Michael Truell
speaker
105 appearances
2 recordings
2 series
first heard Oct 2024
last heard 14 Nov
Michael Truell’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Nov 2025 with 1.
Appearances
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.
I think that this benchmark question is both complicated by what Svali just mentioned, and then also to... What Aman was getting into is that even if you like, you know, there's this problem of like the skew between what can you actually model in a benchmark versus real programming. And that can be sometimes hard to encapsulate because it's like real programming is like very messy.
And sometimes things aren't super well specified what's correct or what isn't. But then it's also doubly hard because of this public benchmark problem. And that's both because public benchmarks are sometimes kind of hill-climbed on, but then it's really, really hard to also get the data from the public benchmarks out of the models.
And so, for instance, one of the most popular agent benchmarks, SweetBench, is really, really contaminated in the training data of these foundation models. And so if you ask these foundation models to do a sweet bench problem, but you actually don't give them the context of a code base, they can like hallucinate the right file pass, they can hallucinate the right function names.
And so it's also just the public aspect of these things is tricky.
I think that given the dearths and benchmarks, there have been a few interesting crutches that places that build systems with these models or build these models actually use to get a sense of are they going in the right direction or not. And in a lot of places, people will actually just have humans play with the things and give qualitative feedback on these things.
like one or two of the foundation model companies, they have people who, that's a big part of their role. And, you know, internally we also, you know, qualitatively assess these models and actually lean on that a lot in addition to like private evals that we have. It's like the vibe. The vibe, yeah. It's like the vibe.
Don't you think this gets at a little bit some of the stuff you were talking about earlier with the difficulty of specifying intent for what you want with software? Where sometimes it might be because the intent is really hard to specify, it's also then going to be really hard to prove that it's actually matching whatever your intent is.
Yeah, or just for a given spec, maybe you can... I think there is a question of can you actually do the formal verification? Is that possible? I think that there's more to dig into there.
Even if you have the spec. Is the spec written in natural language? Yeah, how do you map the spec?
But how easy would that be to draw? So then I think that you care about things that are not going to be easily well-specified in the spec language.
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