Summary
In episode #447 of the Lex Fridman Podcast, the Cursor team, creators of an innovative AI-assisted code editor called Cursor, discusses the evolution of programming tools. They highlight how AI integration is transforming code editors from basic text editing to advanced predictive features, allowing for error checking and context-aware suggestions. The team explains that Cursor provides a tailored experience by leveraging AI functionalities that enhance user efficiency and workflow. Several machine learning models, including GPT and Claude, are compared for their strengths and weaknesses in coding tasks, emphasizing the need for reliable human oversight in code verification. The conversation also touches on the potential future of programming, which may rely more on natural language processing and creative problem-solving rather than traditional coding skills.
Key Takeaways
- 1Cursor is a modern code editor that uses AI to significantly enhance the efficiency and experience of programming.
- 2AI integration into coding tools is reshaping programming practices, making them more intuitive and reducing manual labor.
- 3The functionality of AI models like GPT and Claude varies, highlighting the importance of human oversight in verifying AI-generated code.
- 4The future of programming may see a shift towards natural language processing, allowing more accessibility and creativity.
- 5Human-AI collaboration is projected to greatly enhance programming capabilities, merging the strengths of both for innovative solutions.
- 6Cursor aims to address common programming errors and improve the debugging process through advanced AI methodologies.
Notable Quotes
"So the code editor is largely the place where you build software. And 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."
"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."
"In AI programming, being even just a few months ahead... makes your product much, much, much more useful."
"I think one thing that I think helps us is that we're sort of doing it all in one where we're developing the UX and the way you interact with the model."
"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 going to do next."
"If you're talking about autocomplete, it should be really, really fast to read in all situations."
"Then the next iteration of it, which is sort of funny, would you, you would hold the Mac option button."
"As the models get much smarter, the changes they will be able to propose are much bigger. So as the changes gets bigger and bigger and bigger, the humans have to do more and more verification work."
"And we train a model to then apply that change to the file. It gives you a really damn good suggestion of what new things to do."
"So speculative edits are a variant of speculative decoding and maybe be helpful to briefly describe speculative decoding."
"Yeah, I think there, there's no model that Pareto dominates, uh, others, meaning it is better in all categories that we think matter. The categories being speed, um, ability to edit code, ability to process lots of code, long context, you know, a couple of other things and kind of coding capabilities."
"And as you declare it, it would decide what you want and then it figures out what you want. Um, and, and so we have found that to be, uh, quite helpful. And I think the role of it has, has sort of shifted over time, um, where initially it was to fit to these small context windows."
"So like, if you say just do what you want, I mean, humans are lazy. There's a kind of tension between just being lazy versus like provide more as, uh, be prompted, almost like the system pressuring you or inspiring you to be articulate."
"It's sort of like how to deal with the uncertainty. Ask for more information to reduce the ambiguity? One of the things we, we do is, it's like a recent addition is try to suggest files that you can add."
"So basically you can then have the language model kind of hold the lock on, on saving to disk."
"If you're trying to do things concurrently, that's such an exciting future, by the way, it's a bit of a tangent, but like to allow a model to change files, it's scary for people, but like, it's really cool to be able to just like let the agent do a set of tasks and you come back the next day and kind of observe, like it's a colleague or something like that."
"I think for the more aggressive things, where you're making larger changes that take longer periods of time, you'll probably want to do this in some sandbox remote environment."
"But in terms of coding, I would be fundamentally thinking about bug finding, like many levels of kind of bug finding and also bug finding, like logical bugs, not logical, like spiritual bugs or something."
"I think people will just not write tests anymore. And the model will suggest, like you write a function, the model will suggest a spec and you review the spec."
"You know, my hope initially is... it should, you know, first help with the stupid bugs."
"But eventually it should be able to catch harder bugs too."
"So you guys mostly use AWS. What are some interesting details?"
"A lot of it, you know, most software just does stuff, this heavy computational stuff. Locally. Have you considered doing sort of embeddings locally?"
"Some of our users use the latest MacBook Pro, but most of our users, like more than 80% of our users are on Windows machines, which are not very powerful. Engines like AI need significant computational resources."
"As these models get better, they're going to become more and more economically useful. And so more and more of the world's information will flow through one or two centralized actors."
"But there's also only a small set of companies that are controlling that data, you know, and they obviously have leverage and they could be infiltrated in all kinds of ways."
"But man, it'd be really horrible if sort of like all the world's information is sort of monitored that heavily. It's way too centralized."
"So having a language model kind of output tokens or probability distributions over tokens... then you can train some less capable model on this."
"You could do the same thing where you verify that it's past the test and then train the model and the outputs that have passed the tests."
"It's actually really, really easy for formal languages... It would be using tests or, uh, formal systems."
"I think this works if you have the ability to get a ton of human feedback for this kind of task that you care about."
"The other thing you could do is that we kind of do is like a little bit of a mix of RLAIF and RLHF where usually the model is actually quite correct."
"I think bigger is certainly better for just raw performance. And raw intelligence."
"And then you can iterate much, much faster than you can. Then you don't have to think as much upfront and stay, stand at the blackboard and like think exactly like, how are we going to do this? Because the cost is so high, but you can just try something first and you realize, oh, this is not actually exactly what I want."
"I think the, the, these types of people will really get into the details of how things, um, work, and like there’s that level of programmer where like this obsession and love of programming, um, I think makes really the best programmers."
"Programming will change a lot to just what is it that you want to make. It's sort of higher bandwidth. The communication to the computer just becomes higher and higher bandwidth as opposed to just typing, which is much lower bandwidth than communicating intent."
"We are an applied research lab building extraordinarily productive human-AI systems. That's an order of magnitude more effective than any one engineer."