3 ms·

Same reason LLMs aren't so great for regular usage. They do an excellent job of creating highly plausible looking nonsense, which makes it much more likely for

Same reason LLMs aren't so great for regular usage. They do an excellent job of creating highly plausible looking nonsense, which makes it much more likely for things to slip through the cracks. If there was some sort of magical way to ensure code being submitted was flawless, then we'd no longer have bugs in anything, let alone the endless critical flaws that appear in pretty much everything. For a large highly visible open source project, there's also probably intellectual property concerns. LLMs are being trained wholesale on code that's under IP protections (be that copyright or otherwise) and having that end up in your code base could land you in trouble. 'The AI did it' will probably not be seen as a viable defense.

rubymamis 3y ago
Current LLMs. While I agree, they sometimes create more problems than solving (I'm currently debugging a bug caused by GPT4 code that I initially approved), they are getting better by the day and already 1.5x my productivity.
- passwordoops 3y ago
  That's an oddly specific, very round number. You have metrics for it, or is this the same math being used to declare AGI is a year away?
  - rubymamis 3y ago
    No scientific at all, but I keep a long to-do list for my current project with dates, and it *feels* like LLMs have allowed me to cut a third of development time.
    - passwordoops 3y ago
      If you still have the record of previous projects (with similar tasks, of course) it would be really cool to see the before/after. And if you don't, and therefore cannot quantify, then for future reference simply state "it feels like the LLM makes me more efficient"
      - rubymamis 3y ago
        I could have worded it a bit better - I don't have the before and after since I've been using ChatGPT Pro for the past year, yet only started my new project[1] 7 months ago. What I meant is: by keeping track of every little task I'm doing each day, I'm able to perceive how much time the LLM saved me. Like I said, it's unscientific. BTW, I'll, nonetheless, release this list in a future blog post. [1] https://get-plume.com/ https://get-plume.com/
dagw 3y ago
highly plausible looking nonsense Ask the LLM to generate some unit tests for the code it just generated. Then if the test fails, ask the LLM to fix the bug in the code. I find that this catches and fixes a surprising number of LLM bugs in generated code.
- denton-scratch 3y ago
  > Then if the test fails, ask the LLM to fix the bug in the code. If an LLM is intended to generate code, perhaps it's standing orders should include "Generate a comprehensive test suite, run it, fix the bugs, then iterate. Send me an email when you've finished."
  - dagw 3y ago
    I feel pretty convinced that that is the next iteration here, and would be quite disappointed if there aren't at least three companies working it right now. In fact maybe the correct solution is to generate 3 version of each function using 3 different LLMs and only return if at least 2 of them generate the same results, like back in the very early days of computers.
    - denton-scratch 3y ago
      > and would be quite disappointed if there aren't at least three companies working it right now Why does it need three companies working on it? Isn't this just a matter of prompt engineering? I've never played with an LLM, so I really don't know. Perhaps it requires some ordinary linear code to produce a "comprehensive test suite". Ultimately, the problem I can't see 3 or 300 companies solving, is the correct interpretation of technical instructions in English. Native English speakers have trouble with that, and I doubt that a machine can out-perform a native speaker. Maybe I'm wrong, and it can; but it needs to also be able to convince me that my doubt is misplaced. Writing technical specs is like writing laws; you're using vague words to describe something precise. We don't use machines to interpret laws, and I don't trust machines to interpret specs.
- deleted 3y ago
  [deleted]

rubymamis 3y ago

Current LLMs. While I agree, they sometimes create more problems than solving (I'm currently debugging a bug caused by GPT4 code that I initially approved), they are getting better by the day and already 1.5x my productivity.

passwordoops 3y ago
That's an oddly specific, very round number. You have metrics for it, or is this the same math being used to declare AGI is a year away?
- rubymamis 3y ago
  No scientific at all, but I keep a long to-do list for my current project with dates, and it *feels* like LLMs have allowed me to cut a third of development time.
  - passwordoops 3y ago
    If you still have the record of previous projects (with similar tasks, of course) it would be really cool to see the before/after. And if you don't, and therefore cannot quantify, then for future reference simply state "it feels like the LLM makes me more efficient"
    - rubymamis 3y ago
      I could have worded it a bit better - I don't have the before and after since I've been using ChatGPT Pro for the past year, yet only started my new project[1] 7 months ago. What I meant is: by keeping track of every little task I'm doing each day, I'm able to perceive how much time the LLM saved me. Like I said, it's unscientific. BTW, I'll, nonetheless, release this list in a future blog post. [1] https://get-plume.com/ https://get-plume.com/

dagw 3y ago

highly plausible looking nonsense Ask the LLM to generate some unit tests for the code it just generated. Then if the test fails, ask the LLM to fix the bug in the code. I find that this catches and fixes a surprising number of LLM bugs in generated code.

denton-scratch 3y ago
> Then if the test fails, ask the LLM to fix the bug in the code. If an LLM is intended to generate code, perhaps it's standing orders should include "Generate a comprehensive test suite, run it, fix the bugs, then iterate. Send me an email when you've finished."
- dagw 3y ago
  I feel pretty convinced that that is the next iteration here, and would be quite disappointed if there aren't at least three companies working it right now. In fact maybe the correct solution is to generate 3 version of each function using 3 different LLMs and only return if at least 2 of them generate the same results, like back in the very early days of computers.
  - denton-scratch 3y ago
    > and would be quite disappointed if there aren't at least three companies working it right now Why does it need three companies working on it? Isn't this just a matter of prompt engineering? I've never played with an LLM, so I really don't know. Perhaps it requires some ordinary linear code to produce a "comprehensive test suite". Ultimately, the problem I can't see 3 or 300 companies solving, is the correct interpretation of technical instructions in English. Native English speakers have trouble with that, and I doubt that a machine can out-perform a native speaker. Maybe I'm wrong, and it can; but it needs to also be able to convince me that my doubt is misplaced. Writing technical specs is like writing laws; you're using vague words to describe something precise. We don't use machines to interpret laws, and I don't trust machines to interpret specs.
deleted 3y ago
[deleted]