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A friend was recently telling me about an LLM'd PR he was reviewing submitted by a largely non-technical manager where the feature from the outside entirely app

by donatj 1y ago

A friend was recently telling me about an LLM'd PR he was reviewing submitted by a largely non-technical manager where the feature from the outside entirely appeared to work, but actually investigating the thousands of lines of generated code, it was instead hacking their response cache system to appear to work without actually updating anything on the backend. It took a ton of effort on his part to convince his manager that this wasn't ready to be merged. I wonder how much vibe coded software is out there in the wild that just appears to work?

ebiester 1y ago
Where are these non-technical engineering managers and how did they stay in the business? I haven't seen a truly non-technical manager in over 15 years.
OutOfHere 1y ago
I would report the manager to the CTO or CEO or business owners/investors.
- int_19h 1y ago
  You mean, the very people who keep doubling down on investments into AI combined with layoffs? You'd go and tell them that this thing that they signed off on, pitched to others, and thus are ultimately responsible for if it fails in a way that cannot be denied or covered up, is not working.
  - OutOfHere 1y ago
    > is not working Even a calculator doesn't work if one doesn't use it correctly. Agentic coding works very well if used correctly, such as in the following way: 1. Define your task prompt as well as possible. Refine it via the LLM, having the LLM review it, and repeat this process ad infinitum until there are no important issues left to fix. If possible, use multiple LLMs to identify gaps in your task prompt. You now have your refined task specification. This is the most time consuming step. Sometimes it's necessary to add API docs and SDKs to the context. 2. Use a good reasoning LLM by OpenAI or Claude or Gemini or Grok to execute the spec. 3. Review the generated code line by line. Make any necessary changes, either manually or again using the LLM. With any luck there won't be anything to fix. If used in this way, it works so well.
    - int_19h 1y ago
      You forgot the most important part: 0. Pick a task that's not too complicated for the LLM, and use language and frameworks that it knows about. If you're within that zone, it all feels magical. Step outside and things fall apart really quickly.
rAum 1y ago
lol you should absolutely merge it and go with it in such cases, just collect evidence first to have enough deniability and enjoy the show. You can tell a child not to do the thing over and over or just accept it will very quickly learn for their life that touching hot oven is not a smart thing to do. With so much AI hype induced brainrot seems for certain individuals the only antitode is to make them feel direct consequences of their false beliefs. Without feedback loop there is no learning occurring at all. More dangerous thing is such idiot managers can judge you by their lens of shipping LLM garbage they didn't applied in reality to see consequences, living in fantasy due to lack of technical knowledge. Of course it directly leads to firing people and adding more tasks/balloning expectation on leftover team who are force trapped to burn out and be replaced as trash as that makes total sense in their world view and "evidence".
- ModernMech 1y ago
  That's only an option when it's not you who will have to clean up the mess.
  - int_19h 1y ago
    Even if it's not you, it's still ethically questionable. It won't be the manager in question who will have to deal with the fallout, even in the long run - it will be another software engineer. It would ultimately contribute to this: https://www.scottsmitelli.com/articles/altoids-by-the-fistful/ https://www.scottsmitelli.com/articles/altoids-by-the-fistfu...
iamleppert 1y ago
Have you tried Loveable or seen any of their marketing? They are innovating a new category of software that is passable in all the ways a typical user can examine, but none of the ways of traditional software. And why should they? Most people will pay them, churn out whatever code, it will likely never be deployed or used by anyone (this is true of most code created by a real engineer too). By the time the user has figured out what they have "created" isn't real, Loveable is on to the next mark/user.

by donatj 1y ago

ebiester 1y ago

Where are these non-technical engineering managers and how did they stay in the business? I haven't seen a truly non-technical manager in over 15 years.

OutOfHere 1y ago

I would report the manager to the CTO or CEO or business owners/investors.

int_19h 1y ago
You mean, the very people who keep doubling down on investments into AI combined with layoffs? You'd go and tell them that this thing that they signed off on, pitched to others, and thus are ultimately responsible for if it fails in a way that cannot be denied or covered up, is not working.
- OutOfHere 1y ago
  > is not working Even a calculator doesn't work if one doesn't use it correctly. Agentic coding works very well if used correctly, such as in the following way: 1. Define your task prompt as well as possible. Refine it via the LLM, having the LLM review it, and repeat this process ad infinitum until there are no important issues left to fix. If possible, use multiple LLMs to identify gaps in your task prompt. You now have your refined task specification. This is the most time consuming step. Sometimes it's necessary to add API docs and SDKs to the context. 2. Use a good reasoning LLM by OpenAI or Claude or Gemini or Grok to execute the spec. 3. Review the generated code line by line. Make any necessary changes, either manually or again using the LLM. With any luck there won't be anything to fix. If used in this way, it works so well.
  - int_19h 1y ago
    You forgot the most important part: 0. Pick a task that's not too complicated for the LLM, and use language and frameworks that it knows about. If you're within that zone, it all feels magical. Step outside and things fall apart really quickly.

rAum 1y ago

lol you should absolutely merge it and go with it in such cases, just collect evidence first to have enough deniability and enjoy the show. You can tell a child not to do the thing over and over or just accept it will very quickly learn for their life that touching hot oven is not a smart thing to do. With so much AI hype induced brainrot seems for certain individuals the only antitode is to make them feel direct consequences of their false beliefs. Without feedback loop there is no learning occurring at all. More dangerous thing is such idiot managers can judge you by their lens of shipping LLM garbage they didn't applied in reality to see consequences, living in fantasy due to lack of technical knowledge. Of course it directly leads to firing people and adding more tasks/balloning expectation on leftover team who are force trapped to burn out and be replaced as trash as that makes total sense in their world view and "evidence".

ModernMech 1y ago
That's only an option when it's not you who will have to clean up the mess.
- int_19h 1y ago
  Even if it's not you, it's still ethically questionable. It won't be the manager in question who will have to deal with the fallout, even in the long run - it will be another software engineer. It would ultimately contribute to this: https://www.scottsmitelli.com/articles/altoids-by-the-fistful/ https://www.scottsmitelli.com/articles/altoids-by-the-fistfu...

iamleppert 1y ago

Have you tried Loveable or seen any of their marketing? They are innovating a new category of software that is passable in all the ways a typical user can examine, but none of the ways of traditional software. And why should they? Most people will pay them, churn out whatever code, it will likely never be deployed or used by anyone (this is true of most code created by a real engineer too). By the time the user has figured out what they have "created" isn't real, Loveable is on to the next mark/user.