We're All Addicted To Claude Code

0:00I feel like when I'm using Cloud Code, it's like, oh, I feel like I'm flying through the code. When it's in your CLI, this thing can debug nested delayed jobs like five levels in and figure out what the bug was and then write a test for it and it never happens again. This is insane. I think everyone who's experimenting with this stuff on like a hobbyist level or at like a very small startup, they're just pushing the coding agents as far as they can go. Because it's like you don't really have time to figure out anything else. Like as a startup, you have limited runway. You're just going to orient around speed.

0:27I think at a bigger company, you have a lot more to lose. What are some of the tips to become a top 1 %? user of coding agents. Yeah, what's your stack?

0:44Hey everyone, welcome back to another episode of The Light Cone. Gary, are you ready to record? I'm in plan mode right now, but okay, yeah, I guess it's time. Sorry about that. Well, welcome to another episode of The Light Cone. And today we have an incredible guest, Kelvin French Owen. He's one of the first people to create codecs at OpenAI. And before that, he started Segment, which is a multi-billion dollar company that got to a very successful exit. Kelvin, welcome back. Thanks for having me. I guess what a crazy time for all of us. I recently got very, very addicted to Claude Code. And I would describe it as like 10 years ago, I was a marathon runner and I loved doing it.

1:28And then I suffered a catastrophic knee injury, which is called manager mode. And I stopped coding, which is tragic and horrible. But now the last nine days have been like this incredible unlock of all the things I remember being able to do. And it's like, you know, I got a new total knee replacement and actually it's a bionic knee and it allows me to run five times faster. What's your take on it? Because you're, I mean, right out there at the forefront of it. I mean, Codex pioneered all of the, a lot of the ideas that now like everyone still uses and Codex is still evolving too. For brief context, when I was at OpenAI, I was working on the Codex web product.

2:12At the time, Cursor was out in the market and they had kind of built this shim around, I think it was Sonnet 3.5 and it was able to work in your IDE. Quad code had just come out and it was working as a CLI. And we kind of had this idea like, hey, in the future, coding is really going to feel more like talking to a coworker. Like you're going to send off a question and then they'll go off and do something and come back to you with a PR. And so that's where we started with this web view. And that's what we were building. I think directionally, that's still kind of correct for where things should go.

2:44But obviously now everyone is coding with CLIs instead. Like they're using those tools a lot more, whether it's Cloud Code or whether it's Codex. And I think, at least for me, kind of the lesson in that is I think in some sense you're right that everyone is going to become a manager in the future, or at least that's my hot take. But in order to get there, there are steps along the way, and you have to really build a lot of trust in the model and understand what it's doing. You recently came over to CloudCode. What's the transition been like in terms of as using it as one of your stacks? Yeah, yeah.

3:16So CloudCode is certainly my kind of daily driver today. and honestly this is switched every few months for a while I was deeply in cursor I think their new model which is really fast is actually quite good then I kind of moved over to quad code especially with opus quad code is a really interesting product and I think it's underrated how good the both product and model are working together if you study them closely I think one of the things that quad code does in particular that's really amazing is split up context well and so if you look at uh i don't know things like skills or sub agents like when you ask cloud code to do something it will typically spawn and explore sub agent or like multiple ones and basically each of those are running haiku to traverse the file system and kind of like explore what's there and they're doing it in their own context window and i think anthropic has kind of like figured something out here around given a task does that task fit in the context window or should I actually like split it into many more?

4:14And the models are like insanely good at this, which I think gives them really good results. And I think the fascinating thing is because it's on the terminal, it's the purist form for composable atomic integrations. Because if you came from IDE first world, which is where cursor was, and I suppose codex too, this concept of finding the context more freeform wouldn't come out so natural, right? Because which is so unique. Yeah. Personally, I was surprised. I don't know how you all feel, but I was surprised that CLIs. It's like a weird retro future. Yes. Like the CLIs, which are the technology from 20 years ago, have somehow beaten out all the actual IDEs, which were supposed to be the future.

4:56A hundred percent. Yeah. And I think it's important actually to cloud code that it's not an IDE because it sort of distances you from the code that's being written. Like IDEs are all about exploring files, right? And you're like trying to keep all the state in your head and understand what's going on. But the fact that a CLI is like a totally different thing means that they have a lot more freedom in terms of how it feels. And I don't know about you, but I feel like when I'm using Cloud Code, it's like, oh, I feel like I'm flying through the code. You know, it's like, there's all sorts of things going.

5:22There's like little progress indicators. It's kind of like giving me status updates, but like the code that's being written is not the front and center thing. I mean, dev environments are so messy. I mean, I really like how clean a sandbox conceptually is in Codex, But then I just ran into all these crazy issues, like trying to run just simple testing, right? It needs to access Postgres, and then it can't do it. Or my codex.md ended up being 20 lines long, and even then it didn't work. When it's in your CLI, it could just access your development database. I mean, I'm not sure if I'm supposed to do this, but I've actually also had it access my production database.

6:01Yeah, yeah. And it can just do it. It's like, yeah, OK, here, like I looked into it and I think this happened and I'm going to debug this, you know, concurrency issue. And I was like, oh, my God, like this thing can debug nested delayed jobs like five levels in and figure out what the bug was and then write a test for it. And it never happens again. This is insane. Yeah. And I think that distribution mode is frankly underrated. Like thinking about a cursor or a cloud code or a codex CLI, the fact that you can just download it and use it without having to get IT permissions or anything. makes a huge difference.

6:35And actually, I was playing around with a product the other day where you download a desktop app, and then it execs the Claude code that you have running on your laptop and uses that and communicates back via an MCP server to the desktop product. And it's like, this is a very interesting way of now starting to work with your laptop where you don't have to get anyone's permission to do it. You just download the product and go. Yeah, I was looking at like New Relic has an MCP, but, you know, Century, you can like copy Markdown. But like, it's like an auto bug fixer. Yeah, it's right there. It's super interesting that in a world where things are changing so fast, you really want your pride to have a bottoms up distribution, not top down, because like top down is like just too slow.

7:16Like the CTO of a company is going to be like, have all these concerns about security and privacy and control. Exactly. Versus like the engineers just like install the thing and start using like this thing is amazing. Yeah, I think that's right. The one thing I do struggle with, I mean, I'm like a B2B enterprise guy generally, but I feel like there's some amount of moat that happens when you do that top-down sale. And there's got to be some company who manages to crack it where it's like, oh, this is the thing that everyone has access to. Maybe individual people can take it up. That was the original Netscape Navigator.

7:47It was free for non-commercial use. And then people would just download it and use it for commercial use. and then they could just track down the IPs and figure out exactly how many clients were in all of these different companies and say you should pay for this you're in violation but all you have to do is buy a license yeah yeah curious you could do that work again here I mean that your point about distribution is very interesting because now people are probably just making architecture decisions about what to use directly in cloud code like they might not even know what analytics to use. And it's like, oh yeah, as long as Cloud Code says use PostHog, they're using PostHog.

8:27A hundred percent. One of the companies who I advise was talking about their GEO strategy. This is like the generative optimization or how you show up in chatbots. And what he was saying is funny is one of their competitors had put together a top five list of tools in their category that you should be using. And of course, their tool is ranked at the top of this top five list. And like any human looking at this would be like, oh, this is so obviously biased. It's like the top tool is the one that's in the domain, you know? But the LLMs get fooled and like they're pulling together a bunch of contexts and they're saying like, oh, this is the top.

8:59And then they'll just recommend it. I think, yeah, if you're selling a developer tool, like having good docs that are out there, like having social proof, like maybe being posted on Reddit a little bit more, all of that helps your case tremendously. Which is why I think a lot of the open source projects have taken off a lot more. I think one of the examples is Supabase, actually, which really took off last year. And part of it is because they have such a good open source documentation, how to set up a bunch of stuff. Whenever someone asks how to set up anything that you need, some sort of backend, Firebase type of transaction, the default answer from all the LLMs is actually Supabase.

9:36I was trying some of these questions that comes from that. The thing is, it's winning the internet. And it was like that before when it was like Stack Overflow, searching Google. And then now that nobody uses Google anymore, it's like crazy. Yeah. It's kind of the same deal. I will say it does help open source disproportionately, I would say. Like, I don't know if you all saw there was a ramp blog post that they recently published about building their own coding agent. And they were mentioning that they use open code as a harness because the model can look and see the source code and understand how it's working.

10:07And I do this all the time with open source projects. I'll like clone the repo and then spin up codex or cloud code and be like, hey, give me a walkthrough of what's going on here. and it's really useful. What do you think are some of the tips for anyone that wants to build coding agents since you've done it a lot? What are some now lessons that you learned that you want to share? I mean, I think the number one thing is managing context well. Basically, we kind of had like a checkpoint for, I think it was 03, like one of the reasoning models. And then we did a bunch of fine tuning on it in reinforcement learning where it's like, oh, you're given a bunch of questions to like solve these coding problems or like fix tests or whatever, implement a feature.

10:45And then the model was RL to respond to those. And so I think most people are not going to be doing that, right? But the things that you can do are figure out like, hey, what context should I be supplying to this agent to get the best possible result? And so for Cloud Code, if you watch it working, it's like, oh, I'm going to like spawn a bunch of these Explorer sub agents. They will like search for different patterns in the file system. They will come back. They will have this context. They'll summarize it for me. And then I'll have someplace to go. It's interesting watching like different agents structure this context.

11:16Like I think Cursor takes an approach where they actually do semantic search, where they embed everything and figure out like, hey, what query is closest to this? If you look at a codex or a cloud code, they actually just use like grep. And I think that works because... It works really well. Yeah, it works very well because code is very context dense. Like if you think about lines of code, it's like each line is probably less than 80 characters. there's not a lot of like big like data blobs or like JSON in your code base. Maybe there's some, but not a lot. You can respect git ignore to figure out and like filter out stuff that's just not relevant or is like packaged.

11:50And you can use grep and rip grep to like find context around the code, which probably gives you a good sense for what that code is doing. And you can navigate the folder structure. And also elements are really good at emitting very complicated grep expressions that would like torture a human. Yes. Yeah. Yeah. Yeah. This is like the RL in practice. Yes. Yeah. And so I think all of that, like if you're trying to build a system, well, I'm trying to build systems that integrate agents for non-coding work. I think you can learn a lot of those lessons and say like, hey, how do I get my data in the format that is like maybe closest to code where the model can like peak and look at like areas around it and get the right structured data?

12:27So given this is how a lot of the superpowers for the best coding agents is context engineering, what are some of the tips to become a top 1 % user of coding agents? Yeah, what's your stack? Yeah, what do you do to be so productive with it? One is if you're able to use just generally far less code in plumbing. So a lot of what I do is deploy stacks on like Vercel or Next.js or like Cloudflare workers where there's kind of like already a bunch of boilerplate like taking care of for you. And then you don't really have to think that much about like, hey, I need to stand up like all these different services and deal with like service discovery and like registering on like some sort of central endpoint or like all these databases.

13:07It's like, oh, like everything is pretty roughly defined in this like one or 200 lines of code. I tend to operate more towards microservices for that as well or like individual packages that are fairly well structured. I think it's also worth knowing like what the LLM superpowers are. Like in general, coding agents are, I think Andre Karpathy just tweeted about this. They're like super persistent. So they will keep going no matter what. They end up typically just making more of whatever's there. So if you're trying to direct them to do something, it's worth like, I mean, I can pick on OpenAI slightly in this example.

13:43OpenAI has like a giant monorepo. It's been there for a few years now and has like, I don't know, thousands of engineers who are committing. Some of those engineers are like super senior meta folks who came in and are like know exactly how to write production code. Some are like new PhDs. It's like a pretty wide range. And so the LM will pick up different things depending on where you direct it. I think there's a lot of room actually for coding agents to figure out like what is the like optimal type of code that we should produce. I mean, obviously giving the model a way to check its work helps improve performance drastically.

14:17So the more that you can run tests in Lint, CI, et cetera. Personally, I also use code review bots pretty aggressively. I know like Reptile at YC company is really good. The cursor bug bot has gotten quite good. And I actually like Codex for code review as well. I find it does a very good job on correctness. So those are all things that like the agents are good at. And they're excellent exploring the code base too. I think areas where they don't do well, they make more if your goal is not to make more, They'll often duplicate code and spend a bunch of time re-implementing things that you're like, oh, of course you didn't want to do this.

14:53I think context poisoning is a real thing where it kind of goes down one loop and it will continue because it has this persistence, but it's referring back to tokens which are not right in terms of pursuing a solution. And so one thing that I often do is very actively clear context. Like how often? usually uh when it gets above like 50 tokens oh wow yeah yeah i don't know there's this guy dex uh from this company human layer that was actually another yc company yes that yc company fall 24 yeah yeah and he talks a lot about it yeah he has this concept of like the llms reaching the dumb zone where it's like after a certain amount of tokens uh it just starts like degrading in quality and i actually think that's very true especially if you think about like how the reinforcement learning might work.

15:42Like imagine you're a college student, you're taking an exam. In the first five minutes of that exam, you're like, oh, I have all the time in the world. Like I'll do a great job. I'll think through each of these problems. Let's say you have like five minutes left and you still have half the exam left. You're like, oh man, I just got to do whatever I can. Like that's the LM with the context window, right? One of the tricks that I think founders use is you put like a cannery at the beginning of the context. There's something very esoteric that I would only help. It's like something really funny.

16:09It's like, I don't know, my name is Calvin and blah, blah, blah. I drink tea at 8 a.m. Some random fact. And then as you keep going, you ask it, do you remember what's my name? Do you remember when I drank tea? And then when it starts forgetting that, I think it's a bit of a sign that the context has poison. That's like one trick I've seen people do. They do a random cannery. I have not tried this, but I fully believe it. That's interesting. I haven't run across any bugs before compaction, but maybe I'm not paying attention. But you're saying like that actually is actively something that it just starts doing weirder things that are not like optimal.

16:46Yeah, yeah. Okay, I gotta be on the lookout for that. It seems like it should be solvable within the quad code itself. Like it should be able to basically do some sort of detection like what Tiana said. Yeah, you need to do your own internal heartbeat around it, around the context. Yeah, and I think we're just not there yet. Like I agree with you in the limit. Right now it's definitely hard to manage context well and I think kind of the way it gets around it is like split up context windows and then try and merge everything. But you're sort of still at the limit right now of like everything that lives in context at the end of a cloud code session is kind of fixed.

17:17It's actually interesting. The Codex approach is kind of the opposite. And they just wrote about this on the OpenAI blog, where it will run compaction like periodically after each turn. And so Codex can continue to run for a very long time. And if you look at the percentage in the CLI, you'll see it like move up and down as compaction runs. I guess like there are these very different architectures between CloudCode and Codex sound like they're actually deeper in that Codex is actually meant for much longer running jobs. That's sort of like off the bat, a different use case. And then the architecture is very different as a result.

17:55I guess right now it seems like CLIs, you know, 2026 might be the year of CLI. But then this other idea that AGI is here and it's actually ASI is around the corner. the coding agents right now are really really smart but not smart enough to run on their own for long periods of time but a 10x increase in compute from here are we there like are we at 24 hours or 48 hour running jobs on codex and that architecture is correct for that world yeah i think it's a good question it sort of goes back to like kind of the founding dna of both companies like i feel like anthropic has always been very big on like building tools for humans where it comes to like oh here's the style of the tone and like here's how it should fit with all of the rest of your work and i think quad code is like a very natural extension of that in a lot of ways it like works like a human would where it's like oh you need to build like i don't know a dog house or something it's like oh i'll go to the hardware store and i'll build all these materials and i'll like figure out how they all fit together whereas open ai really leans into this idea of just like we are going to train the best model and reinforce over time and get it to do longer and longer horizon things in this pursuit of artificial general intelligence.

19:06And so it may not work like a human at all. Like going back to the doghouse example, it's like AlphaGo didn't either. Yeah, but AlphaGo didn't either. It's like, oh, it's like instead I will have a 3D printer that can print from scratch like a doghouse and it will be exactly what you want and it will take a long time and it will be like very custom and it will do like weird things, but it will work, you know, and like maybe in the limit, that's the right call. And so it's going to be really interesting to see how they play out. I mean, net-net, it seems like the latter is somewhat inevitable, but I like the former so much.

19:35Yes, yeah, yeah. You know, like even this idea that it greps is like I thought about, you know, 10 years ago, it was like, yeah, I was in there like writing my own really weird regexes to try to figure out where everything was when I was refactoring or trying to understand code or whatever. So that's the feeling I get when I'm using it. It's like I can do five people's worth of work in like a single day. It's like rocket boosters. It's unbelievable. Yeah. I think it's going to be really interesting to see how this plays out across large and small companies. I think everyone who's experimenting with this stuff on like a hobbyist level or at like a very small startup, they're just pushing the coding agents as far as they can go.

20:11Because it's like you don't really have time to figure out anything else. Like as a startup, you have limited runway. You're just going to like orient around speed. I think at a bigger company, you have a lot more to lose. And you have all these other internal processes around code review and you probably already hired like a big eng team. And I think it's going to be very strange as like these individual teams of like one person are like, hey, that team over there isn't doing the right thing. Like, let me just build a prototype that like works better. I think at some point it's going to start working better.

20:40And I think that landscape shift is going to be a very interesting, strange thing. My 10 year old is, you know, he has writing assignments every day. And then yesterday was the first day where he used AI. And then I was like, this is not a turn of a phrase that a 10 year old is capable of doing. And then I think about that in this context, because we are working with a lot of 18 to 22 year olds who, they've done internships, but they haven't done eng manager work. We're saying post product market fit, once you have job queues of millions of jobs and hundreds of thousands of errors. That's real eng management.

21:23It's horribly unglamorous, combing through hundreds of thousands of errors and then manually making sure that the thing works for all of your users in the background. How does the next generation understand that? Can the Cloud Code bot actually teach people about architecture and things like that? Or are you just going to bump your head into it and users just kind of suffer and you know have to figure it out like at least where i find myself spending the most time when it comes to product is figuring out the kind of product model in a sense like what are the things that the user has to understand today um and what are the primitives that they can use to like do whatever they want i always think of slack like this it's like slack was in some ways not really a new concept it's like there were many chats that existed before it.

22:13But the fact that they had like channels, messages and reactions in a simple way that people could just like think about and be like, oh, I understand how to like navigate this. It made a lot of sense for people. But then kind of once they were there, like it's very hard to change that later on for a user, you know, it's like, oh, maybe they wanted to go in more of like a document first way or like maybe right now they're trying to incorporate agents. It's like difficult to change the user's mental model. And so I at least for myself building products. It's like you have to think about that very carefully from an early stage because again, whatever you supply to the coding agents as that kind of kernel is going to be what they run with and make more of forevermore.

22:48YC's next batch is now taking applications. Got a startup in you? Apply at ycombinator.com slash apply. It's never too early and filling out the app will level up your idea. Okay, back to the video. Do you have thoughts just because you know the agents so well, Like what types of engineers are going to benefit more than others from these tools becoming popular? In general, I think that kind of the more senior you are, the more you benefit. Because the agents are so good at taking some sort of idea and then putting it into action. If you're able to prompt that in a few words, it's kind of like, oh, now suddenly I had this idea.

23:30I find this so often in OpenAI, like strolling through the code base. It's like, oh, like, here's the thing that I wish were different. Here's the thing that I wish were different. Here's the thing that I wish were different. Like just being able to kick those off and then have them come back, I think is super empowering and multiplies your impact. I think also being able to detect like which sorts of changes are good or bad architecturally is very important or like have a sense for where you might want to flag something to an agent. I think engineers who are more organized, like manager-ish, and there's probably just a missing product to be built here.

24:00maybe something like Conductor where it's like spread across all of your sessions and kind of reminding you like, hey, you were working on this thing. It's done. It needs your input here. Oh, you should switch your attention over to this other thing. I think that is going to become - Oh, Conductor should add that. Yeah. Like context management for agents, but like we also need context management for humans. Yes, 100%. Yeah. I mean, I want like when I wake up every day, it kind of is like, hey, here's all the work that got done overnight. Like here are the like three decisions that you need to make.