6 ms·

I don't see it that way. Tabs, spaces, curly brace placement, Vim, Emacs, VSCode, etc are largely aesthetic choices with some marginal unproven cognitive implic

by suprjami 11mo ago

I don't see it that way. Tabs, spaces, curly brace placement, Vim, Emacs, VSCode, etc are largely aesthetic choices with some marginal unproven cognitive implications. I find people mostly prefer what they are used to, and if your preference was so superior then how could so many people build fantastic software using the method you don't like? AI isn't like that. AI is a bunch of people telling me this product can do wonderful things that will change society and replace workers, yet almost every time I use it, it falls far short of that promise. AI is certainly not reliable enough for me to jeopardize the quality of my work by using it heavily.

Loughla 11mo ago
>AI is certainly not reliable enough for me to jeopardize the quality of my work by using it heavily. I mean this in sincerity, and not at all snarky, but - have you considered that you haven't used the tools correctly or effectively? I find that I can get what I need from chatbots (and refuse to call them AI until we have general AI just to be contrary) if I spend a couple of minutes considering constraints and being careful with my prompt language. When I've come across people in my real life who say they get no value from chatbots, it's because they're asking poorly formed questions, or haven't thought through the problem entirely. Working with chatbots is like working with a very bright lab puppy. They're willing to do whatever you want, but they'll definitely piss on the floor unless you tell them not to. Or am I entirely off base with your experience?
- dwoldrich 11mo ago
  It would be helpful if you would relate your own bad experiences and how you overcame them. Leading off with "do it better" isn't very instructive. Unfortunately there's no useful training for much of anything in our industry, much less AI. I prefer to use LLM as a sock puppet to filter out implausible options in my problem space and to help me recall how to do boilerplate things. Like you, I think, I also tend to write multi-paragraph prompts repeating myself and calling back on every aspect to continuously hone in on the true subject I am interested in. I don't trust LLM's enough to operate on my behalf agentically yet. And, LLM is uncreative and hallucinatory as heck whenever it strays into novel territory, which makes it a dangerous tool.
- kentm 11mo ago
  > have you considered that you haven't used the tools correctly or effectively? The problem is that this comes off just as tone-deaf as "you're holding it wrong." In my experience, when people promote AI, its sold as just having a regular conversation and then the AI does thing. And when that doesn't work, the promoter goes into system prompts, MCP, agent files, etc and entire workflows that are required to get it to do the correct thing. It ends up feeling like you're being lied to, even if there's some benefit out there. There's also the fact that all programming workflows are not the same. I've found some areas where AI works well, but a lot of my work it does not. Usually things that wouldn't show up in a simple Google search back before it was enshittified are pretty spotty.
  - mattgreenrocks 11mo ago
    I suspect AI appeals very strongly to a certain personality type who revels in all the details in getting a proper agentic coding environment bootstrapped for AI to run amok in, and then supervises/guides the results. Then there’s people like me, who you’d probably term as an old soul, who looks at all that and says, “I have to change my workflow, my environment, and babysit it? It is faster to simply just do the work.” My relationship with tech is I like using as little as possible, and what I use needs to be predictable and do something for me. AI doesn’t always work for me.
    - msikora 11mo ago
      Yes, this rings true, it took me over a month to actually get to at least 1x of my usual productivity with Claude Code. There is a ton of setup and ton of things to learn and try to see what works. What to watch out for and how to babysit it so it doesn't go off the rails (quite heavy handed approach works best for me). It's kind of like a shitty, but very fast and very knowledgable junior developer. At this moment it still maybe isn't "worth it" for a lot of devs if productivity (and developer ergonomics) is the only goal, but it is clear to me that this is where the industry is heading and I think every dev will eventually have to get on board. These tools really just started to be somewhat decent this year. I'm 100% sure that in a year or two it will be the default for everyone in a way that you simply won't be able to compete without it at all. It would be like using a shovel instead of an excavator. Remember, right now is the worst it'll ever be.
- jandrewrogers 11mo ago
  AI is okay (not great) at generating low- to mid-skill code. If you are working in a high-skill software domain that requires pervasive state-of-the-art or first-principles implementation then AI produces consistently terrible code. It frequently is flatly incorrect about esoteric technical details that really matter. It can't reason from first principles and there isn't training data for a lot of state-of-the-art computer science and code implementations. Nothing you can prompt will make it produce non-naive output because it doesn't have that capability. AI works for a lot of things because, if we are honest, AI generated slop is replacing human generated slop. But not all software is slop and there are software domains where slop is not even an option.
- suprjami 11mo ago
  All good, no snark inferred. Yes I have considered this, and I keep considering it every time I get a bad result. Sorry this response is so long. I think I have a good idea how these things work. I have run local LLMs for a couple of years on a pair of video cards here, trying out many open weight models. I have watched the 3blue1brown ML course. I have done several LinkedIn Learning courses (which weren't that helpful, just mandatory). I understand about prompting precisely and personas (though I am not sold personas are a good idea). I understand LLMs do not "know" anything, they just generate the next most likely token. I understand LLMs are not a database with accurate retrieval. I understand "reasoning" is not actual thinking just manipulating tokens to steer a conversation in vector space. I understand LLMs are better for some tasks (summarisation, sentiment analysis, etc) than others (retrieval, math, etc). I understand they can only predict what's in their training data. I feel I have a pretty good understanding of how to get results from LLMs (or at least the ways people say you can get results). I have had some small success with LLMs. They are reasonably good at generating sub-100 line test code when given a precise prompt, probably because that is in training data scraped from StackOverflow. I did a certification earlier this year and threw ~1000 lines of Markdown notes into Gemini and had it quiz me which was very useful revision, it only got one question wrong of the couple of hundred I had it ask me. I'll give a specific example of a recent failure. My job is mostly troubleshooting and reading code, all of which is public open source (so accessible via LLM search tooling). I was trying to understand something where I didn't know the answer, and this was difficult code to me so I was really not confident at all in my understanding. I wrote up my thoughts with references, the normal person I ask was busy so I asked Gemini Pro. It confidently told me "yep you got it!". I asked someone else who saw a (now obvious) flaw in my reasoning. At some point I'd switched from a hash algorithm which generates Thing A, to a hash algorithm which generates Thing B. The error was clearly visible, one of my references had "Thing B" in the commit message title, which was in my notes with the public URL, when my whole argument was about "Thing A". This wasn't even a technical or code error, it was a text analysis and pattern matching error, which I didn't see because I was so focused on algorithms. Even Gemini, the apparent best LLM in the world which is causing "code red" at OpenAI did not pick this up, when text analysis is supposed to be one of its core functionalities. I also have a lot of LLM-generated summarisation forced on me at work, and it's often so bad I now don't even read it. I've seen it generate text which makes no logical sense and/or which uses so many words without really saying anything at all. I have tried LLM-based products where someone else is supposed to have done all the prompt crafting and added RAG embeddings and I can just behave like a naive user asking questions. Even when I ask these things question which I know are in the RAG, they cannot retrieve an accurate answer ~80% of the time. I have read papers which support the idea that most RAG falls apart after about ~40k words and our document set is much larger than that. Generally I find LLMs are at the point where to evaluate the LLM response I need to either know the answer beforehand so it was pointless to ask, or I need to do all the work myself to verify the answer which doesn't improve my productivity at all. About the only thing I find consistently useful about LLMs is writing my question down and not actually asking it, which is a form of Rubber Duck Debugging (https://en.wikipedia.org/wiki/Rubber_duck_debugging https://en.wikipedia.org/wiki/Rubber_duck_debugging) which I have already practiced for many years because it's so helpful. Meanwhile trillions of dollars of VC-backed marketing assures me that these things are a huge productivity increaser and will usher in 25% unemployment because they are so good at doing every task even very smart people can do. I just don't see it. If you have any suggestions for me I will be very willing to look into them and try them.
hectdev 11mo ago
I would say it is like that. No one HAS to use AI. But the shared goal is to get a change to the codebase to achieve a desired outcome. Some will outsource a significant part of that to AI, some won't. And its tricky because I'm trying not to appeal to emotion despite being fascinated with how this tool has enabled me to do things in a short amount of time that it would have taken me weeks of grinding to get to and improves my communication with stakeholders. That feels world changing. Specifically my world and the day-to-day roll I play when it comes to getting things done. I think it is fine that it fell short of your expectations. It often does for me as well but it's when it gets me 80% of the way there in less than a day's work, then my mind is blown. It's an imperfect tool and I'm sorry for saying this but so are we. Treat its imperfections in the same way you would with a JR developer- feedback, reframing, restrictions, and iterate.
- moduspol 11mo ago
  > But the shared goal is to get a change to the codebase to achieve a desired outcome. I'd argue that's not true. It's more of a stated goal. The actual goal is to achieve the desired outcome in a way that has manageable, understood side effects, and that can be maintained and built upon over time by all capable team members. The difference between what business folks see as the "output" of software developers (code) and what (good) software developers actually deliver over time is significant. AI can definitely do the former. The latter is less clear. This is one of the fundamental disconnects in discussions about AI in software development.
  - hectdev 11mo ago
    In my personal use case, I work at a company that has SO MUCH process and documentation for coding standards. I made an AI agent that knows all that and used it to update legacy code to the new standard in a day. Something that would have taken weeks if not more. If your desire is manageable code, make that a requirement. I'm going to say this next thing as someone with a lot of negative bias about corporations. I was laid off from Twitter when Elon bought the company and at a second company that was hemorrhaging users. Our job isn't to write code, it's to make the machine do the thing. All the effort for clean, manageable, etc is purely in the interest of the programmer but at the end of the day, launching the feature that pulls in money is the point.
dwaltrip 11mo ago
You can vibe-code a throwaway UI for investigating some complex data in less than 30 minutes. The code quality doesn't matter, and it will make your life much easier. Rinse and repeat for many "one-off" tasks. It's not going away, you need to learn how to use it. shrugs shoulders
- deleted 11mo ago
  [deleted]
- deleted 11mo ago
  [deleted]
- dwaltrip 11mo ago
  It’s kind of fun watching this comment go up and down :) There’s so much evidence out there of people getting real value from the tools. Some questions you can ask yourself are “why doesn’t it work for me?” and “what can I do differently?”. Be curious, not dogmatic. Ignore the hype, find people doing real work.
  - samdoesnothing 11mo ago
    Most people don't have a problem with using genai for stuff like throwaway UI's. That's not even remotely relevant to the criticisms. People reject having it forced down their throats by companies who are desperate to make us totally reliant on it to justify their insane investments. And people reject the evangelicals who claim that it's going to replace developers because it can spit out mostly working boilerplate.
  - SpicyLemonZest 11mo ago
    They're good questions! The problem is that I've tried to talk to the people who are getting real value from it, and often the answer ends up being that the value is not as real as they think. One guy gave an excited presentation about how AI let him write 7k LOC per day, expounded for an entire session about how the rest of us should follow in his shoes, and then clarified only in Q&A that reviewers couldn't keep up so he exempted himself from code review.

Loughla 11mo ago

>AI is certainly not reliable enough for me to jeopardize the quality of my work by using it heavily. I mean this in sincerity, and not at all snarky, but - have you considered that you haven't used the tools correctly or effectively? I find that I can get what I need from chatbots (and refuse to call them AI until we have general AI just to be contrary) if I spend a couple of minutes considering constraints and being careful with my prompt language. When I've come across people in my real life who say they get no value from chatbots, it's because they're asking poorly formed questions, or haven't thought through the problem entirely. Working with chatbots is like working with a very bright lab puppy. They're willing to do whatever you want, but they'll definitely piss on the floor unless you tell them not to. Or am I entirely off base with your experience?

dwoldrich 11mo ago
It would be helpful if you would relate your own bad experiences and how you overcame them. Leading off with "do it better" isn't very instructive. Unfortunately there's no useful training for much of anything in our industry, much less AI. I prefer to use LLM as a sock puppet to filter out implausible options in my problem space and to help me recall how to do boilerplate things. Like you, I think, I also tend to write multi-paragraph prompts repeating myself and calling back on every aspect to continuously hone in on the true subject I am interested in. I don't trust LLM's enough to operate on my behalf agentically yet. And, LLM is uncreative and hallucinatory as heck whenever it strays into novel territory, which makes it a dangerous tool.
kentm 11mo ago
> have you considered that you haven't used the tools correctly or effectively? The problem is that this comes off just as tone-deaf as "you're holding it wrong." In my experience, when people promote AI, its sold as just having a regular conversation and then the AI does thing. And when that doesn't work, the promoter goes into system prompts, MCP, agent files, etc and entire workflows that are required to get it to do the correct thing. It ends up feeling like you're being lied to, even if there's some benefit out there. There's also the fact that all programming workflows are not the same. I've found some areas where AI works well, but a lot of my work it does not. Usually things that wouldn't show up in a simple Google search back before it was enshittified are pretty spotty.
- mattgreenrocks 11mo ago
  I suspect AI appeals very strongly to a certain personality type who revels in all the details in getting a proper agentic coding environment bootstrapped for AI to run amok in, and then supervises/guides the results. Then there’s people like me, who you’d probably term as an old soul, who looks at all that and says, “I have to change my workflow, my environment, and babysit it? It is faster to simply just do the work.” My relationship with tech is I like using as little as possible, and what I use needs to be predictable and do something for me. AI doesn’t always work for me.
  - msikora 11mo ago
    Yes, this rings true, it took me over a month to actually get to at least 1x of my usual productivity with Claude Code. There is a ton of setup and ton of things to learn and try to see what works. What to watch out for and how to babysit it so it doesn't go off the rails (quite heavy handed approach works best for me). It's kind of like a shitty, but very fast and very knowledgable junior developer. At this moment it still maybe isn't "worth it" for a lot of devs if productivity (and developer ergonomics) is the only goal, but it is clear to me that this is where the industry is heading and I think every dev will eventually have to get on board. These tools really just started to be somewhat decent this year. I'm 100% sure that in a year or two it will be the default for everyone in a way that you simply won't be able to compete without it at all. It would be like using a shovel instead of an excavator. Remember, right now is the worst it'll ever be.
jandrewrogers 11mo ago
AI is okay (not great) at generating low- to mid-skill code. If you are working in a high-skill software domain that requires pervasive state-of-the-art or first-principles implementation then AI produces consistently terrible code. It frequently is flatly incorrect about esoteric technical details that really matter. It can't reason from first principles and there isn't training data for a lot of state-of-the-art computer science and code implementations. Nothing you can prompt will make it produce non-naive output because it doesn't have that capability. AI works for a lot of things because, if we are honest, AI generated slop is replacing human generated slop. But not all software is slop and there are software domains where slop is not even an option.
suprjami 11mo ago
All good, no snark inferred. Yes I have considered this, and I keep considering it every time I get a bad result. Sorry this response is so long. I think I have a good idea how these things work. I have run local LLMs for a couple of years on a pair of video cards here, trying out many open weight models. I have watched the 3blue1brown ML course. I have done several LinkedIn Learning courses (which weren't that helpful, just mandatory). I understand about prompting precisely and personas (though I am not sold personas are a good idea). I understand LLMs do not "know" anything, they just generate the next most likely token. I understand LLMs are not a database with accurate retrieval. I understand "reasoning" is not actual thinking just manipulating tokens to steer a conversation in vector space. I understand LLMs are better for some tasks (summarisation, sentiment analysis, etc) than others (retrieval, math, etc). I understand they can only predict what's in their training data. I feel I have a pretty good understanding of how to get results from LLMs (or at least the ways people say you can get results). I have had some small success with LLMs. They are reasonably good at generating sub-100 line test code when given a precise prompt, probably because that is in training data scraped from StackOverflow. I did a certification earlier this year and threw ~1000 lines of Markdown notes into Gemini and had it quiz me which was very useful revision, it only got one question wrong of the couple of hundred I had it ask me. I'll give a specific example of a recent failure. My job is mostly troubleshooting and reading code, all of which is public open source (so accessible via LLM search tooling). I was trying to understand something where I didn't know the answer, and this was difficult code to me so I was really not confident at all in my understanding. I wrote up my thoughts with references, the normal person I ask was busy so I asked Gemini Pro. It confidently told me "yep you got it!". I asked someone else who saw a (now obvious) flaw in my reasoning. At some point I'd switched from a hash algorithm which generates Thing A, to a hash algorithm which generates Thing B. The error was clearly visible, one of my references had "Thing B" in the commit message title, which was in my notes with the public URL, when my whole argument was about "Thing A". This wasn't even a technical or code error, it was a text analysis and pattern matching error, which I didn't see because I was so focused on algorithms. Even Gemini, the apparent best LLM in the world which is causing "code red" at OpenAI did not pick this up, when text analysis is supposed to be one of its core functionalities. I also have a lot of LLM-generated summarisation forced on me at work, and it's often so bad I now don't even read it. I've seen it generate text which makes no logical sense and/or which uses so many words without really saying anything at all. I have tried LLM-based products where someone else is supposed to have done all the prompt crafting and added RAG embeddings and I can just behave like a naive user asking questions. Even when I ask these things question which I know are in the RAG, they cannot retrieve an accurate answer ~80% of the time. I have read papers which support the idea that most RAG falls apart after about ~40k words and our document set is much larger than that. Generally I find LLMs are at the point where to evaluate the LLM response I need to either know the answer beforehand so it was pointless to ask, or I need to do all the work myself to verify the answer which doesn't improve my productivity at all. About the only thing I find consistently useful about LLMs is writing my question down and not actually asking it, which is a form of Rubber Duck Debugging (https://en.wikipedia.org/wiki/Rubber_duck_debugging https://en.wikipedia.org/wiki/Rubber_duck_debugging) which I have already practiced for many years because it's so helpful. Meanwhile trillions of dollars of VC-backed marketing assures me that these things are a huge productivity increaser and will usher in 25% unemployment because they are so good at doing every task even very smart people can do. I just don't see it. If you have any suggestions for me I will be very willing to look into them and try them.

hectdev 11mo ago

I would say it is like that. No one HAS to use AI. But the shared goal is to get a change to the codebase to achieve a desired outcome. Some will outsource a significant part of that to AI, some won't. And its tricky because I'm trying not to appeal to emotion despite being fascinated with how this tool has enabled me to do things in a short amount of time that it would have taken me weeks of grinding to get to and improves my communication with stakeholders. That feels world changing. Specifically my world and the day-to-day roll I play when it comes to getting things done. I think it is fine that it fell short of your expectations. It often does for me as well but it's when it gets me 80% of the way there in less than a day's work, then my mind is blown. It's an imperfect tool and I'm sorry for saying this but so are we. Treat its imperfections in the same way you would with a JR developer- feedback, reframing, restrictions, and iterate.

moduspol 11mo ago
> But the shared goal is to get a change to the codebase to achieve a desired outcome. I'd argue that's not true. It's more of a stated goal. The actual goal is to achieve the desired outcome in a way that has manageable, understood side effects, and that can be maintained and built upon over time by all capable team members. The difference between what business folks see as the "output" of software developers (code) and what (good) software developers actually deliver over time is significant. AI can definitely do the former. The latter is less clear. This is one of the fundamental disconnects in discussions about AI in software development.
- hectdev 11mo ago
  In my personal use case, I work at a company that has SO MUCH process and documentation for coding standards. I made an AI agent that knows all that and used it to update legacy code to the new standard in a day. Something that would have taken weeks if not more. If your desire is manageable code, make that a requirement. I'm going to say this next thing as someone with a lot of negative bias about corporations. I was laid off from Twitter when Elon bought the company and at a second company that was hemorrhaging users. Our job isn't to write code, it's to make the machine do the thing. All the effort for clean, manageable, etc is purely in the interest of the programmer but at the end of the day, launching the feature that pulls in money is the point.

dwaltrip 11mo ago

You can vibe-code a throwaway UI for investigating some complex data in less than 30 minutes. The code quality doesn't matter, and it will make your life much easier. Rinse and repeat for many "one-off" tasks. It's not going away, you need to learn how to use it. shrugs shoulders

deleted 11mo ago
[deleted]
deleted 11mo ago
[deleted]
dwaltrip 11mo ago
It’s kind of fun watching this comment go up and down :) There’s so much evidence out there of people getting real value from the tools. Some questions you can ask yourself are “why doesn’t it work for me?” and “what can I do differently?”. Be curious, not dogmatic. Ignore the hype, find people doing real work.
- samdoesnothing 11mo ago
  Most people don't have a problem with using genai for stuff like throwaway UI's. That's not even remotely relevant to the criticisms. People reject having it forced down their throats by companies who are desperate to make us totally reliant on it to justify their insane investments. And people reject the evangelicals who claim that it's going to replace developers because it can spit out mostly working boilerplate.
- SpicyLemonZest 11mo ago
  They're good questions! The problem is that I've tried to talk to the people who are getting real value from it, and often the answer ends up being that the value is not as real as they think. One guy gave an excited presentation about how AI let him write 7k LOC per day, expounded for an entire session about how the rest of us should follow in his shoes, and then clarified only in Q&A that reviewers couldn't keep up so he exempted himself from code review.