7 ms·

How many years until manual programming, as we know it, will be obsolete? Meaning that there won't be coders that sit around punching code all day, but rather

How many years until manual programming, as we know it, will be obsolete? Meaning that there won't be coders that sit around punching code all day, but rather just generate diagrams and/or feed models with descriptions of systems, and in return get fully functional systems. 5 years? 10 years? 15 years? 20?

deleted 3y ago
[deleted]
Jensson 3y ago
Bug fixing via gpt interface is going to be fun! It needs to be far better than humans to avoid the following situation, and at that point these models have taken over basically every office job, so at that point you wont have to go through this process anyway. You: Here is a bug: [explanation] GPT: Ok, fixed it! You: It is still there. Gpt: Ok, now fixed for real! You: It is still there...
- PaulRobinson 3y ago
  You're right, and I like the joke, but we should be clar that LLM != GPT != ChatGPT. There's a whole field emerging of prompt engineers, but I think what's desperately missing is good quality integrated UX engineering. Being able to write a comment and/or a method signature and having an LLM spit out a generated piece of code is kinda cool, but the interface for showing that the code is buggy needs refining. I think that's likely to be quickly showing a test case: given these inputs I'm expecting this output. LLM refactors against all tests, and you iterate together towards the correct solution. That's one small example of how we can start to think about the future UX that isn't about having a "chat" about your code. Similar UX thought is needed in other disciplines, it'll be interesting to see how this domain evolves.
  - yunwal 3y ago
    We should probably also be clear that LLMs aren’t the only ML models that can learn to code. If hands-on programming is automated away, it’s not obvious that it’ll be an LLM that does it.
- ilaksh 3y ago
  I have already built a system that puts ChatGPT in a loop with the output of programs and running commands. It can be pretty effective. It's even more effective when you can limit the domain somewhat to things that can fit in the context window, which eliminates out of date API information. We will now shortly be able to add visual feedback of the applications into the loop.
allisdust 3y ago
My guess is it's sooner than later. First to go would be UI programmers and automation testers. Last would probably be the hardware interfacing engineers like embedded etc. May be writing code manually would be equivalent of using punch cards or artisan bread :) Frankly I feel like we are looking at this the wrong way. In the future we might not even have a way to program some things manually. There might be a llm for each device that generates necessary hardware instructions on the fly like today's jit based on natural language or environment. May be there would be a inter llm spec/language that would felicitate cross llm functionality. All short circuiting out the programmers between the user and the utility.
- zer0tonin 3y ago
  UI programmers have to deal with a ton of ambiguity and their job is literally to understand how humans will use a program. I have no idea how the hell you can come to the conclusion that this would be easier to automate than embedded.
  - modernpink 3y ago
    Considering speech is becoming the primary interface for HCI via LLMs, that alone may reduce the need for UI design.
  - throwuwu 3y ago
    Isn’t that the designers job? You don’t need to actually write code to do UX
  - allisdust 3y ago
    Like the other comments say, most likely the product owner describes his requirement in as much detail as possible and the phase-1 LLM will convert that to designs. Based on the feedback, it will be iterated and may be 3-4 designs selected for A/B testing. The phase-2 LLM converts the designs to code in either react/flutter (both of which current LLMs are already quite good at) and deploy for a certain set of users. Basically same flow as today minus the engineers. Most of this is even possible today. The only thing saving engineering necks is the context window size which prevents fitting the whole repo in memory. Hopefully we (the programmers) will build our savings to a sustainable level in the next 5 years before signing off on manual coding or finding another thing to do than typing on keyboards to make computers dance.

deleted 3y ago

[deleted]

Jensson 3y ago

Bug fixing via gpt interface is going to be fun! It needs to be far better than humans to avoid the following situation, and at that point these models have taken over basically every office job, so at that point you wont have to go through this process anyway. You: Here is a bug: [explanation] GPT: Ok, fixed it! You: It is still there. Gpt: Ok, now fixed for real! You: It is still there...

PaulRobinson 3y ago
You're right, and I like the joke, but we should be clar that LLM != GPT != ChatGPT. There's a whole field emerging of prompt engineers, but I think what's desperately missing is good quality integrated UX engineering. Being able to write a comment and/or a method signature and having an LLM spit out a generated piece of code is kinda cool, but the interface for showing that the code is buggy needs refining. I think that's likely to be quickly showing a test case: given these inputs I'm expecting this output. LLM refactors against all tests, and you iterate together towards the correct solution. That's one small example of how we can start to think about the future UX that isn't about having a "chat" about your code. Similar UX thought is needed in other disciplines, it'll be interesting to see how this domain evolves.
- yunwal 3y ago
  We should probably also be clear that LLMs aren’t the only ML models that can learn to code. If hands-on programming is automated away, it’s not obvious that it’ll be an LLM that does it.
ilaksh 3y ago
I have already built a system that puts ChatGPT in a loop with the output of programs and running commands. It can be pretty effective. It's even more effective when you can limit the domain somewhat to things that can fit in the context window, which eliminates out of date API information. We will now shortly be able to add visual feedback of the applications into the loop.

allisdust 3y ago

My guess is it's sooner than later. First to go would be UI programmers and automation testers. Last would probably be the hardware interfacing engineers like embedded etc. May be writing code manually would be equivalent of using punch cards or artisan bread :) Frankly I feel like we are looking at this the wrong way. In the future we might not even have a way to program some things manually. There might be a llm for each device that generates necessary hardware instructions on the fly like today's jit based on natural language or environment. May be there would be a inter llm spec/language that would felicitate cross llm functionality. All short circuiting out the programmers between the user and the utility.

zer0tonin 3y ago
UI programmers have to deal with a ton of ambiguity and their job is literally to understand how humans will use a program. I have no idea how the hell you can come to the conclusion that this would be easier to automate than embedded.
- modernpink 3y ago
  Considering speech is becoming the primary interface for HCI via LLMs, that alone may reduce the need for UI design.
- throwuwu 3y ago
  Isn’t that the designers job? You don’t need to actually write code to do UX
- allisdust 3y ago
  Like the other comments say, most likely the product owner describes his requirement in as much detail as possible and the phase-1 LLM will convert that to designs. Based on the feedback, it will be iterated and may be 3-4 designs selected for A/B testing. The phase-2 LLM converts the designs to code in either react/flutter (both of which current LLMs are already quite good at) and deploy for a certain set of users. Basically same flow as today minus the engineers. Most of this is even possible today. The only thing saving engineering necks is the context window size which prevents fitting the whole repo in memory. Hopefully we (the programmers) will build our savings to a sustainable level in the next 5 years before signing off on manual coding or finding another thing to do than typing on keyboards to make computers dance.