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> AI assistance means that programmers can concern themselves less and less with the particulars of any language. Sure. Until we need to. Then we face some app

> AI assistance means that programmers can concern themselves less and less with the particulars of any language. Sure. Until we need to. Then we face some apparently tiny concern, which is actually deeply intricated with the rest of this whole mess, and we are ready for a ride in the rabbit hole. > most developers today don’t pay much attention to the instruction sets and other hardware idiosyncrasies of the CPUs that their code runs on, which language a program is vibe coded in ultimately becomes a minor detail. This can be very misguided from my part but I have the feeling they are two very different cases here. Ok, not everyone is a ffmpeg level champion who will thrive in code-golfing ASM til the last drop of cycle gain. But there are also probably reasons why third-generation programming language lasted without any other subsequent proposal completely displacing them. It’s all about a tradeoff of expressiveness and precision. What we want to keep in the focus zone, and what we want to delegate to mostly uncontrolled details. If to go faster we need to get rid of a transparent glasses, we will need very sound and solid alternative probes to report what’s going on ahead.

mattsahr 1y ago
>> ...deeply intricated with... I think you invented a new phrase. And it's a good one!
louthy 1y ago
>> most developers today don’t pay much attention to the instruction sets and other hardware idiosyncrasies of the CPUs that their code runs on, which language a program is vibe coded in ultimately becomes a minor detail. If it was even slightly true then we wouldn’t be generating language syntax at all, we’d be generating raw machine code for the chip architectures we want to support. Or even just distributing the prompts and letting an AI VM generate the target machine code later. That may well happen one day, but we’re not even close right now
- skydhash 1y ago
  Also there’s so much patching in the kernel (for unix) to solve hardware bugs. And a lot of languages depends on C (with all its footguns) to probide that stable foundation. It’s all unseen work that are very important.
forinti 1y ago
Oh dear. Using AI for something you don't understand well is surely a recipe for disaster and should not be encouraged.
- epolanski 1y ago
  Your comment lacks so much context and nuance to ultimately be nonsense. You absolutely can, and probably _should_, leverage AI to learn many things you don't understand at all. Simple example: try picking up or learning a programming language like C with or without LLMs. With is going to be much more efficient. C is one of the languages that LLMs have seen the most, they are very, very good at it for learning purposes (also at bug hunting). I have never learned as much about computing as in the last 7/8 months of using LLMs to assist me at summarizing, getting information, finding bugs, explaining concepts iteratively (99% of Software books are crap: poorly written and quickly outdated, often wrong), scanning git repositories for implementation details, etc. You people keep committing the same mistake over and over: there's a million uses to LLMs, and instead of defining the context of what you're discussing about you conflate everything with vibe coding making ultimately your comments nonsense.
  - forinti 1y ago
    My context is that I have seen some colleagues try to make up for not having expertise with a particular technology by using LLMs and ultimately they have managed to waste their time and other people's time. If you want to use LLMs for learning, that's altogether a different proposition.
    - epolanski 1y ago
      I kinda knew what you meant, but I also feel it is important to provide the nuance and context.
    - 300hoogen 1y ago
      seems like a significant skill/intelligence issue. someone i know made a web security/pentesting company without ANY prior knowledge in programming or security in general. and his shit actually works by the way, topping leaderboards on hackerone and having a decent amount of clients. your colleagues might be retarded or just don’t know how to use llms
somethingsome 1y ago
Take into account that this is posted on IEEE. In my opinion, their target audience are scientists rather than programmers, and a scientist most often think of code as a tool to express his ideas (hence, perfect AI generated code is kind of a graal). The faster he can express them, even if the code is ugly, the better. He does not care to reuse the code later most of the time. I have the hint that scientists and not programmers are the target audience as other things may trigger only one category but not the other, for example, they consider Arduino a language, This makes totally sense for scientists, as most of the ones using Arduino dont necessarily know C++, but are proud to be able to code in Arduino.
- jimbokun 1y ago
  That’s a good point. For a professional programmer, code and what it does is the object of study. Saying the programmer shouldn’t look at the code is very odd.
- thfuran 1y ago
  But reproducibility is famously a matter of some concern to scientists.
  - fhd2 1y ago
    Sure, but their tools are complexity management tools: Hypotheses, experiments, empirical evidence, probabilities. To my knowledge, they deal far less with the determism programmers rely on. It's reproducible if you get similar results with the same probability.
- bitwize 1y ago
  If code is actually viewed as a tool to express ideas, making it easy to read and figure out should be a goal.
  - somethingsome 1y ago
    I like programming, I like clean code, so it's something I struggled with when I began research. But actually, producing easy to read code when you don't have specifications, because you don't know yet if the idea will work, and you are discovering problems on that idea as you go doesn't lead to readable code naturally. You refactor all the time, but then something that you misunderstood becomes a concern, and you need to refactorer again everything, and again and again.. You loose much time, and research is fast paced. Scientists that spend too much time cleaning code often miss deadlines and deliverables that are actually what they need to produce. Nobody cares about their code, as when the idea is fully developed, other scientist will just rewrite a better software with full view of the problem. (some scientists rewrite their full software when everything is discovered) I think a sensible goal would be easy to write code instead of easy to read for scientists.
BobbyJo 1y ago
> This can be very misguided from my part but I have the feeling they are two very different cases here They are indeed very different. If your compiler doesn't emit the right output for your architecture, or the highly optimized library you imported breaks on your hardware, you file a bug and, depending on the third party, have help in fixing the issue. Additionally, those types of issues are rare in popular libraries and languages unless you're pushing boundaries, which likely means you are knowledgeable enough to handle those type of edge cases anyway. If your AI gives you the wrong answer to a question, or outputs incorrect code, it's entirely on you to figure it out. You can't reach out to OpenAI or Anthropic to help you fix the issue. The former allows you to pretty safely remain ignorant. The latter does not.
mpalmer 1y ago
All they've done is widened the plank over the abyss.

mattsahr 1y ago

>> ...deeply intricated with... I think you invented a new phrase. And it's a good one!

louthy 1y ago

>> most developers today don’t pay much attention to the instruction sets and other hardware idiosyncrasies of the CPUs that their code runs on, which language a program is vibe coded in ultimately becomes a minor detail. If it was even slightly true then we wouldn’t be generating language syntax at all, we’d be generating raw machine code for the chip architectures we want to support. Or even just distributing the prompts and letting an AI VM generate the target machine code later. That may well happen one day, but we’re not even close right now

skydhash 1y ago
Also there’s so much patching in the kernel (for unix) to solve hardware bugs. And a lot of languages depends on C (with all its footguns) to probide that stable foundation. It’s all unseen work that are very important.

forinti 1y ago

Oh dear. Using AI for something you don't understand well is surely a recipe for disaster and should not be encouraged.

epolanski 1y ago
Your comment lacks so much context and nuance to ultimately be nonsense. You absolutely can, and probably _should_, leverage AI to learn many things you don't understand at all. Simple example: try picking up or learning a programming language like C with or without LLMs. With is going to be much more efficient. C is one of the languages that LLMs have seen the most, they are very, very good at it for learning purposes (also at bug hunting). I have never learned as much about computing as in the last 7/8 months of using LLMs to assist me at summarizing, getting information, finding bugs, explaining concepts iteratively (99% of Software books are crap: poorly written and quickly outdated, often wrong), scanning git repositories for implementation details, etc. You people keep committing the same mistake over and over: there's a million uses to LLMs, and instead of defining the context of what you're discussing about you conflate everything with vibe coding making ultimately your comments nonsense.
- forinti 1y ago
  My context is that I have seen some colleagues try to make up for not having expertise with a particular technology by using LLMs and ultimately they have managed to waste their time and other people's time. If you want to use LLMs for learning, that's altogether a different proposition.
  - epolanski 1y ago
    I kinda knew what you meant, but I also feel it is important to provide the nuance and context.
  - 300hoogen 1y ago
    seems like a significant skill/intelligence issue. someone i know made a web security/pentesting company without ANY prior knowledge in programming or security in general. and his shit actually works by the way, topping leaderboards on hackerone and having a decent amount of clients. your colleagues might be retarded or just don’t know how to use llms

somethingsome 1y ago

Take into account that this is posted on IEEE. In my opinion, their target audience are scientists rather than programmers, and a scientist most often think of code as a tool to express his ideas (hence, perfect AI generated code is kind of a graal). The faster he can express them, even if the code is ugly, the better. He does not care to reuse the code later most of the time. I have the hint that scientists and not programmers are the target audience as other things may trigger only one category but not the other, for example, they consider Arduino a language, This makes totally sense for scientists, as most of the ones using Arduino dont necessarily know C++, but are proud to be able to code in Arduino.

jimbokun 1y ago
That’s a good point. For a professional programmer, code and what it does is the object of study. Saying the programmer shouldn’t look at the code is very odd.
thfuran 1y ago
But reproducibility is famously a matter of some concern to scientists.
- fhd2 1y ago
  Sure, but their tools are complexity management tools: Hypotheses, experiments, empirical evidence, probabilities. To my knowledge, they deal far less with the determism programmers rely on. It's reproducible if you get similar results with the same probability.
bitwize 1y ago
If code is actually viewed as a tool to express ideas, making it easy to read and figure out should be a goal.
- somethingsome 1y ago
  I like programming, I like clean code, so it's something I struggled with when I began research. But actually, producing easy to read code when you don't have specifications, because you don't know yet if the idea will work, and you are discovering problems on that idea as you go doesn't lead to readable code naturally. You refactor all the time, but then something that you misunderstood becomes a concern, and you need to refactorer again everything, and again and again.. You loose much time, and research is fast paced. Scientists that spend too much time cleaning code often miss deadlines and deliverables that are actually what they need to produce. Nobody cares about their code, as when the idea is fully developed, other scientist will just rewrite a better software with full view of the problem. (some scientists rewrite their full software when everything is discovered) I think a sensible goal would be easy to write code instead of easy to read for scientists.

BobbyJo 1y ago

> This can be very misguided from my part but I have the feeling they are two very different cases here They are indeed very different. If your compiler doesn't emit the right output for your architecture, or the highly optimized library you imported breaks on your hardware, you file a bug and, depending on the third party, have help in fixing the issue. Additionally, those types of issues are rare in popular libraries and languages unless you're pushing boundaries, which likely means you are knowledgeable enough to handle those type of edge cases anyway. If your AI gives you the wrong answer to a question, or outputs incorrect code, it's entirely on you to figure it out. You can't reach out to OpenAI or Anthropic to help you fix the issue. The former allows you to pretty safely remain ignorant. The latter does not.

mpalmer 1y ago

All they've done is widened the plank over the abyss.