this post was submitted on 28 Jun 2023
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Programming

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The economic impact of the AI-powered developer lifecycle and lessons from GitHub Copilot (github.blog)
submitted 1 year ago by starman to c/programming
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[–] [email protected] 7 points 1 year ago (1 children)

Alright, guess I'll reiterate my usual beats here. AI code assistance is interesting, and I'm not against it. However, every current solution is inadequate, until it does the following:

  1. Runs locally, or in an on-prem instance. I'm not taking it up with legal or security if I'm allowed to send our proprietary code off to be analyzed on a foreign server. And I'm not doing it without asking. It just isn't happening.
  2. It has to be free, or paid for by my company. It's cool, and it might help me work, but paying a subscription fee on something that only benefits me at work is essentially the same as a pay cut. Not interested.
  3. It has to analyze the entire repo. In my current tests of ChatGPT, for most cases I've spent long enough giving it context that I could've just... solved the problem myself. It needs to have that context already.
[–] starman 1 points 1 year ago (1 children)
  1. I agree
  2. GitHub Copilot is free for students, and that's the way I use it
  3. GitHub Copilot takes some context from recent files, but iirc they are working on "copilot for your codebase" or something like that
[–] [email protected] 1 points 1 year ago

Ah, it makes way more sense for students, absolutely. None of your code is proprietary, so that's not a concern, student pricing makes things easier.

Plus, your tech stacks are much simpler. Usually just... Java, or Python, or something. Not a python webserver using X framework for templating, Y framework for typing, and Z framework for API calls to some undocumented internal API.