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Yeah, yesterday I just told it to write a Dockerfile for me, with exactly what I wanted. instead of changing anything by hand, I told it what to fix, without te

by Reptur 4y ago

Yeah, yesterday I just told it to write a Dockerfile for me, with exactly what I wanted. instead of changing anything by hand, I told it what to fix, without telling it how to. Took 20-30 follow-up messages, telling it to add things, remove things, fix things, make things compatible with the versions of software it was running etc. It worked. Running the container now, works great.

qayxc 4y ago
> Took 20-30 follow-up messages, telling it to add things, remove things, fix things, make things compatible with the versions of software it was running etc. So in other words you basically spent just as much time and effort as if you did it yourself?
- bordercases 4y ago
  The arc of software development is to make people depend on vaguer specifications to tell systems what they want, and rely on recognition of what they see being what they want with fast feedback loops, versus slowly recalling the syntax of how to state it. It could be the same time spent, but not the same amount of cognitive effort.
- randomdigitz 4y ago
  This is why I stopped using it as much. Realized it was taking more time asking it questions than just doing the work.
  - fnordpiglet 4y ago
    I don’t use it to do work I can do already, I use it to bootstrap in areas of confusion.
- Reptur 4y ago
  I understand your point, and you are right. Had I been familiar with the image I was working with, and the version differences in configuration etc. It probably would have taken me the same amount of time. I look at this from a more zoomed out perspective, this is just the beginning. The point is its capable and will improve.
  - nr2x 4y ago
    Exactly.
  - wruza 4y ago
    Will it improve though? I’m not a GPT hater or denier, but how do you even predict that it hasn’t already hit the wall? They can increase parameter count x100 again, but correctness is not some knob they can just dial up to 10. What if a learning dataset simply has not enough info for a correct answer to have a greater weight than all the “con” noise? What if an answer requires a sort of reasoning inaccessible to LLMs? Stories itt can as well be boiled down to “I fed it with corrections for some time and it didn’t f..k up this last time and finally included everything into the answer”. What makes you think it would not do just that better or quicker? Edit: Another probably highly related question is, can it answer “I don’t know this / not sure about these parts”? Never seen that in chat logs.
    - shmoogy 4y ago
      Isn't that what the thumbs up/down are for? Some kind of annotating that can be used to improve future iterations of training ? They've got millions of people feeding potentially billions of queries, probably tons of feedback - would this not result in an improvement over time?
      - wruza 4y ago
        Assuming that the existing corpus was already coherent with what experts find true (afaik, they used all available books and common knowledge resources), why would any amount of additional corrective statements make a difference for a retrained model? It’s not that our written knowledge was wrong all the time and we tolerated it until mid 2022. I don’t really understand how it works, how its iterations are different or what the roadmap is. But what I managed to learn (better say feel) about LLMs isn’t very consistent with such linear predictions. Well, maybe it will use downvotes as anti-prompts? Existing sources must have had votes too, but it was probably only a subset. Maybe the current iteration didn’t rank by vote at all, so the next one will really shine? Guess we’ll see soon.
- randomcarbloke 4y ago
  So far this has been my experience developing with it, lightning fast but inaccurate results, then just as much time getting it to work as I would've spent writing it myself.
- fennecfoxy 4y ago
  Lmao, the difference being, of course, one tab open vs 20 different tabs for various aspects of Dockerfile docs, SO's providing more detail that the docs lack, etc. Yeah we can all write this stuff by hand but it's incredibly exciting; when it first came out I was asking it to write snippets of JS for stuff, additions, removals, asking it to write unit tests and then update them when the overall code changed and it maintained several different "threads" of conversation all related to a singular exercise just fine. Sure it's not perfect, but it's kind of cool having a super junior dev who happens to have instant access to most documentation around at the time in its head.
holoduke 4y ago
Incredible useful data to feed back into the system for futher learning. Chatgpt is miles ahead on this part conpared to competitors who yet have to release. Imagine the amount of data chatgpt is receiving now. Google should be very very worried.

by Reptur 4y ago

qayxc 4y ago

> Took 20-30 follow-up messages, telling it to add things, remove things, fix things, make things compatible with the versions of software it was running etc. So in other words you basically spent just as much time and effort as if you did it yourself?

bordercases 4y ago
The arc of software development is to make people depend on vaguer specifications to tell systems what they want, and rely on recognition of what they see being what they want with fast feedback loops, versus slowly recalling the syntax of how to state it. It could be the same time spent, but not the same amount of cognitive effort.
randomdigitz 4y ago
This is why I stopped using it as much. Realized it was taking more time asking it questions than just doing the work.
- fnordpiglet 4y ago
  I don’t use it to do work I can do already, I use it to bootstrap in areas of confusion.
Reptur 4y ago
I understand your point, and you are right. Had I been familiar with the image I was working with, and the version differences in configuration etc. It probably would have taken me the same amount of time. I look at this from a more zoomed out perspective, this is just the beginning. The point is its capable and will improve.
- nr2x 4y ago
  Exactly.
- wruza 4y ago
  Will it improve though? I’m not a GPT hater or denier, but how do you even predict that it hasn’t already hit the wall? They can increase parameter count x100 again, but correctness is not some knob they can just dial up to 10. What if a learning dataset simply has not enough info for a correct answer to have a greater weight than all the “con” noise? What if an answer requires a sort of reasoning inaccessible to LLMs? Stories itt can as well be boiled down to “I fed it with corrections for some time and it didn’t f..k up this last time and finally included everything into the answer”. What makes you think it would not do just that better or quicker? Edit: Another probably highly related question is, can it answer “I don’t know this / not sure about these parts”? Never seen that in chat logs.
  - shmoogy 4y ago
    Isn't that what the thumbs up/down are for? Some kind of annotating that can be used to improve future iterations of training ? They've got millions of people feeding potentially billions of queries, probably tons of feedback - would this not result in an improvement over time?
    - wruza 4y ago
      Assuming that the existing corpus was already coherent with what experts find true (afaik, they used all available books and common knowledge resources), why would any amount of additional corrective statements make a difference for a retrained model? It’s not that our written knowledge was wrong all the time and we tolerated it until mid 2022. I don’t really understand how it works, how its iterations are different or what the roadmap is. But what I managed to learn (better say feel) about LLMs isn’t very consistent with such linear predictions. Well, maybe it will use downvotes as anti-prompts? Existing sources must have had votes too, but it was probably only a subset. Maybe the current iteration didn’t rank by vote at all, so the next one will really shine? Guess we’ll see soon.
randomcarbloke 4y ago
So far this has been my experience developing with it, lightning fast but inaccurate results, then just as much time getting it to work as I would've spent writing it myself.
fennecfoxy 4y ago
Lmao, the difference being, of course, one tab open vs 20 different tabs for various aspects of Dockerfile docs, SO's providing more detail that the docs lack, etc. Yeah we can all write this stuff by hand but it's incredibly exciting; when it first came out I was asking it to write snippets of JS for stuff, additions, removals, asking it to write unit tests and then update them when the overall code changed and it maintained several different "threads" of conversation all related to a singular exercise just fine. Sure it's not perfect, but it's kind of cool having a super junior dev who happens to have instant access to most documentation around at the time in its head.

holoduke 4y ago

Incredible useful data to feed back into the system for futher learning. Chatgpt is miles ahead on this part conpared to competitors who yet have to release. Imagine the amount of data chatgpt is receiving now. Google should be very very worried.