4 ms·

I'll give you examples of how it helps me: 1) copilot is a terrific auto complete, and writes tremendous amounts of repetitive boilerplate 2) copilot can help

I'll give you examples of how it helps me: 1) copilot is a terrific auto complete, and writes tremendous amounts of repetitive boilerplate 2) copilot can help me kickstart writing some complex functions starting from a comment where I tell it what is the input and expected output. Is the implementation always perfect or bug free? No. But in general I just need to review and check rather than come up with the instruction entirely. 3) copilot chat helps me a lot in those situations where I would've googled to find how to do this or that and spent a lot of time with irrelevant or outdated search results 4) I have found use cases for LLMs in production. I had lots of unformatted plain text that I wanted to transform in markdown. All I needed to do is to provide few examples and it did everything on its own. No need to implement complex parsers, but make a query to OpenAI with the prompt and context. Few euros per month in OpenAI credits is still insanely cheaper than paying tons of money in writing and maintaining software by humans for that use case. 5) It helps me tremendously when trying to learn new programming languages or remembering some APIs. Writing CSS selectors is actually a very good example. But I don't feed it an entire HTML as you do, I literally tell him "how do I target the odd numbered list elements that are descendants of .foo-bar for this specific media query". Not sure why would you need to feed it an entire HTML. 6) LLMs have been extremely useful to generate images and icons for an entire frontend application I wrote 7) I instruct him to write and think about test cases about my code. And it does and writes the code and tests. Often thinks about test cases I would've never thought of and catches nice bugs. I really don't buy nor think it can write too much on its own. The promise of it writing anything but simple boilerplate, I find it ridiculous because there's way too much nuance in our products, business, devices, systems that you need to follow and work on. But as a helper? It's terrific. I'm 100% sure that people not using these tools are effectively limiting themselves and their productivity. It's like arguing you're better off writing code without a type checker or without intellisense. Sure you can do it, but you're gonna be less effective.

infecto 3y ago
I agree with all of your points and experience the same benefits. 1) Autocomplete is more often than not what I want or pretty darn close. 2) Sometimes I need a discrete function that I am not sure how I want to write. I use a prompt with 3.5/4 inside of my IDE to ask it to write that function. It is definitely not writing complete programs any time soon but I can see where it's heading in the near term. Couple it with something like RAG to answer questions on library/api implementations. Maybe give it a stronger opinion about what good Python code looks like. For the naysayers I don't know how you use it but it is certainly useful enough for me to pay for.
javier123454321 3y ago
I ended up getting annoyed with the autocomplete feature taking over things such as snippet expansion in vscode, so I turned it off personally. I felt that the battling against the assistant made around a break even productivity gain overall. Except for regular expressions, that I have basically offloaded to AI almost in its entirety for non trivial things.
- RSHEPP 3y ago
  Completely agree. I turned it off and realized I can absolutely fly writing code when copilot stops getting in the way. I only turn on for writing tests now.
- zeroonetwothree 3y ago
  Regex is easier to write than read so it seems not the best use of AI given you actually verify what it gives you and don't just take it on faith :/
2devnull 3y ago
But people read much less of what you type now.
ThalesX 3y ago
> 1) copilot is a terrific auto complete, and writes tremendous amounts of repetitive boilerplate I agree. I have it active on VSCode and enjoy it. It has introduced subtle bugs but the souped up autocomplete is nice. > 2) copilot can help me kickstart writing some complex functions starting from a comment where I tell it what is the input and expected output. Is the implementation always perfect or bug free? No. But in general I just need to review and check rather than come up with the instruction entirely. I don't find it very useful for anything non trivial. If anything I found it more useful for generating milestones and tasks for a product, than even making a moderately complex input -> output without me having to check it in a way that annoys me. > 3) copilot chat helps me a lot in those situations where I would've googled to find how to do this or that and spent a lot of time with irrelevant or outdated search results I find I don't use copilot chat, almost at all. Nowadays I prefer to go to Gemini and throw in my question. > 4) I have found use cases for LLMs in production. I had lots of unformatted plain text that I wanted to transform in markdown. All I needed to do is to provide few examples and it did everything on its own. No need to implement complex parsers, but make a query to OpenAI with the prompt and context. Few euros per month in OpenAI credits is still insanely cheaper than paying tons of money in writing and maintaining software by humans for that use case. This is mostly what I'm using it for in this current project. It does it job nicely but it's very far away from replacing myself as a programmer. It's more like a `fn:magic(text) -> nicer text`. This is a good use case. But it's a tool, not a replacement. > 5) It helps me tremendously when trying to learn new programming languages or remembering some APIs. Writing CSS selectors is actually a very good example. But I don't feed it an entire HTML as you do, I literally tell him "how do I target the odd numbered list elements that are descendants of .foo-bar for this specific media query". Not sure why would you need to feed it an entire HTML. Because I get random websites with complex markup, and more often than not every page has its unique structure. I can't just say give me `.foo-bar` because `.foo-bar` might not exist. Which is where the manual process comes in. Currently, I'm using hand crafted queries that get fed into GPT / Claude / LLama, but the actual query is what I wanted it to do. > 6) LLMs have been extremely useful to generate images and icons for an entire frontend application I wrote I'm very curious how this behaves in different resolutions. There's a reason vector graphics are a thing. I've used it for this purpose before but it doesn't compare to vectorial formats. > 7) I instruct him to write and think about test cases about my code. And it does and writes the code and tests. Often thinks about test cases I would've never thought of and catches nice bugs. What is the context size of your code? It works for trivial snippets but as soon as the system is a bit more complex, I find that it becomes irellevant fairly fast. > The promise of it writing anything but simple boilerplate, I find it ridiculous because there's way too much nuance in our products, business, devices, systems that you need to follow and work on. > But as a helper? It's terrific. > I'm 100% sure that people not using these tools are effectively limiting themselves and their productivity. Totally agree. But I'm not complaining about its usefulness. I'm a paying user of LLM systems. I use them almost every day. They're part of my products. But this particular hype about it replacing ... me. I don't buy. Yet. It could come tomorrow and I'd be happier for it.
sanderjd 3y ago
Yep, pretty much exactly this. Especially #5. I'm certain that I've been at least 10x more productive in learning new tools since chatgpt hit the scene. And then knowing it helps so much with that has had even more leverage in opening up possibilities for thing I'm newly confident in learning / figuring out in a reasonable amount of time. It is much easier for me to say "yep, no big deal, I'm on it" when people are looking for someone to take on some ambiguous project using some toolset that nobody at the company is strong with. It solves the "blank page" issue with figuring out how to use unfamiliar-to-me-but-widely-used tools, and that is like a superpower, truly. It's pretty decent for "happy path" test cases, but not that good at thinking of interesting edge or corner cases IME, which comprise the most useful tests at least at the unit level. I'm pretty skeptical of #4. I would be way too fearful that it is doing that plain text to markdown transform wrong in important-but-non-obvious cases. But it depends on which quadrant you need with respect to Type I vs. Type II errors. I just never seem to be in the right quadrant to rely on this in my production projects. The "really good intellisense" use cases #1-#3 also make up a "background radiation" of usefulness for me, but would not be nearly worth all the hype this stuff is getting if that were all it is good for.
newalexandria 3y ago
thanks, was looking for the sane and verbose reply.
the-mitr 3y ago
some of my work involves copyediting/formatting ocred text, for that it works quite well and saves me a lot of time. Especially if it involves completing/guessing badly ocred text.
earwin 3y ago
> 1) copilot is a terrific auto complete, and writes tremendous amounts of repetitive boilerplate I term this "low-entropy code". Copilot is great at writing heaps and heaps of low-entropy code. The thing is, if you're not paid by LOC, and care about your system as a whole, you normally strive to get rid of code if possible (any code is liability), and make the rest of it high-entropy. Today's terrific autocomplete is tomorrow's legacy shit you have to deal with.

infecto 3y ago

I agree with all of your points and experience the same benefits. 1) Autocomplete is more often than not what I want or pretty darn close. 2) Sometimes I need a discrete function that I am not sure how I want to write. I use a prompt with 3.5/4 inside of my IDE to ask it to write that function. It is definitely not writing complete programs any time soon but I can see where it's heading in the near term. Couple it with something like RAG to answer questions on library/api implementations. Maybe give it a stronger opinion about what good Python code looks like. For the naysayers I don't know how you use it but it is certainly useful enough for me to pay for.

javier123454321 3y ago

I ended up getting annoyed with the autocomplete feature taking over things such as snippet expansion in vscode, so I turned it off personally. I felt that the battling against the assistant made around a break even productivity gain overall. Except for regular expressions, that I have basically offloaded to AI almost in its entirety for non trivial things.

RSHEPP 3y ago
Completely agree. I turned it off and realized I can absolutely fly writing code when copilot stops getting in the way. I only turn on for writing tests now.
zeroonetwothree 3y ago
Regex is easier to write than read so it seems not the best use of AI given you actually verify what it gives you and don't just take it on faith :/

2devnull 3y ago

But people read much less of what you type now.

ThalesX 3y ago

> 1) copilot is a terrific auto complete, and writes tremendous amounts of repetitive boilerplate I agree. I have it active on VSCode and enjoy it. It has introduced subtle bugs but the souped up autocomplete is nice. > 2) copilot can help me kickstart writing some complex functions starting from a comment where I tell it what is the input and expected output. Is the implementation always perfect or bug free? No. But in general I just need to review and check rather than come up with the instruction entirely. I don't find it very useful for anything non trivial. If anything I found it more useful for generating milestones and tasks for a product, than even making a moderately complex input -> output without me having to check it in a way that annoys me. > 3) copilot chat helps me a lot in those situations where I would've googled to find how to do this or that and spent a lot of time with irrelevant or outdated search results I find I don't use copilot chat, almost at all. Nowadays I prefer to go to Gemini and throw in my question. > 4) I have found use cases for LLMs in production. I had lots of unformatted plain text that I wanted to transform in markdown. All I needed to do is to provide few examples and it did everything on its own. No need to implement complex parsers, but make a query to OpenAI with the prompt and context. Few euros per month in OpenAI credits is still insanely cheaper than paying tons of money in writing and maintaining software by humans for that use case. This is mostly what I'm using it for in this current project. It does it job nicely but it's very far away from replacing myself as a programmer. It's more like a `fn:magic(text) -> nicer text`. This is a good use case. But it's a tool, not a replacement. > 5) It helps me tremendously when trying to learn new programming languages or remembering some APIs. Writing CSS selectors is actually a very good example. But I don't feed it an entire HTML as you do, I literally tell him "how do I target the odd numbered list elements that are descendants of .foo-bar for this specific media query". Not sure why would you need to feed it an entire HTML. Because I get random websites with complex markup, and more often than not every page has its unique structure. I can't just say give me `.foo-bar` because `.foo-bar` might not exist. Which is where the manual process comes in. Currently, I'm using hand crafted queries that get fed into GPT / Claude / LLama, but the actual query is what I wanted it to do. > 6) LLMs have been extremely useful to generate images and icons for an entire frontend application I wrote I'm very curious how this behaves in different resolutions. There's a reason vector graphics are a thing. I've used it for this purpose before but it doesn't compare to vectorial formats. > 7) I instruct him to write and think about test cases about my code. And it does and writes the code and tests. Often thinks about test cases I would've never thought of and catches nice bugs. What is the context size of your code? It works for trivial snippets but as soon as the system is a bit more complex, I find that it becomes irellevant fairly fast. > The promise of it writing anything but simple boilerplate, I find it ridiculous because there's way too much nuance in our products, business, devices, systems that you need to follow and work on. > But as a helper? It's terrific. > I'm 100% sure that people not using these tools are effectively limiting themselves and their productivity. Totally agree. But I'm not complaining about its usefulness. I'm a paying user of LLM systems. I use them almost every day. They're part of my products. But this particular hype about it replacing ... me. I don't buy. Yet. It could come tomorrow and I'd be happier for it.

sanderjd 3y ago

Yep, pretty much exactly this. Especially #5. I'm certain that I've been at least 10x more productive in learning new tools since chatgpt hit the scene. And then knowing it helps so much with that has had even more leverage in opening up possibilities for thing I'm newly confident in learning / figuring out in a reasonable amount of time. It is much easier for me to say "yep, no big deal, I'm on it" when people are looking for someone to take on some ambiguous project using some toolset that nobody at the company is strong with. It solves the "blank page" issue with figuring out how to use unfamiliar-to-me-but-widely-used tools, and that is like a superpower, truly. It's pretty decent for "happy path" test cases, but not that good at thinking of interesting edge or corner cases IME, which comprise the most useful tests at least at the unit level. I'm pretty skeptical of #4. I would be way too fearful that it is doing that plain text to markdown transform wrong in important-but-non-obvious cases. But it depends on which quadrant you need with respect to Type I vs. Type II errors. I just never seem to be in the right quadrant to rely on this in my production projects. The "really good intellisense" use cases #1-#3 also make up a "background radiation" of usefulness for me, but would not be nearly worth all the hype this stuff is getting if that were all it is good for.

newalexandria 3y ago

thanks, was looking for the sane and verbose reply.

the-mitr 3y ago

some of my work involves copyediting/formatting ocred text, for that it works quite well and saves me a lot of time. Especially if it involves completing/guessing badly ocred text.

earwin 3y ago

> 1) copilot is a terrific auto complete, and writes tremendous amounts of repetitive boilerplate I term this "low-entropy code". Copilot is great at writing heaps and heaps of low-entropy code. The thing is, if you're not paid by LOC, and care about your system as a whole, you normally strive to get rid of code if possible (any code is liability), and make the rest of it high-entropy. Today's terrific autocomplete is tomorrow's legacy shit you have to deal with.