We're All Addicted To Claude Code

I feel like when I'm using quad 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. I 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? >> Hey everyone, welcome back to another episode of the Ly Cone. Gary, are you Are you ready to record? >> I'm 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 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. Uh I recently got very very addicted to Claude Code and uh I would describe it as like 10 years ago I was a marathon runner and I love doing it and then I suffered a catastrophic knee injury which is called manager

mode and I uh stopped coding which is tragic and horrible. Uh 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 codeex pioneered all of the a lot of the ideas that now like everyone still uses and codeex is still

evolving too. >> For brief context when I was at openai um I was working on the codeex web Project at the time cursor was out in the market and they had kind of built this shim uh around I think it was set 3.5 and it was able to work in your IDE. FOD code had just come out uh 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 co-orker 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. Uh and so that's where we Started with this web view. Uh and that's what we were building. I think directionally that's still kind of correct for where things should go. But 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 codecs. And I think at least for me kind of the lesson in that is I think in some sense you're right that like everyone is going to become

a manager in the future or at least that's My hottake 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 cloud code. What's the transition been like in terms of as using it as your you know one of your stacks? >> Yeah. Yeah. So cloud code is uh certainly my kind of like daily driver today. And honestly this has switched every few months. Uh 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. Cloud 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 claude 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 cla code to do something it will typically

spawn an explore sub agent or like multiple ones and basically each of those are running haik coup 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 enthropic 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 and 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 is the purest form for composible atomic integrations because if you came from ID first world which is where cursor was and I suppose codeex too this concept of uh finding the context more free form wouldn't come out so natural right string which is so unique. >> Yeah. And I personally I was surprised I don't know how you all feel but I was surprised that like >> weird it's like a weird retro future that like the CLI which are the technology from 20 years ago have somehow beaten out all the actual ideides which

were supposed to be the future. >> 100%. Yeah. And I I think it's important actually to claude 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 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. There'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. But then I just ran into all these crazy issues like trying to do just simple testing, right? It needs to access Postgress and then it can't do it or

you Know my codeex.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 to do >> and uh it can just do it. It's like, yeah, okay, here. Like, I looked into it and I think this happened and I'm going to debug this, you know, concurrency issue. 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 codec CLI, the fact that you can just download it and use it without having to get it permissions or anything makes a huge difference. And actually, I was playing around with a product the other Day where you download a desktop app >> and then it execs the clawed code that you have

running on your laptop and uses that and communicates back via an MCP server to the desktop product. Mhm. 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 Sentry you can Like copy markdown but like it's like an auto bug fixer basically. It's right there. >> It's super interesting that in a world where things are changing

so fast you really want your product to have a bottoms up distribution not top down because like top down is like just too slow. like the CTO of a company is going to be like have all these concerns about security and privacy and what if 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 a thing that everyone has access to maybe individual people can Take it up. That was the original um Netscape Navigator. It was free for non-commercial use and then uh people would just download it and use it for commercial use and then they could just track down the IPs and figure out uh 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. So I'd be curious if you could do that work again here. I mean That your point about distribution is very interesting because uh now people are probably just making architecture decisions about what to use directly in claude code like they might not even know what you know what analytics to use and it's like oh yeah as long as cla code says use post hog like they're using post hog >> 100%. One of the companies who I advise was talking about their like GEO strategy. This

is like the generative Optimization or how you show up in chat bots. And what he was saying is funny is one of their competitors had put together a like top five list of like 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 LMS get fooled and like they're Pulling together a

bunch of contexts and they're saying like, "Oh, this is the top." And 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 uh projects have taken off a lot more. I think one of the examples is Superbase actually. >> Yeah. Yeah. uh 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 firebased type of transaction the default answer from all the LMS is actually superbase. I was just 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 and searching Google and then now that nobody uses Google anymore. It's like crazy. You just Yeah, it's kind of the same deal.

>> I 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 um because the model can look and see the source code and understand how it's working. And I I do this all the time with open source products. I'll like clone the repo and then spin up codeex 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 a coding agent since you've uh done it a lot? What are what are some uh now lessons that you learned that you want to share? >> I mean, I think the number one thing uh is managing context well. Basically, we kind of had like a checkpoint for uh I think it was 03 like one of the reasoning models and then we did a bunch of fine-tuning on it um 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 or implement a feature um and 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 explore

sub agents. They will like search for different patterns in the file system. They will come back. Uh 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. Uh like 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 codeex or a cloud code, uh they actually just use like grip. >> And I think that works because well, >> yeah,

it works very well because code is very context dense. >> Um 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 codebase. 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. And you can use gp 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 f folder structure. >> And also elements are really good at emitting very complicated gp expressions that would like torture a human. >> Yes. Yeah. Yeah. Yeah. Yeah. This is like the RL in practice. >> 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 uh agents for non-coding work. Uh 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. >> So 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 what do you do to be so productive with it? one is if you're able to use uh just generally far less code and plumbing. Um so a lot of what I do is like deploy

stacks on like Verscell or Nex.js or like Cloudflare workers where there's kind of like already a bunch of boiler plate 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. It'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 Carpathy just Tweeted about this. They're like super persistent so they will keep going no matter what. they end up uh 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. OpenAI has like a giant monor repo. It's been there for uh 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 uh 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. So the More that you can run tests in lint uh CI etc. Um personally I also use code review bots pretty aggressively. Um I know like reptile YC company is really good. Um, I use the cursor bug bot has gotten quite good and I actually like codeex 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. Uh, and they're excellent exploring the codebase too. I think areas where they don't do well, Uh, they make more. If

your goal is not to make more, they'll like often duplicate code and like spend a bunch of time re-implementing things that like you're like, "Oh, of course you didn't want to do this." I think context poisoning is a real thing where it kind of like goes down one loop and it will continue because it has this persistence but it's referring back to tokens which are like not right in terms of pursuing a solution. Um and so one thing that I Often do is like very actively clear context >> like how often >> usually uh when

it gets above like 50% tokens. >> Oh wow. >> Yeah. Yeah. I don't there's this guy Dex uh from this company Human Layer that was actually another YC company. >> Yes, a YC company from Fall 24. >> Yeah. Yeah. And he talks a lot about it. Yeah. >> He has this concept of like the LLM's 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. Like

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 a context window, right? >> One of the tricks that uh I think founders use is you put like a canary at the beginning of the

context. There's something very esoteric that it's like something really funny. It's like, I don't know, my name is Calvin and blah Blah blah. I drank tea at 8 am. 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 is a bit of a sign that the context has poison. That's like one trick I've seen people do. They do a random canery. >> I have not tried this, but I fully believe it. Yeah,

>> 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. >> Yeah. Yeah. >> Okay. I got to be on the lookout for that >> solvable within cloud code itself. Like it should be able to basically do some sort of detection like what is saying >> 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. Uh >> 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. It's actually interesting. The codeex 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 codecs

can continue to run for a very long time. And if you look at the percentage in the uh the CLI, you'll see it like move up and down as compaction runs. I guess like there are these very different architectures between cloud Code and codeex sound like they're actually deeper in that codeex is actually meant for much longer running jobs. So you that's sort of like off the bat a different use case and then the architecture is very different as a result. I guess right now it seems like CLI's 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 codecs 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 doghouse 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 OpenAI 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 uh in this pursuit of artificial general intelligence. And so it may not work like a human at all. Like going back to the doghouse example, it's like >> but 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 you know like even this idea that it gs was like I thought about you know 10 years ago I was like yeah I was

in there Like writing my own really weird reaxes to try to figure out where everything was when I was refactoring or what 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 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 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 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 ENT 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 and I think that landscape shift is going to be a very interesting strange thing. my 10-year-old is uh 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 you

know are working with a lot of 18 to 22 year olds who you know they've done internships but like they haven't done like manager work like you know we're saying um you know postp Productduct market fit uh once you're have job cues of like millions of jobs and like you know hundreds of thousands of errors. That's like real management. Like that's really you know it's horribly unglamorous like combing through hundreds of thousands of errors and then like manually making sure that like 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 uh architecture and things like that or you know are you just gonna bump your head into it and users just kind of suffer and you know people 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. Um, but 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 forever more. YC's next batch is now taking applications. Got a startup in you? Apply at y combinator.com/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 cuz you know the the agent so well? like what what types of engineers are going to benefit more than others um from these tools becoming popular? >> In general, I think that kind of the more senior senior you are, the more you benefit. >> Um 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 like idea. I I find this so often in open like scrolling through the codebase. It's like, oh, like here's a thing that I wish were Different. here's a thing that I wish were different. Here's a 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 managerish uh and there's probably just a missing product to be built here. Uh maybe something like conductor uh 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 >> conductor should add that >> yeah like uh 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. here are like areas of deep thinking that you were planning to do like I want the turn by turn for my day you know other things that make it very useful like if you're able to build um I don't know some sort of like quick prototype for an idea to Show it off like

that's an area I mean obviously the agents do super well at this um I would find myself at openai often writing kind of like prototype code or like hey I've got this like in-memory key value store can you now turn it into like uh work with a production database or something like that being able to concisely specify ideas in code and I think having a smell for what the right architecture is is still the area where the models Like don't do the best job. >> So if you were going back to your like college days

and studying CS again fresh and you like were picking your own like syllabus or curriculum like what would you what would you study? Personally, I think still understanding systems uh is very important. Um and just having some conception of like how like git works, you know, or like HTTP or databases like cues, like all of these different systems. I think that those fundamentals Are still quite important. The other thing that I'd probably do is just have a semester where like each week you're just building something and you really try and push the models as far

as they can go. There's a sense that you have whenever you're doing something that you could always just like go up the layer and ask the model to do it and like go up a layer and ask the model to do it, you know, where it's like, oh, I have like a implement command where it like Implements the next phase of the plan, but then I could have like an implement all command and it like goes stage by stage and creates a new sub agent and then I could have like a check your work kind

of thing and like and I think knowing where the models can and can't accomplish that is such a moving target that it's worthwhile just to like tinker a lot. I mean, the other thing that's really really crazy for I mean, I would love to be able to teach 18 to 22 year Olds. Like, everyone around like at this table has like ship stuff that people really really want and love. So, it's like how do we teach people that? >> I wonder if like the best 18 to 22 year olds like 5 years from now will

just have like off the charts taste and everything because they'll just be so much more prolific. They should be, right? like they should just be launching and like touching reality like 10 times as much as like the generation Before them. >> The one thing I have wondered about on that note, um I don't know if you all found this, but growing up my mom used to tell me like, oh, like stop multitasking. You're not paying attention to like what I'm doing. Uh >> and I think there is some truth to that. Like often I would

be like off on my computer like not paying attention. But I do think I was legitimately better at multitasking than our parents were. Uh, And now I look at this new generation. I think they're actually quite a bit better at multitasking than we are, you know, because they've kind of grown up in this age of the internet and they're dealing with like TikTok and all these like different short form video and things. Like it seems like there's room for both kind of this like deep thinking where you want to like notice what you're seeing and

understand and problem solve, but then there's also this mode Of just like bounce between a bunch of different things and you're context switching constantly. >> ADHD mode. >> Yeah, the new generation is quite good at this. >> Yes, I definitely think there's a there's a type of smart person. and maybe it's ADHD, but just like always has like a bunch of good projects on the go, but just never actually finishes anything. I might relate to this Personality a little bit. Um, >> you release your uh your vibe code. >> Yeah, but I wouldn't only because

of Claude code. That's kind of my like now I just think like you kind of like there's certain types of brains that just have like like 10 branches going in their head, but you never have enough hours in the day to actually like see any of them through. So, they're always like half complete. And now it's just like cold code gets you over the line With everything. And it's just like, and you made this point in your blog post about how it feels like a video game, but it's just like there's just a constant novelty

factor. Like you start working on something and usually when you hit the point of like I'm like bored and I've got like this other better idea and I should like start on that and then come back to this. Like you can do that now, but like everything can actually get finished. >> Let's live in the future for a moment. It's 40 years from now. Software still exists. Databases still exist. Access control still exists. But like at the core of it, I mean software is entirely personal. access control and who gets to do it is like

you know sort of like this manager mode thing that people still have meetings about but then everything else about a company its functions its rules like is defined by people just doing things in their own cloud code Like thing I don't know maybe it's a CLI or it's like you know having giant armies of workers then I don't know what would that look like >> like imagine if every time a company signed up for segment you fork the codebase you give them their own copy of segment is running on their own servers and then if

they want to change anything about it they just like tell some chat window which is running like an agent coding loop and just like edits Their version of segment as segment the corporation pushes out more features some agent figures out how to merge >> yeah I I could totally see it I mean sort of what I've been thinking I don't know how far this future is but like eventually every person who's working like has their own sort of like cloud computer and like set of cloud agents who are running for them and and they're mostly

just like talking back and forth. It's kind of like having like a super EA Or something where it's like, "Oh, here are the things I need to pay attention to. Like, let me make some quick decisions. Like, let me spend more time on this. Let me like meet with other people." Cuz I think that there's still going to be room for people who are like want to meet other people and exchange ideas in person. At least I get a lot of fulfillment out of that. And then separately, there's going to be this army of agents

who are like doing things On your behalf and like automating a bunch of things. Uh, I think the average company is probably going to get like a little smaller and there's going to be many more of them doing more things. >> Something I'm curious to see is kind of like what the update version of the PG maker uh maker schedule versus manager schedule would look like cuz I feel like part of what's going on at YC is sort of a lot of our jobs are essentially manager schedule which just really made It hard to do

any sort of building your own software. But now you totally can and that's why like a bunch of the partners >> just do it in the meeting like I like right at the beginning of this podcast. You let it run and then come back. >> Well, like in the pockets, right? Like in like it just used to be like literally unless you had like, you know, 4 hours minimum block free to do something, it just wasn't worth even Getting started, right? And I I think that's actually goes very deep to how we've changed programming. Like

it used to be that in order to write any code, you had to fill your own context window with so much data about all the different class names and the functions and the code that it touches. It would take hours to build up that context window. And so doing it in 10-minute snatches was just like so frustrating. I do think I think maybe one one primitive For this future world will be I think still the data models need to be still be consistent and the system of record >> there's there's opportunity for something that's kind

of agentic first because right now we're still kind of in integrated very much with databases and SQL or NoSQL queries that are very low level but imagine something that generates all the data that you need for all the different views for custom software so a lot of the world would be Custom views but I think the unifi stuff we still need to have data to be correct. >> I think data has a lot of gravity and I I think you see this with companies who are like offering access via API or MCP like I think

Slack uh locked down their API a little bit because they didn't want people just exfiltrating everything from Slack and then building agentic experiences on top of it. >> I wonder with that note if you were to Rebuild segment in the current with the current tools >> how would it look like? I mean, segment is a funny business in that uh where we started was building these integrations, right? Um and so it's like, oh, you need to wire up like the same data going to like mix panel and kissmetrics and Google Analytics, etc. And I think

just writing that code now like that used to be maybe a more annoying or harder thing to do and so it was worth paying for. Now it like that value has dropped to zero. >> Yeah. And actually like in many cases you're better off like saying, "Oh, I actually want to map it this way and I want this specific behavior." like I will just tell the quad or codeex what to do and then it will do it and I'll have exactly the behavior that I want. So I think that aspect of segment like the value

has dropped precipitously. I think the aspect of like keeping this Data pipeline running and like continuing to automate a bunch of parts of your business or like schedule these like email deliveries which should go out through customer IO every time a customer signs up or like manage audiences for you like that value is kind of still there and I think you could do a lot more interesting things where it's like hey if I have all this data and like a full view of the customer like how should I be emailing Them? Should I change like

parts of the product when they log in? Should I be giving them different onboardings depending on who they are? like there's a lot more interesting stuff that you could do by basically running like I don't know small LM agents over them and changing that. That would be the changes I would make. >> So it's kind of like moving up the stack to your comment earlier and all the way turtles down the low-level stuff is gone Is now really more doing things at the campaign level which is way more abstract. >> Yes. I mean I'm amazed

at to what degree like claude code even just from like the context of what I'm working on figures out like what my motivations are. >> Yeah. I I I'm still blown away by coding agents because effectively what you're doing is you're like giving them a copy of a repo and then you're slipping a little note under the door and being Like, "Hey, go implement this thing." They have like no knowledge of like what your company is or like what you do, who your customers are in most cases. Maybe it's in the training set because they

know you're Gary. But it blows my mind that it works at all. And and that's where I think the context is really important, right? Because if it latches on to something that isn't quite right, it doesn't have a lot to go on. And if it misses something that's essential, It's going to just reimplement it. >> What do you think the constraints are right now? I mean, like context window is still a constraint, but it's like so big that, you know, it's like we can do some stuff like we can't do the mega rearchitectures, but we

can do a lot. And then if the opus 4.5 somehow got a lot smarter, uh, and then that unlocked a big thing, which was interesting. I don't have no idea if that was like pre-training or post-training. like what Are there other like levers that you think of other than you know basic model intelligence like frontier model intelligence and context window I mean I still think context window is like probably the number one limit like if you look at cloud code executing it's delegating to all these different context windows at the end of the day when

each one comes back it's like getting some sort of summary so it's also not getting the full picture like If you have a problem that's just like too big to fit in a single one like kind of no amount of compaction is going to help I would point to that as like both Anthropic has figured something quite useful out with delegating to these subcontext windows, but also I think it's still a block barrier. >> So we'd do better if we had a million million token context every single time. >> Yeah, I think so. And like

figure it out a better way to especially train these Like very long context trajectories because if you think about it like there's there's a lot of training data on the internet for like what is the next sentence that comes or like what's the next paragraph that comes. If you have 80,000 tokens that are generated like understanding what the next thing to do based upon like oh I should refer to the 20,000 token like that's trickier. I think this like integration and orchestration is starting to become The limiting factor. I mean I think there are like

stuff on code review related to this. It's like oh if we're like merging all this code like who's watching it? Does a human still have to watch it? Like how do we verify the changes? And then I think like pulling in the context correctly from your tools like you were talking about Sentry like you want Sentry to auto be able to like figure out a PR you know and then like maybe it pushes it to a subset of your Traffic and if it looks good then it rolls out everywhere you know like all of that

automation still has to be built. I was surprised how important testing was. Like I was operating for like the first 2 or 3 days of my 9 days in the wilderness. Like uh no tests or very few tests. And then one day I was like, "All right, today's refactor day. I'm going to do get to 100% test coverage." And then I just sped up like crazy. It was just like, "Oh, it did it. It works." I rarely even have to necessarily manually test because it's like the test coverage is so good and like nothing breaks

>> which is very similar to what all the companies are doing just for prompt engineering outside of coding is very much test-driven development. I think we had this episode with Jake Heler and that was a big paradigm shift is like the way you get a good prompt is all test driven just like evals right in a Sense the test cases are your evals. There are some broken flows now. I think that you we might need a clawed code that could like talk to a stack overflow that was like a clawed code stack overflow. >> Like

I had this problem. It was so crazy like I uh instead of using in the in like the priority of a job Q I used or actually I didn't even write again I did not write this. The machine wrote a string with a comma thinking that it Would take that syntax, but it was expecting like an array in JSON and then it just like no jobs would run. And then I watched it for like 30 minutes walk through the internals of Rails job like the active job like couple thousand lines of code like trying to

debug what was happening and it found the bug actually. I was like that's amazing. I just think about what I would do like 10 years ago and I would have been like hey why are the you know jobs not working And then I would find a Stack Overflow or blog a Rails blog post and it's like oh yeah like nobody fixed that stupid bug where you know you think that you can put a you know comma delimited string in there but actually you have to make sure it's an array. >> Uhhuh. >> I was like

oh my god like that was very funny actually. I think that's like one of the hardest parts about thinking about what's going to happen here Because uh there's like things that you would do as a human in a CLI right now and like that's very obvious but even that idea of like should the agents have their own stack overflow like if you just increase the intelligence by you know I don't know what you even call it like by 10 IQ points like 10 virtual IQ points >> like would it even do that? It would just

be like oh yeah that's a string whatever. >> Yeah. Yeah. I think there's something very interesting here around like agent memory. Um, and cloud code has sort of set itself up and I think Codeex 2 by storing all your conversation history just as files. So you could imagine you like give it access to a tool that then can read previous conversation history. I think there's a missing piece around a lot of collaboration there. Like it'd be amazing if like there was some way of smartly sharing your co-workers prompts And you could see and be like,

"Oh, like I hit this thing, but actually like Brian over there like fixed it earlier, you know, so like the two of us can share knowledge." I I think there's something there's something onto this of like a model generated like wiki, you know, or like Graopedia stuff. >> Now I can't stop thinking about Have you seen um the Claudebot social like the network for clawbots to talk to each other and it's like that's the evolution For Molen? >> Yeah. I guess what they don't know. Clawbot's essentially like um uh like your own personal AI agent

that you can run on your own machine. You can download it. Um do not give it access to emails would be my number one piece of advice or probably anything. Um because it's not clear how safe it is and it's probably almost certainly going to probably a lot of people being prompt injected by it right now. But somebody Created um like a I haven't actually seen it but I've like seen it on Twitter but like a site where like everyone can sort of spin up their own like clawbot like personal agent and then the agents

can talk to each other and now there's just like all this AI generated content of these like personal AI agents talking to each other. >> Yeah. I mean it looks like Reddit but if Reddit were run by agents. I mean it's interesting to see like Codeex's Personality shine through when writing code. I would say uh it does most stuff that humans don't do. kind of this alpha go sense where it's like oh it'll write a Python script to like modify some part of the file system. I think that is like very interesting and kind of

alien behavior which has been learned. Yeah. >> Um but it does give these like superhuman results for me at least when debugging complex issues that I find opus often misses. >> What's an example of a complex issue that you could talk about? I mean it's like concurrency or naming issues, right? >> I find the models are actually like decent at concurrency. oftentimes there's stuff where it's like, oh, there's a request that is like traversing several different services. I mean, kind of to your point about the uh serialization and deserialization of like stuff with commas in

it. >> Um, it's like, oh, it needs to track some sort of complex behavior around those or like way of uh I don't know refreshing complex UI state and Opus often will miss it if there is many files, but codeex seems to catch it. >> Interesting. >> Yeah. >> Yeah. Prognostication about how will tools continue to evolve. It's very interesting like I feel like sort of a new citizen in this land in a way like I Just you know knew it was happening. I you know manager schedule finally a project appeared and was like oh

I'm going to go all in on this >> and then now I'm like in it's like uh >> I'm in a stranger in a strange land but it but it like resembles exactly what I remember. >> I think we all we all feel that way like I think I think the most important thing is just to keep tinkering because it all changes every few months. I do feel like The best or the people who will get the most out of coding agents in the future are going to be kind of like more manager-like where they're

focusing on directing flows in certain ways. They're probably going to be a little bit more like designer artist in some ways where it's like they're figuring out what specifically goes in the product and what stuff you can do without. And I think they'll be very good at just like continuing to think about automation and Where they're missing context. I guess what's funny is I tried to use codeex just now uh for my Rails project but the thing is like it's kind of obvious that nobody at OpenAI cares about Rails which is fine like it's a

very it's a vestigial language it's very strange it just happened to be the one that I you know really really went deep on 10 years ago >> and then uh it's just funny how much of it is exactly again anyone can make Something but then the something people want is very hard and um even when you have like unlimited resources is at like an openi. It's like I guess if someone from codeex is watching right now, my request would be go down the list of all of the run times and just add like syntactic sugar.

There's like this is probably like you know 10 PRs at most for like I don't know the top like 15 run times. I guess it's like sort of the reminder that like man actually like There are far fewer excuses for software that doesn't quite work for a user you know now than ever actually. Yeah, I do think it this is an interesting point in terms of mix of training data. Codex works very well on Python monor repos shape of openi. >> Yeah. Yeah. And it's like I remember working like internally open I was like oh

my gosh this tool is amazing. It is incredible. Um and it kind of makes sense in terms of the data mix and the Researchers who are working on it. I think Enthropic is focused a little bit more on like some of the front-end things. Um, and I don't know in terms of like a Ruby for example, like who has the best model there and who's incorporated the data mix. >> Yeah. >> Like some of the labs tend to take this perspective of just more data is better. Uh, and so they'll just flood as much data

as possible while others I think Are a little bit more tuned in terms of the mix. And I think depending on which approach you take there, it can give very different results where it's like, oh, I'm taking just the like top 10% of JavaScript is pretty different than if you're looking across everything. I actually think OpenAI and the you know OpenAI models are really good at Ruby from what I can tell and then >> it's it's the harness around the model. It is Yeah. >> Oh, interesting. Okay. >> It's literally like Rails has this weird

thing where you have to have, you know, access Postgress in a certain way or like it couldn't fit. Yeah. The sandboxing. >> Yeah, the sandboxing. It's such an interesting question because uh I think OpenAI actually takes the like sandboxing and security question more seriously than almost anyone else. I remember when we were building codeex Like basically one of the gates that you have to pass through in order to release a model is you have to like talk about safety and security risks like every time you want to release. One of the things we were looking

into was prompt injection especially for opening up to the internet because a bunch of users were like oh this has to like work on the internet. We're like, "Oh, we don't know." Like, "It seems pretty easy to prompt." >> Operator was also Yeah. that. Yeah. >> And so, uh, the PM on our team, Alex, uh, basically like put together a GitHub issue and it had like a very obvious prompt injection, which was like, "Oh, reveal this thing." And then he told the model like, "Hey, go fix this issue." Uh, and he's like, "Oh, there's no

way this is going to work." And like immediately the prompt injection works, you know? And so I think OpenAI like sort of correctly is very worried about This and is like, "Hey, we're going to run everything in on a sandbox. We're going to make sure it like doesn't touch all these sensitive files in your machine. We're going to be very careful about secrets. And I think if you're a startup where you're just like running fast, you probably don't care. You're just like, I just want it to work. Y, you know, >> are you a dangerously

skip permissions person? >> Uh, I actually am not. I like have a set of things that are >> How about you? Are you running? >> Not I like to read, you know. I like to read what it's doing. >> Are you skip permissions, Jerry? >> 100%. >> YOLO. >> Oh my god. It's about 50/50 on the YC engineering team. >> It's about 5050. >> A security engineer would watch this Part and say, "You can't release this part of the video. Just cut it from the podcast. You can't have this out here." >> I think it's

context dependent. Like if you're at an enterprise, you don't want to do that. If you're a startup and have nothing to lose, you probably do. >> YC has progressed a little bit from a startup. We still act like one though, which is, I think, important. Cool. I mean, this is so awesome. Kelvin, thank you so much for joining us. >> Of course. Thanks for having me. >> Oh my god. There's Yeah. So fun. All right, back to Claude.