41–50 of 66 posts
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You're telling me you've never once pressed "TAB" to auto-fill the line or even several lines with Copilot? Because about half of the letters in my source code is now Copilot generated. The value added is that you don't send your source code to OpenAI and/or their parent org Microsoft.
Microsoft has owned GitHub since 2018.
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I feel like I live in an alternate universe to this comment. Every engineer I know (devops, software, hell even datascience) is using GPT-4 or Copilot (especially Copilot with the new chat interface) and people, myself included, use it every day. Refactor this code for me, add a function that does X, why is Y failing, align this code with a different coding pattern, I cobbled together this React app, clean it up and…
It's not better then me. But I use it when I'm lazy, tired or at times drinking. And then it's really nice. At times it costs me too much time solving it's mistakes. But then it nails something perfectly and I'm impressed again.
It's useful to recognize when a dialog is a dead-end, otherwise it's easy to enter a rabbit hole that the llm can't get out of. Best to poke at it from another angle and/or within a new dialog, then.
This is neat, but, as with all projects in this vein, I fail to see the real value add. We've had ML-powered code generation for several years now (Copilot released publicly in late 2021), and apart from a cute novelty, there's no real use case I've found. ( Maybe except for generating tests, but when dealing with a novel code base, the models really can't figure out dependency injection, mocks, weird edge cases I'd…
I feel like I live in an alternate universe to this comment. Every engineer I know (devops, software, hell even datascience) is using GPT-4 or Copilot (especially Copilot with the new chat interface) and people, myself included, use it every day. Refactor this code for me, add a function that does X, why is Y failing, align this code with a different coding pattern, I cobbled together this React app, clean it up and…
hi, can you write me a function in rust that, if running on windows:
1. figures out if the topmost window is a windows explorer window
2. if it's a windows explorer window, it returns the path (as a string) that the windows explorer window is actively viewing
3. if it's not a windows explorer window, it returns a blank string
#[cfg(windows)]
fn get_active_explorer_path() -> String {
use std::ptr;
use winapi::um::winuser::{EnumWindows, GetWindowTextW, IsWindowVisible};
use winapi::shared::windef::HWND;
use std::os::raw::c_int;
use std::ffi::OsString;
use std::os::windows::ffi::OsStringExt;
let mut path = String::new();
let mut active_explorer = None;
unsafe extern "system" fn enum_windows_callback(hwnd: HWND, lparam: LPARAM) -> c_int {
let active_explorer = &mut *(lparam as *mut Option);
let mut buffer: [u16; 512] = [0; 512];
if IsWindowVisible(hwnd) != 0 && GetWindowTextW(hwnd, buffer.as_mut_ptr(), 512) != 0 {
let window_title = OsString::from_wide(&buffer[..]).to_string_lossy();
if window_title == "File Explorer" {
*active_explorer = Some(hwnd);
return 0; // Stop enumeration
}
}
1 // Continue enumeration
}
unsafe {
EnumWindows(Some(enum_windows_callback), &mut active_explorer as *mut _ as LPARAM);
}
if let Some(hwnd) = active_explorer {
let mut buffer: [u16; 512] = [0; 512];
if GetWindowTextW(hwnd, buffer.as_mut_ptr(), 512) != 0 {
let window_title = OsString::from_wide(&buffer[..]).to_string_lossy();
if window_title == "File Explorer" {
let shell32 = winapi::um::shellapi::GetShellWindow();
if shell32 != hwnd {
let mut path_buffer: [u16; 512] = [0; 512];
if winapi::um::shellapi::SHGetPathFromIDListW(ptr::null_mut(), path_buffer.as_mut_ptr()) != 0 {
let path_str = OsString::from_wide(&path_buffer[..]).to_string_lossy();
path = path_str.into_owned();
}
}
}
}
}
path
}
#[cfg(target_os = "macos")]
fn get_active_finder_path() -> String {
use std::ptr;
use objc::runtime::{Class, Object};
use objc::{class, msg_send, sel, sel_impl};
use objc_id::Id;
let mut path = String::new();
unsafe {
let workspace: Id = msg_send![class!(NSWorkspace), sharedWorkspace];
let front_window: Id = msg_send![workspace, frontmostApplication];
let front_window_pid: u32 = msg_send![front_window, processIdentifier];
let running_apps: Id = msg_send![workspace, runningApplications];
let app_count: usize = msg_send![running_apps, count];
for i in 0..app_count {
let app: Id = msg_send![running_apps, objectAtIndex: i];
let app_pid: u32 = msg_send![app, processIdentifier];
if app_pid == front_window_pid {
let bundle_url: Id = msg_send![app, bundleURL];
let bundle_path: *const Object = msg_send![bundle_url, path];
let path_str: *const i8 = msg_send![bundle_path, UTF8String];
if !path_str.is_null() {
let c_str = std::ffi::CStr::from_ptr(path_str);
path = c_str.to_string_lossy().to_string();
}
break;
}
}
}
path
}
#[cfg(not(target_os = "macos"))]
fn get_active_finder_path() -> String {
String::new()
}
fn main() {
let active_finder_path = get_active_finder_path();
println!("Active Finder Path: {}", active_finder_path);
}
So what is ML code generation useful for? Apart from helping on the Nth CRUD app someone's building, I just don't see it.
This is neat, but, as with all projects in this vein, I fail to see the real value add. We've had ML-powered code generation for several years now (Copilot released publicly in late 2021), and apart from a cute novelty, there's no real use case I've found. ( Maybe except for generating tests, but when dealing with a novel code base, the models really can't figure out dependency injection, mocks, weird edge cases I'd…
I feel like I live in an alternate universe to this comment. Every engineer I know (devops, software, hell even datascience) is using GPT-4 or Copilot (especially Copilot with the new chat interface) and people, myself included, use it every day. Refactor this code for me, add a function that does X, why is Y failing, align this code with a different coding pattern, I cobbled together this React app, clean it up and…
Copilot/GPT is excellent at writing lots of new lines of code. It's also really good at getting you started in code/frameworks that you don't really understand.
However, Copilot/GPT is not nearly as good at troubleshooting problems in existing code. If your job involves lots of bug fixing or tweaks to existing features, Copilot and GPT are next to useless.
I've noticed that if my work falls into the first category, Copilot often speeds me up something like 30% to 40%. If my work falls into the second category, it's 0%.
Earlier quoted context omitted.
I feel like I live in an alternate universe to this comment. Every engineer I know (devops, software, hell even datascience) is using GPT-4 or Copilot (especially Copilot with the new chat interface) and people, myself included, use it every day. Refactor this code for me, add a function that does X, why is Y failing, align this code with a different coding pattern, I cobbled together this React app, clean it up and…
Forgive the long reply, but I'm going to go ahead and "press X to doubt," because I have been trying to use both ChatGPT and Copilot, and the results are abysmal apart from anything but "write me a linked list" or "give me some React boilerplate." If you have examples of it working for you, please share them. Here's a very salient example of it not working for me: I spent about 1-2 days figuring this out, so it's def…
It's not perfect at "zero-shot" answers but from my experience is very good when you work with it conversationally.
Earlier quoted context omitted.
I feel like I live in an alternate universe to this comment. Every engineer I know (devops, software, hell even datascience) is using GPT-4 or Copilot (especially Copilot with the new chat interface) and people, myself included, use it every day. Refactor this code for me, add a function that does X, why is Y failing, align this code with a different coding pattern, I cobbled together this React app, clean it up and…
Forgive the long reply, but I'm going to go ahead and "press X to doubt," because I have been trying to use both ChatGPT and Copilot, and the results are abysmal apart from anything but "write me a linked list" or "give me some React boilerplate." If you have examples of it working for you, please share them. Here's a very salient example of it not working for me: I spent about 1-2 days figuring this out, so it's def…
The real value proposition of GPT isn't that it can solve really hard problems. The value proposition is that it's about as capable as a junior engineer, except it can write code much faster than any junior engineer, so it can speed you along on the boilerplate-y parts of coding that otherwise would be a lot of manual effort. It's especially useful for things that feel "easy" but which I don't have the relevant domain expertise. For instance, the other day I needed to write a fairly trivial shell script to parse some JSON files. I never write shell scripts and I always forget the syntax, but GPT wrote it correctly on the first try. That probably saved me 20-30 minutes of googling for how to do things like read files, etc in shell scripts.
Earlier quoted context omitted.
> I haven't seen or heard of anyone that seriously uses ChatGPT to generate code and uses it in prod I have. And I'm sure others have too. I dare say there are many who probably _shouldn't_ be using it because of privacy/IP concerns and so you won't hear about them. > cute novelty [...] I thought the same not so long ago. But gpt4 for me was a game changer. It's helped me debug and fix some legitimately complex code,…
> I imagine it's already happening. Not quite, that I know of, but some of us are working on it :) I have a feeling that while the glorious future you describe can probably be realized using LLMs as a foundational technology, the software engineering effort needed to get there is on par with other AI moonshot projects e.g. autonomous vehicles. If you or others reading this are interested in this topic, see this post…
Earlier quoted context omitted.
I feel like I live in an alternate universe to this comment. Every engineer I know (devops, software, hell even datascience) is using GPT-4 or Copilot (especially Copilot with the new chat interface) and people, myself included, use it every day. Refactor this code for me, add a function that does X, why is Y failing, align this code with a different coding pattern, I cobbled together this React app, clean it up and…
Copilot shines with a number of things and isn't very good at a number of other things. Whether you get value out of Copilot says more, I think, about the kind of work you do on a day-to-day basis than it does about the utility of Copilot. Copilot/GPT is excellent at writing lots of new lines of code. It's also really good at getting you started in code/frameworks that you don't really understand. However, Copilot/GP…
Earlier quoted context omitted.
Copilot shines with a number of things and isn't very good at a number of other things. Whether you get value out of Copilot says more, I think, about the kind of work you do on a day-to-day basis than it does about the utility of Copilot. Copilot/GPT is excellent at writing lots of new lines of code. It's also really good at getting you started in code/frameworks that you don't really understand. However, Copilot/GP…
How about this? Taking a dense obtusely written ML algorithm with a ton of mathematical notation, plug it into GPT4, and get torch code? Cuz I do that often.
Earlier quoted context omitted.
I feel like I live in an alternate universe to this comment. Every engineer I know (devops, software, hell even datascience) is using GPT-4 or Copilot (especially Copilot with the new chat interface) and people, myself included, use it every day. Refactor this code for me, add a function that does X, why is Y failing, align this code with a different coding pattern, I cobbled together this React app, clean it up and…
Forgive the long reply, but I'm going to go ahead and "press X to doubt," because I have been trying to use both ChatGPT and Copilot, and the results are abysmal apart from anything but "write me a linked list" or "give me some React boilerplate." If you have examples of it working for you, please share them. Here's a very salient example of it not working for me: I spent about 1-2 days figuring this out, so it's def…
ChatGPT is not really equipped to do this level of code design because there are too many steps for it to handle at once. It can handle your instructions if it could first write a detailed design spec, then write code and unit tests, then run them, read the compiler output, and iteratively make changes.
IOW, this would require a purpose-built solution which uses ChatGPT's underlying engine to take your list of requirements and turn them into built and tested code.