#447 – Cursor Team: Future of Programming with AI

Summary

Lex Fridman Podcast Episode #447 – Cursor Team: Future of Programming with AI

Release Date: October 6, 2024

In Episode #447 of the Lex Fridman Podcast, host Lex Fridman engages in an in-depth conversation with the founding members of the Cursor team: Michael Truel, Swali, Arvid Lunmark, and Aman Sanger. Cursor, a powerful AI-assisted code editor based on VS Code, has garnered significant attention within the programming and AI communities. This episode delves into the transformative role of AI in programming, exploring the future of human-AI collaboration in designing and engineering complex systems.

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1. Introduction to Cursor and the Team

Lex introduces the Cursor team, highlighting their mission to revolutionize programming through AI integration. Cursor aims to enhance the traditional coding experience by embedding advanced AI features directly into the code editor, fostering a more efficient and enjoyable programming environment.

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2. The Evolution from VS Code to Cursor

Origin Story and Transition

The team shares their journey from being dedicated VIM users to adopting VS Code, primarily driven by the integration of GitHub Copilot in 2021. Copilot’s AI-assisted autocomplete capabilities impressed them enough to switch from their preferred editor to VS Code.

Aman Sanger [12:06]:

"We were originally VIM users. But when Copilot came out, VS Code was the only editor supporting it, and the experience was compelling enough to make us switch."

Decision to Fork VS Code

Recognizing the limitations of existing AI integrations within VS Code, the team decided to fork the editor to build Cursor. This decision was fueled by their vision of creating a more adaptable and robust coding environment tailored to leverage burgeoning AI advancements.

Swali [19:59]:

"We wanted to build the most useful stuff without being locked into the limitations of existing editors."

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3. Innovative Features of Cursor

Cursor Tab: AI-Powered Code Assistance

Cursor Tab stands out as a flagship feature, offering an enhanced autocomplete experience. Unlike traditional autocomplete, Cursor Tab predicts entire code changes, not just the next few characters, allowing for more substantial and accurate code suggestions.

Swali [24:18]:

"Cursor Tab helps programmers by predicting the next entire change, jump diff, and transforming instructions into code with ergonomic and smart editing experiences."

Intuitive Diff Interface

Cursor incorporates a sophisticated diff interface that visually represents code changes, making it easier for programmers to accept or reject AI-suggested modifications.

Aman Sanger [31:35]:

"We optimized the diff interface to be fast and easy to read, ensuring that even large code changes are manageable and less distracting."

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4. Integrating Advanced AI Models

Custom Models and Frontier Models

Cursor utilizes an ensemble of custom-trained models alongside leading-edge (frontier) models like GPT-4 and OpenAI’s OpenAI01. This hybrid approach ensures high-quality code generation and efficient handling of complex programming tasks.

Aman Sanger [39:57]:

"Cursor operates using custom models trained specifically for our tasks alongside frontier models, enhancing performance in code generation and editing."

Performance Optimization Techniques

To achieve low latency and high efficiency, Cursor employs various optimization strategies, including mixture-of-experts (MOE) models and speculative decoding techniques.

Aman Sanger [28:46]:

"Speculative edits and MOE models allow us to handle long contexts and maintain high performance, making Cursor Tab both fast and reliable."

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5. Overcoming Technical Challenges

Handling Latency and Caching

One of the primary challenges discussed is minimizing latency to ensure a seamless user experience. The team explains their use of KV caching and speculative edits to reduce response times and manage computational loads effectively.

Aman Sanger [63:15]:

"By reusing the KV cache and employing speculative edits, we significantly reduce latency, providing a faster and more responsive editing experience."

Scaling Infrastructure

Managing a vast number of requests and maintaining system integrity at scale is another critical topic. The team describes their hierarchical reconciliation system, akin to Merkle trees, to efficiently synchronize client and server states without overwhelming network or database resources.

Swali [97:10]:

"Scaling to handle each extra zero in requests introduces unique challenges, from cache management to efficient retrieval systems, which we've addressed through innovative hashing and reconciliation methods."

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6. The Future of Programming: Human-AI Collaboration

Enhancing Programmer Agency and Speed

Cursor envisions a future where programmers are empowered with AI tools that amplify their speed and creativity without diminishing their control over the codebase. The emphasis is on maintaining human oversight while leveraging AI to handle repetitive and low-entropy tasks.

Swali [148:40]:

"We aim to keep programmers in the driver's seat, enhancing their speed and control, and making programming more fun and productive."

AI-Assisted Bug Finding and Code Verification

The team discusses the potential of AI in identifying and fixing bugs, distinguishing between trivial and critical errors, and integrating verification systems to ensure code reliability.

Aman Sanger [89:10]:

"Our hope is that AI can first catch simple bugs and eventually handle more complex ones, providing a robust safety net for developers."

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7. Synthetic Data and Model Training

Taxonomy of Synthetic Data

Cursor explores different types of synthetic data—distillation, data augmentation through introducing bugs, and generating verifiable outputs—to enhance model training and performance.

Aman Sanger [128:35]:

"Synthetic data can be used for distillation, bug detection, and generating verifiable outputs, each serving unique roles in improving model capabilities."

Enhancing Model Capabilities with Synthetic Data

By leveraging synthetic data, Cursor aims to train models that are better at specific tasks like bug detection and context-aware code generation, beyond what pre-trained models offer.

Aman Sanger [131:35]:

"Using synthetic data allows us to push models towards specialized tasks, enhancing their usefulness in real-world programming scenarios."

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8. Scaling Laws and Future Research

Understanding Scaling Laws

The discussion delves into scaling laws, the relationship between model size, data, and performance. The team reflects on the evolution of scaling laws and their implications for future model development.

Aman Sanger [137:00]:

"Scaling laws have evolved, highlighting the importance of not just model and data size, but also factors like inference compute and context length."

Balancing Compute and Innovation

Cursor emphasizes the need to balance computational resources with innovative research to continue advancing AI capabilities in programming tools.

Aman Sanger [140:36]:

"While compute is crucial, innovative engineering and research are equally important to push the boundaries of what's possible with AI-assisted programming."

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9. Addressing AI Safety and Ethical Considerations

Chain of Thought and Model Transparency

The team touches on the ethical implications of AI models’ internal processes, such as hiding the chain of thought to prevent misuse and protect proprietary technology.

Swali [123:47]:

"Hiding the chain of thought may prevent others from replicating our technology, but it also raises concerns about data centralization and surveillance."

Privacy and Decentralization

Cursor discusses the importance of privacy-preserving techniques like homomorphic encryption to ensure user data remains secure while using AI-powered tools.

Arvid Lunmark [124:25]:

"Implementing homomorphic encryption could allow us to offer powerful AI features without compromising user privacy, though it's still in the research phase."

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10. Infrastructure and Scaling Solutions

Leveraging AWS for Robust Infrastructure

The team explains their reliance on AWS for its reliability and scalability, despite its complex interface.

Arvid Lunmark [97:10]:

"AWS provides the robust infrastructure we need, ensuring our systems work seamlessly even as we scale."

Efficient Data Indexing and Retrieval

Cursor employs advanced data indexing techniques to manage large codebases efficiently, ensuring quick and accurate retrieval of relevant code snippets for AI processing.

Swali [97:10]:

"Our retrieval system uses chunked embeddings and hash-based reconciliation to handle large codebases without excessive network or compute overhead."

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11. Competitive Landscape and Innovation

Staying Ahead in a Rapidly Evolving Market

Despite competitors like GitHub Copilot integrating advanced models, Cursor remains committed to continuous innovation, believing that staying agile allows them to outperform larger, less flexible organizations.

Swali [127:07]:

"The best product will always outshine others, especially in a space where innovation moves swiftly. We focus on building the best system and user experience."

Model Selection and Customization

Cursor differentiates itself by selecting models like Sonnet for their superior performance in understanding and generating code, rather than relying solely on mainstream models.

Aman Sanger [45:38]:

"Sonnet offers a balance of speed and accuracy that aligns well with our needs, outperforming other models in maintaining capabilities across diverse coding tasks."

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12. Concluding Insights on the Future of Programming

Transformation of Programming Practices

The episode concludes with an optimistic outlook on how AI will transform programming, making it faster, more creative, and enjoyable. The team envisions a landscape where AI handles mundane tasks, allowing programmers to focus on innovative solutions and complex problem-solving.

Swali [150:41]:

"Programming will become more fun, with AI handling boilerplate tasks, enabling programmers to concentrate on creative and high-level decision-making."

Embracing Human-AI Synergy

Cursor aims to foster a symbiotic relationship between human ingenuity and AI efficiency, ensuring that programmers remain central to the development process while leveraging AI to enhance their capabilities.

Aman Sanger [152:22]:

"We believe in enhancing the programmer's experience, allowing for rapid iteration and deeper creative engagement through AI assistance."

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Notable Quotes:

• Swali [24:18]:

  "Cursor Tab helps programmers by predicting the next entire change, jump diff, and transforming instructions into code with ergonomic and smart editing experiences."

• Aman Sanger [39:57]:

  "Cursor operates using custom models trained specifically for our tasks alongside frontier models, enhancing performance in code generation and editing."

• Swali [97:10]:

  "Scaling to handle each extra zero in requests introduces unique challenges, from cache management to efficient retrieval systems, which we've addressed through innovative hashing and reconciliation methods."

• Swali [150:41]:

  "Programming will become more fun, with AI handling boilerplate tasks, enabling programmers to concentrate on creative and high-level decision-making."

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This episode provides an extensive exploration of how AI is poised to redefine programming, emphasizing the importance of human-AI collaboration, technical innovation, and ethical considerations. The Cursor team’s insights offer a glimpse into a future where programming is not only more efficient but also more enjoyable and creatively fulfilling.

Transcript

[Lex Friedman]

The following is a conversation with the founding members of the Cursor team, Michael Truel, Swali, Asif, Arvid Lunmark and Aman Sanger. Cursor is a code editor based on VS code that adds a lot of powerful features for AI assisted coding. It has captivated the attention and excitement of the programming and AI communities. So I thought this is an excellent opportunity to dive deep into the role of AI in programming. This is a super technical conversation that is bigger than just about one code editor. It's about the future of programming and in general the future of human AI collaboration in designing and engineering complicated and powerful systems. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We got Encore for unifying your machine learning Stack, Masterclass for learning, Shopify for selling stuff online, NetSuite for your business and AG1 for your health. Choose wisely my friends. Also, if you want to get in touch with me for whatever reason, or take a survey or submit questions for an AMA, all of that would be great. Go to lexfreeman.com contact and now onto the full ad reads. I try to make them interesting but if you skip them please still check out our sponsors. I enjoy their stuff. Maybe you will too. This episode is brought to you by Encore, a platform that provides data focused AI tooling for data annotation, curation management and for model evaluation. One of the things I love about these guys is they have a great blog that describes cleanly. I mean it's technical but it's not too technical but it's sufficiently technical to where it's actually describing ideas, not BS blog posts on the sort of the state of the art like the OpenAI 01 model that was just released. So sometimes they integrate it into why this is a part of Encore, why this makes sense and sometimes not. And so I love that I recommend their blog just in general. That said, you know when they are looking at state of the art models they are always looking for ways to integrate it into their platform. Basically it's a place to organize your data and data is everything. This was true before the popularity and the explosion of attention methods of Transformers and it is still very much true now. Sort of the non synthetic, the human generated data is extremely important. How you generate that data, how you organize that data, how you leverage it, how you train on it, how you fine tune on it, the pre training, the post training, all of it, the whole thing. Data is extremely extremely important. And so Encore takes data Very seriously. Anyway, go try out Encore to create, annotate and manage your AI [e-mail] lex that's encore.com lex this episode is also brought to you by Masterclass, where you can watch over 200 classes from the best people in the world in their respective disciplines. Carlos Santana on guitar, for example, I loved that one. There's a few guitar ones. Tom Morello too. Great, great, great stuff. But Carlos Santana, his instrumental Europa, I haven't quite tried to play that, but it's on my to do list. Is sort of one of those things, you know for sure. This is a thing I will play because it's too beautiful, it's too soulful. It feels like once you play, you understand something about the guitar that you didn't before. It's not blues. It's not. I don't know what it is. It's some kind of dreamlike teleportation into a psychedelic world where the tone is warmer than anything else I've ever heard and still the guitar can cry. I don't know. I love it. He's a genius. So it's such a gift that you can get a genius like that to teach us about his secrets. Get unlimited access to every Masterclass and get an additional 15% off an annual [e-mail] lexpod that's masterclass.com Lex Pod this episode is also brought to you by Shopify, a platform designed for anyone to sell anywhere with a great looking online store or simple looking online store like the one I put [e-mail] store. I have a few shirts on there in case you're interested. And speaking of shirts, I'm reminded of thrift stores which I very much loved for a long time. I still love. Thrift stores were a nice place to get stuff like, I don't know, kitchen stuff and clothing and the kind of clothing you get at thrift stores actually pretty interesting because there's shirts there. They're just unlike anything else you would get anywhere else. So if you're sort of selective and creative minded, there's a lot of interesting fashion that's there. And in terms of T shirts, there's just like hilarious T shirts. T shirts that are very far away from the kind of trajectories you have taken in life or are not, but you just haven't thought about it. Like a band that you love but you never would have thought to wear their T shirt anyway. A little bit I think of Shopify is the Internet's thrift store. Of course you can do super classy, you can do super fancy, or you can do super thrift. All of it is possible. Sign up for a $1 per month trial period at shopify.com lex that's all lowercase. Go to shopify.com lex to take your business to the next level today. This episode is also brought to you by netsuite and all in one cloud business management system. Sometimes I think that NetSuite is supporting this podcast because they're trolling me. They're saying, hey, Lex, aren't you doing a little too much talking? Maybe you should be building more. I agree with you, netsuite. I agree with you. And so every time I do an ad read for NetSuite, it is a chance for me to confront my Jungian shadow. Some of the demons emerge from the subconscious and ask questions that I don't have answers to. Questions about one's mortality and that life is short. And that one of the most fulfilling things in life is to have a family and kids. And all of these things I would very much like to have. And also the reality that I love programming and I love building. I love creating cool things that people can use and share and that would make their life better. All of that. Of course, I also love listening to podcasts, and I kind of think of this podcast as me listening to a podcast where I can also maybe participate by asking questions. So all these things that you love, but you ask the hard question of like, okay, well, life is slipping away. It's short. It really, really is short. What do you want to do with the rest of the minutes and the hours that make up your life?

[Michael Truel]

Yeah.

[Lex Friedman]

So thank you for the existential crisis, natsuite.

[Michael Truel]

I appreciate it.

[Lex Friedman]

If you're running a business, if you have taken the leap into the unknown and started a company, then you should be using the right tools to manage that company. In fact, over 37,000 companies have upgraded to NetSuite. Take advantage of NetSuite's flexible financing plan and netsuite.com lex that's netsuite.com lex. This episode is also brought to you by the Delicious. The delicious AG one. It's an all in one daily drink to support better health and peak performance. Is basically a super awesome multivitamin that makes me feel like I have my life together, even when everything else feels like it's falling apart. At least have AG1. At least I have that nutritional foundation to my life. So all the fasting I'm doing, all the carnivore diets, all the physical endurance events and the mental madness of staying up all night or just the stress of certain things. I'm going through all of that. AG1 is there. @ least they have the vitamins. Also, I sometimes wonder, they used to be called athletic greens and now they're called AG1. I always wonder, is AG2 coming? Like, why is it just one? It's an interesting branding decision. Like AG1. Me as an OCD kind of programmer type. It's like, okay, is this a versioning thing? Okay, Is this like AG 0.1 alpha? What's. When's the final release? What's the. Anyway, the thing I like to say and to consume is AG1. They'll give you one month supply of fish oil when you sign [e-mail] Lex this is the Lex Friedman podcast to support it. Please check out our sponsors in the description and now, dear friends, here's Michael, Swale, Arvid and Amman.

[Michael Truel]

All right, this is awesome. We have Michael, Aman, Swale, Arvid here.

[Lex Friedman]

From the Cursor team. First up, big ridiculous question, what's the point of a code editor?

[Swali]

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 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.

[Michael Truel]

I think also a code editor should just be fun.

[Arvid Lunmark]

Yes, that is very important. That is very important. And it's actually sort of an underrated aspect of how we decide what to build. Like a lot of the things that we build and then we try them out, we do an experiment and Then we actually throw them out because they're not fun. And so a big part of being fun is like being fast a lot of the time. Fast is fun.

[Michael Truel]

Yeah, Fast. Yeah, that should be a T shirt.

[Swali]

But fundamentally, I think one of the things that draws a lot of people to building stuff on computers is this insane iteration speed where in other disciplines you might be sort of gate capped by resources or the ability, even the ability to get a large group together. And coding is this amazing thing where it's you in the computer and that alone you can build really cool stuff really quickly.

[Michael Truel]

So for people who don't know, Cursor is this super cool new editor that's a fork of VS code. It would be interesting to get your kind of explanation of your own journey of editors. How did you, I think all of you were big fans of VS code with Copilot. How did you arrive to VS code and how did that lead to your journey with Cursor?

[Aman Sanger]

Yeah, so I think a lot of us, well, all of us were originally VIM users.

[Unnamed Speaker]

Pure, Pure vim.

[Aman Sanger]

Pure vim, yeah. No Neo Vim, just pure VIM in a terminal. And at least for myself, it was around the time that copilot came out, so 2021, that I really wanted to try it. So I went into VS code, the only platform, the only code editor in which it was available. And even though I really enjoyed using vim, just the experience of Copilot with VS code was more than good enough to convince me to switch. And so that kind of was the default until we started working on Cursor.

[Michael Truel]

And maybe we should explain what Copilot does. It's like a really nice autocomplete. It suggests as you start writing a thing, it suggests one or two or three lines how to complete the thing. And there's a fun experience in that, you know, like when you have a close friendship and your friend completes your sentences, like when it's done well, there's an intimate feeling. There's probably a better word than intimate, but there's a. There's a cool feeling of like, holy shit, it gets me now. And then there's an unpleasant feeling when it doesn't get you. And so there's that kind of friction. But I would say for a lot of people, the feeling that it gets me overpowers that it doesn't.

[Arvid Lunmark]

And I think actually one of the underrated aspects of Get Up Co Pilot is that even when it's wrong is like a little bit annoying. But it's not that bad because you just type another character and then maybe then it gets you, or you type another character and then it gets you. So even when it's wrong, it's not that bad.

[Unnamed Speaker]

Yeah, you can sort of iterate and fix it. I mean, the other underrated part of Copilot for me sort of was just the first real AI product. It's like the first language model consumer product.

[Michael Truel]

So Copilot was kind of like the first killer app for LLMs.

[Swali]

Yeah. And the beta was out in 2021.

[Michael Truel]

Right. Okay, so what's the origin story of Cursor?

[Swali]

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 looks like you could make these models a lot better if you had more compute and more data.

[Michael Truel]

By the way, we'll probably talk for three to four hours on the topic of scaling loss. But just to summarize, it's a paper and a set of papers and a set of ideas that say bigger might be better for model size and data size. In the realm of machine learning, it's.

[Unnamed Speaker]

Bigger and better, but predictably better.

[Michael Truel]

Okay, that's another topic of conversation.

[Swali]

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 do useful work in AI. It 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 clicked together was actually getting early access to GPT4. So sort of end of 2022 was when we were tinkering with that model and the step open 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 we were building, building tools for financial professionals who have to work within a jupyter notebook or playing around with can you do static analysis with these models? And then the step up in GPT4 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. 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 then.

[Unnamed Speaker]

There'S one that I distinctly remember. So my roommate is an IMO gold winner and there's a competition in the US called the Putnam which is sort of the IMO for college people. And it's this math competition is exceptionally good. So Shang Tong and Amman, I remember it sort of June of 2022 had this bet on whether the mod like 2024, June or July, you were going to win a gold medal in the IMO with models.

[Michael Truel]

IMO is international math Olympian.

[Unnamed Speaker]

Yeah, IMO is international Math Olympiad. And so Arvid and I are both also competed in it so was sort of personal. And I remember thinking Matt, this is not going to happen. This was like even though I sort of believed in progress, I thought I'm a girl just like Aman is just delusional. And to be honest, I was, to be clear, very wrong. But that was maybe the most prescient bet in the group.

[Michael Truel]

So the new results from DeepMind, it turned out that you were correct. That's what the.

[Arvid Lunmark]

Well, it was technically.

[Aman Sanger]

Not technically incorrect, but one point away.

[Swali]

Iman was very enthusiastic about this stuff back then and before Iman had this scaling laws T shirt that he would walk around with. It had the charts and the formulas on it.

[Michael Truel]

So you felt the AGI or you felt the scaling laws.

[Aman Sanger]

Yeah, I distinctly remember there was this one conversation I had with Michael where before it hadn't thought super deeply and critically about scaling laws. And he kind of posed the question why isn't scaling all you need? Or why isn't scaling going to result in massive gains in progress? And I think I went through the stages of grief. There is anger, denial and then finally at the end just thinking about it, acceptance. And I think I've been quite hopeful and optimistic about progress since. I think one thing I'll caveat is I think it also depends on which domains you're going to see progress. Math is A great domain, especially formal theorem proving, because you get this fantastic signal of actually verifying if the thing was correct. And so this means something like RL can work really, really well. And I think you could have systems that are perhaps very superhuman at math and still not technically have AGI.

[Michael Truel]

Okay, so can we take it all the way to cursor? And what is cursor? It's a fork of VS code. And VS code is one of the most popular editors for a long time. Like everybody fell in love with it. Everybody left vim, I left Emacs for it. Sorry. So unified in some fundamental way, the developer community. And then you look at the space of things, you look at the scaling laws. AI is becoming amazing. And you decided, okay, it's not enough to just write an extension for VS code because there's a lot of limitations to that. If AI is going to keep getting better and better and better, we need to really rethink how the AI is going to be part of the editing process. And so you decided to fork VS code and start to build a lot of the amazing features we'll be able to talk about. But what was that decision like? Because there's a lot of extensions, including copilot of VS code that are doing sort of AI type stuff. What was the decision like to just fork VS code?

[Swali]

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 now 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 plug into 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.

[Michael Truel]

Okay, well then the natural question is VS code is kind of with Copilot, a competitor. So how do you win? Is it basically just the speed and the quality of the features?

[Aman Sanger]

Yeah, I mean, I think this is a space that is quite interesting, perhaps quite unique, where if you look at previous tech waves, maybe there's kind of one major thing that happened and unlocked a new wave of companies. But every single year, every single model capability or jump you get in model capabilities, you now unlock this new wave of features, things that are possible especially in programming. And so I think in AI programming, being even just a few months ahead, let alone a year ahead, makes your product much, much, much more useful. I think the cursor a year from now will need to make the cursor of today look obsolete. And I think Microsoft has done a number of fantastic things, but I don't think they're in a great place to really keep innovating and pushing on this in the way that a startup can.

[Michael Truel]

Just rapidly implementing features.

[Aman Sanger]

And kind of doing the research, experimentation necessary to really push the ceiling.

[Unnamed Speaker]

I don't know if I think of it in terms of features as I think of it in terms of capabilities for programmers. It's that as the new one model came out, and I'm sure there are going to be more models of different types, like longer context and maybe faster, there's all these crazy ideas that you can try and hopefully 10% of the crazy ideas will make it into something kind of cool and useful. And we want people to have that sooner. To rephrase, it's like an underrated fact is we're making it for ourselves. When we started Cursor, you really felt this frustration that models, you could see models getting better, but the COBOL experience had not changed. It was. It's like, man, these guys, like the ceiling is getting higher. Like, why are they not making new things? Like, they should be making new things. They should be like, where's all the alpha features? There were no alpha features. It was like, I'm sure it was selling well, I'm sure it was a great business, but it didn't feel. I'm one of these people that really want to try and use new things and was just there's no new thing for like a very long while.

[Michael Truel]

Yeah, it's interesting. I don't know how you put that into words, but when you compare a cursor with Copilot, Copilot pretty quickly became. Started to feel stale for some reason.

[Arvid Lunmark]

Yeah, 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 and at the same time as we're developing how we actually make the model give better answers. So how you build up the prompt or how do you find the context and for a cursor tab, how do you train the model? So I think that helps us to have all of it sort of like the same people working on the entire experience end to end.

[Unnamed Speaker]

Yeah, it's like the person making the UI and the person training the model sit to 18ft away.

[Aman Sanger]

Often they're the same person even.

[Unnamed Speaker]

Yeah, often even the same person. So you can create things that are sort of not possible if you're not talking, you're not experimenting and you're using.

[Michael Truel]

Like you said, cursor to write. Cursor, of course.

[Aman Sanger]

Oh, yeah.

[Michael Truel]

Well, let's talk about some of these features. Let's talk about the all knowing, the all powerful. Praise be to the Tab. You know, autocomplete on steroids, basically. So how does Tab work?

[Swali]

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 it helps programmers with. One is this idea of looking over your shoulder and being a really fast colleague who can kind of jump ahead of you and type and figure out what you're going to do next. And that was the original idea behind. That was kind of the kernel of the idea behind good autocomplete was predicting what you're going to 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 going to make, the next diff, the next place you're going to jump to. And the second thing cursor 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.

[Unnamed Speaker]

One of the things we really wanted was we wanted the model to be able to edit code for us. That was kind of a wish. And we had multiple attempts at it before we had a sort of a good model that could edit code for you. Then after we had a good model, I think there have been a lot of effort to make the inference fast for having a good experience. And we've been starting to incorporate, I mean, Michael sort of mentioned this ability to jump to different places. And that jump to different places I think came from a feeling of once you accept an edit, it's like, man, it should be just really obvious where to go next. It's like I'd made this change. The model should just know that the next place to go to is like 18 lines down. Like if you're a WIM user, you could press 18 JJ or whatever. But why am I doing this? The model should just know it. And then. So the idea was you just press tab, it would go 18 lines down and then show you the next edit and you would press tab. So it's just you as long as you could keep pressing tab. And so the internal competition was how many tabs can we make someone press? Once you have the idea more sort of abstractly, the thing to think about is sort of like, how are the edits sort of zero entropy? So once you've sort of expressed your intent and the edit is there's no new bits of information to finish your thought, but you still have to type some characters to make the computer understand what you're actually thinking, then maybe the model should just read your mind and. And all the zero entropy bits should just be tabbed away. Yeah, that was sort of the abstract version.

[Aman Sanger]

There's this interesting thing where if you look at language model loss on different domains, I believe the bits per byte, which is kind of character normalized loss for code is lower than language, which means in general there are a lot of tokens in code that are super predictable, a lot of characters that are super predictable. And this is I think even magnified when you're not just trying to autocomplete code, but predicting what the user is going to do next in their editing of existing code. And so the goal cursor tab is let's eliminate all the low entropy actions you take inside of the editor when the intent is effectively determined. Let's just jump you forward in time, skip you forward.

[Michael Truel]

Well, what's the intuition and what's the technical details of how to do nuxt cursor prediction that jump? That's not so intuitive, I think to people.

[Aman Sanger]

Yeah, I think I can speak to a few of the details on how to make these things work. They're incredibly low latency. So you need to train small models on this task in particular. They're incredibly pre fill token hungry. What that means is they have these really, really long prompts where they see a lot of your code and they're not actually generating that many tokens. And so the perfect fit for that is using a sparse model, meaning an MOE model. So that was kind of one breakthrough we made that substantially improved its performance at longer context. The other being a variant of speculative decoding that we kind of built out called speculative edits. These are two, I think, important pieces of what make it quite high quality and very fast.

[Michael Truel]

Okay, so moe mixture of experts. The input is huge, the output is small. Yeah, okay, so what else can you say about how to make. Does caching play a role in.

[Aman Sanger]

Caching plays a huge role because you're dealing with this many input tokens. If every single keystroke that you're typing in a given line, you had to rerun the model on all of those tokens passed in, you're just going to one, significantly degrade latency. Two, you're going to kill your GPUs with loads. So you need to design the actual prompts you use for the model such that they're caching aware. And then, yeah, you need to reuse the KV cache across requests just so that you're spending less work less compute.

[Michael Truel]

Again, what are the things that tab is supposed to be able to do kind of in the near term just to sort of linger on that generate code like fill empty space. Also edit code across multiple lines and then jump to different locations inside the same file and then hopefully jump to different files also.

[Unnamed Speaker]

So if you make an edit in one file and maybe you have to go to another file to finish your thought, it should go to the second file.

[Arvid Lunmark]

Also the full generalization is like next action prediction. Sometimes you need to run a command in the terminal and it should be able to suggest the command based on the code that you wrote too. Or sometimes you actually need to it suggests something but it's hard for you to know if it's correct because you actually need some more information to learn. You need to know the type to be able to verify that it's correct. And so maybe it should actually take you to a place that's the definition of something and then take you back so that you have all the requisite knowledge to be able to accept the next completion.

[Michael Truel]

So providing the human the knowledge.

[Arvid Lunmark]

Yes.

[Michael Truel]

Right.

[Unnamed Speaker]

Yeah.

[Michael Truel]

Can you integrate like I just gotten to know a guy named primegen who I believe has an SS you can order coffee via ssh.

[Aman Sanger]

Oh yeah, we did that.

[Unnamed Speaker]

We did that.

[Michael Truel]

So can also the model do that feed you and. Yeah, and provide you with caffeine. Okay, so that's the general framework.

[Aman Sanger]

Yeah.

[Swali]

And the magic moment would be if programming is this weird discipline where sometimes the next five minutes, not always, but sometimes the next five minutes of 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.

[Michael Truel]

As we're talking about this, I should mention that one of the really cool and noticeable things about Cursor is that there's this whole diff interface situation going on. So like the model suggests with the red and the green of like, here's how we're going to modify the code. And in the chat window you can apply and it shows you the diff and you can accept the diff. So maybe can you speak to whatever direction of that?

[Unnamed Speaker]

We probably have like four or five different kinds of diffs. So we have optimized the diff for the autocomplete. So that has a different diff interface than when you're reviewing larger blocks of code. And then we're trying to optimize another diff thing for when you're doing multiple different files and sort of at a high level. The difference is for when you're doing autocomplete, it should be really, really fast to read. Actually it should be really fast to read in all situations. But in autocomplete it's sort of, you're really like your eyes focused on one area. You can't be in too many. The humans can't look in too many different places.

[Michael Truel]

So you're talking about on the interface side.

[Unnamed Speaker]

On the interface side. So it currently has this box on the side. So we have the current box. And if it tries to delete code in some place and tries to add other code, it tries to show you a box on the side, you can.

[Aman Sanger]

Maybe show it if we pull it up on cursor.com? this is what we're talking about.

[Unnamed Speaker]

So that box, it was like three or four different attempts at trying to make this thing work. Where first the attempt was like these blue crossed out lines. So before it was a box on the side, it used to show you the code to delete by showing you like Google Doc style. You would see like a line through it, then you would see the new code that was super distracting. And then we tried many different. There was sort of deletions, there was trying to red highlight. Then the next iteration of it, which is sort of funny, you would hold the, on Mac, the option button. So it would sort of highlight a region of code to show you that there might be something coming. So maybe in this example the input and the value would all get blue and the blue would highlight that the AI had a suggestion for you. So instead of directly showing you the thing, it would show you that it would just hint that the AI had a suggestion. And if you really wanted to see it, you would hold the option button and then you would see the new suggestion. Then if you release the option button, you would then see your original code.

[Michael Truel]

So that's. By the way, that's pretty nice, but you have to know to hold the option button.

[Swali]

Yeah.

[Michael Truel]

By the way, I'm not a Mac user, but I got it. It's a button. I guess you people have.

[Unnamed Speaker]

It's, you know, it's again, it's just, it's just non intuitive. I think that's the, that's the key thing.

[Aman Sanger]

And there's a chance this is also not the final version of it.

[Arvid Lunmark]

I am personally very excited for making a lot of improvements in this area. We often talk about it as the verification problem, where these diff's are great for small edits, for large edits, or when it's multiple files or something. It's actually a little bit prohibitive to review these diffs. And so there are a couple of different ideas here. One idea that we have is, okay, parts of the diffs are important, they have a lot of information and then parts of the diff are just very low entropy. They're the same thing over and over again. And so maybe you can highlight the important pieces and then gray out the not so important pieces. Or maybe you can have a model that looks at the diff and sees, oh, there's a likely bug here. I will mark this with a little red squiggly and say you should probably review this part of the diff and ideas in that vein I think are exciting.

[Michael Truel]

Yeah, that's a really fascinating space of UX design engineering. So you're basically trying to guide the human programmer through all the things they need to read and nothing more. Optimally, yeah.

[Arvid Lunmark]

And you want an intelligent model to do it. Currently, diff algorithms are just like normal algorithms. There is no intelligence. There's intelligence that went into designing the algorithm, but then you don't care if it's about this thing or this thing. And so you want a model to do this.

[Unnamed Speaker]

So I think the general question is, Matt, these models are going to get much smarter. 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 and more verification work. It gets more and more and more hard. You need to help them out. I don't want to spend all my time reviewing code.

[Michael Truel]

Can you say a little more across multiple files?

[Arvid Lunmark]

Yeah.

[Aman Sanger]

I mean, so GitHub tries to solve this, right, with code review. When you're doing code review, you're reviewing multiple diffs across multiple files. But like Arvid said earlier, I think you can do much better than code review. Code review kind of sucks. You spend a lot of time trying to grok this code that's often quite unfamiliar to you, and it often doesn't even actually catch that many bugs. And I think you can significantly improve that review experience using language models, for example, using the kinds of tricks that Arp had described of maybe pointing you towards the regions that actually matter. I think also if the code is produced by these language models and it's not produced by someone else, the code review experience is designed for both the reviewer and the person that produced the code. In the case where the person that produced the code is a language model, you don't have to care that much about their experience and you can design the entire thing around the reviewer such that the reviewer's job is as fun, as easy, as productive as possible. And I think that feels like the issue with just kind of naively trying to make these things look like code review. I think you can be a lot more creative and push the boundary on what's possible.

[Arvid Lunmark]

Just one idea there is. I think ordering matters. Generally, when you review a pr, you have this list of files and you're reviewing them from top to bottom. But actually you actually want to understand this part first, because that came logically first. And then you want to understand the next part. And you don't want to have to figure out that yourself. You want a model to guide you through the thing.

[Michael Truel]

And is the step of creation going to be more and more natural language is the goal versus with actual.

[Arvid Lunmark]

I think sometimes I don't think it's going to be the case that all of programming will be natural language. And the reason for that is if I'm pair programming with Swale, and Swale is at the computer and the keyboard, and sometimes if I'm driving, I want to say to Swale, hey, implement this function. And that works. And then sometimes it's just so annoying to explain to Svale what I want him to do. And so I actually take over the keyboard and I show him, I write part of the example and then it makes sense and that's the easiest way to communicate. And so I think that's also the case for AI, Sometimes the easiest way to communicate with the AI will be to show an example and then it goes and does the thing everywhere else. Or sometimes if you're making a website, for example, the easiest way to show to the AI what you want is not to tell it what to do, but drag things around or draw things and yeah, and maybe eventually we will get to brain machine interfaces or whatever and kind of understand what you're thinking. And so I think natural language will have a place. I think it will definitely not be the way most people program most of the time.

[Michael Truel]

I'm really feeling the AGI with this editor. It feels like there's a lot of machine learning going on underneath.

[Lex Friedman]

Tell me about some of the ML stuff that makes it all work where.

[Aman Sanger]

Cursor really works via this ensemble of custom models that we've trained alongside the frontier models that are fantastic at the reasoning intense things. And so Cursor tab, for example, is a great example of where you can specialize this model to be even better than even frontier models. If you look at evals on the task, we set it at the other domain, which it's kind of surprising that it requires custom models, but it's kind of necessary and works quite well is in applying. So I think these models are like the frontier models are quite good at sketching out plans for code and generating rough sketches of the change. But actually creating diffs is quite hard for frontier models. For your training models, you try to do this with Sonnet with 01, any Frontier model and it really messes up stupid things like counting line numbers, especially in super, super large files. And so what we've done to alleviate this is we let the model kind of sketch out this rough code block that indicates what the change will be and we train a model to then apply that change to the file.

[Michael Truel]

And we should say that apply is the model looks at your code, it gives you a really damn good suggestion of what new things to do. And the seemingly for humans trivial step of combining the two, you're saying is not so trivial.

[Unnamed Speaker]

Contrary to popular perception, it is not a deterministic algorithm.

[Aman Sanger]

Yeah, I think you see shallow copies of apply elsewhere and it just breaks most of the time because you think you can kind of try to do some deterministic matching and then it fails at least 40% of the time and that just results in a terrible product experience. I think in general this regime of you are going to get smarter and smarter models. So one other thing that Appli lets you do is it lets you use fewer tokens with the most intelligent Models. This is both expensive in terms of latency for generating all these tokens and cost. So you can give this very, very rough sketch and then have your small models go and implement it, because it's a much easier task to implement this very, very sketched out code. And I think that this regime will continue where you can use smarter and smarter models to do the planning. And then maybe the implementation details can be handled by the less intelligent ones. Perhaps you'll have maybe 01, maybe it'll be even more capable models given an even higher level plan that is kind of recursively applied by Sonnet and then the model.

[Unnamed Speaker]

Maybe we should talk about how to make it fast. Fast is always an interesting detail.

[Michael Truel]

Fast is good. Yeah. How do you make it fast?

[Aman Sanger]

Yeah. So one big component of making it fast is speculative edits. So speculative edits are a variant of speculative decoding. And maybe it'd be helpful to briefly describe speculative decoding. With speculative decoding, what you do is you can kind of take advantage of the fact that most of the time, and I'll add the caveat that it would be when you're memory bound in language model generation, if you process multiple tokens at once, it is faster than generating one token at a time. So this is the same reason why if you look at tokens per second, prompt tokens versus generated tokens, it's much, much faster for prompt tokens. So what we do is instead of using what speculative decoding normally does, which is using a really small model to predict these draft tokens that your larger model will then go in and verify with code edits, we have a very strong prior of what the existing code will look like. And that prior is literally the same exact code. So what you can do is you can just feed chunks of the original code back into the model and then the model will just pretty much agree most of the time that, okay, I'm just going to spit this code back out. And so you can process all of those lines in parallel. And you just do this with sufficiently many chunks and then eventually you'll reach a point of disagreement where the model will now predict text that is different from the ground truth original code. It'll generate those tokens and then we kind of will decide after enough tokens match the original code to restart speculating in chunks of code, what this actually ends up looking like is just a much faster version of normal editing code. So it's just like it looks like a much faster version of the model rewriting all the code so we can Use the same exact interface that we use for diffs, but it will just stream down a lot faster.

[Unnamed Speaker]

And then the advantages that wireless streaming, you can just also be reviewing, start reviewing the code exactly before it's done. So there's no big loading screen, so maybe that is part of the advantage.

[Michael Truel]

So the human can start reading before the thing is done.

[Unnamed Speaker]

I think the interesting riff here is something like speculation is a fairly common idea nowadays. It's like not only in language models, there's obviously speculation in CPUs and there's speculation for databases and speculation all over the place.

[Michael Truel]

Let me ask the ridiculous question of which LLM is better at coding GPT Claude, who wins in the context of programming? And I'm sure the answer is much more nuanced because it sounds like every single part of this involves a different model.

[Aman Sanger]

Yeah, I think there's no model that Pareto dominates others, meaning it is better in all categories that we think matter. Categories being speed, ability to edit code, ability to process lots of code, long context, a couple of other things and kind of coding capabilities. The one that I'd say right now is just kind of net best is Sonnet. I think this is a consensus opinion. R1's really interesting and it's really good at reasoning. So if you give it really hard programming interview style problems or leetcode problems, it can do quite well on them. But it doesn't feel like it kind of understands your rough intent as well as Sonnet does. If you look at a lot of the other frontier models, one qualm I have is it feels like they're not necessarily overfit. I'm not saying they train on benchmarks, but they perform really well in benchmarks relative to everything that's kind of in the middle. So if you tried on all these benchmarks and things that are in the distribution of the benchmarks they're evaluated on, they'll do really well. But when you push them a little bit outside of that sonnet's, I think the one that kind of does best at maintaining that same capability. You kind of have the same capability in the benchmark as when you try to instruct it to do anything with coding.

[Michael Truel]

Another ridiculous question is the difference between the normal programming experience versus what benchmarks represent. Where do benchmarks fall short, do you think? When we're evaluating these models, by the.

[Unnamed Speaker]

Way, that's really hard. It's like critically important detail how different benchmarks are versus real coding where real coding, it's not interview style coding. You're doing these Humans are saying half broken English sometimes, sometimes you're saying like, oh, do what I did before. Sometimes you're saying, go add this thing and then do this other thing for me and then make this UI element. And then it's just like a lot of things are sort of context dependent. You really want to understand the human and then do what the human wants as opposed to sort of this, maybe the way to put it is sort of abstractly is the interview problems are very well specified, they lean a lot on specification while the human stuff is less specified.

[Swali]

Yeah, I think that this benchmark question is both complicated by what Swali just mentioned and then also what AMAN was getting into is that even if there's this problem of the skew between what can you actually model in a benchmark versus real programming? And that can be sometimes hard to encapsulate because real programming is 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 like agent benchmarks, Sweet bench, is really, really contaminated in the training data of these foundation models. And so if you ask these foundation models to do a sweep bench problem, but you actually don't give them the context of a code base, they can hallucinate the right file paths, they can hallucinate the right function names. And so it's also just the public aspect of these things is tricky.

[Aman Sanger]

Yeah, in that case it could be trained on the literal issues or pull requests themselves and maybe the labs will start to do a better job. Or they've already done a good job at decontaminating those things, but they're not going to omit the actual training data of the repository itself. These are all some of the most popular Python repositories, like Sympy is one example. I don't think they're going to handicap their models on Sympy and all these popular Python repositories in order to get true evaluation scores in these benchmarks.

[Swali]

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. Like one or two of the foundation model companies, they have people who. That's a big part of their role. And 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.

[Unnamed Speaker]

It's like the vibe.

[Michael Truel]

The vibe? Yeah, the vibe. The vibe. Benchmark. Human benchmark. The humans. You pull in the humans to do a vibe check.

[Arvid Lunmark]

Yeah.

[Swali]

Okay.

[Michael Truel]

I mean, that's kind of what I do. Like, just like reading online forums and Reddit and X just like. Well, I don't know how to properly load in people's opinions because they'll say things like I feel like Claude or GPT has gotten dumber or something. They'll say, I feel like. And then I sometimes feel like that too. But I wonder if it's the model's problem or mine.

[Aman Sanger]

Yeah, with Claude, there's an interesting take I heard where I think AWS has different chips and I suspect they have slightly different numerics than Nvidia GPUs. And someone speculated that Claude's degraded performance had to do with maybe using the quantized version that existed on AWS bedrock versus whatever was running on anthropic GPUs.

[Michael Truel]

I interview a bunch of people that have conspiracy theories, so I'm glad spoke to this conspiracy.

[Unnamed Speaker]

Well, it's not, not like conspiracy theory as much as they're just like they're, you know, humans. Humans are humans. And there's these details and you know, you're doing like this queasy amount of flops and you know, chips are messy and man, you can just have bugs. Like bugs are. It's hard to overstate how hard bugs are to avoid.

[Michael Truel]

What's the role of a good prompt in all this? You mentioned that benchmarks have really structured, well formulated prompts. What should a human be doing to maximize success and what's the importance of what the humans. You wrote a blog post on, you called it prompt design.

[Arvid Lunmark]

Yeah, I think it depends on which model you're using. And all of them are slightly different and they respond differently to different prompts. But I think the original GPT4 and the original sort of breedable models last year, they were quite sensitive to the prompts. They also had a very small context window. And so we have all of these pieces of information around the code base that would maybe be relevant in the prompt. Like you have the docs, you have the files that you add, you have the conversation history. And then there's a problem, like how do you decide what you actually put in the prompt? And when you have a limited space. And even for today's models, even when you have long context, filling out the entire context window means that it's slower. It means that sometimes the model actually gets confused, and some models get more confused than others. And we have this one system internally that we call Preempt, which helps us with that a little bit. And I think it was built for the era before where we had 8,000 token context windows. And it's a little bit similar to when you're making a website, you want it to work on mobile, you want it to work on a desktop screen, and you have this dynamic information, which you don't have. For example, if you're designing a print magazine, you know exactly where you can put stuff. But when you have a website or when you have a prompt, you have these inputs and then you need to format them to always work, even if the input is really big, then you might have to cut something down. And so the idea was, okay, let's take some inspiration. What's the best way to design websites? Well, the thing that we really like is React and the declarative approach where you use JSX in JavaScript and then you declare, this is what I want. And I think this has higher priority or this has higher Z index than something else. And then you have this rendering engine in web design, it's like Chrome, and in our case it's a preempt renderer which then fits everything onto the page. And as you declare, you decide what you want and then it figures out what you want. And so we have found that to be quite helpful. And I think the role of it has sort of shifted over time. Where initially it was to fit to these small context windows. Now it's really useful because it helps us with splitting up the data that goes into the prompt and the actual rendering of it. And so it's easier to debug because you can change the rendering of the prompt and then try it on old prompts because you have the raw data that went into the prompt. And then you can see, did my change actually improve it for this entire eval set?

[Michael Truel]

So do you literally prompt with jsx?

[Aman Sanger]

Yes.

[Arvid Lunmark]

So it kind of looks like React. There are components. Like we have one component that's a file component, and it takes in the cursor. Usually there's one line where the cursor is in your file, and that's probably the most important line, because that's the one you're looking at. And so then you can give priorities so that line has the highest priority. And then you subtract one for every line that is farther away. And then eventually when it's rendered, it figures out how many lines can I actually fit and it centers around that thing.

[Michael Truel]

That's amazing.

[Unnamed Speaker]

Yeah.

[Aman Sanger]

And you can do like other fancy things where if you have lots of code blocks from the entire code base, you could use retrieval and things like embedding and re ranking scores to add priorities for each of these components.

[Michael Truel]

So should humans, when they ask questions, also try to use something like that? Would it be beneficial to write JSX in the problem or. The whole idea is this should be loose and messy.

[Arvid Lunmark]

I think our goal is kind of that you should just do whatever is the most natural thing for you. And then we, our job is to figure out how do we actually retrieve the relative things so that your thinking actually makes sense.

[Michael Truel]

Well, this is the discussion I had with Arvind of perplexity is like his whole idea is you should let the person be as lazy as he wants. But that's a beautiful thing. But I feel like you're allowed to ask more of programmers, right? 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 be prompted. Almost like the system pressuring you or inspiring you to be articulate not in terms of the grammar of the sentences, but in terms of the depth of thoughts that you convey inside the problems.

[Aman Sanger]

I think even as a system gets closer to some level of perfection, often when you ask the model for something you just are not. Not enough intent is conveyed to know what to do. And there are a few ways to resolve that intent. One is the simple thing of having the model just ask you. I'm not sure how to do these parts based on your query. Could you clarify that? I think the other could be maybe there are five or six possible generations. Given the uncertainty present in your query so far, why don't we just actually show you all of those and let you pick them?

[Michael Truel]

How hard is it for the model to choose to talk back? It's hard to sort of like how to deal with the uncertainty. Do I choose to ask for more information to reduce the ambiguity?

[Unnamed Speaker]

So I mean, one of the things we do is, it's like a recent addition is try to suggest files that you can add. So while you're typing one can guess what the uncertainty is and maybe suggest that maybe you're writing your API and we can guess using the commits that you've made previously in the same file that the client and the server is super useful. And there's like a hard technical problem of how do you resolve it across all commits. Which files are the most important given your current prompt. And we're still sort of initial version is rolled out and I'm sure we can make it much more accurate. It's very experimental. But then the idea is we show you like, do you just want to add this file, this file, this file also to tell the model to edit those files for you. Because if maybe you're making the API, you should also edit the client and the server that is using the API and the other one resolving the API. So that'll be kind of cool. Both there's the phase where you're writing the prompt and before you even click enter, maybe we could help resolve some of the uncertainty.

[Michael Truel]

To what degree do you use agentic approaches? How useful are agents?

[Arvid Lunmark]

We think agents are really, really cool. I think agents is like, it resembles sort of like a human. It's sort of like the things like you can kind of feel that you're getting closer to AGI because you see a demo where it acts as a human would. And it's really, really cool. I think agents are not yet super useful for many things. I think we're getting close to where they will actually be useful. And so I think there are certain types of tasks where having an agent would be really nice. I would love to have an agent. For example, we have a bug where you sometimes can't command C and command V inside our chat input box. And that's a task that's super well specified. I just want to say in two sentences, this does not work. Please fix it. And then I would love to have an agent that just goes off, does it? And then a day later I come back and I review the thing.

[Michael Truel]

You mean it goes finds the right file?

[Arvid Lunmark]

Yeah, it finds the right files, it tries to reproduce the bug, it fixes the bug, and then it verifies that it's correct. And this could be a process that takes a long time. And so I think I would love to have that. And then I think a lot of programming there is often this belief that agents will take over all of programming. I don't think we think that that's the case because a lot of programming, a lot of the value is in iterating or you don't actually want to specify something Upfront, because you don't really know what you want until you seen an initial version and then you want to iterate on that and then you provide more information. And so for a lot of programming, I think you actually want a system that's instant, that gives you an initial version instantly back, and then you can iterate super, super quickly.

[Michael Truel]

What about something like that recently came out, Replit Agent that does also like setting up the development environment, installing software packages, configuring everything, configuring the databases, and actually deploying the app. Is that also in the set of things you dream about?

[Arvid Lunmark]

I think so. I think that would be really cool for certain types of programming. It would be really cool.

[Michael Truel]

Is that within scope of cursor?

[Arvid Lunmark]

Yeah, we aren't actively working on it right now, but it's definitely like we want to make the programmer's life easier and more fun. And some things are just really tedious and you need to go through a bunch of steps and you want to delegate that to an agent. And then some things you can actually have an agent in the background while you're working. Like, let's say you have a PR that's both backend and front end, and you're working in the front end. And then you can have a background agent that does some work and figure out kind of what you're doing. And then when you get to the backend part of your pr, then you have some initial piece of code that you can iterate on. And so that would also be really cool.

[Michael Truel]

One of the things we already talked about is speed, but I wonder if we can just linger on that some more in the various places that the technical details involved in making this thing really fast. So every single aspect of cursor, most aspects of cursor feel really fast. Like I mentioned, the apply is probably the slowest thing. And for me, from. I'm sorry, the pain.

[Arvid Lunmark]

I know it's a pain. It's a pain that we're feeling and we're working on fixing it.

[Michael Truel]

Yeah, I mean, it says something that. Something that feels. I don't know what it is, like one second or two seconds that feels slow. That means that actually shows that everything else is just really, really fast. So is there some technical details about how to make some of these models? How to make the chat fast, how to make the diffs fast? Is there something that just jumps to mind?

[Aman Sanger]

Yeah, I mean, so we can go over a lot of the strategies that we use. One interesting thing is cache warming. And so what you can do is if as the user is typing, you can have, you're probably going to use some piece of context and you can know that before the user's done typing. So as we discussed before, reusing the KV cache results in lower latency, lower cost cross requests. So as the user starts typing, you can immediately warm the cache with, let's say, the current file contents. And then when they've pressed enter, there's very few tokens. It actually has to prefill and compute before starting the generation. This will significantly lower TTFT.

[Michael Truel]

Can you explain how KVCache works?

[Aman Sanger]

Yeah. So the way transformers work, I like it. One of the mechanisms that allow transformers to not just independently like the mechanism that allows transformers to not just independently look at each token, but see previous tokens are the keys and values, it's attention. And generally the way attention works is you have at your current token some query and then you have all the keys and values of all your previous tokens, which are some kind of representation that the model stores internally of all the previous tokens in the prompt. And by default, when you're doing a chat, the model has to for every single token, do this forward pass through the entire model. That's a lot of matrix multiplies that happen and that is really, really slow. Instead, if you have already done that and you stored the keys and values and you keep that in the gpu, then let's say I have stored it for the last N tokens. If I now want to compute the output token for the n +1th token, I don't need to pass those first n tokens through the entire model because I already have all those keys and values. And so you just need to do the forward pass through that last token. And then when you're doing attention, you're reusing those keys and values that have been computed, which is the only kind of sequential part or sequentially dependent part of the transformer.

[Michael Truel]

Is there higher level caching of caching of the prompts or that kind of stuff?

[Aman Sanger]

I see help. Yeah, there's other types of caching you can kind of do. One interesting thing that you can do for cursor tab is you can basically predict ahead as if the user would have accepted the suggestion and then trigger another request. And so then you've cached, you've done this speculative, it's a mix of speculation and caching, right? Because you're speculating what would happen if they accepted it. And then you have this value that is cached this suggestion. And then when they press tab, the Next one would be waiting for them immediately. It's a kind of clever heuristic trick that uses a higher level caching and can give the. It feels fast despite there not actually being any changes in the model.

[Unnamed Speaker]

And if you can make the KV cache smaller, one of the advantages you get is maybe you can speculate even more. Maybe you can guess. Here's the 10 things that could be useful. Predict the next 10 and it's possible the user hits the one of the 10. It's much higher chance than the user hits the exact one that you show them. Maybe they type another character in and we sort of hit something else in the cache. So there's all these tricks where the general phenomena here is I think it's also super useful for RL is maybe a single sample from the model isn't very good. But if you predict 10 different things, turns out that one of the 10 that's right, the probability is much higher. There's these passed K curves and part of RL what RL does is you can exploit this pass it K phenomena to make many different predictions. And one way to think about this, the model knows internally has has some uncertainty over which of the casings is correct or which of the key things does the human want. When we RL our cursor tab model, one of the things we're doing is we're predicting which of the hundred different suggestions the model produces is more amenable for humans. Which of them do humans more like than other things? Maybe there's something where the model can predict very far ahead versus a little bit and maybe somewhere in the middle. And then you can give a reward to the things that humans would like more and punish the things that it won't like and then train the model to output the suggestions that humans would like more. You have these RL loops that are very useful that exploit these passive K curves. Oman maybe can go into even more detail.

[Aman Sanger]

Yeah, it is a little different than speed, but I mean technically you tie it back in because you can get away with the smaller model if you rl your smaller model and it gets the same performance as the bigger one. And while I was mentioning stuff about reducing the size of your KB cache, there are other techniques there as well that are really helpful for speed. So kind of back in the day, all the way, two years ago, people mainly use multi head attention and I think there's been a migration towards more efficient attention schemes like group query or multi query attention. And this is really helpful for then with larger batch sizes being able to generate the tokens much faster. The interesting thing here is this now has no effect on that time to first token prefill speed. The thing this matters for is now generating tokens. And why is that? Because when you're generating tokens instead of being bottlenecked by doing these super parallelizable matrix multiplies across all your tokens, you're bottlenecked by how quickly for long context with large batch sizes, by how quickly you can read those cache keys and values. And so then that's memory bandwidth. And how can we make this faster? We can try to compress the size of these keys and values. So multi query attention is the most aggressive of these. Where normally with multi head attention you have some number of attention heads and some number of kind of query heads. Multiquery just preserves the query heads, gets rid of all the key value heads. So there's only one kind of key value head and there's all the remaining query heads. With groupquery you instead preserve all the query heads and then your keys and values are kind of. There are fewer heads for the keys and values, but you're not reducing it to just one. But anyways, the whole point here is you're just reducing the size of your KB cache.

[Arvid Lunmark]

And then there is mla.

[Aman Sanger]

Yeah, multilatant, that's a little more complicated. And the way that this works is it kind of turns the entirety of your keys and values across all your heads into this kind of one latent vector that is then kind of expanded inference time.

[Unnamed Speaker]

But MLA is from this company called Deepseek. It's quite an interesting algorithm. Maybe the key idea is in both MQA and in other places what you're doing is sort of reducing the the number of KV heads. The advantage you get from that is there's less of them. But maybe the theory is that you actually want a lot of different. You want each of the keys and values to actually be different. So one way to reduce the size is you keep one big shared vector for all the keys and values and then you have smaller vectors for every single token so that when you can, you can store only the smaller thing as some sort of like low rank reduction and the low rank reduction. And at the end of the time when you eventually want to compute the final thing, remember that you're memory bound, which means that you still have some compute left that you can use for these things. And so if you can expand the latent vector back at and somehow this is far more efficient because you're reducing, for example, maybe reducing 32 or something like the size of the vector that you're keeping.

[Aman Sanger]

Yeah, there's perhaps some richness in having a separate set of keys and values and query. That kind of pairwise matchup versus compressing that all into one and that interaction at least.

[Michael Truel]

Okay. And all of that is dealing with being memory bound. And I mean, ultimately how does that map to the user experience trying to get the thing?

[Aman Sanger]

Yeah, the two things that it maps to is you can now make your cache a lot larger because you've less space allocated for the KV cache. You can maybe cache a lot more aggressively in a lot more things. So you get more cache hits, which are helpful for reducing the time to first token for the reasons that were kind of described earlier. And then the second being when you start doing inference with more and more requests and larger and larger batch sizes, you. You don't see much of a slowdown in as it's generating the tokens, the speed of that.

[Unnamed Speaker]

Well, it also allows you to make your prompt bigger for certain.

[Aman Sanger]

Yeah, yeah. So like the basic, the size of your KV cache is both the size of all your prompts multiplied by the number of prompts being processed in parallel. So you could increase either of those dimensions. Right. The batch size or the size of your prompts without degrading the latency of generating tokens.

[Michael Truel]

Arvid, you wrote a blog post Shadow workspace iterating on code in the background. So what's going on?

[Arvid Lunmark]

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 K prompts, for example. But the idea is if you can actually spend computation in the background, then you can 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 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 you can get higher performance by just letting the model think for longer. And so 01 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. It exists for most different languages and there's like a separate language server per language and it can tell you 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 understand the structure of your code. So language servers are extensions developed by like There is a TypeScript language server developed by the TypeScript people, a Rust language server developed by the Rust people.

[Aman Sanger]

And.

[Arvid Lunmark]

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 for linting purposes. It's for linting. It's for going to definition and for seeing the right types that you're using.

[Michael Truel]

So it's doing type checking also?

[Arvid Lunmark]

Yes, type checking and going to references. And that's like when you're working in a big project, you kind of need that. If you don't have that, it's really hard to code in a big project.

[Michael Truel]

Can you say again how that's being used inside Cursor, the language server protocol communication thing.

[Arvid Lunmark]

So it's being used in Cursor to show to the programmer, just like in VS code. But then the idea is you want to show that same information to the models, the I O models, and you want to do that in a way that doesn't affect the user, because you want to do it in background. And so the idea behind the Chatter workspace was, okay, one way we can do this is we spawn a separate window of Cursor that's hidden and so you can set this flag and Electron is hidden. There is a window, but you don't actually see it. And inside of this window, the AI agents can modify code however they want, as long as they don't save it because it's still the same folder. And then can get feedback from the linters and go to definition and iterate on their code.

[Michael Truel]

So like literally run everything in the background, like as if, right? Yeah, maybe even run the code.

[Arvid Lunmark]

So that's the eventual version. Okay, that's what you want. And a lot of the blog post is actually about how do you make that happen? Because it's a little bit tricky. You want it to be on the user's machine so that it exactly mirrors the user's environment. And then on Linux you can do this cool thing where you can actually mirror the file system and have the AI make changes to the files, and it thinks that it's operating on the file level, but actually that's stored in memory and you can create this kernel extension to make it work. Whereas on Mac and Windows it's a little bit more difficult, but it's a fun technical problem.

[Aman Sanger]

So that's why one maybe hacky but interesting idea that I like is holding a lock on saving. And so basically you can then have the language model kind of hold the lock on saving to disk. And then instead of you operating in the ground truth version of the files that are saved to disk, you actually are operating what was the shadow workspace before? And these unsaved things that only exist in memory that you still get linter errors for and you can code in. And then when you try to maybe run code, it's just like there's a small warning that there's a lock and then you kind of will take back the lock from the language server if you're trying to do things concurrently, or from the shadow workspace if you're trying to do things concurrently.

[Michael Truel]

That's such an exciting future, by the way. That's a bit of a tangent, but to allow a model to change files, it's scary for people, but it's really cool to be able to just let the agent do a set of tasks and you come back the next day and observe it's a colleague or something like that.

[Aman Sanger]

Yeah. And I think there may be different versions of runability where for the simple things where you're doing things in the span of a few minutes on behalf of the user as they're programming, it makes sense to make something work locally on their machine. 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. And that's another incredibly tricky problem of how do you exactly reproduce or mostly reproduce to the point of it being effectively equivalent for running code, the user's environment with this remote sandbox.

[Unnamed Speaker]

I'm curious, what kind of agents you want for coding? Do you want them to find bugs? Do you want them to implement new features? What agents do you want?

[Michael Truel]

So, by the way, when I think about agents, I don't think just about coding. I think so for the practices this particular podcast, there's video editing and a lot of. If you look in Adobe a lot, there's code behind Find. It's very poorly documented code, but you can interact with Premiere, for example, using code and basically all the uploading. Everything I do on YouTube, everything as you could probably imagine, I do all that through code and so, and including translation and overdubbing all of this. So I envision all those kinds of tasks so automating many of the tasks that don't have to do directly with the editing so that. Okay, that's what I was thinking about. 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. Ones like sort of big directions of implementation, that kind of stuff.

[Unnamed Speaker]

That's opine and bug finding.

[Aman Sanger]

Yeah. I mean it's really interesting that these models are so bad at bug finding. When just naively prompted to find a bug, they're incredibly poorly calibrated.

[Arvid Lunmark]

Even the smartest models.

[Aman Sanger]

Exactly, even.01.

[Michael Truel]

How do you explain that? Is there a good intuition?

[Aman Sanger]

I think these models are a really strong reflection of the pre training distribution and I do think they generalize as the loss gets lower and lower. But I don't think the loss and the scale is quite or the loss is low enough such that they're really fully generalizing in code. The things that we use these things for, the frontier models that they're quite good at are really code generation and question answering. And these things exist in massive quantities in pre training with all of the code on GitHub on the scale of many, many trillions of tokens and questions and answers on things like stack Overflow and maybe GitHub issues. And so when you try to push into these things that really don't exist very much online, like for example, the cursor tab objective of predicting the next edit, given the edits done so far, the brittleness kind of shows. And then bug detection is another great example where there aren't really that many examples of actually detecting real bugs and then proposing fixes and the models just kind of really struggle at it. But I think it's a question of transferring the model in the same way that you get this fantastic transfer from pre trained models just on code in general to the cursor tab objective. You'll see a very, very similar thing with generalized models that are really good at code to bug detection. It just takes a little bit of nudging in that direction to be clear.

[Unnamed Speaker]

I think they understand code really well while they're being pre trained. The representation that's being built up almost certainly like somewhere in the stream. The model knows that maybe there's something sketchy going on. Right. It sort of has some sketchiness but actually eliciting the sketchiness to. Actually part of it is that humans are really calibrated on which bugs are really important. It's not just actually saying like there's something sketchy. It's like, is this sketchy trivial? Is this sketchy? Like you're going to take the server down? It's like part of it is maybe the cultural knowledge of why is a staff engineer a staff engineer? A staff engineer is good because they know that three years ago someone wrote a really sketchy piece of code that took the server down. And as opposed to maybe this thing is like an experiment. So a few bugs are fine. You're just trying to experiment and get the feel of the thing. And so if the model gets really annoying when you're writing an experiment, that's really bad. But if you're writing something for super production, you're like writing a database. Right. You're writing code in Postgres or Linux or whatever, like your Linus Torvalds. It's sort of unacceptable to have even in Edge case and just having the calibration of how paranoid is the user.

[Aman Sanger]

But even then, if you're putting in a maximum paranoia, it still just doesn't quite get it.

[Unnamed Speaker]

Yeah, yeah, yeah.

[Michael Truel]

I mean, but this is hard for humans too, to understand what which line of code is important. Which is not like you. I think one of your principles on a website says if a code can do a lot of damage, one should add a comment that say this line of code is dangerous and all caps 10 times. No, you say like for every single line of code inside the function, you have to. And that's quite profound. That says something about human beings because the engineers move on. Even the same person might just forget how it can sync the Titanic a single function. You might not intuit that quite clearly by looking at the single piece of code.

[Arvid Lunmark]

Yeah, and I think that one is also partially also for today's AI models, where if you actually write dangerous, dangerous, dangerous in every single line, the models will pay more attention to that and will be more likely to find bugs in that region.

[Michael Truel]

That's actually just straight up a really good practice of labeling code of how much damage this can do.

[Arvid Lunmark]

Yeah, I mean, it's controversial. Some people think it's ugly. Swallow.

[Unnamed Speaker]

In fact, I actually think this is one of the things I learned from arid is I sort of aesthetically, I don't like it, but I think there's certainly something where it's useful for the models and humans just forget a lot and it's really easy to make a small mistake and cause just bring down the server. Of course we test a lot and whatever, but there's always these things that you have to be very careful.

[Aman Sanger]

Yeah. With just normal docstrings, I think people will often just skim it when making a change and think, oh, I know how to do this. And you kind of really need to point it out to them so that doesn't slip through.

[Michael Truel]

Yeah. You have to be reminded that you could do a lot of damage. That's like we don't really think about that. You think about, okay, how do I figure out how this works so I can improve it? You don't think about the other direction that it could.

[Arvid Lunmark]

Until we have formal verification for everything, then you can do whatever you want. And you know for certain that you have not introduced a bug. If the proof pass.

[Aman Sanger]

But concretely, what do you think that future would look like?

[Arvid Lunmark]

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. And in the meantime, smart reasoning model computes a proof that the implementation follows the spec. And I think that happens for most functions.

[Swali]

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.

[Arvid Lunmark]

You think that spec is hard to generate.

[Swali]

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. But then also, even if you have.

[Unnamed Speaker]

The spec, if you have the spec.

[Aman Sanger]

But how do you.

[Swali]

Even if you have the spec, is the spec written in natural language or is it mathematical?

[Arvid Lunmark]

No, the spec would be formal.

[Aman Sanger]

But how easy would that be? Formal.

[Swali]

So then I think that you care about things that are not going to be easily well specified in the spec language.

[Arvid Lunmark]

I see, I see.

[Swali]

Maybe an argument against formal verification is all you need.

[Arvid Lunmark]

Yeah.

[Aman Sanger]

The worry is there's this massive document.

[Swali]

Replacing something like unit tests. Sure.

[Arvid Lunmark]

Yeah. I think you can probably also evolve the spec languages to capture some of the things that they don't really capture right now. I don't know. I think it's very exciting.

[Michael Truel]

And you're speaking not just about single functions, you're speaking about entire code bases.

[Arvid Lunmark]

I think entire Code bases is harder, but that is what I would love to have and I think it should be possible because there's a lot of work recently where you can prove formally verify down to the hardware. So you formally verify the C code and then you formally verify through the GCC compiler and then through the verilog down to the hardware. And that's an incredibly big system, but it actually works. And I think big code bases are sort of similar in that they're like multilayered system. And if you can decompose it and formally verify each part, then I think it should be possible. I think the specification problem is a real problem.

[Aman Sanger]

How do you handle side effects or how do you handle, I guess, external dependencies like calling the Stripe API?

[Unnamed Speaker]

Maybe Stripe would write a spec for the API.

[Aman Sanger]

But you can't do this for everything. Can you do this for everything you use? How do you do it for. If there's a language model, maybe people will use language models as primitives in the programs they write and there's a dependence on it. And how do you now include that?

[Arvid Lunmark]

I think you might be able to prove that still.

[Aman Sanger]

Prove what? About language models, I think it feels.

[Arvid Lunmark]

Possible that you could actually prove that a language model is aligned, for example, or you can prove that it actually gives the right answer.

[Unnamed Speaker]

That's the dream.

[Michael Truel]

Yeah, that is. I mean, that's if it's possible. I have a dream speech, if it's possible. That will certainly help with, you know, making sure your code doesn't have bugs and making sure AI doesn't destroy all of human civilization. So the full spectrum of AI safety to just bug finding. So you said the models struggle with bug finding. What's the hope?

[Unnamed Speaker]

You know, my hope initially is, and I can let Michael chime in too, but it should first help with the stupid bugs. It should very quickly catch the stupid bugs, like off by one errors. Sometimes you write something in a comment and do the other way. It's very common. I do this, I write less than in a comment and I maybe write the greater than or something like that. And the model is like looks sketchy. Are you sure you want to do that? But eventually it should be able to catch 100 bugs too.

[Swali]

Yeah. And I think that it's also important to note that having good bug finding models feels necessary to get to the highest reaches of having AI do more and more programming for you, where you're going to. If the AI is building more and more of the system for you. You need to not just generate but also verify and without that, some of the problems that we've talked about before with programming with these models will just become untenable. So it's not just for humans like you write a bug, I write a bug, find the bug for me. But it's also being able to verify the AI's code and check it is really important.

[Arvid Lunmark]

Yeah. And then how do you actually do this? We have had a lot of contentious dinner discussions of how do you actually train a bug model, but one very popular idea is it's kind of potentially easy to introduce a bug than actually finding the bug. And so you can train a model to introduce bugs in existing code, and then you can train a reverse bug model then that can find bugs using this synthetic data. So that's like one example. But yeah, there are lots of ideas for how to.

[Swali]

You can also do a bunch of work, not even at the model level, of taking the biggest models and then maybe giving them access to a lot of information. That's not just the code. It's kind of a hard problem to stare at a file and be like, where's the bug? And that's hard for humans often. Right. And so often you have to run the code. And being able to see things like traces and step through a debugger, there's a whole other direction where it kind of tends toward that. And it could also be that there are kind of two different product form factors here. It could be that you have a really specialty model that's quite fast, that's kind of running in the background and trying to spot bugs. And it might be that sometimes, sort of to Arvid's earlier example about some nefarious input box bug, it might be that sometimes you want to, like, you know there's a bug. You're not just like checking hypothesis free. You're like, this is a problem, I really want to solve it. And you zap that with tons and tons and tons of compute and you're willing to put in like $50 to solve that bug or something even more.

[Michael Truel]

Have you thought about integrating money into this whole thing? I would pay probably a large amount of money for if you found a bug or even generated code that I really appreciated. I had a moment a few days ago when I started using Cursor, where it generated.

[Lex Friedman]

Perfect.

[Michael Truel]

Perfect three functions for interacting with the YouTube API, to update captions, and for localization, like different in different languages. The API documentation is not very good. And the code across, like if I googled it for a while, I couldn't find exactly. There's A lot of confusing information and cursor generated perfectly. And I was like, I just said back. I read the code. I was like, this is correct. I tested it. It's correct. I was like, I want a tip on a button that goes, here's $5. One that's really good just to support the company and support what the interface is. And the other is that probably sends a strong signal, like, good job. Right. There's a much stronger signal than just accepting the code. Right. You just actually send like a strong good job. That and for bug finding, obviously there's a lot of people that would pay a huge amount of money for a bug bounty thing. Right? You guys think about that?

[Unnamed Speaker]

Yeah.

[Arvid Lunmark]

It's a controversial idea inside the company. I think it sort of depends on how much you believe in humanity almost. I think it would be really cool if you spend nothing to try to find a bug, and if it doesn't find a bug, you spend $0. And then if it does find a bug and you click accept, then it also shows in parentheses, like $1. And so you spend $1 to accept the bug. And then of course, there is a worry, like, okay, we spent a lot of computation. Maybe people will just copy paste. I think that's a worry. And then there is also the worry that introducing money into the product makes it kind of. It doesn't feel as fun anymore. You have to think about money and all you want to think about is code. And so maybe it actually makes more sense to separate it out. And you pay some fee every month and then you get all of these things for free.

[Michael Truel]

But there could be a tipping component.

[Arvid Lunmark]

Which is not like it still has that dollar symbol. I think it's fine. But I also see the point where maybe you don't want to introduce it.

[Aman Sanger]

Yeah, I was going to say the moment that it feels like people do this is when they share it. When they have this fantastic example, they just share it with their friends.

[Swali]

There is also a potential world where there's a technical solution to this, like on our system problem too, where if we can get to a place where we understand the output of the system more, I mean, to the stuff we were talking about with error checking with the LSP and then also running the code. But if you could get to a place where you could actually somehow verify, oh, I have fixed the bug, maybe then the bounty system doesn't need to rely on the honor system too.

[Michael Truel]

How much interaction is there between the terminal and the code? Like how much information is gained from. If you run the code in the terminal. Like can you use. Can you do like a loop where it runs the code and suggest how to change the code if the code in runtime gives an error? Because right now they're separate worlds completely. Like I know you can do control K inside the terminal to help you write the code.

[Aman Sanger]

You can use terminal contacts as well. Inside of Check mankind of everything. We don't have the looping part yet, though we suspect something like this could make a lot of sense. There's a question of whether it happens in the foreground too, or if it happens in the background, like what we've been discussing.

[Michael Truel]

Sure. The background is pretty cool. You can do it running the code in different ways. Plus there's a database side to this which how do you protect it from not modifying the database?

[Unnamed Speaker]

But okay, there's certainly cool solutions there. There's this new API that is being developed for. It's not in AWS, but it certainly is. I think it's in PlanetScale. I don't know if PlanetScale was the first one to add it. This ability to add branches to a database which if you're working on a feature and you want to test against the PROD database, but you don't actually want to test against the PROD database. You could add a branch to the database and the way they do that is to add a branch to the write ahead log. And there's obviously a lot of technical complexity in doing it correctly. I guess database companies need new things to do. They have good databases now and I think like Turbo Buffer, which is one of the databases we use, is going to add hope, maybe branching to the RAD log. Maybe the AI agents will use, will use branching. They'll test against some branch and it's sort of going to be a requirement for the database to support branching or something.

[Aman Sanger]

It'd be really interesting if you could branch a file system. Right?

[Unnamed Speaker]

Yeah, I feel like everything needs branching.

[Michael Truel]

That's the problem with the multiverse, Right. If you branch on everything, that's like a lot.

[Unnamed Speaker]

There's obviously these super clever algorithms to make sure that you don't actually sort of use a lot of space or CPU or whatever.

[Michael Truel]

Okay, this is a good place to ask about infrastructure. So you guys mostly use aws. What are some interesting details? What are some interesting challenges? Why'd you choose aws? Why is AWS still winning?

[Arvid Lunmark]

Hashtag AWS is just really, really good. It's really good. Like whenever you use an AWS product, you just know that it's Going to work like it might be absolute hell to go through the steps to set it up.

[Michael Truel]

Why is the interface so horrible?

[Arvid Lunmark]

Because it's just so good. It doesn't need to.

[Michael Truel]

It's the nature of winning.

[Arvid Lunmark]

I think it's exactly.

[Unnamed Speaker]

It's just nature. They're winning.

[Arvid Lunmark]

Yeah, yeah. But aws, you can always trust, like it will always work. And if there is a problem, it's probably your problem. Yeah.

[Michael Truel]

Okay. Is there some interesting challenges to you guys? A pretty new startup to get scaling to so many people?

[Swali]

Yeah. I think that it has been an interesting journey adding each extra zero to the request per second. You run into all of these with the general components you're using for caching. And databases run into issues as you make things bigger and bigger. And now we're at the scale where we get int overflows on our tables and things like that. And then also there have been some custom systems that we've built, like for instance, our retrieval system for computing a semantic index of your code base and answering questions about a code base that have continually, I feel like, been one of the trickier things to scale.

[Unnamed Speaker]

I have a few friends who are super senior engineers and one of their sort of lines is like, it's very hard to predict where systems will break when you scale them. You can sort of try to predict in advance, but there's always something weird that's going to happen when you add this extra zero. And you thought, you thought through everything, but you didn't actually think through everything. But I think for that particular system. So for concrete details, the thing we do is obviously we upload, we chunk up all of your code and then we send up sort of the code for embedding, and we embed the code and then we store the embeddings in a database, but we don't actually store any of the code. And then there's reasons around making sure that we don't introduce client bugs because we're very, very paranoid about client bugs. We store much of the details on the server. Like everything is sort of encrypted. So one of the technical challenges is always making sure that the local index, the local code base state, is the same as the state that is on the server. And the way technically we ended up doing that is so for every single file you can keep this hash, and then for every folder you can keep a hash which is the hash of all of its children. And you can recursively do that until the top. And why do something complicated? One thing you could do is you could keep a hash for every file. Then every minute you could try to download the hashes that are on the server, figure out what are the files that don't exist on the server. Maybe you just created a new file, maybe you just deleted a file, maybe you checked out a new branch and try to reconcile the state between the client and the server. But that introduces absolutely ginormous network overhead, both on the client side. I mean, nobody really wants us to hammer their wifi all the time if you're using Cursor. But also it would introduce ginormous overhead on the database. It would sort of be reading this tens of terabyte database, sort of approaching like 20 terabytes or something database every second. That's just sort of, kind of crazy. You definitely don't want to do that. So what do you do? You sort of. You just try to reconcile the single hash which is at the root of the project. And then if something mismatches, then you go, you find where all the things disagree. Maybe you look at the children and see if the hashes match. And if the hashes don't match, go look at their children and so on. But you only do that in the scenario where things don't match. And for most people, most of the time the hashes match.

[Michael Truel]

So it's a kind of like hierarchical reconciliation.

[Unnamed Speaker]

Yeah, something like that.

[Aman Sanger]

Yeah, it's called the Merkle tree.

[Michael Truel]

Yeah, Merkle, yeah. So, yeah, this is cool to see that. You kind of have to think through.

[Unnamed Speaker]

All these problems and I mean, the reason it's gotten hard is just because the number of people using it and if some of your customers have really, really large code bases to the point where we originally reordered our code base, which is big, but I mean, it's just not the size of some company that's been there for 20 years and has a ginormous number of files. And you sort of want to scale that across programmers. There's all these details where building the simple thing is easy, but scaling it to a lot of people, like a lot of companies, is obviously a difficult problem, which is sort of independent of actually. So there's part of this scaling. Our current solution is also coming up with new ideas that obviously we're working on, but then scaling all of that in the last few weeks once.

[Aman Sanger]

Yeah, and there are a lot of clever things like additional things that go into this indexing system. For example, the bottleneck in terms of costs is not storing things in the vector database or the database, it's actually embedding the code. And you don't want to re embed the code base for every single person in a company that is using the same exact code, except for maybe they're in a different branch with a few different files or they've made a few local changes. And so because again, embeddings are the bottleneck, you can do this one clever trick and not have to worry about the complexity of dealing with branches and the other databases where you just have some cache on the actual vectors computed from the hash of a given chunk. And so this means that when the nth person at a company goes and embeds their code base, it's really, really fast. And you do all this without actually storing any code on our servers at all, no code data stored. We just store the vectors in the vector database and the vector cache.

[Michael Truel]

What's the biggest gains at this time you get from indexing the code base? Just out of curiosity, what benefit do users have? It seems like longer term there'll be more and more benefit, but in the short term, just asking questions of the code base, what's the usefulness of that?

[Arvid Lunmark]

I think the most obvious one is just you want to find out where something is happening in your large code base and you sort of have a fuzzy memory of okay, I want to find the place where we do X. But you don't exactly know what to search for in a normal text search. And so you ask a chat, you hit command enter to ask with the codebase chat and then very often it finds the right place that you were thinking of.

[Aman Sanger]

I think, like you mentioned in the future, I think this is only going to get more and more powerful where we're working a lot on improving the quality of our retrieval. And I think the ceiling for that is really, really much higher than people give it credit for.

[Michael Truel]

One question that's good to ask here. Have you considered and why haven't you much done local stuff to where you can do the. It seems like everything we just discussed is exceptionally difficult to do. To go to the cloud, you have to think about all these things with the caching and the large code bases. With a large number of programmers are using the same code base, you have to figure out the puzzle of that. A lot of it. Most software just does stuff this heavy computational stuff locally. Have you considered doing sort of embeddings locally?

[Arvid Lunmark]

Yeah, we thought about it and I think it would be cool to do it locally. I think it's just really hard. And one thing to keep in mind is that some of our Users use the latest MacBook Pro. But most of our users, like more than 80% of our users, are in Windows machines and many of them are not very powerful. And so local models really only works on the latest computers. And it's also a big overhead to build that in. And so even if we would like to do that, it's currently not something that we are able to focus on. And I think there are some people that do that and I think that's great. But especially as models get bigger and bigger and you want to do fancier things with bigger models, it becomes even harder to do it locally.

[Aman Sanger]

Yeah.

[Unnamed Speaker]

And it's not a problem of weaker computers. It's just that, for example, if you're some big company, you have big company code base. It's just really hard to process big company code base even on the beefiest MacBook Pros. So even if it's not even a matter of if you're just a student or something, I think if you're the best programmer at a big company, you're still going to have a horrible experience. If you do everything locally, you could do it and scrape by, but again it wouldn't be fun anymore.

[Aman Sanger]

Yeah, at approximate nearest neighbors. And this massive code base is going to just eat up your memory and your cpu. And that's just that. Let's talk about also the modeling side, where as Arvid said, there are these massive headwinds against local models where one things seem to move towards MOEs, which one benefit is maybe they're more memory bandwidth bound, which plays in favor of local versus using GPUs or using Nvidia GPUs. But the downside is these models are just bigger in total and they're going to need to fit often not even on a single node, but multiple nodes. There's no way that's going to fit inside of even really good MacBooks. And I think especially for coding, it's not a question as much of does it clear some bar of the model's good enough to do these things and then we're satisfied, which may be the case for other problems and maybe where local models shine. But people are always going to want the best, the most intelligent, the most capable things. And that's going to be really hard to run for almost all people locally.

[Unnamed Speaker]

Don't you want the most capable model? You want Sonnet and also with O.

[Michael Truel]

I like how you're pitching me. Would you be satisfied with an inferior model? Listen, I'm. Yes, I'm one of those, but there's some people that like to do stuff locally especially like there's a whole obviously open source movement that kind of resists. And it's good that they exist actually, because you want to resist. The power centers that are growing are.

[Arvid Lunmark]

There's actually an alternative to local models that I am particularly fond of. I think it's still very much in the research stage, but you could imagine to do homomorphic encryption for language model inference. So you encrypt your input on your local machine, then you send that up and then the server can use lots of computation. They can run models that you cannot run locally on this encrypted data, but they cannot see what the data is. And then they send back the answer and you decrypt the answer and only you can see the answer. So I think that's still very much research and all of it is about trying to make the overhead lower because right now the overhead is really big. But if you can make that happen, I think that would be really, really cool and I think it would be really, really impactful. Because I think one thing that's actually kind of worrisome is that as these models get better and better, they're going to become more and more economically useful. And so more and more of the world's information and data will flow through one or two centralized actors. And then there are worries about there can be traditional hacker attempts, but it also creates this kind of scary part where if all of the world's information is flowing through one node in plain text, you can have surveillance in very bad ways. And sometimes that will happen initially will be good reasons. People will want to try to protect against bad actors using AI models in bad ways. And then you will add in some surveillance code and then someone else will come in and you're on a slippery slope and then you start doing bad things with a lot of world's data. And so I'm very hopeful that we can solve homomorphic encryption for language modeling.

[Michael Truel]

Doing privacy preserving machine learning. But I would say that's the challenge we have with all software these days. It's like there's so many features that can be provided from the cloud and all of us increasingly rely on it and make our life awesome. But there's downsides and that's why you rely on really good security to protect from basic attacks. But there's also only a small set of companies that are controlling that data, you know, and they, they obviously have leverage and they could be infiltrated in all kinds of ways. That's the world we live In Yeah.

[Unnamed Speaker]

I mean the thing I'm just actually quite worried about is sort of the world where anthropic has this responsible scaling policy and so where we're on like the low, low asls, which is the anthropic security level or whatever of like of the models. But as we get to like quote unquote ASL3, ASL4, whatever models which are sort of very powerful but for mostly reasonable security reasons, you would want to monitor all the prompts. But I think that's so reasonable and understandable where everyone is coming from. But Matt, it'd be really horrible if all the world's information is sort of monitor that heavily. It's way too centralized. It's really fine line you're walking where on the one side you don't want the models to go rogue, on the other side humans. I don't know if I trust all the world's information to pass through three model providers.

[Michael Truel]

Yeah.

[Aman Sanger]

Why do you think it's different than cloud providers?

[Arvid Lunmark]

Because I think a lot of this data would never have gone to the cloud providers in the first place. Where this is often like you want to give more data to the EIA models, you want to give personal data that you would never have put online in the first place to these companies or to these models. And it also centralizes control where right now for cloud you can often use your own encryption keys and AWS can't really do much, but here it's just centralized actors that see the exact plain text of everything.

[Michael Truel]

On the topic of context, that's actually been a friction for me when I'm writing code in Python there's a bunch of stuff imported. You could probably intuit the kind of stuff I would like to include in the context. How hard is it to auto figure out the context?

[Swali]

It's tricky. I think we can do a lot better at computing the context automatically in the future. One thing that's important to note is there are trade offs with including automatic context. So the more context you include for these models, first of all, the slower they are and the more expensive those requests are, which means you can then do less model calls and do less fancy stuff in the background. Also for a lot of these models, they get confused if you have a lot of information in the prompt. So the bar for accuracy and for relevance of the context you include should be quite high. But this is already. We do some automatic context in some places within the product. It's definitely something we want to get a lot better at. And I think that there are a lot of cool ideas to try there both on the learning better retrieval systems, like better embedding models, better rerankers. I think that there are also cool academic ideas, stuff we've tried out internally, but also the field is grappling with writ large about can you get language models to a place where you can actually just have the model itself understand a new corpus of information? And the most popular talked about version of this is can you make the context Windows infinite? Then if you make the context Windows infinite, can you make the model actually pay attention to the infinite context? And then after you can make it pay attention to the infinite context to make it somewhat feasible to actually do it, can you then do caching for that infinite context? You don't have to recompute that all the time. But there are other cool ideas that are being tried that are a little bit more analogous to fine tuning of actually learning this information and the weights of the model. And it might be that you actually get sort of a qualitatively different type of understanding if you do it more at the weight level than if you do it at the in context learning level. I think the jury's still a little bit out on how this is all going to work in the end. But in the interim, us as a company, we are really excited about better retrieval systems and picking the parts of the code base that are most relevant to what you're doing. We could do that a lot better.

[Aman Sanger]

One interesting proof of concept for the learning this knowledge directly in the weights is with VS code. So we're in a VS code fork and VS code, the code is all public. So these models in pre training have seen all the code. They've probably also seen questions and answers about it. And then they've been fine tuned and RLA chef to be able to answer questions about code in general. So when you ask it a question about VS code, sometimes it'll hallucinate, but sometimes it actually does a pretty good job at answering the question. And I think this is just it happens to be okay, but what if you could actually specifically train or post train a model such that it really was built to understand this code base? It's an open research question, one that we're quite interested in. And then there's also uncertainty of do you want the model to be the thing that end to end is doing everything that is it's doing the retrieval and its internals and then kind of answering your question, creating the code or do you want to separate the retrieval from the frontier model where Maybe you'll get some really capable models that are much better than the best open source ones in a handful of months. And then you'll want to separately train a really good open source model to be the retriever, to be the thing that feeds in the context to these larger models.

[Michael Truel]

Can you speak a little more to the post training a model to understand the code base? What do you mean by that? Is this a synthetic data direction?

[Aman Sanger]

Yeah, I mean there are many possible ways you could try doing it. There's certainly no shortage of ideas. It's just a question of going in and trying all of them and being empirical about which one works best. One very naive thing is to try to replicate what's done with VS Code and these frontier models. So let's continue pre training, some kind of continued pre training that includes general code data, but also throws in a lot of the data of some particular repository that you care about. And then in post training, meaning let's just start with instruction fine tuning. You have a normal instruction fine tuning dataset about code, but you throw in a lot of questions about code in that repository. So you could either get ground truth ones, which might be difficult, or you could do what you kind of hinted at or suggested using synthetic data, that is kind of having the model ask questions about various pieces of the code. So you kind of take the pieces of the code, then prompt the model or have a model propose a question for that piece of code and then add those as instruction fine tuning data points. And then in theory this might unlock the model's ability to answer questions about that code base.

[Michael Truel]

Let me ask you about OpenAI01. What do you think is the role of that kind of test time compute system in programming?

[Aman Sanger]

I think test time compute is really, really interesting. So there's been the pre training regime which will kind of as you scale up the amount of data and the size of your model, get you better and better performance both on loss and then on downstream benchmarks and just general performance when we use it for coding or other tasks. We're starting to hit a bit of a data wall, meaning it's going to be hard to continue scaling up this regime. And so scaling up test time compute is an interesting way of now increasing the number of inference time flops that we use, but still getting, as you increase the number of flops used inference time getting corresponding improvements in the performance of these models. Traditionally we just had to literally train a bigger model that always uses, that always used that many more flops, but now we could perhaps use the same size model and run it for longer to be able to get an answer at the quality of a much larger model. And so the really interesting thing I like about this is there are some problems that perhaps require 100 trillion parameter model intelligence trained on 100 trillion tokens. But that's maybe 1%, maybe 0.1% of all queries. So are you going to spend all of this effort, all of this compute training a model that costs that much and then run it so infrequently it feels completely wasteful when instead you get the model that can you train the model that is capable of doing the 99.9% of queries, then you have a way of inference time running it longer. For those few people that really, really want max intelligence, how do you figure.

[Michael Truel]

Out which problem requires what level of intelligence is that possible to dynamically figure out when to use GPT4, when to use like when to use a small model and when you need the 01?

[Aman Sanger]

I mean, yeah, that's an open research problem certainly I don't think anyone's actually cracked this model routing problem quite well. We'd like to. We have kind of initial implementations of this for things for something like cursor tab, but at the level of going between Foro Sonet to 01, it's a bit trickier. Perhaps there's also a question of what level of intelligence do you need to determine if the thing is too hard for the four level model? Maybe you need the 01 level model.

[Michael Truel]

It's really unclear but you mentioned so there's a pre training process, then there's post training and then there's test time compute. That fare does separate. Where's the biggest gains?

[Aman Sanger]

Well, it's weird because test time compute, there's a whole training strategy needed to get test time compute to work. And the other really weird thing about this is outside of the big labs and Maybe even just OpenAI, no one really knows how it works. There have been some really interesting papers that show hints of what they might be doing and so perhaps they're doing something with tree search using process reward models. But yeah, I think the issue is we don't quite know exactly what it looks like, so it would be hard to kind of comment on where it fits in. I would put it in post training, but maybe the compute spent for getting test time compute to work for a model is going to dwarf pre training eventually.

[Michael Truel]

So we don't Even know if O1 is using just chain of thought RL we don't know how they're using any of these. We don't know anything.

[Aman Sanger]

It's fun to speculate.

[Michael Truel]

If you were to build a competing model, what would you do?

[Aman Sanger]

Yeah. So one thing to do would be I think you probably need to train a process reward model. So maybe we can get into reward models and outcome reward models versus process reward models. Outcome reward models are the kind of traditional reward models that people are trained for, for language modeling. And it's just looking at the final thing. So if you're doing some math problem, let's look at that final thing. You've done everything and let's assign a grade to it. How likely we think, what's the reward for this outcome? Process reward models instead, try to grade the chain of thought. And so OpenAI had some preliminary paper on this, I think last summer, where they use human labelers to get this pretty large several hundred thousand dataset of creating chains of thought. Ultimately, it feels like I haven't seen anything interesting in the ways that people use process reward models outside of just using it as a means of affecting how we choose between a bunch of samples. So what people do in all these papers is they sample a bunch of outputs from the language model and then use the process reward models to grade all those generations alongside maybe some other heuristics and then use that to choose the best answer. The really interesting thing that people think might work and people want to work is tree search with these process reward models. Because if you really can grade every single step of the chain of thought, then you can kind of branch out and explore multiple paths of this chain of thought and then use these process reward models to evaluate how good is this branch that you're taking.

[Michael Truel]

Yeah. When the quality of the branch is somehow strongly correlated with the quality of the outcome at the very end. So you have a good model of knowing which branch to take. So not just in the short term and in the long term.

[Aman Sanger]

Yeah. And the interesting work that I think has been done is figuring out how to properly train the process or the interesting work that has been open sourced. And people I think talk about is how to train the process reward models maybe in a more automated way. I could be wrong here. Could not be mentioning some papers. I haven't seen anything super that seems to work really well for using the process reward models creatively to do tree search and code.

[Michael Truel]

This is kind of an AI safety, maybe a bit of a philosophy question. So OpenAI says that they're hiding the chain of thought from the user and they've said that that was a difficult decision to make. Instead of showing the chain of thought, they're asking the model to summarize the chain of thought. They're also in the background saying they're going to monitor the chain of thought to make sure the model is not trying to manipulate the user, which is a fascinating possibility. But anyway, what do you think about hiding the chain of thought?

[Swali]

One consideration for OpenAI, and this is completely speculative, could be that they want to make it hard for people to distill these capabilities out of their model. It might actually be easier if you had access to that hidden chain of thought to replicate the technology, because that's pretty important data, like seeing the steps that the model took to get to the final result.

[Michael Truel]

So you could probably train on that also.

[Swali]

And there was sort of a mirror situation with this with some of the large language model providers. And also this is speculation, but some of these APIs used to offer easy access to log probabilities for all the tokens that they're generating, and also log probabilities over the prompt tokens. And then some of these APIs took those away. And again, complete speculation. But one of the thoughts is that the reason those were taken away is if you have access log probabilities similar to this hidden train of thought, that can give you even more information to try and distill these capabilities out of the APIs, out of these biggest models into models you control as an asterisk on also the previous discussion about us integrating O1, I think that we're still learning how to use this model. So we made O1 available in cursor because when we got the model, we were really interested in trying it out. I think a lot of programmers are going to be interested in trying it out, but O1 is not part of the default cursor experience in any way up. And we still haven't found a way to yet integrate it into an edit, into the editor in a way that we reach for sort of every hour, maybe even every day. And so I think that the jury's still out on how to use the model. And we haven't seen examples yet of people releasing things where it seems really clear like, oh, that's now the use case. The obvious one to turn to is maybe this can make it easier for you to have these background things running, to have these models in loops, to have these models be a gentic. But we're still discovering, to be clear.

[Unnamed Speaker]

We have ideas we need to try and get something incredibly useful before we put it out there.

[Aman Sanger]

But it has these significant limitations, like even barring capabilities, it does not stream. And that means it's really, really painful to use for things where you want to supervise the output and instead you're just waiting for the wall of text to show up. Also, it does feel like the early innings of test time compute and search where it's just very, very much a V0 and there's so many things that don't feel quite right. And I suspect in parallel to people increasing the amount of pre training data and the size of the models and pre training and finding tricks there, you'll now have this other thread of getting search to work better and better.

[Michael Truel]

So let me ask you about Strawberry Tomorrow Eyes. So it looks like GitHub Copilot might be integrating 01 in some kind of way. And I think some of the comments are saying, does this mean cursor is done? I think I saw one comment saying.

[Arvid Lunmark]

That it's time to shut down Cursory.

[Michael Truel]

Time to shut down Cursair. So is it time to shut down Cursair?

[Swali]

I think this space is a little bit different from past software spaces over the 2010s where I think that the ceiling here is really, really, really, incredibly high. And so I think that the best product in three to four years will just be so much more useful than the best product today. And you can wax poetic about moats this and brand that and you know, this is our advantage. But I think in the end, just if you don't have like, if you stop innovating on the product, you will, you will lose. And that's also great for startups. That's great for people trying to enter this market because it means you have an opportunity to win against people who have, you know, lots of users already by just building something better. And so I think, yeah, over the next few years it's just about building the best product, building the best system. And that both comes down to the modeling engine side of things. And it also comes down to the editing experience.

[Aman Sanger]

Yeah, I think most of the additional value from Cursor versus everything else out there is not just integrating the new model fast like 01. It comes from all of the depth that goes into these custom models that you don't realize are working for you in every facet of the product as well as the really a thoughtful UX with every single feature.

[Michael Truel]

All right, from that profound answer, let's descend back down to the technical you mentioned. Do you have a taxonomy of synthetic data?

[Aman Sanger]

Oh yeah.

[Michael Truel]

Can you please explain?

[Aman Sanger]

Yeah, I think there are Three main kinds of synthetic data. So what is synthetic data first? So there's normal data like non synthetic data, which is just data that's naturally created. That is usually it'll be from humans having done things. So from some human process you get this data, synthetic data. The first one would be distillation. So having a language model output tokens or probability distributions over tokens and then you can train some less capable model on this. This approach is not going to get you a net more capable model than the original one that has produced the tokens. But it's really useful for if there's some capability you want to elicit from some really expensive high latency model, you can then distill that down into some smaller task specific model. The second kind is when one direction of the problem is easier than the reverse. And so a great example of this is bug detection like we mentioned earlier, where it's a lot easier to introduce reasonable looking bugs than it is to actually detect them. And this is probably the case for humans too. And so what you can do is you can get a model that's not training that much data, that's not that smart to introduce a bunch of bugs in code and then you can use that to then train, use a synthetic data to train a model that can be really good at detecting bugs. The last category I think is, I guess the main one that it feels like the big labs are doing for synthetic data, which is producing text with language models that can then be verified easily. So an extreme example of this is if you have a verification system that can detect if language is Shakespeare level and then you have a bunch of monkeys typing in typewriters, you can eventually get enough training data to train a Shakespeare level language model. And I mean this is the case like very much the case for math, where verification is actually really, really easy for formal languages. And then what you can do is you can have an okay model, generate a ton of rollouts and then choose the ones that you know have actually proved the ground truth theorems and then train that further. There's similar things you can do for code with leetcode like problems or where if you have some set of tests that you know correspond to, if something passes these tests, it has actually solved the problem. You could do the same thing where we verify that it's passed the test and then train the model and the outputs that have passed the tests. I think it's going to be a little tricky getting this to work in all domains or just in general having the perfect verifier feels really, really hard to do with just open ended miscellaneous tasks you give the model or more long horizon tasks even in coding.

[Michael Truel]

That's because you're not as optimistic as Arvid. But yeah, that third category requires having a verifier.

[Aman Sanger]

Yeah, verification. It feels like it's best when you know for a fact that it's correct. And then it wouldn't be using a language model to verify. It would be using tests or formal.

[Swali]

Systems or running the thing too. Doing the human form of verification where you just do manual quality control. Yeah, but the language model version of that where it's running the thing and actually understands the output.

[Aman Sanger]

Yeah, no, that's true.

[Swali]

Sort of somewhere between.

[Aman Sanger]

Yeah, I think that's the category that is most likely to result in massive gains.

[Michael Truel]

What about RL with feedback side RLHF vs Rlaif? What's the role of that in getting better performance on the models?

[Aman Sanger]

Yeah, so RLHF is when the reward model you use is trained from some labels you've collected from humans giving feedback. 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. RLAI f is interesting because you're kind of depending on like this is actually kind of going to. It's depending on the constraint that verification is actually a decent bit easier than generation because it feels like, okay, what are you doing? Are you using this language model to look at the language model outputs and then improve the language model? But no, it actually may work. If the language model has a much easier time verifying some solution than it does generating it, then you actually could perhaps get this kind of recursive loop. I don't think it's going to look exactly like that. The other thing you could do is that we kind of do is a little bit of a mix of RLAIF and RLHF where usually the model is actually quite correct. And this is in the case of cursor tab at picking between two possible generations of what is the better one. And then it just needs a little bit of human nudging with only on the order of 50, 100 examples to kind of align that prior the model has exactly with what you want. It looks different than I think normal RLHF where you're usually training these reward models in tons of examples.

[Michael Truel]

What's your intuition when you compare generation and verification or generation and ranking? Is ranking way easier than generation?

[Aman Sanger]

My intuition would just say, yeah, it should be. This is kind of going Back to if you believe P does not equal np then there's this massive class of problems that are much much easier to verify given a proof than actually proving it.

[Michael Truel]

I wonder if the same thing will prove P not equal to NP or P equal to np.

[Arvid Lunmark]

That would be really cool.

[Michael Truel]

That'd be a. Whatever Fields medal by AI who gets the credit? Another open philosophical question.

[Unnamed Speaker]

I'm actually prompted it. I'm actually surprisingly curious. What, what, what like a good bet for when one AI will get the Fields medal will be. I actually don't know.

[Swali]

Is this a mon specialty?

[Unnamed Speaker]

I don't know what Amon's bet here is.

[Michael Truel]

Oh sorry. Nobel Prize or Fields metal first level.

[Arvid Lunmark]

Feels metal level field needs to solve.

[Michael Truel]

Fields metal comes first. Well you would say that of course.

[Arvid Lunmark]

But it's also this isolated system you verify and Sure.

[Unnamed Speaker]

I don't even know if I don't.

[Arvid Lunmark]

Need to do I feel like I.

[Aman Sanger]

Have much more tricky there. It felt like the path to get to IMO was a little bit more clear because it already could get a few IMO problems and there was a bunch of low hanging fruit given the literature at the time of what tactics people could take. I think I'm one much less versed in the space of theorem proving now and. And to yeah less intuition about how close we are to solving these really really hard open problems.

[Michael Truel]

So you think it'll be Fields model first? It won't be like in physics or.

[Unnamed Speaker]

In oh a hundred percent. I think that's probably more likely. It's probably much more likely that it'll get. Yeah well I think it puts the I don't know like BSD which is a Burt Spinner turn diet conjecture or Riemann ipods or any one of these hard math problems are just actually really hard. It's sort of unclear what a path to get even a solution looks like. We don't even know what a path looks like let alone and you rebuild.

[Arvid Lunmark]

Yeah, this is like an isolated system and you can actually have a good reward system and it feels like it's easier to train for that.

[Aman Sanger]

I think we might get Fields metal before AGI.

[Unnamed Speaker]

I'd be very happy, very happy. But I don't know if I think 20, 28, 2030.

[Michael Truel]

It feels metal.

[Unnamed Speaker]

Feels metal. All right.

[Michael Truel]

It feels like forever from now given how fast things have been going. Speaking of how fast things have been going, let's talk about scaling laws. So for people who don't know, maybe it's good to talk about this whole idea of scaling laws. What are they where do things stand and where do you think things are going?

[Aman Sanger]

I think it was interesting the original scaling laws paper by OpenAI was slightly wrong because I think of some issues they did with learning rate schedules and then Chinchilla showed a more correct version. And then from then people have again kind of deviated from doing the compute optimal thing because people start now optimizing more so for making the thing work really well given an inference budget. And I think there are a lot more dimensions to these curves than what we originally used of just compute number of parameters and data like inference compute is the obvious one. I think context length is another obvious one. So if you care, let's say you care about the two things of inference compute and then context windows, maybe the thing you want to train is some kind of SSM because they're much, much cheaper and faster at super super long context. And even if Maybe it has 10x worse scaling properties during training, meaning you have to spend 10x more compute to train the thing to get the same level of capabilities, it's worth it because you care most about that inference budget for really long context windows. So it'll be interesting to see how people play with all these dimensions.

[Michael Truel]

So yeah, I mean you speak to the multiple dimensions. Obviously the original conception was just looking at the variables of the size of the model as measured by parameters and the size of the data as measured by the number of tokens and looking at the ratio of the two.

[Aman Sanger]

Yeah.

[Michael Truel]

And it's kind of a compelling notion that there is a number or at least a minimum and it seems like one was emerging. Do you still believe that there is a kind of bigger is better?

[Aman Sanger]

I think bigger is certainly better for just raw performance, raw intelligence and raw intelligence. I think the path that people might take is I'm particularly bullish on distillation and how many knobs can you turn to? If we spend a ton ton of money on training, get the most capable cheap model really caring as much as you can. Because the naive version of caring as much as you can about inference time compute is what people have already done done with the LLAMA models or just overtraining the shit out of 7B models on way, way, way more tokens than is tinchill optimal. But if you really care about it, maybe the thing to do is what Gemma did, which is let's not just train on tokens, let's literally train on minimizing the KL divergence with the distribution of Gemma27b so knowledge distillation there and you're spending the compute of literally training this 27 billion model, billion parameter model on all these tokens just to get out this, I don't know, smaller model.

[Michael Truel]

And the distillation gives you just a faster model. Smaller means faster?

[Aman Sanger]

Yeah. Distillation in theory is I think getting out more signal from the data that you're training on. And it's perhaps another way of getting over, not completely over, but partially helping with the data wall where you only have so much data to train on. Let's train this really, really big model on all these tokens and we'll distill it into this smaller one and maybe we can get more signal per token for this much smaller model than we would have originally if we trained it.

[Michael Truel]

So if I gave you $10 trillion, how would you spend it? I mean, you can't buy an island or whatever. How would you allocate it in terms of improving big model versus maybe paying for HF in the RLHF or.

[Aman Sanger]

Yeah, I think there's a lot of these secrets and details about training these large models that I just don't know and are only privy to the large labs. And the issue is I would waste a lot of that money if I even attempted this because I wouldn't know those things. Suspending a lot of disbelief and assuming you had the know how or if you're saying like you have to operate with like the limited information you have now.

[Michael Truel]

No, no, no. Actually I would say you swoop in and you get all the information, all the little heuristics, all the little parameters, all the, all the parameters that define how the thing is trained. If we look in how to invest money for the next five years in terms of maximizing what you called raw.

[Unnamed Speaker]

Intelligence, I mean, isn't the answer really simple? You just try to get as much compute as possible. At the end of the day, all you need to buy is the GPUs and then the researchers can find all the you can tune whether you want to pre train a big model or a small model.

[Aman Sanger]

Well, this gets into the question of are you really limited by compute and money or are you limited by these other things and driving?

[Unnamed Speaker]

I'm more privy to Arvid's belief that we're sort of ideal limited. But there's always.

[Arvid Lunmark]

But if you have a lot of compute, you can run a lot of experiments.

[Michael Truel]

So you would run a lot of experiments versus use that compute to train a gigantic model.

[Arvid Lunmark]

I would, but I do believe that we are limited in terms of the ideas that we have.

[Aman Sanger]

I Think. Yeah, because even with all this compute and all the data you could collect in the world, I think you really are ultimately limited by not even ideas, but just really good engineering. Even with all the capital in the world, would you really be able to assemble? There aren't that many people in the world who really can make the difference here. And there's so much work that goes into research that is just pure really, really hard engineering work. As a very hand wavy example, if you look at the original transformer paper, how much work was joining together a lot of these really interesting concepts embedded in the literature versus then going in and writing all the code like maybe the CUDA kernels, maybe whatever else. I don't know if it ran on GPUs or GPUs originally such that it actually saturated the GPU performance. Getting GNOME to zero to go in and do all this code. And GNOME is probably one of the best engineers in the world or maybe going a step further, the next generation of models having these things like getting model parallelism to work and scaling it on thousands of, or maybe tens of thousands of V1 hundreds, which I think GPT3 may have been. There's just so much engineering effort that has to go into all of these things to make it work. If you really brought that cost down to maybe not zero, but just made it 10x easier, made it super easy for someone with really fantastic ideas to immediately get to the version of the new architecture they dreamed of, that is getting 50, 40% utilization on the GPUs. I think that would just speed up research by a ton.

[Unnamed Speaker]

I think if you see a clear path to improvement, you should always take the low hanging fruit first. I think probably OpenAI and all the other labs did the right thing to pick off the low hanging fruit. Where the low hanging fruit is sort of, you could scale up to a GPT 4.25 scale and you just keep scaling and things keep getting better. And as long as there's no point of experimenting with new ideas when everything is working and you should sort of bang on and try to get as much juice out as possible. And then maybe when you really need new ideas for I think if you're spending $10 trillion, you probably want to spend then actually reevaluate your ideas, probably your idea limited at that point, I.

[Aman Sanger]

Think all of us believe new ideas are probably needed to get all the way there to hei. And all of us also probably believe there exist ways of testing out those ideas at smaller scales and being fairly confident they'll play out. It's just quite difficult for the labs in their current position to dedicate their very limited research and engineering talent to exploring all these other ideas when there's this core thing that will probably improve performance for some decent amount of time.

[Michael Truel]

Yeah. But also these big labs like winning. This is going wild. Okay, so how. Big question. Looking out into the future, you're now at the center of the programming world. How do you think programming, the nature of programming changes in the next few months, in the next year, in the next two years, the next five years, 10 years?

[Swali]

I think we're really excited about a future where the programmers in the driver's seat for a long time and you've heard us talk about this a little bit, but one that emphasizes speed and agency for the programmer and control. The ability to modify anything you want to modify, the ability to iterate really fast on what you're building. And this is a little different, I think, than where some people are jumping to in the space where I think one idea that's captivated people is can you talk to your computer? Can you have it build software for you as if you're talking to an engineering department or an engineer over slack? And can it just be this sort of isolated text box? And part of the reason we're not excited about that is some of the stuff we've talked about with latency, but then a big piece, a reason we're not excited about that is because that comes with giving up a lot of control. It's much harder to be really specific when you're talking in the text box. And if you're necessarily just going to communicate with a thing like you would be communicating with an engineering department, you're actually abdicating tons of tons of really important decisions to this bot. And this kind of gets at fundamentally what engineering is. I think that some people who are a little bit more removed from engineering might think of it as, you know, the spec is completely written out and then the engineers just come and they just implement and it's just about making the thing happen in code and making the thing exist. But I think a lot of the best engineering, the engineering we enjoy, involves tons of tiny micro decisions about what exactly you're building and about really hard trade offs between speed and cost and all the other things involved in a system. And we want, as long as humans are actually the ones making, you know, designing the software and the ones specifying what they want to be built, and it's not just like company run by all AIs we think you'll really want the humor, the human in a driver's seat dictating these decisions. And so there's. The jury's still out on kind of what that looks like. I think that one weird idea for what that could look like is it could look like you can control the level of abstraction you view a code base at, and you can point at specific parts of a code base that maybe you digest a code base by looking at it in the form of pseudocode. And you can actually edit that pseudocode too and then have changes get made down at the formal programming level. And you keep the. You can gesture at any piece of logic in your software component of programming. You keep the inflow, text editing component of programming. You keep the control of. You can even go down into the code. You can go at higher levels of abstraction while also giving you these big productivity gains.

[Michael Truel]

It'd be nice if you can go up and down the abstraction stack.

[Swali]

Yeah. And there are a lot of details to figure out there that's sort of like a fuzzy idea. Time will tell if it actually works. But these principles of control and speed in the human in the driver's seat, we think are really important. We think for some things, like Arvind mentioned before, for some styles of programming, you can kind of hand it off chatbot style if you have a bug that's really well specified. But that's not most of programming, and that's also not most of the programming we think a lot of people value.

[Michael Truel]

What about the fundamental skill of programming? There's a lot of people, young people right now, kind of scared, thinking because they love programming, but they're scared about will I be able to have a future if I pursue this career path? Do you think the very skill of programming will change fundamentally?

[Swali]

I actually think this is a really, really exciting time to be building software. We remember what programming was like in 2013, 2012, whatever it was. And there was just so much more cruft and boilerplate and looking up something really gnarly. And that stuff still exists. It's definitely not at zero. But programming today is way more fun than back then. It's like we're really getting down to the delight, concentration, and all the things that really draw people to programming. Like for instance, this element of being able to build things really fast and speed and also individual control. All those are just being turned up a ton. And so I think it's going to be a really, really fun time for people who build software. I think that the skills will probably change Too. I think that people's taste and creative ideas will be magnified and it will be less about, maybe less, a little bit about boilerplate text editing, maybe even a little bit less about carefulness, which I think is really important today if you're a programmer. I think it'll be a lot more fun.

[Michael Truel]

What do you guys think?

[Arvid Lunmark]

I agree. I'm very excited to be able to change. Just one thing that happened recently was we wanted to do a relatively big migration to our code base. We were using async local storage in Node js, which is known to be not very performant, and we wanted to migrate to our context object. And this is a big migration that affects the entire code base. And Swall and I spent, I don't know, five days working through this, even with today's AI tools. And I'm really excited for a future where I can just show a couple of examples and then the AI applies that to all of the locations and then it highlights, oh, this is a new example. What should I do? And then I show exactly what to do there and then that can be done in 10 minutes and then you can iterate much, much faster. Then you don't have to think as much upfront and stand at the blackboard and think, exactly 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. And then you can change it instantly again after. And so, yeah, I think being a programmer in the future is going to be a lot of fun.

[Aman Sanger]

Yeah, I really like that point about it feels like a lot of the time with programming, there are two ways you can go about it. One is you think really hard, carefully upfront about the best possible way to do it, and then you spend your limited time of engineering to actually implement it. But I much prefer just getting in the code and taking a crack at it, seeing how it kind of lays out and then iterating really quickly on that. That feels more fun.

[Michael Truel]

Yeah, just speaking to generating the boilerplate is great. So you just focus on the difficult design, nuanced, difficult design decisions. Migration. I feel like this is, this is a cool one. It seems like large language models are able to basically translate from one programming language to another or translate migrate in the general sense of what migrate is. But that's in the current moment. The fear has to do with, okay, as these models get better and better, then you're doing less and less creative decisions and is it going to move to a place where you're operating in the design space of natural language, where natural language is the main programming language. And I guess I could ask that by way of advice, like, if somebody's interested in programming now, what do you think they should learn? Like, do they. You guys started in Java and I forget the. Oh, some php. Objective C, Objective C. There you go. Yeah. I mean, in the end we all know JavaScript is going to win and not TypeScript. It's just, it's going to be like vanilla JavaScript. It's going to eat the world and maybe a little bit php. And I mean it also brings up the question of like, I think Don Knuth has this idea that some percent of the population is geeks and like there's a particular kind of psychology in mind required for programming. And it feels like more and more that expands. The kind of person that should be able to, can do great programming might expand.

[Aman Sanger]

I think different people do programming for different reasons. But I think the true, maybe the best programmers are the ones that really love, just absolutely love programming. For example, there are folks on our team who literally when they're, they get back from work, they go and then they boot up Cursor and then they start coding on their side projects for the entire night and they stay until 3am doing that. And when they're sad, they said, I just really need to code. And I think there's that level of programmer where this obsession and love of programming I think makes really the best programmers. And I think these types of people will really get into the details of how things work.

[Michael Truel]

I guess the question I'm asking that exact program, let's think about that person. When the Super Tab, the super awesome praise be the tab succeeds and you keep pressing tab, that person in the.

[Unnamed Speaker]

Team loves Cursor Tab more than anybody else, right?

[Arvid Lunmark]

Yeah. And it's also not just like, like pressing tab is like the just pressing tab that's like the easy way to say it in the catchphrase, you know. But what you're actually doing when you're pressing tab is that you're injecting intent all the time while you're doing it. Sometimes you're rejecting it, sometimes you're typing a few more characters and that's the way that you're sort of shaping the things that's being created. And I think programming will change a lot to just what is it that you want to make.

[Unnamed Speaker]

It's sort of higher bandwidth. The communication to the computer just becomes higher and higher bandwidth as opposed to just typing is much lower bandwidth than communicating intent.

[Michael Truel]

I mean, this goes to your manifesto titled Engineering Genius. We are an applied research lab building extraordinary, productive human AI systems. So speaking to this hybrid element, to start, we're building the engineer of the future. A human AI programmer that's an order of magnitude more effective than any one engineer. This hybrid engineer will have effortless control over their code base and no low entropy keystrokes. They will iterate at the speed of their judgment, even in the most complex systems. Using a combination of AI and human ingenuity, they will outsmart and out engineer the best pure AI systems. We are a group of researchers and engineers. We build software and models to invent at the edge of what's useful and what's possible. Our work has already improved the lives of hundreds of thousands of programmers. And on the way to that, we'll at least make programming more fun. So thank you for talking today.

[Arvid Lunmark]

Thank you.

[Swali]

Thanks for having us.

[Unnamed Speaker]

Thank you, thank you.

[Lex Friedman]

Thanks for listening to this conversation with Michael, Swale, Arvid and Aman to support this podcast. Please check out our sponsors in the description. And now let me leave you with a random, funny, and perhaps profound programming code I saw on Reddit. Nothing is as permanent as a temporary solution that works.

[Michael Truel]

Thank you for listening and hope to.

[Lex Friedman]

See you next time.