3 ms·

This might not pan out to be the glorious victory of human craft as you’re imagining it to be. Here’s a slightly different future - these AI rescue consultants

by fooker 5mo ago

This might not pan out to be the glorious victory of human craft as you’re imagining it to be. Here’s a slightly different future - these AI rescue consultants are bots too, just trained for this purpose. Plausible? I have already experienced claude 4.7 handle pretty complex refactors without issues. Scale and correctness aren’t even 1% of the issue it was last year. You just have to get the high level design right, or explicitly ask it critique your design before building it.

malfist 5mo ago
> You just have to get the high level design right, or explicitly ask it critique your design before building it. Do you think people are not giving their agents specs and asking for input?
- fooker 5mo ago
  Very often, no.
- literalAardvark 5mo ago
  The ones who end up with messes, no
- djeastm 5mo ago
  Maybe the professional devs, but not the vibecoders
- vasco 5mo ago
  A thing I've noticed is that everyone thinks they prompt better than the next guy.
  - iugtmkbdfil834 5mo ago
    This. I have this buddy, who is not an idiot by stretch of the imagination and more adventurous than me in some ways ( I don't really run agents on my machine ), but when I was looking at his prompts, I sometimes question how he gets anything done at all. It is vague and angry demands.
    - jgilias 5mo ago
      Not sure about the angry part, but vague sometimes works really good. The important part is to have enough good context pushed into the context window beforehand (codebase explorations, docs, etc). Then a vague prompt of the general direction gives the autocomplete more “freedom” to figure out the “best” approach given the context. Doesn’t work well ofc in a one shot situation with no context.
      - iugtmkbdfil834 5mo ago
        Yeah, sorry, I myself was being vague, because I don't want to give any identifying info even by mistake. You are right; generalizations here are not as useful. I was talking about something the lines of 'can you make it better', but without llm having the context to understand what better could potentially mean. For brainstorming sessions, I love to start broad. Admittedly, I have limited experience with agents ( though current project intends to bridge that gap ) so it is possible I am missing something ( plus, to your point, I don't know his full setup ).
kilroy123 5mo ago
I think that will happen. I think several things can be true at the same time: - AI Hype - AI Psychosis - AI keeps getting better and better until it can work around big AI slop code bases
- bluefirebrand 5mo ago
  > AI keeps getting better and better until it can work around big AI slop code bases The belief in this is a form of AI psychosis, I think. Maybe in the future but certainly no evidence of this anytime soon
  - fooker 5mo ago
    > Maybe in the future but certainly no evidence of this anytime soon Here's some anecdotal evidence from me - I cleaned up multiple GPT 4.x era vibecoded projects recently with the latest claude model and integrated one of those into a fairly large open source codebase. This is something AI completely failed at last year. Maybe you should try something like this or listen to success stories before claiming 'certainly no evidence' in future?
  - whimsicalism 5mo ago
    No evidence? Chatgpt came out 3 years ago. You basically just need to stick a ruler up on a curve
    - asveikau 5mo ago
      I'm no expert, but the skeptic's opinion I've heard would be to ask: What evidence is there that we're not at or close to a plateau of what LLMs are capable of? How do you know the growth rate from 2023 to present will continue into 2029? eg. Is it more training data? More GPUs? What if we're kind of reaching the limits of those things already?
      - literalAardvark 5mo ago
        Since we're not experts, we treat it as a black box. What are the results? Is the quality of the results improving? Is the improvement accelerating or decelerating? And the answer appears to be that the improvement is accelerating. So how could it be stopping? https://metr.org/time-horizons/ https://metr.org/time-horizons/
mattmanser 5mo ago
One AI can't vibe code out of the mess, so you'd make another AI trained on getting out of vibe coded messes? That's serious levels of circular thinking right there.
- fooker 5mo ago
  This is literally how training humans have worked for thousands of years. We train humans to do things untrained humans can not do.
  - mattmanser 5mo ago
    No it's not, don't be facetious. That's not at all how AI training works.
  - bigstrat2003 5mo ago
    Humans, unlike LLMs, are capable of reasoning and thinking. Thus humans, unlike LLMs, can actually be taught and improve.
dullcrisp 5mo ago
And the bots training the bots are just bots that were trained to train bots?
- fooker 5mo ago
  Nothing that sexy, just thirty odd years of software engineering data from humans. Commits, design reviews, whitepapers, code reviews, test suites. And pretty concerning : chat logs and even keystrokes from employees nowadays. The way we train specialized bots now is incredibly inefficient, that part is rapidly improving.

by fooker 5mo ago

malfist 5mo ago

> You just have to get the high level design right, or explicitly ask it critique your design before building it. Do you think people are not giving their agents specs and asking for input?

fooker 5mo ago
Very often, no.
literalAardvark 5mo ago
The ones who end up with messes, no
djeastm 5mo ago
Maybe the professional devs, but not the vibecoders
vasco 5mo ago
A thing I've noticed is that everyone thinks they prompt better than the next guy.
- iugtmkbdfil834 5mo ago
  This. I have this buddy, who is not an idiot by stretch of the imagination and more adventurous than me in some ways ( I don't really run agents on my machine ), but when I was looking at his prompts, I sometimes question how he gets anything done at all. It is vague and angry demands.
  - jgilias 5mo ago
    Not sure about the angry part, but vague sometimes works really good. The important part is to have enough good context pushed into the context window beforehand (codebase explorations, docs, etc). Then a vague prompt of the general direction gives the autocomplete more “freedom” to figure out the “best” approach given the context. Doesn’t work well ofc in a one shot situation with no context.
    - iugtmkbdfil834 5mo ago
      Yeah, sorry, I myself was being vague, because I don't want to give any identifying info even by mistake. You are right; generalizations here are not as useful. I was talking about something the lines of 'can you make it better', but without llm having the context to understand what better could potentially mean. For brainstorming sessions, I love to start broad. Admittedly, I have limited experience with agents ( though current project intends to bridge that gap ) so it is possible I am missing something ( plus, to your point, I don't know his full setup ).

kilroy123 5mo ago

I think that will happen. I think several things can be true at the same time: - AI Hype - AI Psychosis - AI keeps getting better and better until it can work around big AI slop code bases

bluefirebrand 5mo ago
> AI keeps getting better and better until it can work around big AI slop code bases The belief in this is a form of AI psychosis, I think. Maybe in the future but certainly no evidence of this anytime soon
- fooker 5mo ago
  > Maybe in the future but certainly no evidence of this anytime soon Here's some anecdotal evidence from me - I cleaned up multiple GPT 4.x era vibecoded projects recently with the latest claude model and integrated one of those into a fairly large open source codebase. This is something AI completely failed at last year. Maybe you should try something like this or listen to success stories before claiming 'certainly no evidence' in future?
- whimsicalism 5mo ago
  No evidence? Chatgpt came out 3 years ago. You basically just need to stick a ruler up on a curve
  - asveikau 5mo ago
    I'm no expert, but the skeptic's opinion I've heard would be to ask: What evidence is there that we're not at or close to a plateau of what LLMs are capable of? How do you know the growth rate from 2023 to present will continue into 2029? eg. Is it more training data? More GPUs? What if we're kind of reaching the limits of those things already?
    - literalAardvark 5mo ago
      Since we're not experts, we treat it as a black box. What are the results? Is the quality of the results improving? Is the improvement accelerating or decelerating? And the answer appears to be that the improvement is accelerating. So how could it be stopping? https://metr.org/time-horizons/ https://metr.org/time-horizons/

mattmanser 5mo ago

One AI can't vibe code out of the mess, so you'd make another AI trained on getting out of vibe coded messes? That's serious levels of circular thinking right there.

fooker 5mo ago
This is literally how training humans have worked for thousands of years. We train humans to do things untrained humans can not do.
- mattmanser 5mo ago
  No it's not, don't be facetious. That's not at all how AI training works.
- bigstrat2003 5mo ago
  Humans, unlike LLMs, are capable of reasoning and thinking. Thus humans, unlike LLMs, can actually be taught and improve.

dullcrisp 5mo ago

And the bots training the bots are just bots that were trained to train bots?

fooker 5mo ago
Nothing that sexy, just thirty odd years of software engineering data from humans. Commits, design reviews, whitepapers, code reviews, test suites. And pretty concerning : chat logs and even keystrokes from employees nowadays. The way we train specialized bots now is incredibly inefficient, that part is rapidly improving.