• frezik ( frezik@midwest.social ) 
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    1 year ago

    The general comments that Ben received were that experienced developers can use AI for coding with positive results because they know what they’re doing. But AI coding gives awful results when it’s used by an inexperienced developer. Which is what we knew already.

    That should be a big warning sign that the next generation of developers are not going to be very good. If they’re waist deep in AI slop, they’re only going to learn how to deal with AI slop.

    As a non-programmer, I have zero understanding of the code and the analysis and fully rely on AI and even reviewed that AI analysis with a different AI to get the best possible solution (which was not good enough in this case).

    What I’m feeling after reading that must be what artists feel like when AI slop proponents tell them “we’re making art accessible”.

    • In so many ways, LLMs are just the tip of the iceberg of bad ideology in software development. There have always been people that come into the field and develop heinously bad habits. Whether it’s the “this is just my job, the only thing I think about outside work is my family” types or the juniors who only know how to copy paste snippets from web forums.

      And look, I get it. I don’t think 60-80 hour weeks are required to be successful. But I’m talking about people who are actively hostile to their own career paths, who seem to hate programming except that it pays good and let’s them raise families. Hot take: that sucks. People selfishly obsessed with their own lineage and utterly incurious about the world or the thing they spend 8 hours a day doing suck, and they’re bad for society.

      The juniors are less of a drain on civilization because they at least can learn to do better. Or they used to could, because as another reply mentioned, there’s no path from LLM slop to being a good developer. Not without the intervention of a more experienced dev to tell them what’s wrong with the LLM output.

      It takes all the joy out of the job too, something they’ve been working on for years. What makes this work interesting is understanding people’s problems, working out the best way to model them, and building towards solutions. What they want the job to be is a slop factory: same as the dream of every rich asshole who thinks having half an idea is the same as working for years to fully realize an idea in all it’s complexity and wonder.

      They never have any respect for the work that takes because they’ve never done any work. And the next generation of implementers are being taught that there are no new ideas. You just ask the oracle to give you the answer.

    • swlabr ( swlabr@awful.systems ) 
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      1 year ago

      When they say “art” they mean “metaphorical lead paint” and when they say “accessible” they mean “insidiously inserted into your neural pathways”

    • Dragonstaff ( Dragonstaff@leminal.space ) 
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      I dunno. I feel like the programmers who came before me could say the same thing about IDEs, Stack Overflow, and high level programming languages. Assembly looks like gobbledygook to me and they tell me I’m a Senior Dev.

      If someone uses ChatGPT like I use StackOverflow, I’m not worried. We’ve been stealing code from each other since the beginning.“Getting the answer” and then having to figure out how to plug it into the rest of the code is pretty much what we do.

      There isn’t really a direct path from an LLM to a good programmer. You can get good snippets, but “ChatGPT, build me a app” will be largely useless. The programmers who come after me will have to understand how their code works just as much as I do.

    • rumba ( rumba@lemmy.zip ) 
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      1 year ago

      All the newbs were just copying lines from stackexchange before AI. The only real difference at this point is that the commenting is marginally better.

  • Baldur Bjarnason’s given his thoughts on Bluesky:

    My current theory is that the main difference between open source and closed source when it comes to the adoption of “AI” tools is that open source projects generally have to ship working code, whereas closed source only needs to ship code that runs.

    I’ve heard so many examples of closed source projects that get shipped but don’t actually work for the business. And too many examples of broken closed source projects that are replacing legacy code that was both working just fine and genuinely secure. Pure novelty-seeking

  • vivendi ( vivendi@programming.dev ) 
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    1 year ago

    No the fuck it’s not

    I’m a pretty big proponent of FOSS AI, but none of the models I’ve ever used are good enough to work without a human treating it like a tool to automate small tasks. In my workflow there is no difference between LLMs and fucking grep for me.

    People who think AI codes well are shit at their job

    • V0ldek ( V0ldek@awful.systems ) 
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      In my workflow there is no difference between LLMs and fucking grep for me.

      Well grep doesn’t hallucinate things that are not actually in the logs I’m grepping so I think I’ll stick to grep.

      (Or ripgrep rather)

      • vivendi ( vivendi@programming.dev ) 
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        1 year ago

        Hallucinations become almost a non issue when working with newer models, custom inference, multishot prompting and RAG

        But the models themselves fundamentally can’t write good, new code, even if they’re perfectly factual

          • vivendi ( vivendi@programming.dev ) 
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            You need to run the model yourself and heavily tune the inference, which is why you haven’t heard from it because most people think using shitGPT is all there is with LLMs. How many people even have the hardware to do so anyway?

            I run my own local models with my own inference, which really helps. There are online communities you can join (won’t link bcz Reddit) where you can learn how to do it too, no need to take my word for it

        • scruiser ( scruiser@awful.systems ) 
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          The promptfarmers can push the hallucination rates incrementally lower by spending 10x compute on training (and training on 10x the data and spending 10x on runtime cost) but they’re already consuming a plurality of all VC funding so they can’t 10x many more times without going bust entirely. And they aren’t going to get them down to 0%, hallucinations are intrinsic to how LLMs operate, no patch with run-time inference or multiple tries or RAG will eliminate that.

          And as for newer models… o3 actually had a higher hallucination rate because trying to squeeze rational logic out of the models with fine-tuning just breaks them in a different direction.

          I will acknowledge in domains with analytically verifiable answers you can check the LLMs that way, but in that case, its no longer primarily an LLM, you’ve got an entire expert system or proof assistant or whatever that can operate independently of the LLM and the LLM is just providing creative input.

          • swlabr ( swlabr@awful.systems ) 
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            1 year ago

            We should maximise hallucinations, actually. That is, we should hack the environmental controls of the data centers to be conducive for fungi growth, and flood them with magic mushrooms spores. We can probably get the rats on board by selling it as a different version of nuking the data centers.

          • vivendi ( vivendi@programming.dev ) 
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            1 year ago

            O3 is trash, same with closedAI

            I’ve had the most success with Dolphin3-Mistral 24B (open model finetuned on open data) and Qwen series

            Also lower model temperature if you’re getting hallucinations

            For some reason everyone is still living in 2023 when AI is remotely mentioned. There is a LOT you can criticize LLMs for, some bullshit you regurgitate without actually understanding isn’t one

            You also don’t need 10x the resources where tf did you even hallucinate that from

              • vivendi ( vivendi@programming.dev ) 
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                My most honest goal is to educate people which on lemmy is always met with hate. people love to hate, parroting the same old nonsense that someone else taught them.

                If you insist on ignorance then be ignorant in peace, don’t try such misguided attempts at sneer

                There are things in which LLMs suck. And there are things that you wrongly believe as part of this bullshit twitter civil war.

                • froztbyte ( froztbyte@awful.systems ) 
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                  My most honest goal is to educate people

                  oh and I suppose you can back that up with verifiable facts, yes?

                  and that you, yourself, can stand as a sole beacon against the otherwise regularly increasing evidence and studies that both indicate toward and also prove your claims to be full of shit? you are the saviour that can help enlighten us poor unenlightened mortals?

                  sounds very hard. managing your calendar must be quite a skill

            • scruiser ( scruiser@awful.systems ) 
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              GPT-1 is 117 million parameters, GPT-2 is 1.5 billion parameters, GPT-3 is 175 billion, GPT-4 is undisclosed but estimated at 1.7 trillion. Token needed for training and training compute scale linearly (edit: actually I’m wrong, looking at the wikipedia page… so I was wrong, it is even worse for your case than I was saying, training compute scales quadratically with model size, it is going up 2 OOM for every 10x of parameters) with model size. They are improving … but only getting a linear improvement in training loss for a geometric increase in model size, training time. A hypothetical GPT-5 would have 10 trillion training parameters and genuinely need to be AGI to have the remotest hope of paying off it’s training. And it would need more quality tokens than they have left, they’ve already scrapped the internet (including many copyrighted sources and sources that requested not to be scrapped). So that’s exactly why OpenAI has been screwing around with fine-tuning setups with illegible naming schemes instead of just releasing a GPT-5. But fine-tuning can only shift what you’re getting within distribution, so it trades off in getting more hallucinations or overly obsequious output or whatever the latest problem they are having.

              Lower model temperatures makes it pick it’s best guess for next token as opposed to randomizing among probable guesses, they don’t improve on what the best guess is and you can still get hallucinations even picking the “best” next token.

              And lol at you trying to reverse the accusation against LLMs by accusing me of regurgitating/hallucinating.

              • vivendi ( vivendi@programming.dev ) 
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                1 year ago

                Small scale models, like Mistral Small or Qwen series, are achieving SOTA performance with lower than 50 billion parameters. QwQ32 could already rival shitGPT with 32 billion parameters, and the new Qwen3 and Gemma (from google) are almost black magic.

                Gemma 4B is more comprehensible than GPT4o, the performance race is fucking insane.

                ClosedAI is 90% hype. Their models are benchmark princesses, but they need huuuuuuge active parameter sizes to effectively reach their numbers.

                Everything said in this post is independently verifiable by taking 5 minutes to search shit up, and yet you couldn’t even bother to do that.

      • vivendi ( vivendi@programming.dev ) 
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        These views on LLMs are simplistic. As a wise man once said, “check yoself befo yo wreck yoself”, I recommend more education thus

        LLM structures arw over hyped, but they’re also not that simple

        • MonkderVierte ( MonkderVierte@lemmy.ml ) 
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          From what i know from recent articles about retracing LLM indepth, they are indeed best suited for language translation and perfectly explain the halucinations. And i think i’ve read somewhere that this was the originally intended purpose of the tech?

          Ah, here, and here more tabloid-ish.

          • froztbyte ( froztbyte@awful.systems ) 
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            many of the proponents of things in this field will propose/argue $x thing to be massively valuable for $x

            thing is, that doesn’t often work out

            yes, there’s some value in the tech for translation outcomes. to anyone even mildly online, “so are language teaching apps/sites using this?” is probably a very nearby question. and rightly so!

            and then when you go digging into how that’s going in practice, wow fuck damn doesn’t that Glorious AI Future sheen just fall right off…

      • Blackmist ( Blackmist@feddit.uk ) 
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        I’m guessing if it would actually work for that, somebody would have done it by now.

        But it probably just does it’s usual thing of bullshitting something that looks like code, only now you’re wasting the time of maintainers as well who have to confirm that it is bobbins.

        • gens ( gens@programming.dev ) 
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          Yea it’s a problem already for security bugs, llms just waste maintainers time and make them angry.

          They are useless and make more work for programmers, even on python and js codebases that they are trained on the most and are the “easiest”.

        • Natanox ( Natanox@discuss.tchncs.de ) 
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          It’s already doing that, some FOSS projects regularly get weird PRs that on first glance look good, but if you look closer are either total nonsense or riddled with bugs. Especially awful are security-related PRs; although those are never made in good faith, that’s usually grifting (throwing AI at the wall trying to cash in as many bounties as possible). The project lead of curl recently announced that anyone who posts a PR that’s obviously AI, or is made with AI, will get banned.

          Like, it’s really good as a learning tool as long as you don’t blindly believe everything it says given you can ask stuff in natural language and it will resolve possible knowledge dependencies for you that you’d otherwise get stuck on in official docs, and since you can ask contextual questions receiving contextual answers (no logical abstraction). But code generation… please don’t.

          • froztbyte ( froztbyte@awful.systems ) 
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            it’s really good as a learning tool as long as you don’t blindly believe everything it says given you can ask stuff in natural language

            the poster: “it’s really good as a learning tool”

            the poster: “but don’t blindly believe it”

            the learner: “how should I know when to believe it?”

            the poster: “check everything”

            the learner: “so you’re saying I should just read the actual documentation and/or source?”

            the poster: “how are you going to ask that anything? how can you fondle something that isn’t a prompt?!”

            the learner: “thanks for your time, I think I’m going to find another class”

            • Natanox ( Natanox@discuss.tchncs.de ) 
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              Nice conversation you had right there in your head. I assume you also took a closer look at it to get a neutral opinion and didn’t just ride one of the two waves “blind AI hype” or “blind AI hate”?

              I’ve taken a closer look at Codestral (which is locally hostable), threw stuff at it and got a sense for what it can and can’t do. The general gist is that its (Python) syntax is basically always correct, however it sometimes messes up the actual code logic or gets the user request wrong. That makes it a good tool for code questions aimed at specific features, how certain syntax in a language works or to look up potential alternative solutions for smaller code snippets. However it should absolutely not be used to create huge chunks of your code logic, that will always backfire.

              And since some people will read this and think I’m some AI worshipper, fuck no. They’re amoral as fuck, the only models not screwed up through their creation process are those very few truly FOSS ones. But if you hate on something you have to actually know shit about it and understand its appeal and non-hyped usecases (they do have them, even LLMs). Otherwise you’ll end up in a social corner filled with bitterness and, depending on the topic, perhaps even increasingly extreme opinions (not saying we shouldn’t smash OpenAI and other corposcum into tiny pieces, we absolutely should).

              There are technologies that are utter bullshit like NFTs. However (unfortunately?) that isn’t the case for AI. We just live in an economy that’s good in abusing everything and everyone.

              • froztbyte ( froztbyte@awful.systems ) 
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                Nice conversation you had right there in your head

                that you recognize none of this is telling. that someone else got it, more so.

                I assume

                you could just ask, you know. since you seem so comfortable fondling prompts, not sure why you wouldn’t ask a person. is it because they might tell you to fuck off?

                I’ve taken a closer look…

                fuck off with the unrequested advertising. never mind that no-one asked you for how you felt for some fucking piece of shit. oh, you feel happy that the logo is a certain tint of ? bully for you, now fuck off and do something worthwhile

                That makes it a good tool

                a tool you say? wow, sure glad you’re going to replace your *spins the wheel* Punctured Car Tyre with *spins the wheel again* Needlenose Pliers!

                think I’m some AI worshipper, fuck no. They’re amoral as fuck

                so, you think there’s moral problems, but only sometimes? it’s supes okay to do your version of leveraged exploitation? cool, thanks for letting us know

                those very few truly FOSS ones

                oh yeah, right, the “truly FOSS ones”! tell me again how those are trained - who’s funding that compute? are the licenses contextually included in the model definition?

                wait, hold on! why are you squealing away like a deflating balloon?! those are actual questions! you’re the one who brought up morals!

                Otherwise you’ll end up in a social corner filled with bitterness

                I’ve met people like you at parties. they’re often popular, but they’re never fun. and I always regret it.

                There are technologies that are utter bullshit like NFTs. However (unfortunately?) that isn’t the case for AI

                citation. fucking. needed.

              • swlabr ( swlabr@awful.systems ) 
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                Otherwise you’ll end up in a social corner filled with bitterness

                This is a standard Internet phenomenon (I generalize) called a Sneer Club, i.e. people who enjoy getting together and picking on designated targets. Sneer Clubs (I expect) attract people with high Dark Triad characteristics, which is (I suspect) where Asshole Internet Atheists come from - if you get a club together for the purpose of sneering at religious people, it doesn’t matter that God doesn’t actually exist, the club attracts psychologically f’d-up people. Bullies, in a word, people who are powerfully reinforced by getting in what feels like good hits on Designated Targets, in the company of others doing the same and congratulating each other on it.

  • swlabr ( swlabr@awful.systems ) 
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    The headlines said that 30% of code at Microsoft was AI now! Huge if true!

    Something like MS word has like 20-50 million lines of code. MS altogether probably has like a billion lines of code. 30% of that being AI generated is infeasible given the timeframe. People just ate this shit up. AI grifting is so fucking easy.

    • froztbyte ( froztbyte@awful.systems ) 
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      yeah, the “some projects” bit is applicable, as is the “machine generated” phrasing

      @gsuberland pointed out elsewhere on fedi just how much of the VS-/MS- ecosystem does an absolute fucking ton of code generation

      (which is entirely fine, ofc. tons of things do that and it exists for a reason. but there’s a canyon in the sand between A and B)

      • swlabr ( swlabr@awful.systems ) 
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        1. Perhaps you didn’t read the linked article. Nadella didn’t claim that 30% of MS’s code was written by AI. What he said was garbled up to the eventual headline.
        2. We don’t have to play devil’s advocate for a hyped-up headline that misquotes what an AI glazer said, dawg.
        3. “Existing code generation codes can write 30%” doesn’t imply that AI possibly/plausibly wrote 30% of MS’s code. There’s no logical connection. Please dawg, I beg you, think critically about this.
              • swlabr ( swlabr@awful.systems ) 
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                “Oh man, this brain fog I have sure makes it hard to think. Guess I’ll use my trusty LLM! ChatGPT says lead paint is tastier and better for your brain than COVID? Don’t mind if I do!”

                • Soyweiser ( Soyweiser@awful.systems ) 
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                  I’m on a diet of rocks, glue on my pizza, lead paint, and covid infections, according to Grok this is called the Mr Burns method which should prevent diseases, as they all work together to block all bad impulses. Can’t wait to try this new garlic oil I made, using LLM instructions. It even had these cool bubbles while fermenting, nature is great.

            • froztbyte ( froztbyte@awful.systems ) 
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              I’ve been beating this drum for like 4~5y but: I don’t think the tech itself is going anywhere. published, opensourced, etc etc - the bell can’t be unrung, the horses have departed the stable

              but

              I do also argue that an extremely large amount of wind in the sails right now is because of the constellation of VC/hype//etc shit

              can’t put a hard number on this, but … I kind see a very massive reduction; in scope, in competence, in relevance. so much of this shit (esp. the “but my opensource model is great!” flavour) is so fucking reliant on “oh yeah this other entity had a couple fuckpiles of cash with which to train”, and once that (structurally) evaporates…

    • David Gerard ( dgerard@awful.systems ) OP
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      This discussion has made it clear to me that LLM enthusiasts do not value the time or preferences of open-source maintainers, willfully do not understand affirmative consent, and that I should take steps to explicitly ban the use of such tools in the open source projects I maintain.

    • gerikson ( gerikson@awful.systems ) 
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      Well that took a turn.

      The initial comment spawned a GIANT THREAD which I haven’t had time to parse, but after 2 days or so the initiator (username Kerrick) ragequit.

      Here’s the modlog message (timestamp 2025-05-15 23:02)

      User 7u026ne9se

      Action: Banned

      Reason: Was ~Kerrick from kerrick.blog who picked and lost every fight in /s/gkpmli, deleted/disowned all his comments, lied about stalking bc someone told a maintainer he admitted to misleading them, and asked us to delete his username change from the modlog. No.

      Here’s the old profile:

      http://web.archive.org/web/20250312141826/https://lobste.rs/~Kerrick

      I suggest open source projects keep an eye out for this username and maybe take an extra look at their contributions.

      Edit found the last comment they made before deleting. Textbook DARVO, considering they have almost unmerited amounts of positive karma in the thread itself.

      I came here to give a simple explanation of why people aren’t noticing as many open source vibe coded contributions as they’d expect. Fights were picked with me by others: I was called a sneak, incapable, a pedant, an ignorer of consent, and a threat to human expression. All through that I’ve worked extremely hard to steer it away from such abhorrent behavior and towards the free expression of ideas, rather than engaging in the same kind of name calling.

      Even so, I’ve been emailed, text messaged, and even called on my cell phone about this thread. Someone stalked me to other social media to bring it up there. This thread has brought about the most toxicity I’ve ever experienced on any forum, and these last couple days have been the among the worst in my life.

      • gerikson ( gerikson@awful.systems ) 
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        I have downloaded the relevant page from an open tab in Firefox ("Save as HTML), but it doesn’t contain the latest craziness. If anyone is interested, let me know the best way to host such content.

        What’s scary is the raw number of upvotes he got for basically admitting to defrauding a FLOSS maintainer.

  • MNByChoice ( MNByChoice@midwest.social ) 
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    Damn, this is powerful.

    If AI code was great, and empowered non-programmers, then open source projects should have already committed hundreds of thousands of updates. We should have new software releases daily.

  • Coding is hard, and its also intimidating for non-coders. I always used to look at coders as kind of a different kind of human, a special breed. Just like some people just glaze over when you bring up math concepts but are otherwise very intelligent and artistic, but they can’t bridge that gap when you bring up even algebra. Well, if you are one of those people that want to learn coding its a huge gap, and the LLMs can literally explain everything to you step by step like you are 5. Learning to code is so much easier now, talking to an always helpful LLM is so much better than forums or stack overflow. Maybe it will create millions of crappy coders, but some of them will get better, some will get great. But the LLM’s will make it possible for more people to learn, which means that my crypto scam now has the chance to flourish.

    • murtaza64 ( murtaza64@programming.dev ) 
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      so what are the sentiments about langchain? I was recently working with it to try to build some automatic PR generation scripts but I didn’t have the best experience understanding how to use the library. the documentation has been quite messy, repetitive and disorganized—somehow both verbose and missing key details. but it does the job I wanted it to, namely letting me use an LLM with tool calling and custom tools in a script

      • Architeuthis ( Architeuthis@awful.systems ) 
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        Given the volatility of the space I don’t think it could have been doing stuff much better, doubt it’s getting out of alpha before the bubble bursts and stuff settles down a bit, if at all.

        Automatic pr generation sounds like something that would need a prompt and a ten-line script rather than langchain, but it also seems both questionable and unnecessary.

        If someone wants to know an LLM’s opinion on what the changes in a branch are meant to accomplish they should be encouraged to ask it themselves, no need to spam the repository.

      • zbyte64 ( zbyte64@awful.systems ) 
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        I’ve deployed LangChain to production shudders. My use case involved sending images results back to the “agent” and that use case is an after thought for many of these services. I ended up extending the Gemini Vertex client to fake it. The artifacts system is basically pass around a dictionary and pray both ends agree on the shape.

        This is not an endorsement of LLMs in general. I’m working to replace it with a decision tree.

  • TheObviousSolution ( TheObviousSolution@lemm.ee ) 
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    Had a presentation where they told us they were going to show us how AI can automate project creation. In the demo, after several attempts at using different prompts, failing and trying to fix it manually, they gave up.

    I don’t think it’s entirely useless as it is, it’s just that people have created a hammer they know gives something useful and have stuck it with iterative improvements that have a lot compensation beneath the engine. It’s artificial because it is being developed to artificially fulfill prompts, which they do succeed at.

    When people do develop true intelligence-on-demand, you’ll know because you will lose your job, not simply have another tool at your disposal. The prompts and flow of conversations people pay to submit to the training is really helping advance the research into their replacements.

    • My opinion is it can be good when used narrowly.

      Write a concise function that takes these inputs, does this, and outputs a dict with this information.

      But so often it wants to be overly verbose. And it’s not so smart as to understand much of the project for any meaningful length of time. So it will redo something that already exists. It will want to touch something that is used in multiple places without caring or knowing how it’s used.

      But it still takes someone to know how the puzzle pieces go together. To architect it and lay it out. To really know what the inputs and outputs need to be. If someone gives it free reign to do whatever, it’ll just make slop.

      • swlabr ( swlabr@awful.systems ) 
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        1 year ago

        That’s the problem, isn’t it? If it can only maybe be good when used narrowly, what’s the point? If you’ve managed to corner a subproblem down to where an LLM can generate the code for it, you’ve already done 99% of the work. At that point you’re better off just coding it yourself. At that point, it’s not “good when used narrowly”, it’s useless.

        • It’s a tool. It doesn’t replace a programmer. But it makes writing some things faster. Give any tool to an idiot and they’ll fuck things up. But a craftsman can use it to make things a little faster, because they know when and how to use it. And more importantly when not to use it.

          • swlabr ( swlabr@awful.systems ) 
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            1 year ago

            The “tool” branding only works if you formulate it like this: in a world where a hammer exists and is commonly used to force nails into solid objects, imagine another tool that requires you to first think of shoving a nail into wood. You pour a few bottles of water into the drain, whisper some magic words, and hope that the tool produces the nail forcing function you need. Otherwise you keep pouring out bottles of water and hoping that it does a nail moving motion. It eventually kind of does it, but not exactly, so you figure out a small tweak which is to shove the tool at the nail at the same time as it does its action so that the combined motion forces the nail into your desired solid. Do you see the problem here?

          • froztbyte ( froztbyte@awful.systems ) 
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            1 year ago

            It’s a tool.

            (if you persist to stay with this dogshit idiotic “opinion”:) please crawl into a hole and stay there

            fucking what the fuck is with you absolute fucking morons and not understand the actual literal concept of tools

            read some fucking history goddammit

            (hint: the amorphous shifting blob, with a non-reliable output, not a tool; alternative, please, go off about how using a php hammer is definitely the way to get a screw in)

      • frezik ( frezik@midwest.social ) 
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        1 year ago

        There’s something similar going on with air traffic control. 90% of their job could be automated (and it has been technically feasible to do so for quite some time), but we do want humans to be able to step in when things suddenly get complicated. However, if they’re not constantly practicing those skills, then they won’t be any good when an emergency happens and the automation gets shut off.

        The problem becomes one of squishy human psychology. Maybe you can automate 90% of the job, but you intentionally roll that down to 70% to give humans a safe practice space. But within that difference, when do you actually choose to give the human control?

        It’s a tough problem, and the benefits to solving it are obvious. Nobody has solved it for air traffic control, which is why there’s no comprehensive ATC automation package out there. I don’t know that we can solve it for programmers, either.

  • AI isn’t bad when supervised by a human who knows what they’re doing. It’s good to speed up programmers if used properly. But business execs don’t see that.

    Even when I supervise it, I always have to step in to clean up it’s mess, tell it off because it randomly renames my variables and functions because it thinks it knows better and oversteps. Needs to be put in it’s place like a misbehaving dog, lol