Swale Asif

And then there is MLA.

And then there is MLA.

And then there is MLA.

So to be clear, we want there to be a lot of stuff happening in the background, and we're experimenting with a lot of things. Right now, we don't have much of that happening, other than the cache warming or figuring out the right context that goes into your command key prompts, for example. But the idea is, if you can actually spend computation in the background, then you can help...

So to be clear, we want there to be a lot of stuff happening in the background, and we're experimenting with a lot of things. Right now, we don't have much of that happening, other than the cache warming or figuring out the right context that goes into your command key prompts, for example. But the idea is, if you can actually spend computation in the background, then you can help...

So to be clear, we want there to be a lot of stuff happening in the background, and we're experimenting with a lot of things. Right now, we don't have much of that happening, other than the cache warming or figuring out the right context that goes into your command key prompts, for example. But the idea is, if you can actually spend computation in the background, then you can help...

help the user maybe at a slightly longer time horizon than just predicting the next few lines that you're going to make. But actually, in the next 10 minutes, what are you going to make? And by doing it in the background, you can spend more computation doing that. And so the idea of the shadow workspace that we implemented, and we use it internally for experiments, is that

help the user maybe at a slightly longer time horizon than just predicting the next few lines that you're going to make. But actually, in the next 10 minutes, what are you going to make? And by doing it in the background, you can spend more computation doing that. And so the idea of the shadow workspace that we implemented, and we use it internally for experiments, is that

help the user maybe at a slightly longer time horizon than just predicting the next few lines that you're going to make. But actually, in the next 10 minutes, what are you going to make? And by doing it in the background, you can spend more computation doing that. And so the idea of the shadow workspace that we implemented, and we use it internally for experiments, is that

to actually get advantage of doing stuff in the background, you want some kind of feedback signal to give back to the model. Because otherwise, like you can get higher performance by just letting the model think for longer. And so like O1 is a good example of that. But another way you can improve performance is by letting the model... iterate and get feedback.

to actually get advantage of doing stuff in the background, you want some kind of feedback signal to give back to the model. Because otherwise, like you can get higher performance by just letting the model think for longer. And so like O1 is a good example of that. But another way you can improve performance is by letting the model... iterate and get feedback.

to actually get advantage of doing stuff in the background, you want some kind of feedback signal to give back to the model. Because otherwise, like you can get higher performance by just letting the model think for longer. And so like O1 is a good example of that. But another way you can improve performance is by letting the model... iterate and get feedback.

And so one very important piece of feedback when you're a programmer is the language server, which is this thing that exists for most different languages, and there's like a separate language server per language. And it can tell you, you know, you're using the wrong type here, and then gives you an error, or it can allow you to go to definition and sort of understands the structure of your code.

And so one very important piece of feedback when you're a programmer is the language server, which is this thing that exists for most different languages, and there's like a separate language server per language. And it can tell you, you know, you're using the wrong type here, and then gives you an error, or it can allow you to go to definition and sort of understands the structure of your code.

And so one very important piece of feedback when you're a programmer is the language server, which is this thing that exists for most different languages, and there's like a separate language server per language. And it can tell you, you know, you're using the wrong type here, and then gives you an error, or it can allow you to go to definition and sort of understands the structure of your code.

So language servers are extensions developed by, like there's a TypeScript language server developed by the TypeScript people, a Rust language server developed by the Rust people, and then they all interface over the language server protocol to VS Code.

So language servers are extensions developed by, like there's a TypeScript language server developed by the TypeScript people, a Rust language server developed by the Rust people, and then they all interface over the language server protocol to VS Code.

So language servers are extensions developed by, like there's a TypeScript language server developed by the TypeScript people, a Rust language server developed by the Rust people, and then they all interface over the language server protocol to VS Code.

So that VS Code doesn't need to have all of the different languages built into VS Code, but rather you can use the existing compiler infrastructure.

So that VS Code doesn't need to have all of the different languages built into VS Code, but rather you can use the existing compiler infrastructure.