Michael Truell: evaluation

Transcript context

…I want to come back to Cursor. A question I like to ask everyone that's building a tool like this, if you could sit next to every new user that uses Cursor for the first time, just whisper a couple tips in their ear to be more successful, most successful with Cursor, what would be 1 or 2 tips? I think right now, and we'd want to fix this at a product level, a lot of being successful with Cursor is kind of having a taste for what the models can do, both what complexity of a task they can handle, and how much you need to specify things to that model, but having a taste for the quality of the model, and where its gaps exist, and what it can do and what it can't. And right now, we don't do a good job in the product of educating people around that, and maybe giving people some swim lanes, giving people some guidelines. But to develop that taste, would give two tips. So one is, as mentioned before, would bias less toward, trying in one go to tell the model, "Hey, here's exactly what I want you to do." Then seeing the output, and then either being disappointed or accepting the entire thing for an entire big task. Instead what I would do is I would chop things up into bits, and you can spend basically the same amount of time specifying things overall, but chopped up more. So you're specifying a little bit, you're getting a little bit of work, you're specifying a little bit, getting a little bit of work, and not doing as much the, "Let's write a giant thing telling the model exactly what to do." I think that will be a little bit of a recipe for disaster right now. And so biasing toward chopping things up. At the same time, and it might make sense to do this on a side project and not on your professional work, I would encourage people to, especially developers who are used to existing workflows for building software, I would encourage people to explicitly try to fall on their face, and try to discover the limits of what these models can do by being ambitious in a safe environment, like perhaps a side project, and trying to kind of go around town, use AI to the fullest. Because a lot of the time, we run into people who haven't given the AI yet a fair shake, and are underestimating its abilities. So generally biasing towards chopping things up and making things smaller, but to discover the limits of what you can do there, explicitly just try to go for broke in a safe environment, and get a taste for... You might be surprised in some of the places where the model doesn't break. What I'm essentially hearing is build a gut feeling of what the model can do, and how far it can take an idea versus just kind of guiding it along. And I bet that you need to rebuild this gut every time there's a new model launch, when it's on... I don't know, 4.0 comes out, you have to do this again. Is that generally right?…

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