Michael Truel
speaker
75 appearances
1 recordings
1 series
first heard Sep 2024
last heard Sep 2024
Michael Truel’s voice in public audio — every appearance, attributed to the second.
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So the code editor is largely the place where you build software. And today, or for a long time, that's meant the place where you text edit a formal programming language. And for people who aren't programmers, the way to think of a code editor is like a really souped-up word processor for programmers, where the reason it's souped up is code has a lot of structure.
And so the quote-unquote word processor, the code editor, can actually do a lot for you that word processors sort of in the writing space haven't been able to do for people editing text there.
And so that's everything from giving you visual differentiation of the actual tokens in the code so you can scan it quickly, to letting you navigate around the code base, sort of like you're navigating around the internet with hyperlinks. You're going to sort of definitions of things you're using, to error checking to catch rudimentary bugs.
And so traditionally, that's what a code editor has meant. And I think that what a code editor is is going to change a lot over the next 10 years as what it means to build software maybe starts to look a bit different. I think also a code editor should just be fun.
Like fundamentally, I think one of the things that draws a lot of people to building stuff on computers is this like insane integration speed where, you know, in other disciplines, you might be sort of gatecapped by resources or the ability, even the ability, you know, to get a large group together and coding is this like amazing thing where it's you and the computer and that alone, you can build really cool stuff really quickly.
Okay. So what's the origin story of Cursor? So around 2020, the scaling loss papers came out from OpenAI. And that was a moment where this looked like clear, predictable progress for the field, where even if we didn't have any more ideas, it looked like you could make these models a lot better if you had more compute and more data.
So around that time, for some of us, there were a lot of conceptual conversations about what's this going to look like? What's the story going to be for all these different knowledge worker fields about how they're going to be made better by this technology getting better?
And then I think there were a couple of moments where the theoretical gains predicted in that paper started to feel really concrete. And it started to feel like a moment where you could actually go and not do a PhD if you wanted to work on, do useful work in AI. Actually felt like now there was this whole set of systems one could build that were really useful.
And I think that the first moment we already talked about a little bit, which was playing with the early bit of Copilot, that was awesome and magical. I think that the next big moment where everything kind of clicked together was actually getting early access to GPT-4. So it was sort of end of 2022 was when we were tinkering with that model. And the step up in capabilities felt enormous.
And previous to that, we had been working on a couple of different projects. We had been because of Copilot, because of scaling Oz, because of our prior interest in the technology, we had been tinkering around with tools for programmers, but things that are like very specific.
So, you know, we were building tools for financial professionals who have to work within a Jupyter notebook or like, you know, playing around with, can you do static analysis with these models? And then the stuff up in GPT-4 felt like, look, that really made concrete the theoretical gains that we had predicted before. Felt like you could build a lot more just immediately at that point in time.
And also, if we were being consistent, it really felt like this wasn't just going to be a point solution thing. This was going to be all of programming was going to flow through these models. And it felt like that demanded a different type of programming environment, a different type of programming. And so we set off to build that sort of larger vision around that.
Technically incorrect, but one point away. Amon was very enthusiastic about this stuff. Yeah. And before, Amon had this, like, Scaling Laws t-shirt that he would walk around with, where it had the, like- charts and like the formulas on it.
of vs code that are doing sort of ai type stuff what was the decision like to just fork vs code so the decision to do an editor seemed kind of self-evident to us for at least what we wanted to do and achieve because when we started working on the editor the idea was these models are going to get much better their capabilities are going to improve and it's going to entirely change how you build software both in a you will have big productivity gains but also radical and not like the act of building software is going to change a lot
And so you're very limited in the control you have over a code editor if you're a plugin to an existing coding environment. And we didn't want to get locked in by those limitations. We wanted to be able to just build the most useful stuff.
What is tab? To highlight and summarize at a high level, I'd say that there are two things that Cursor is pretty good at right now. There are other things that it does. But two things that it helps programmers with. One is this idea of looking over your shoulder and being like a really fast colleague who can kind of jump ahead of you and type and figure out what you're gonna do next.
And that was the original idea behind, that was kind of the kernel of the idea behind a good autocomplete was predicting what you're gonna do next. But you can make that concept even more ambitious by not just predicting the characters after your cursor, but actually predicting the next entire change you're gonna make, the next diff, next place you're gonna jump to.
And the second thing Kirscher is pretty good at right now, too, is helping you sometimes jump ahead of the AI and tell it what to do and go from instructions to code. And on both of those, we've done a lot of work on making the editing experience for those things ergonomic and also making those things smart and fast.
Yeah, and the magic moment would be if... it is programming is this weird discipline where sometimes the next five minutes, not always, but sometimes the next five minutes, what you're going to do is actually predictable from the stuff you've done recently.
And so can you get to a world where that next five minutes either happens by you disengaging and it taking you through, or maybe a little bit more of just you seeing next step, what it's going to do. And you're like, okay, that's good. That's good. That's good. That's good. And you can just sort of tap, tap, tap through these big changes.
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