen, probably in weeks to months. A company, like Microsoft for example, could build something like this, never release it to the public, and if successful, can suddenly out-compete every other software company on the planet. 100%.

            Your attitude is a carbon copy of the same naysaying attitude that could be see all over hackernews before ChatGPT found its way to the front page. That AI wasn’t ever going to do XY or Z. Then it does. Then the goal posts have to move.

            AI will be writing end to end architecture, writing teh requirements documents, filling out the jira tickets. Building the unit tests. If you don’t think that a company would LOVE to depart with its 250k+ per year software engineers, bro…

            • Scrubbles@poptalk.scrubbles.tech
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              10 months ago

              lol okay dude. Flippantly you ignored all of the limitations I pointed out. Sure it could happen, but not on the timeline you’re discussing. There is no way within a year that they have replaced software engineers, I call absolute BS on that. I doubt it will rise above copilot within a year. I see it being used alongside code for a long time, calling out potential issues, optimizing where it can, and helping in things like building out yaml files. It cannot handle an entire solution, the hardware doesn’t exist for it. It also can’t handle specific contexts for business use-cases. Again maybe, but it’ll be a while - and even then our jobs shift to building out models and structuring AI prompts in a stable way.

              My attitude is the same because these are the same issues that it’s faced. I’m not arguing that it’s not a great tool to be used, and I see a lot of places for it. But it’s naiive to say that it can replace an engineer at it’s stage, or in the near future. Anyone who has worked with it would tell you that.

              I firmly do think companies want to replace their 250k engineers. That’s why I know that most of it is hype. The same hype that existed 20 years ago when they came out with designers for UIs, the same hype when react and frontend frameworks came out. Python was built to allow anyone to code, and that was another “end of engineers”. Cloud claimed to be able to remove entire IT departments, but those jobs just shifted to DevOps engineers. The goalposts moved each time, but the demand for qualified engineers went up because now they needed to know these new technologies.

              Why do you think I worked with AI so much over the last year? I see my job evolving, I’m getting ready for it. This has happened before - those who don’t learn new tech get left behind, those who learn it keep going. I may not be coding in python in 10 years, god knows I wasn’t doing what I was 10 years ago - but it’s laughable to me to think that engineers are done and over with.

              • TropicalDingdong@lemmy.world
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                10 months ago

                You seem mad and strongly opinionated, but I hate arguing when there is nothing on the line. Would you be interested in a gentleman’s bet then?

                My thesis is that we’ll have (or some one will, you and I may not have access) to a form of interactive AI that can effectively code from scratch some kind of large-ish application (like a website), make changes to that website, add features, etc, in the next few years, like, very few.

                I’d like to come to terms with you and lay down a bet. If need be we can start a sublemmy to post the bet publicly and we can be held accountable for public shaming if we fail to put up.

                For the purposes of a bet, I want to suggest that a code base ‘as complicated’ as Lemmy is a good barometer. My getting this prediction right will be to show you an example of that happening in media, or ideally, being able to show it in use. I think in media should be considered acceptable.

                In my circles, we usually make these bets beers or bottles of the counterparties favorite drink, and I’m willing to offer you the following terms: 3:1 in the first year, 2:1 in the second year, and 1:1 in the first year. If the above thesis isn’t confirm, I’m wrong and I’ll make it clear that I acknowledge that I’m wrong.

                I would like to bet 12 bottles on my thesis based on the above terms, (where a case of 12 bottles of the preferred liquor or beer or whatever does not exceed $200, so like a 12 pack of good beer or mid tier wine).

                Is that a deal you can agree to?

                • relevants@feddit.de
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                  10 months ago

                  It will happen, probably in weeks to months.

                  in the next few years, like, very few

                  Now who’s moving the goalposts…?

    • marcos@lemmy.world
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      10 months ago

      There are no models good enough to just ask for something to be done and it gets done.

      We call those “compilers”. There are many of them.