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I Spent 24 Hours with GitHub Copilot Workspaces

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Re: I Spent 24 Hours with GitHub Copilot Workspaces

#51

Post by kbenson »

vundercind wrote: post #15
kbenson wrote: post #5

Earlier quoted context omitted.

I don't know, but I think you and I have vastly different base assumptions. Its a huge legal liability to have statements about how data won't be used and then use it, when you're a company that might compete in similar spaces, and Microsoft competes almost everywhere. While I trusted githib when they were independent, I trust this feature from MS owned github more than I would them because the liability misuse opens…

I mean “here’s some telemetry (spying) data on your employees, in a nice little dashboard”

Ah, that makes more sense. I was misinterpreting corporate Spyware and corporate espionage. I imagine providing additional info to employers is something MS would offer as a value add to organizations using this, but that's sort of expected with all organization based tooling in my eyes.

Dont use your personal account for work, and don't assume any service you use for work provided by work isn't giving data on you to you employer, and if at all possible try to work for a company that cares what you deliver and not how you do it (meaning they aren't micromanage, not that they want you to skirt laws.. ). Some of those are obviously easier than others to control.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#52

Post by sottol »

Swizec wrote: post #23
sottol wrote: post #19

The main thing that makes me skeptical is still what happens to a code base when you do this longer-term. And not just the code base but also the company when nobody understands the code any longer, but maybe neither are problems. A couple questions: * Will the codebase turn into a mess over time by having the AI apply changes over changes over changes? Do we even care? Or do we want a human to still be able to follo…

> Are product-based tech companies/startups still defensible if anyone can basically recreate the product with some English? The code is not the asset. It never has been. Deeply understanding your customer, their problem, and how to solve it is the asset. The code is just the current manifestation of that understanding. Problem is that for many companies the code is also the only manifestation of that understanding.

Maybe put another way, if I can get an LLM/AI to build exactly the product that I need, is a company that serves many customers simultaneously but probably worse still necessary?

I think it'll be hard enough to reason about what you really want that most customers won't care enough to roll their own. And personally, I'd happily pay someone to keep the product maintained. A product is usually not one and done.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#53

Post by Marsymars »

kbenson wrote: post #5
vundercind wrote: post #2

… am I wrong for thinking the actual play Workspaces is making is in corporate spyware, and the rest is mostly secondary as far as what may get businesses to pay for it?

I don't know, but I think you and I have vastly different base assumptions. Its a huge legal liability to have statements about how data won't be used and then use it, when you're a company that might compete in similar spaces, and Microsoft competes almost everywhere. While I trusted githib when they were independent, I trust this feature from MS owned github more than I would them because the liability misuse opens…

> Its a huge legal liability to have statements about how data won't be used and then use it, when you're a company that might compete in similar spaces, and Microsoft competes almost everywhere.

Almost everywhere in tech, but almost nowhere outside of tech. I work for a large non-tech conglomerate, and as far as I'm aware, we don't compete with any MS products/services.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#54

Post by willsmith72 »

extr wrote: post #4

I've noticed the same issue with AI coding, where you start to write requirements and then realize that you yourself don't have a perfect idea of what exactly this feature should be, or how it should work. It's easy to say the answer should be to simply think harder, or enter a dialogue with the AI about missing details, but if you try that you'll find yourself supplying an enormous amount of context you didn't expec…

and that's because software engineering is TDD is a great way to show exactly how much you understand what you're about to build. the make all the decisions about edge cases and various conditions ahead of time, before even getting to the code

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#55

Post by extr »

duxup wrote: post #47
extr wrote: post #4

I've noticed the same issue with AI coding, where you start to write requirements and then realize that you yourself don't have a perfect idea of what exactly this feature should be, or how it should work. It's easy to say the answer should be to simply think harder, or enter a dialogue with the AI about missing details, but if you try that you'll find yourself supplying an enormous amount of context you didn't expec…

I don't know if we're talking about exactly the same thing but this is my side story: Even small requests to AI I find myself accidentally including some words or phrases that seem to indicate to AI "Oh he wants this as a function that does all the things very manually". So I get some fairly capable, but very verbose and often inflexible code. Yet, that's not what I was asking for, but something in the context set th…

Yeah this is a similar phenomenon. AI is not so good at recognizing that you're looking for the "general" solution to the problem, one that will holistically fit in with the rest of the codebase/objective, and what has been provided as an example is really just a special case.

I think part of the problem is that instruction fine tuning is not done on full codebases, just shorter problems that fit into reasonable (8K, 32K) context windows. By nature these problems are more specific, so they are biased in that direction from the start.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#56

Post by ike2792 »

I might be a curmudgeon, but I think that even teaching CS in Python is too new-fangled and high-level for CS students. Learning the hard way with C/C++ (or for a more modern flair Go or Rust) and understanding how to handle pointers and memory allocation makes it a lot easier to debug things when the higher level languages and frameworks have issues. A class or two on coding with AI would be great at the undergrad level, but not basing an entire curriculum on it.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#57

Post by extr »

chasd00 wrote: post #50
extr wrote: post #4

I've noticed the same issue with AI coding, where you start to write requirements and then realize that you yourself don't have a perfect idea of what exactly this feature should be, or how it should work. It's easy to say the answer should be to simply think harder, or enter a dialogue with the AI about missing details, but if you try that you'll find yourself supplying an enormous amount of context you didn't expec…

it sounds like pseudocode sort of, like analyzing the requirements and needs to a point all that's left is typing it out in whatever programming language you're using. I can see an LLM being pretty good at that but then that's just a higher level version of a compiler going from a programming language to what the machine understands. You start with very well structured human language, the llm turns that into somethin…

Yeah, this is my experience as well. Once I've fully fleshed out the requirements to the point that there is zero ambiguity in what I want, I've basically written a pseudocode implementation already and the AI is just saving me some typing.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#59

Post by FrustratedMonky »

ike2792 wrote: post #56

I might be a curmudgeon, but I think that even teaching CS in Python is too new-fangled and high-level for CS students. Learning the hard way with C/C++ (or for a more modern flair Go or Rust) and understanding how to handle pointers and memory allocation makes it a lot easier to debug things when the higher level languages and frameworks have issues. A class or two on coding with AI would be great at the undergrad l…

Agree.

And not joking, I think there should be engineering classes taught with slide rule, to get students to learn old school ability to work with orders of magnitude in their head.

Of course students have to learn new things too. But do think we are really losing some of the basic skills, methods of thinking, that you get with the old methods.

Like tracking down some pointer errors, it takes time, it's a difficult struggle, but you do learn a lot about how things work.

Have classes with 'new' tech, then have classes that require 'old' tech. Exams without calculators, or make an Assembly language class mandatory.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#60

Post by doug_durham »

frereit wrote: post #31

I'm honestly surprised at the relatively positive reception to this. While there isn't any problem with the code shown, the same effect couldn've probably been achieved with a few well thought out shortcuts in any IDE (delete outerHTML of svg tag, add new tag, add attributes). The only "more complex" output that is shown is the specification that CW produces, which literally contains an error in the first line ("Sp r…

This is a pretty reductive argument. I'm not quite sure what "a few well thought out IDE shortcuts" are. I've never experienced an IDE that allows any kind of sophisticated "shortcut" that will write arbitrary code.
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