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> I’ve been coding for a long time... I think having been coding for a long time, I don't think you fall into the same category. Dart having paradigms not too

by zkry 2y ago

> I’ve been coding for a long time... I think having been coding for a long time, I don't think you fall into the same category. Dart having paradigms not too different from other standard languages, a lot of these skills are probably transferable. I've seen beginners on the other hand using LLMs who couldn't even write a proper for-loop without AI assistance. They lack the fundamental ability to "run code in their head." This type of person I feel would be utterly limited by the capabilities of the AI model and would fail in lockstep with it.

brookst 2y ago
This is kind of the classic “kids these days” argument: that because we understand something from what we consider the foundational level up to what we consider the goal level, anyone who comes along later and starts at higher levels will be limited / less capable / etc. It is true, but also irrelevant. Just like most programmers today do not need to understand CPU architecture or even assembly language, programmers in the future will not need to understand for loops the way we do. They will get good at writing LLM-optimized specifications that produce the desired results. And that will be fine, even if we old-timers bemoan that they don’t really program the way we do. Yes, the abstractions required will be inefficient. And we will always be able to say that when those kids solve our kinds of problems, our methods are better. Just like assembly programmers can look at simple python programs and be astounded at the complexity “required” to solve simple problems.
- zkry 2y ago
  I agree with this take actually. I do imagine how programming in the future could be comprised of mostly interactions with LLMs. Such interactions would probably constrained enough to get the success rate of LLMs sufficiently high, maybe involving specialized DSLs made for LLMs. I do think the future may be more varied. Just like today where I look at kernel/systems/DB engineering and it seems almost arcane to me, I feel like there will be another stratum created, working on things where LLMs don't suffice. A lot of this will also depend on how far LLMs get. I would think that there would have to be more ChatGPT-like breakthroughs before this new type of developer can come.
- rafaelmn 2y ago
  I feel like you underestimate how much effort goes into making CPUs reliable and how low level/well specified the problem of building a VM/compiler is compared to getting a natural language specification to executable program. Solving that reliably is basically AGI - I doubt there will be many humans in the loop if we reach that point.
  - brookst 2y ago
    I get CPU’s; I worked at Intel and cut my teeth on x86 assembly. But the fact that some people need to understand CPU architecture does not mean all people need to. The vast, vast majority of programmers do not need to understand CPUs or compilers today. That’s fine. It is also fine that many new programmers won’t even think in the form of functions and return values and types the way we do. I’m not saying traditional hard science tech is useless. I am saying it is not mandatory for everyone.
    - rafaelmn 2y ago
      Yeah but what I'm saying is the level of engineering power that goes into making such relatively simple abstraction is huge and we have decades of experience. I think if AI ever gets to the point where it's so reliable for natural language -> code - we're into the AGI era and I don't see the role of programmers at all - bridging that layer successfully requires some very careful analysis and context awareness. Unless you think we're headed off in a direction where LLLms are gluing idiot proof boxes that are super inefficient but get the job done. I can sort of see that happening but in my experience reasoning through/debugging natural language specs is harder than going through equivalent code - I don't think we're getting much value here and adding a huge layer of inefficiency.

by zkry 2y ago

brookst 2y ago

This is kind of the classic “kids these days” argument: that because we understand something from what we consider the foundational level up to what we consider the goal level, anyone who comes along later and starts at higher levels will be limited / less capable / etc. It is true, but also irrelevant. Just like most programmers today do not need to understand CPU architecture or even assembly language, programmers in the future will not need to understand for loops the way we do. They will get good at writing LLM-optimized specifications that produce the desired results. And that will be fine, even if we old-timers bemoan that they don’t really program the way we do. Yes, the abstractions required will be inefficient. And we will always be able to say that when those kids solve our kinds of problems, our methods are better. Just like assembly programmers can look at simple python programs and be astounded at the complexity “required” to solve simple problems.

zkry 2y ago
I agree with this take actually. I do imagine how programming in the future could be comprised of mostly interactions with LLMs. Such interactions would probably constrained enough to get the success rate of LLMs sufficiently high, maybe involving specialized DSLs made for LLMs. I do think the future may be more varied. Just like today where I look at kernel/systems/DB engineering and it seems almost arcane to me, I feel like there will be another stratum created, working on things where LLMs don't suffice. A lot of this will also depend on how far LLMs get. I would think that there would have to be more ChatGPT-like breakthroughs before this new type of developer can come.
rafaelmn 2y ago
I feel like you underestimate how much effort goes into making CPUs reliable and how low level/well specified the problem of building a VM/compiler is compared to getting a natural language specification to executable program. Solving that reliably is basically AGI - I doubt there will be many humans in the loop if we reach that point.
- brookst 2y ago
  I get CPU’s; I worked at Intel and cut my teeth on x86 assembly. But the fact that some people need to understand CPU architecture does not mean all people need to. The vast, vast majority of programmers do not need to understand CPUs or compilers today. That’s fine. It is also fine that many new programmers won’t even think in the form of functions and return values and types the way we do. I’m not saying traditional hard science tech is useless. I am saying it is not mandatory for everyone.
  - rafaelmn 2y ago
    Yeah but what I'm saying is the level of engineering power that goes into making such relatively simple abstraction is huge and we have decades of experience. I think if AI ever gets to the point where it's so reliable for natural language -> code - we're into the AGI era and I don't see the role of programmers at all - bridging that layer successfully requires some very careful analysis and context awareness. Unless you think we're headed off in a direction where LLLms are gluing idiot proof boxes that are super inefficient but get the job done. I can sort of see that happening but in my experience reasoning through/debugging natural language specs is harder than going through equivalent code - I don't think we're getting much value here and adding a huge layer of inefficiency.