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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

#41

Post by layer8 »

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…

The same is true on the code level, which can be viewed as a more detailed specification.

Part of the fun of software development is exploring the solution space by implementing, and gaining a deeper understanding in the process, as well as coming up with the corresponding design decisions.

It seems that with current AI, in order to steer it and evaluate its output, you would have to build that deeper understanding up front without doing the work, which seems difficult.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#43

Post by tbeseda »

My experience was similar[0] and my conclusions line up with the author here. Summed up: thinking about the problem is the hard part. I can think faster than I can code, but I can code faster than I can write out (in a detailed enough way to achieve my goal with Copilot Workspace) the spec.

[0] https://tbeseda.com/blog/previewing-github-copilot-workspace...

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#44

Post by datadrivenangel »

lordswork wrote: post #30
ossobuco wrote: post #22

Earlier quoted context omitted.

> but if you try that you'll find yourself supplying an enormous amount of context you didn't expect to have to communicate. Context not even directly related to the code at hand, but about the broader business or industry, past lessons learned, something the CEO said to you last week about the feature, etc. Basically you go from programmer to product manager, except you also get to micromanage a non-sentient program…

What prevents an AI agent from becoming the product manager as well, and communicating with you (the customer) to clarify requirements?

A failure mode of product managers is to just pass customer requests to the developers.

I don't see an AI agent doing a good job of avoiding that.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#45

Post by layer8 »

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…

I think there is a natural trend for implementations to drift in complexity toward the edge of what can be understood (and quite often beyond that edge). I would expect the same to happen for AI-authored code with respect to what the AI can understand. Maybe refactoring and reducing tech debt will have to be a more explicit part of development and maintenance in the future?

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#46

Post by whamlastxmas »

siliconc0w wrote: post #35

This debunking video( https://www.youtube.com/watch?v=tNmgmwEtoWE ) of Devin really questioned the usefulness for me. It created a file in the repo and spent a lot of time debugging its own unnecessary code rather than reading the Read Me to understand that the code it needed to use already existed and just needed to be run with different inputs. It's not clear it we're even near a point where it can independently an…

Waymo is definitely not the closest

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#47

Post by duxup »

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 the AI off in that direction. In reality I'm not sure what I want and I'm open to anything.

Often I suddenly realize "Wait, there's gotta be some built in things in this language that does this or part of this..." and often there is that is far more reliable and a better way to do it. Somehow AI skipped that and gave me a different answer.

It strikes me as similar to customers who come to me with "I want an email that's sent on Tuesdays that are single digit calendar dates and this field contains the letter Q in them and ..." But when I ask them what they're trying to accomplish I find all that specificity isn't needed, and they really mean they order all their grapes on Tuesdays at the begging of the month and they just want a list of their grapes orders every few weeks.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#49

Post by steve1977 »

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…

And that is why the I in AI is still misleading.

Re: I Spent 24 Hours with GitHub Copilot Workspaces

#50

Post by chasd00 »

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 something the compiler understands, and then that is turned into something the machine understands.

It sounds like using an LLM to write code requires careful preparation and wording ahead of time that it's basically like writing in a very high level programming language itself.

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