There are some things in your comment that are wrong, or don't quite follow.
> Copilot’s architecture is monolithic: “one model to rule them all.” It is also completely centralized - only Microsoft can train the model
It's true that only Microsoft can train the model—for now. But GPT-3 is a monolithic model, and OpenAI allows fine-tuning. There's nothing about the architecture that prohibits Microsoft from offering customers the ability to fine-tune the model.
> Copilot does not suggest code in the middle of the line, as its AI model is not best suited for this purpose.
Here's a screenshot of GitHub Copilot suggesting code in the middle of a line:
> Copilot uses one universal AI model, which means that every user is getting the same generic assistance based on an “average of GitHub”, regardless of the project they're working on.
If the model is big enough that seems to be an advantage, not a disadvantage. At least, when I use GPT-3 to create a 4chan greentext, or an X-Files script, or a presidential speech, or an article by Hunter S. Thompson (or a presidential speech by Hunter S. Thompson) it doesn't particularly feel like it's averaging things out.
On the highlighted line I typed `panic!("Error while ` and Copilot suggested finishing the line I started with `converting GeoJSON geometry to geometry: {}", e);`
> Copilot’s architecture is monolithic: “one model to rule them all.” It is also completely centralized - only Microsoft can train the model
It's true that only Microsoft can train the model—for now. But GPT-3 is a monolithic model, and OpenAI allows fine-tuning. There's nothing about the architecture that prohibits Microsoft from offering customers the ability to fine-tune the model.
> Copilot does not suggest code in the middle of the line, as its AI model is not best suited for this purpose.
Here's a screenshot of GitHub Copilot suggesting code in the middle of a line:
https://imgur.com/gallery/Jp2qg7X
> Copilot uses one universal AI model, which means that every user is getting the same generic assistance based on an “average of GitHub”, regardless of the project they're working on.
If the model is big enough that seems to be an advantage, not a disadvantage. At least, when I use GPT-3 to create a 4chan greentext, or an X-Files script, or a presidential speech, or an article by Hunter S. Thompson (or a presidential speech by Hunter S. Thompson) it doesn't particularly feel like it's averaging things out.