5 ms·

This is like saying there's nothing to learn about programming, you're just telling a computer what to do. :) It's partly true, but where this logic gets stuck

by fooker 4d ago

This is like saying there's nothing to learn about programming, you're just telling a computer what to do. :) It's partly true, but where this logic gets stuck is software engineering, for both the cases. Just telling the AI what to do won't get you to good software, especially when you want to use dozens of agents working in parallel, when you have something correctness sensitive, when you want the AI to try and solve a research problem you have a hunch about but not a concrete solution.

zem 4d ago
but the solution, ironically, is not learning more about ai, but learning more about good software engineering, so you can tell when the ai is not doing it
- fooker 4d ago
  No, software engineering is fundamentally changed because of AI. No amount of digging our heads in the sand is going to undo that. For example, the traditional best practice cycle of "small incremental change, code review, test, commit" is pretty much obsolete now. The way ten collaborating agents can make short work of a complex project is not something that fits in with our existing software engineering wisdom at all. We are still trying to figure out the new science of software engineering. And the best way to get better now is to just figure out what works and what does not for your use case.
  - Thanemate 3d ago
    >No, software engineering is fundamentally changed because of AI. No amount of digging our heads in the sand is going to undo that. Yet anyone who claims that fails to procure sufficient evidence or instruction on how exactly training the to-be software engineer in the age of AI should be. Until that happens, people still learn DSA, write code manually, and train their problem solving skills with programming exercises. And I mean handwriting them, not ordering "one solution for Leetcode 1133 in Rust, please" so we can proclaim that we're writing Rust so fast that I don't have to read a book about it anymore.
    - fooker 3d ago
      > fails to procure sufficient evidence or instruction on how exactly training the to-be software engineer in the age of AI should be. Of course there's no instruction on how to succeed in a rapidly changing new field. Do you think someone is more likely to succeed by getting their hands dirty and trying things out or waiting around for 'instruction' to be available? > people still learn DSA, write code manually, and train their problem solving skills with programming exercises. Maybe you're confusing computer science with software engineering? I agree that you'd still need to learn about algorithms, just like calculators do not reduce the need of learning algebra and trigonometry.
  - spwa4 4d ago
    Yeah I'm hoping AI can bring us back to software engineering of 20 years ago. Where studying the solution to a problem, finding clever solutions, getting everything to work without getting buried in little concerns is really important and "best practices" can just be a line in agents.md and the uninteresting part can just be handed off.
  - zem 4d ago
    I'm not talking about the transient aspects of producing software; I'm happy to accept that AI will change that, but also I feel that it will change it in ways that really aren't very hard to learn, and truth be told the more time that passes the smaller the gap will get as the agents get more capable. what I am talking about is principles that govern what good software looks like, what properties it has to satisfy in order to be extensible and maintainable and performant and all that other good stuff, and the AIs are not going to make that knowledge obsolete any time soon.
    - Seattle3503 3d ago
      > truth be told the more time that passes the smaller the gap will get as the agents get more capable Yup. A lot of work is going in to reducing the skill required to operate AI agents.
    - fooker 3d ago
      > what good software looks like I think this will change quite a bit too. Code being readable is one of our core tenets. Functions fitting within, approximately, a screen of code used to be ideal. Modern AI as already past solving that problem. You can give it a million line codebase, ask where something specific is happening, and it'll tell you in less than a minute. If we can de-emphasize readability, what do we gain? Custom hand rolled containers and data structures for your use cases are somewhat frowned upon unless really needed. Well, probably not anymore. What about a manually inlined mega-function with loops unrolled that pre-empts some of the compiler's work? No way that would fly a couple of years ago. Already seeing this in performance sensitive code.

by fooker 4d ago

zem 4d ago

but the solution, ironically, is not learning more about ai, but learning more about good software engineering, so you can tell when the ai is not doing it

fooker 4d ago
No, software engineering is fundamentally changed because of AI. No amount of digging our heads in the sand is going to undo that. For example, the traditional best practice cycle of "small incremental change, code review, test, commit" is pretty much obsolete now. The way ten collaborating agents can make short work of a complex project is not something that fits in with our existing software engineering wisdom at all. We are still trying to figure out the new science of software engineering. And the best way to get better now is to just figure out what works and what does not for your use case.
- Thanemate 3d ago
  >No, software engineering is fundamentally changed because of AI. No amount of digging our heads in the sand is going to undo that. Yet anyone who claims that fails to procure sufficient evidence or instruction on how exactly training the to-be software engineer in the age of AI should be. Until that happens, people still learn DSA, write code manually, and train their problem solving skills with programming exercises. And I mean handwriting them, not ordering "one solution for Leetcode 1133 in Rust, please" so we can proclaim that we're writing Rust so fast that I don't have to read a book about it anymore.
  - fooker 3d ago
    > fails to procure sufficient evidence or instruction on how exactly training the to-be software engineer in the age of AI should be. Of course there's no instruction on how to succeed in a rapidly changing new field. Do you think someone is more likely to succeed by getting their hands dirty and trying things out or waiting around for 'instruction' to be available? > people still learn DSA, write code manually, and train their problem solving skills with programming exercises. Maybe you're confusing computer science with software engineering? I agree that you'd still need to learn about algorithms, just like calculators do not reduce the need of learning algebra and trigonometry.
- spwa4 4d ago
  Yeah I'm hoping AI can bring us back to software engineering of 20 years ago. Where studying the solution to a problem, finding clever solutions, getting everything to work without getting buried in little concerns is really important and "best practices" can just be a line in agents.md and the uninteresting part can just be handed off.
- zem 4d ago
  I'm not talking about the transient aspects of producing software; I'm happy to accept that AI will change that, but also I feel that it will change it in ways that really aren't very hard to learn, and truth be told the more time that passes the smaller the gap will get as the agents get more capable. what I am talking about is principles that govern what good software looks like, what properties it has to satisfy in order to be extensible and maintainable and performant and all that other good stuff, and the AIs are not going to make that knowledge obsolete any time soon.
  - Seattle3503 3d ago
    > truth be told the more time that passes the smaller the gap will get as the agents get more capable Yup. A lot of work is going in to reducing the skill required to operate AI agents.
  - fooker 3d ago
    > what good software looks like I think this will change quite a bit too. Code being readable is one of our core tenets. Functions fitting within, approximately, a screen of code used to be ideal. Modern AI as already past solving that problem. You can give it a million line codebase, ask where something specific is happening, and it'll tell you in less than a minute. If we can de-emphasize readability, what do we gain? Custom hand rolled containers and data structures for your use cases are somewhat frowned upon unless really needed. Well, probably not anymore. What about a manually inlined mega-function with loops unrolled that pre-empts some of the compiler's work? No way that would fly a couple of years ago. Already seeing this in performance sensitive code.