4 ms·

In my case its coding real world apps that people use and pay money for. I no longer personally type most of my code, Instead I describe stuff or write pseudo c

by mrtksn 2y ago

In my case its coding real world apps that people use and pay money for. I no longer personally type most of my code, Instead I describe stuff or write pseudo code that LLMs end up converting into the real thing. It's very good at handling the BS part of coding but also its very good at knowing things that I don't know. I recently used it to hack a small bluetooth printer which requires its own iOS app to print, using DeepSeek and ChatGPT I was able to reverse engineer the printer communication and then create an app that will print whatever I want from my macOS laptop. Before AI I would have to study how Bluetooth works now I don't have to. Instead, I use my general knowledge of protocols and communications and describe it to the machine and I'm asking for ideas. Then I try things and ask the stuff that I noticed but I don't understand, then I figure out how this particular device works and then describe it to the machine and ask it to generate me code that will do the thing that I discovered. LLMs are amazing at filling the gaps in a patchy knowledge, like my knowledge of Bluetooth. Because I don't know much about Bluetooth, I ended up creating a CRUD for Bluetooth because that's what I needed when trying to communicate and control my bluetooth devices(it's also what I'm used to from Web tech). I'm bit embarrassed about it but I think I will release it commercially anyway. If I have a good LLM under my hand, I don't have to know specialised knowledge on frameworks or tools. General understanding of how things works and building up from there is all I need. It's like a CNC machine but for coding.

righthand 2y ago
I see, for single operator, no customers products it works nicely. You may find you use it less and less and will actually require that Bluetooth knowledge eventually as you grow a product. LLMs so far seem to be good at developing prototype apps. But most of my projects already have codegen and scaffolding tools so I guess I don’t get that use out of them. I predict that once you release your embarrassing app, you will find all the corner cases and domain secrets come rearing out with little ability of the LLM to help you (especially with Bluetooth).
- mrtksn 2y ago
  The Bluetooth app thing is just an example of LLMs helping me build something I don't have beyond-basics knowledge of. For other stuff, I still find it very useful because why would I bother to code something non-novel when I can just tell the LLM what I need? For example, if I need a code that finds the device that given characteristics belongs(bluetooth stuff, again) to I can just tell the LLM to write it for me. It doesn't take a genius to write such a code, its elemental stuff and I would rather not spend my working memory on remembering the structures and names of variables. I copy+paste the current class that handles the bluetooth comms, tell it that I need a function for sending data to the printer and it gives me back the result. There's no art in writing such a code, its standard code for an API and I would prefer not to bother with it.
- eitally 2y ago
  You seem to be overestimating the quality of a many production software products.
  - righthand 2y ago
    Don’t worry I am not. I understand that most prod deployed softwares are prototypes before LLMs came around.
kamranjon 2y ago
“Before AI I would have to study how Bluetooth works now I don't have to.” And “It's very good at handling the BS part of coding…” This is the part that I think is difficult in a team situation. Learning and understanding is the important part, and certainly isn’t BS. I understand that it really can make it seem like velocity has increased when you really are shipping things that more or less “work”, but it’s really a good practice to understand your code. I’ve had to spend a significant amount of time fixing work that was admittedly generated using AI by other engineers, and I really fear engineers are beginning to trade deep understanding for the high of getting something that “works” with little effort. It might “work” but you might be ignoring the work everyone around you is doing to clean up your brittle code that doesn’t scale and wasn’t thought through at inception.
- mrtksn 2y ago
  It is effective because you can spend your mental energy on the things that matter, things that make difference. Code quality actually doesn't matter when you remove the human from the loop as long as it works correctly because it becomes something made by a machine to be interpreted by a machine.
  - kamranjon 2y ago
    Code isn’t a binary scale of works or doesn’t - there is inefficient code and insecure code and everything else in between that still technically “works” - but a lack of understanding will eventually cause these “working” solutions to catch up to you.
    - mrtksn 2y ago
      You can always revisit that part of code if it doesn’t perform. For vast majority of code running on consumer devices there’s no difference between smart implementation and mediocre implementation. LLMs are great at being mediocre by default. As for security, that mostly stems from the architecture. LLMs mediocracy also helps with following industry conventions and best practices. In my case I never get the code being written at once, instead I make LLMs write pieces that I put together myself. Never got used to copilot or Cursor, I feel in control only with the chat interface.

by mrtksn 2y ago

righthand 2y ago

I see, for single operator, no customers products it works nicely. You may find you use it less and less and will actually require that Bluetooth knowledge eventually as you grow a product. LLMs so far seem to be good at developing prototype apps. But most of my projects already have codegen and scaffolding tools so I guess I don’t get that use out of them. I predict that once you release your embarrassing app, you will find all the corner cases and domain secrets come rearing out with little ability of the LLM to help you (especially with Bluetooth).

mrtksn 2y ago
The Bluetooth app thing is just an example of LLMs helping me build something I don't have beyond-basics knowledge of. For other stuff, I still find it very useful because why would I bother to code something non-novel when I can just tell the LLM what I need? For example, if I need a code that finds the device that given characteristics belongs(bluetooth stuff, again) to I can just tell the LLM to write it for me. It doesn't take a genius to write such a code, its elemental stuff and I would rather not spend my working memory on remembering the structures and names of variables. I copy+paste the current class that handles the bluetooth comms, tell it that I need a function for sending data to the printer and it gives me back the result. There's no art in writing such a code, its standard code for an API and I would prefer not to bother with it.
eitally 2y ago
You seem to be overestimating the quality of a many production software products.
- righthand 2y ago
  Don’t worry I am not. I understand that most prod deployed softwares are prototypes before LLMs came around.

kamranjon 2y ago

“Before AI I would have to study how Bluetooth works now I don't have to.” And “It's very good at handling the BS part of coding…” This is the part that I think is difficult in a team situation. Learning and understanding is the important part, and certainly isn’t BS. I understand that it really can make it seem like velocity has increased when you really are shipping things that more or less “work”, but it’s really a good practice to understand your code. I’ve had to spend a significant amount of time fixing work that was admittedly generated using AI by other engineers, and I really fear engineers are beginning to trade deep understanding for the high of getting something that “works” with little effort. It might “work” but you might be ignoring the work everyone around you is doing to clean up your brittle code that doesn’t scale and wasn’t thought through at inception.

mrtksn 2y ago
It is effective because you can spend your mental energy on the things that matter, things that make difference. Code quality actually doesn't matter when you remove the human from the loop as long as it works correctly because it becomes something made by a machine to be interpreted by a machine.
- kamranjon 2y ago
  Code isn’t a binary scale of works or doesn’t - there is inefficient code and insecure code and everything else in between that still technically “works” - but a lack of understanding will eventually cause these “working” solutions to catch up to you.
  - mrtksn 2y ago
    You can always revisit that part of code if it doesn’t perform. For vast majority of code running on consumer devices there’s no difference between smart implementation and mediocre implementation. LLMs are great at being mediocre by default. As for security, that mostly stems from the architecture. LLMs mediocracy also helps with following industry conventions and best practices. In my case I never get the code being written at once, instead I make LLMs write pieces that I put together myself. Never got used to copilot or Cursor, I feel in control only with the chat interface.