# Building Claude Code with Boris Cherny Markdown copy of https://realalpha.app/v/julbw1JuAz0/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy. Boris Cherny argues that AI shifts engineering toward judgment, verification and cross-disciplinary work as models take on more implementation. - Show: The Pragmatic Engineer - Hosts: [Gergely Orosz](https://realalpha.app/people/gergely-orosz/) - Guest: [Boris Cherny](https://realalpha.app/builders/boris-cherny/) is creator and Head of Claude Code at Anthropic. - Published: Mar 4, 2026 - Length: 1 h 38 m ## The best minutes - [24:03](https://realalpha.app/v/julbw1JuAz0/?t=1443) [Claude Code grew from letting the model use tools](https://realalpha.app/s/claude-code-grew-from-letting-the-model-use-tools/) (3 min) - [40:26](https://realalpha.app/v/julbw1JuAz0/?t=2426) [Repeated review comments can become lint rules](https://realalpha.app/s/repeated-review-comments-can-become-lint-rules/) (6 min) - [1:05:11](https://realalpha.app/v/julbw1JuAz0/?t=3911) [Cowork was built quickly, with safety as the hard part](https://realalpha.app/s/cowork-was-built-quickly-with-safety-as-the-hard-part/) (6 min) ## Also worth watching - [32:54](https://realalpha.app/v/julbw1JuAz0/?t=1974) [A clear plan lets parallel agents implement code](https://realalpha.app/s/a-clear-plan-lets-parallel-agents-implement-code/) (8 min) - [49:39](https://realalpha.app/v/julbw1JuAz0/?t=2979) [Agentic search beat a local vector index](https://realalpha.app/s/agentic-search-beat-a-local-vector-index/) (3 min) - [17:00](https://realalpha.app/v/julbw1JuAz0/?t=1020) [Code quality contributed double-digit productivity gains at Meta](https://realalpha.app/s/code-quality-contributed-double-digit-productivity-gains-at/) (3 min) - [1:14:42](https://realalpha.app/v/julbw1JuAz0/?t=4482) [Uncorrelated context windows improve complex agent tasks](https://realalpha.app/s/uncorrelated-context-windows-improve-complex-agent-tasks/) (3 min) - [1:25:45](https://realalpha.app/v/julbw1JuAz0/?t=5145) [The printing press multiplied output before literacy spread](https://realalpha.app/s/the-printing-press-multiplied-output-before-literacy-spread/) (4 min) Skip the opening coding and startup stories, the sponsor reads, and the closing book recommendations. ## Summary Boris Cherny is the creator and Head of Claude Code at Anthropic. He previously worked at Meta as a Principal Engineer and wrote Programming TypeScript. Cherny says Claude Code grew from a terminal experiment into an Anthropic tool after its team treated models as agents that could use tools, then released it to study safety in real use. He describes a workflow built around plans and parallel agents, with automated checks and human review for code changes. At Anthropic, he says engineers prototype frequently and take on work across product, design and engineering; Claude Cowork brings agent workflows to non-engineers with additional safeguards. Cherny argues that as models take on more implementation, engineers will need methodical problem solving, adaptability and cross-disciplinary judgment. ### Minute by minute - [1:59](https://realalpha.app/v/julbw1JuAz0/?t=119) **Coding began as practical problem-solving.** Cherny says he learned HTML to improve eBay listings for his Pokémon cards and programmed math solvers on a TI-83 calculator. In an early medical-software startup, shadowing doctors showed him that their clinical decision tool did not fit their time and social workflow, so he concluded that product ideas need to be tested against how people actually work. - [11:09](https://realalpha.app/v/julbw1JuAz0/?t=669) **Code quality supports productivity.** At Meta, Cherny worked on Facebook Groups, Instagram infrastructure and code quality across several products. He says Meta’s Better Engineering program asked engineers to spend 20% of their time on tech debt, and his team added ways to prioritize the work and measure its effects. He says code quality contributed a double-digit percentage to engineering productivity. - [19:40](https://realalpha.app/v/julbw1JuAz0/?t=1180) **A rejected PR changed his workflow.** Cherny says he joined Anthropic because of its safety mission. His first pull request, written by hand, was rejected by his ramp-up buddy, who told him to use Clyde, an early predecessor to Claude Code. Cherny says the tool produced a working PR in one shot and changed his view of what models could do. - [23:11](https://realalpha.app/v/julbw1JuAz0/?t=1391) **Claude Code began as a terminal experiment.** Cherny says he first built a terminal chat tool to try Anthropic’s API, then gave it a Bash tool to experiment with tool use. When he asked what music he was listening to, the model wrote an AppleScript to query his music player. He argues that models work better when given tools and room to use them than when boxed into a conventional software component. - [27:20](https://realalpha.app/v/julbw1JuAz0/?t=1640) **Release enabled safety research in the wild.** Cherny says Anthropic debated whether to release Claude Code, then chose to study how the model behaved with users. He says technical employees at Anthropic now use it daily and that it writes about 80% of the company’s code. He says Opus 4.5 made him stop using his IDE and rely on the tool to write his code. - [32:54](https://realalpha.app/v/julbw1JuAz0/?t=1974) **Plans help him run work in parallel.** Cherny says he uses five terminal tabs, each with a separate checkout, and usually starts tasks in plan mode. He is increasingly using the desktop app’s worktree support and starts agents from the iOS app, where they run in the cloud. When he knows a codebase, he focuses on getting the plan right, then works across several tasks; he says he now ships 20–30 pull requests a day. - [40:26](https://realalpha.app/v/julbw1JuAz0/?t=2426) **Review combines automation and human approval.** Cherny says he used to track recurring review comments and turn patterns into lint rules. Now Claude runs tests, sometimes tests itself, and reviews every Anthropic pull request in CI; he estimates it catches maybe 80% of bugs. He says deterministic checks such as type checkers and linters complement AI review, and a person still approves changes before production. - [47:13](https://realalpha.app/v/julbw1JuAz0/?t=2833) **Safety relies on multiple layers.** Cherny describes Claude Code’s architecture as a core agent loop, tools, a terminal interface and security measures. For prompt injection, he cites model alignment, runtime classifiers and subagents that summarize fetched web pages. He says the team dropped a local vector database for retrieval because of stale indexes and permission concerns; agentic search with Glob and Grep worked better. Permissions also use classifiers, static analysis, allowlists and prompts to ask the user. - [55:08](https://realalpha.app/v/julbw1JuAz0/?t=3308) **Generalists build through prototypes.** Cherny says Anthropic’s shared Member of Technical Staff title encourages people to work across disciplines; on the Claude Code team, designers, data scientists and finance staff also use code. He says the team often prototypes instead of writing PRDs, because building is cheap and people may be wrong about what will work. Agent Teams, for example, went through hundreds of prototypes before the team settled on an experience. - [1:05:11](https://realalpha.app/v/julbw1JuAz0/?t=3911) **Cowork adds safeguards for non-engineers.** Cherny says people outside engineering were already using Claude Code for tasks such as monitoring plants and recovering photos, which prompted the team to build Cowork. A small team built it in about 10 days, reusing the desktop app and agent SDK while adding a virtual machine, classifiers and other safeguards. He says the Chrome extension lets Cowork use browser-based tools, and privacy requirements mean the team cannot inspect user data or logs when investigating a reported bug. - [1:14:42](https://realalpha.app/v/julbw1JuAz0/?t=4482) **Agent teams use fresh context windows.** Cherny says agent teams let a lead delegate work to agents with separate, uncorrelated context windows. He says the approach improved results on complex internal evaluations with Opus 4.6 and can help when one agent struggles, but it uses many tokens, so the feature is opt-in and a research preview. - [1:18:15](https://realalpha.app/v/julbw1JuAz0/?t=4695) **Model progress rewards intellectual humility.** Cherny says ideas that failed with earlier models can work as models improve, so he tries old approaches again and learns from colleagues who may find better methods. He describes coding as both an art he once loved for its own sake and a practical way to build things. As models take on more coding, he says engineers have to adjust their expectations and their sense of where their work comes from. - [1:25:45](https://realalpha.app/v/julbw1JuAz0/?t=5145) **AI resembles the printing press.** Cherny compares AI coding tools to the printing press, which made written material accessible beyond the small group of scribes who knew how to produce it. He says scribes did not simply disappear: writers and authors emerged as the market for written work expanded. He argues that, as with the printing press, it is hard to predict what new roles or products will follow. - [1:30:11](https://realalpha.app/v/julbw1JuAz0/?t=5411) **Engineers need method, curiosity and range.** Cherny sees standout engineers as prototypers, product-market-fit finders and people who span disciplines. He says methodical, hypothesis-driven work, curiosity and willingness to cross roles matter more than rigid preferences about languages or code style. He recommends Liu’s short stories, Accelerando by Strauss and Functional Programming in Scala, including doing its exercises. ## Transcript The Pragmatic Engineer, hosted by Gergely Orosz, with Boris Cherny. Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there. [0:59](https://realalpha.app/v/julbw1JuAz0/?t=59) What happens when you join one of the top AI labs in the world, and your first pull request gets rejected? Not because the code was bad, but because you wrote it by hand. This is exactly what happened to Boris Cherny when he joined Anthropic. Boris is the creator and engineering lead behind CloudCode. Before joining Anthropic, he spent seven years at Meta, where he led code quality across Instagram, Facebook, WhatsApp, and Messenger, and was one of the most prolific code authors and code reviewers at the company. In today's episode, we cover how CloudCode went from a side project to one of the fastest-growing developer tools, and the internal debate on Tropic whether to release it at all. Boris's daily workflow of shipping 20-30 pull requests a day with zero handwritten code, and how code review works when AI writes everything. Why Boris believes we're living through a time as transformative as a printing press, and which engineering skills matter more now, and which ones do not. If you want to understand how one of the people closest to AI coding agents actually builds software today, and what that means for the rest of us engineers, this episode is for you. This episode is presented by Statsig, the unified platform for [2:00](https://realalpha.app/v/julbw1JuAz0/?t=120) flags, analytics, experiments, and more. Check out the show notes to learn more about them and our other season sponsors, Sonar and WorkOS. How did you get into tech, software engineering, and coding in general? It starts a while back. I think there was kind of like two parable paths that crossed. So when I was maybe 13 or something like this, I started selling my old Pokemon cards on ebay and i realized that on ebay you can actually like write html and i was looking at other people's pokemon card listings and i realized like some of them have like big colors and fonts and stuff like this and then i discovered the blink tag and i really was blink tag and if i put the blink tag on it i could sell my card you know for like 99 cents instead of 49 cents or whatever so i kind of learned about html this way then i got an html book and kind of learned about html and then uh the second thing was this was also i think sometime in middle school we had these old ti83 uh graphing calculators and we used them for math and what i realized is i can get a better answer on the math test if i just program the answers to the math test into my calculator and so i wrote these little programs to just program the answers and then the test got [3:11](https://realalpha.app/v/julbw1JuAz0/?t=191) harder so then i had to program solvers instead of the actual questions because i didn't know what what you know the coefficients and stuff would be ahead of time and then the math got more advanced like the next year and so i had to drop down from basic to assembly to just make the program run a little bit faster oh so like in high school you dropped down to assembly i think this is like middle school or high school maybe like eighth or ninth grade or something like this then the thing i realized is uh everyone in my class was starting to realize that i had the solver and they got kind of jealous and so i bought this little serial cable so i can give it to them too and then the The next math test, everyone on the class just got A's. [3:46](https://realalpha.app/v/julbw1JuAz0/?t=226) And the teacher was like, what's going on? Eventually, she realized it. She was like, OK, you get away with it once and knock it off. But for me, it was very practical. So, you know, in school, I studied economics. I actually dropped out to start startups. And I never thought that coding would be a career at all. It was always very practical to me. Coding is a means to build things and to make useful things. This startup, the first one was, I think it's like my friends and I were trying to get weed. And so we started this like weed review startup. We made like a website, we called kind of different dispensaries, I think. [4:25](https://realalpha.app/v/julbw1JuAz0/?t=265) And then we just tried to get kind of like weed samples so we could like review it for them. And it actually kind of blew up. And then I actually got more interested in, at the time, no one was like testing this stuff. And so I got into kind of the like chemical testing, kind of chemical analysis. And then after this, I kind of did a bunch of other startups. And then I joined YC actually pretty early. And I was the first hire of this YC startup up in Palo Alto after. How did you decide to go to one startup after the other? Kind of vibes. Vibes, I'd say. Because, you know, like, you know. It's it's never a linear path. You always kind of pivot, pivot, pivot. You have to figure out what the market wants and what users want. And it's never the thing that you think. You always try a thing, but the idea is always a hypothesis. And then almost always you have to pivot once, twice, three times. [5:14](https://realalpha.app/v/julbw1JuAz0/?t=314) You know, at this at this medical software company, this is called Agile Diagnosis. This was kind of an early YC company. This was back in maybe 2011, 2012, something like that. It was medical software for doctors. And the idea was there's these clinical decision protocols that vary a lot, hospital or hospital. And our idea was there was one hospital in Chicago that had a really great protocol specifically for cardiac symptoms. outcomes and so we're like wouldn't outcomes be great if every hospital in the u.s would use the same protocol and so we tried to standardize it and we made this like decision tree software for doctors to use and i wrote you know some of the software the team was like it was it was just a few of us it was a pretty small team and i wrote the software it was in a web browser and i remember this was back in like the internet explorer six days that's what hospitals were using and i wrote this like SVG render because it was this visual decision tree and we launched it and then we had a DAU chart and the DAUs were flat and couldn't figure it out and we were piloting it with a few hospitals at the time and at the time we were based in Palo Alto we were piloting it with [6:21](https://realalpha.app/v/julbw1JuAz0/?t=381) you know a few hospitals including UCSF and I rode a motorcycle at the time so I rode my motorcycle up to you know UCSF and I shadowed doctors for a couple days just to see how how do they actually use this. And I realized that actually doctors don't have time to sit down and use a computer because you're seeing a patient. Then you have maybe five minutes until the next patient. And in those five minutes, you have to walk down the hall. You have to go to the computer station. You have to open up this totally legacy computer. By the time it boots up, that's like three minutes. Then you open up in our Explorer 6. That takes like 30 seconds. Then you have to open up this like app that we built you have to sign in and your five minutes are up you don't even have time to use it and so we reroute everything to run on android and they still weren't using it and the thing we realized is doctors are walking around with a bunch of residents behind them in this kind of situation it's like a social situation right like the thing that matters is they're seen as an authority they don't want to be seen on their phones and then we pivoted again so at that point we're like okay so maybe the doctor isn't the target user actually we want it to be used by [7:25](https://realalpha.app/v/julbw1JuAz0/?t=445) maybe nurses or x-ray technicians or something like this at that point i left because i was like this is actually pretty far off from kind of what i wanted to do this is like the most fun thing for me is finding this this product market fit because it's always surprising you can't have one big idea because the idea is probably going to be wrong so you kind of form hypotheses you you follow it down and you see what's right also i find it so interesting how you're telling us this story because i feel behind a lot of sort of success stories we hear the success story we hear the path of how it went but first of all a lot of startups are like this and second of all what struck me is you you were hired as a software engineer right and this was back before product engineers or anything was the thing which we're now talking about but you just like you rode your motorbike and you went there and you shadowed the people and you understood how they're using it why You're not using it, getting ideas. [8:18](https://realalpha.app/v/julbw1JuAz0/?t=498) I feel this is what makes a great software engineer back then and even today. It doesn't seem to me that you were focused on the technology. You were focused on the outcome, no? Yeah, I mean, look, there's different kinds of engineers and there's different ways to do it. And even on our team right now, I look at an engineer like Jared Sumner and he's just incredible technical mind. He understands systems better than anyone I've met. and you know you need you need people like this you need people with this kind of depth for me engineering has always been a practical thing uh and you know for me i've always been a generalist and like it doesn't matter if i'm doing you know like design or you know if I'm doing engineering or user research or whatever. The investment thesis for AI and software engineering is straightforward. As AI writes more code, more code needs to be verified. But there's a catch. 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Instead of context switching, your AI agent becomes a full-fledged code review and quality assurance copilot capable of analyzing code snippets for issues, filtering bugs by severity, and even checking your project's quality gate status before you ever commit code. Whether you're working with coding assistants or scaling up with full agenda workflows, Sonar provides the automated verification that 75% of the Fortune 100 rely on. It's about giving your developers the freedom to innovate without the fear of breaking the code base. Head to sonarsource.com slash pragmatic to learn more about how Sonar enables the confidence to develop at the speed of AI. [10:38](https://realalpha.app/v/julbw1JuAz0/?t=638) With this, let's get back to Boris's career and what he learned working at startups. My first job I ever had, I was like, I think I was 16, and I just wanted to buy an electric guitar. And so what I did was I just started freelancing. And so I was like, okay, I guess I'll make websites. And I think Fiverr was not a thing back then, so there were some other freelancing websites. So I just started, like, I put up a website, I started bidding on stuff. And my first paycheck, I just spent the entire thing on an electric guitar. But it was very practical, right? Because it's like, when you're in this kind of setup, you have to do the engineering, you have to do kind of the accounting, you have to do the design, you have to talk to customers. [11:14](https://realalpha.app/v/julbw1JuAz0/?t=674) It's just always been like that for me. After a couple of these startups, you ended up at facebook now now called meta and there you spent seven years there can you just talk us through what you worked there what you've learned there you've also had a very remarkable career growth in terms of four promotions over over over seven years and what do you take away from that that experience yeah so i started on facebook groups that was the first time i worked on uh vlad kolesnikov uh hired me i think i think he's actually still a facebook um i think he's on some other team now and it was cool actually there there's a big group of people that i worked with that were these kind of early javascript people too and you know like i did a bunch of javascript stuff and it's funny like i kept crossing paths with these people and so vlad he worked on bolt.js which was the software it was the framework that powered ads manager which later became react.js i kept crossing paths with these people and later on for yeah later on there was a bunch more people like this but anyway so i was working on facebook of groups. I was really excited about it because of this mission of connecting people to their community. This is the thing that drew me in. And at the time, I was a big Reddit user. I became a [12:27](https://realalpha.app/v/julbw1JuAz0/?t=747) Reddit user back when I was a teenager because I didn't know anyone else that coded. Even in college, I didn't really know anyone that coded. And honestly, I was always kind of embarrassed about it because I thought it was this nerdy thing. And I thought it was kind of this thing that i knew how to do but i wanted you know i wanted to be like a cool kid and you know like i couldn't like tell people that i coded it was like it was very nerdy um and and at some point i discovered it was some like programming community on reddit and i was i was just shocked like there's other people that are into this thing. It's like such a weird hobby. It's so niche. And it was just so exciting to find like-minded people like this and get this connection. And so I just wanted to work on this. I wanted to kind of contribute to this in some way. So I worked on Facebook groups for a while. And then, you know, there's a bunch of different projects. I have to kind of get into details for any of these. Eventually, I became the tech lead for Facebook groups and kind of grew into this. And the org grew. The work really changed. It changed from kind of building to a lot of like doc writing and coordination and kind of delegating to others the culture was [13:32](https://realalpha.app/v/julbw1JuAz0/?t=812) changing at the time so you know this early facebook culture was disappearing the docs were coming in the you know alignment meetings were coming in uh there was a lot of a lot more work around this kind of foundational stuff like privacy security things like this that i think honestly early on a lot of corners were cut in order to grow but at some point you just have to pay that debt and that was the time when that happened then i spent a few years at instagram after um and that was also a funny story my wife got a got a job offer and she was just really excited about it and she came to me and was like hey like i got this offer but we're gonna have to move is that okay and i was like yeah that's fine you know like i work in tech we can work remotely anywhere where's the job and she was like it's a nara and i was like where's that and uh nara is like rural japan and this was a different time zone as well different time zone yeah this was 12 hours or something different or something like that something like that yeah it was like 2021 and then I tried to kind of find a team that would sponsor me because there were these kind of arcane HR rules about like the time zone you have to be in [14:31](https://realalpha.app/v/julbw1JuAz0/?t=871) and the team you have to be co-located with and so on and so there was a little kind of nascent team for Instagram in Tokyo and Will Bailey was running this team he was also the guy that made Instagram stories and so he was my manager for a while and so we decided to grow that team together and I worked remotely from Nara and then most of the team was in Tokyo and uh during this time i started hacking on instagram and the stack was just insane like facebook was the single best web serving stack in the world the the way that hh everything is optimized like from from the hack language to the hhvm runtime to the to graphql as the transport layer to like the client libraries like relay and and all the stuff it was just and and react it was just amazing there's no other dev stack in the world that was this good and it's just fully optimized. And then I went to Instagram and it's like, you know, Python where the type checker didn't work and click to definition didn't work. And it was this like kind of hack together Django and then like a fork of, you know, the Cython runtime and just nothing really worked. And so I came to Instagram, I joined the labs team, you know, in Japan. And the idea was to find the next big thing for Instagram. We tried some stuff, but what I very [15:44](https://realalpha.app/v/julbw1JuAz0/?t=944) quickly realized is that I was just not effective at working on the stack because it was such a terrible stack and so i just went and started working on dev infra because uh we we needed to fix it and there's a few projects that we worked on so one was migrating from python to the big facebook monolith another one was migrating from rest to graphkill and uh these projects they're actually in progress you know like these are things that involve it takes hundreds of engineers many years to do this it's a big code base it's a big migration um now it's it's much faster yeah with with these tools that we have, the AI tools. [16:18](https://realalpha.app/v/julbw1JuAz0/?t=978) And migrations are a pretty good use case for them, though. Yeah, it's like the, it's the perfect use case for it. And then I just started getting kind of deeper into this. And by the end, by the time I left Instagram, so I was working on this on DevInfra and kind of leading a bunch of these migrations. That's also where I intersected with Fiona Fung, who is now the manager for the QuadCode team. I just worked with her and she was just such an amazing leader, this incredible depth and kind of history in tech. And I just thought like, there's no better, there's no better manager for this team. And then I also started working on code quality. [16:47](https://realalpha.app/v/julbw1JuAz0/?t=1007) And so the work on Instagram kind of expanded a bit. And by the time I left, I was leading code quality for all of Meta. And so I was responsible for the quality of the code bases across Instagram. Facebook, Messenger, WhatsApp, Reality Labs, kind of all these code bases. At Meta, it was this program called Better Engineering. And the idea was, I think it's sort of like 2016 or 2018 or something. But Zuck mandated that every engineer at the company, 20% of their time has to be spent fixing tech debt. Oh, interesting. And we called this Better Engineering. And some of this is kind of bottom up where, you know, a team knows best the tech debt that they have to fix. [17:26](https://realalpha.app/v/julbw1JuAz0/?t=1046) And then some of it is dropped down where you need to do, you know, very big migrations. You need to migrate to new language features, new frameworks, things like this. And at Facebook scale, you know, there was tens of thousands of these migrations every year. And so I just started leading all this. And I realized very quick that you just need a little bit more order to it. There was no goals. No one knew kind of like what the outcomes were. There wasn't any tracking. And so we developed a bunch of stuff. One of the ideas was a centralized way to prioritize the different kind of code quality efforts. [17:58](https://realalpha.app/v/julbw1JuAz0/?t=1078) The second thing was figuring out the impact of code quality on engineering productivity, which turned out to be significant. How did you measure? What did you find there? There was a bunch of stuff. I think some of this has been published. I don't know if all of it has, but essentially you try to do like causal analysis and causal inference. This is the methodology. You try to figure out like what are the factors that make it so engineers are more productive. Some of it is code quality. Some of it is outside of code quality. So, for example, meta went back to, you know, return to office instead of work from home. [18:25](https://realalpha.app/v/julbw1JuAz0/?t=1105) That was partially driven by this because we just found some, you know, fairly strong relations that we thought were causal about this. [18:32](https://realalpha.app/v/julbw1JuAz0/?t=1112) **Boris Cherny:** But code quality actually contributes like, you know, double digit percent to productivity, it turns out, even at the biggest scale. [18:38](https://realalpha.app/v/julbw1JuAz0/?t=1118) It's kind of comforting to hear because I think it's rare to have a place where you actually measure this, but I think we feel it. Like when you have a clean code base, modular, or it can get easier to work with. And I think, you know, reasoning, could it also be easier for LLMs to work with it? And my hint would be, yes, it should be, right? But I think there's just very little data. But that's a feeling that I would have. Yeah, I think a lot of the big companies have published about this. Like, I think Facebook published something. Microsoft publishes a bunch about this. Google does. But yeah, totally. [19:11](https://realalpha.app/v/julbw1JuAz0/?t=1151) If every time that you build a feature, you have to think about, do I use framework X or Y or Z? These are all options that you can consider because the code base is in a partially migrated state where all of these are around the code somewhere. As an engineer, you're going to have a bad time. As a new hire, you're going to have a bad time. As a model, you might just pick the wrong thing. And then, you know, like the user has to course correct you. So actually, you know, the better thing to do is just always have, you know, a clean code base. Always make sure that when you start a migration, you finish the migration. [19:41](https://realalpha.app/v/julbw1JuAz0/?t=1181) And this is great for engineers. And nowadays it's great for models too. And then you joined Anthropic. And I've heard the story, which you can confirm or give more color to it, that your first pull request was rejected by Adam Wolf. He was my ramp-up buddy. So I joined Anthropic. I was trying to figure out kind of like what to do next. And, you know, I met a bunch of people at all the different labs and Anthropic was just the obvious choice for me because of the mission. This is the thing that personally I know that I need the most. And also just kind of seeing all this change that's happening. It's important to have some sort of framework to think about this and to think about our role in it. I'm also a really big sci-fi reader. That's definitely my genre. I'm a big reader. I have a giant bookshelf at home and stuff. And I just know how bad this thing can go. And I just felt like this is a place that has serious thinkers. People are taking this very seriously and thinking about what can we do to make this thing go better. So when I joined Anthropic, I did a bunch of ramp-up projects, just various stuff that I was hacking on. And I wrote my first pull request by hand because i thought that's how you write code that used to be how you write code that used [20:45](https://realalpha.app/v/julbw1JuAz0/?t=1245) to be how you write code but even at the time at anthropic there was this thing called clyde and it was the it was the predecessor to quad code it was it was super janky it was like it was python you know it took like 40 seconds to start up it was a research code it was not not agentic, but if you prompt it very carefully and hold the tool just right, it can write code for you. And so Adam rejected my PR and he was like, actually, you should use this Clyde thing for it instead. And I was like, okay, cool. It took me like half a day to figure out how to use this tool because you have to like pass in a bunch of flags and like use it correctly. [21:17](https://realalpha.app/v/julbw1JuAz0/?t=1277) But then it sped out a working PR. It just one-shotted it. And this was like 2024, It's like September 2024 or August, something like that. And I think for me, this was my first field AGI moment at Anthropic because I was just, oh my God, like I didn't know the model could do this. Like I was used to these like kind of tab completions, line level completions and IDE. I had no idea that it could just make a working pull request for me. Boris just talked about how he had a true wow moment at work using their AI model. A very different wow moment is when you use a tool at work that makes things so much easier than before. And this leads us nicely to our presenting sponsor, Statsig. Statsig offers engineering teams a tooling for experimentation and feature flagging that used to require years of internal work to build. It's the kind of tool that was so complex to build that only large companies like Meta or Uber had their own custom advanced tooling for it. Here's what Statsig looked like in practice. You ship a change behind a feature gate and roll it out gradually, say to 1% or 10% of users at first. You watch what happens, not just did it crash, but what did it do to the metrics you care about? Conversion, retention, error rates, latency. If something [22:31](https://realalpha.app/v/julbw1JuAz0/?t=1351) looks off, you turn it off quickly. If it's trending the right way, you keep it rolling forward. And the key is that measurement is part of the workflow. You're not switching between three tools and trying to match up segments and dashboards after the fact. Feature flags, experiments, and analytics are all in one place, using the same underlying user assignments, and data. This is why teams at companies like Notion, Brex, and Atlassian use Statsig. Statsig has a generous free tier to get started, and pro pricing for Teams starts at $150 per month. To learn more and get a 30-day enterprise trial, go to statsig.com slash pragmatic. [23:04](https://realalpha.app/v/julbw1JuAz0/?t=1384) And with this, let's get back to Boris and the origin story of CloudCode. And then when you joined Entrofic, we've covered this in a deep dive, but we could recap briefly on how CloudCode came to be out of what seemed like a side project or just a cool hack. So yeah, I started hacking on a bunch of different stuff. I was working on some things in product. I worked on reinforcement learning for a little bit just to kind of understand the layer under the layer at which I was building. This is still advice that I give to a lot of engineers is always understand the layer under. It's really important because that just gives you the depth and you kind of like you have a little bit more levers to work at the layer that you actually work at. [23:44](https://realalpha.app/v/julbw1JuAz0/?t=1424) This was the advice 10 years ago. It's still the advice today. But the layer under is a little bit different now. You know, before it was like, understand, you know, the Java. If you're writing JavaScript, understand the JavaScript VM and frameworks and stuff. Yeah. Now it's like, understand the model. So I was hacking on a bunch of different stuff. Something shipped, something didn't ship. And at some point, I just wanted to understand the public anthropic API because I'd never used it before. And I didn't want to build a UI. I just wanted to, you know, hack something up quite quickly because we didn't have cloud code back then. [24:14](https://realalpha.app/v/julbw1JuAz0/?t=1454) We're still writing code by hand. And I wrote this little batch tool that all it did was it hit the Anthropic API and it was essentially like a chat-based application, but just in the terminal, because that's what AI used to be. And, you know, I still think about it, like engineers are the first adopters. And so when we started to move out of conversational AI to agentic AI, it took a little bit, but engineers understood it pretty quick. And I think now when you ask non-engineers about like, what is AI? they would say it's this conversational AI. It's like a chatbot or something. And that's why I'm actually very excited for, you know, Cowork, this new product that we launched, because it's going to bring the same thing that engineers saw very early to everyone. [25:00](https://realalpha.app/v/julbw1JuAz0/?t=1500) But when I think about, you know, co-work, I think back to this moment that we're talking about, like very early on. QuadCode originally wasn't QuadCode. It was a chatbot because that's what I thought AI was. But we had to kind of figure out kind of what is the next thing. And so at the time I built this chatbot, it was somewhat useful, but it was just a chatbot. And the next thing that I tried was I wanted it to use tools because tool use just came out and I didn't know what it was. And I was like, let's experiment. it and i i gave it a single tool which was the bash tool and i didn't know what to do with the bash tool and so i asked it you know like i actually didn't know if it could even do this but i asked like what music am i listening to and uh it just wrote a little apple script program using like said or whatever to uh open up my music player and then like query it to see what music it's listening to and just one shot at this with sonnet 3.5 this is actually my second aj field agi moment very quickly after the first one and the model just wants to use tools that that's that's just what i realized like this thing like if you give it a tool it will figure out how to use it to get the thing done and i think at the time when when i think about the [26:12](https://realalpha.app/v/julbw1JuAz0/?t=1572) way that people were approaching ai encoding everyone essentially had this mental model of you take the model and you put it in a box and you figure out like what is the interface like How do you want to interact with this model? What do you need it to do? Essentially, it's like if you have a program, you stub out some module, stub out some function, and you say, okay, this is now AI, but otherwise the rest of the program is just a program. And so this is just not the way to think about the model. [26:35](https://realalpha.app/v/julbw1JuAz0/?t=1595) **Boris Cherny:** The way to think about it is the model is its own thing. You give it tools. [26:40](https://realalpha.app/v/julbw1JuAz0/?t=1600) You give it programs that it can run. You let it run programs. You let it write programs. But you don't make it a component of this larger system in this way. And I think there's just like, you know, this is a version of the bitter lesson. So there's, the bitter lesson is a very specific framing, but there's many corollaries to it. This is one of the corollaries is just let the model do its thing. Don't try to put it in a box. Don't try to force it to behave a particular way. One of the first ways you saw it was giving it tools, giving access to the bash and then later to the file system and then to more tools, right? [27:12](https://realalpha.app/v/julbw1JuAz0/?t=1632) That's right. Yeah, we give it, we give it bash. Then I say we, it was just me the first three months, but then the team grew. so it was bash it was uh and and file edit that was the second one and one of the interesting thing we talked about uh last time for the deep dive is when you built it and it started to actually write code with with the tools that you had you've had an internal debate inside and traffic should we just keep it to ourselves because making suddenly it spread across engineering and it was making all of you a lot more productive right yeah that's right in the end the decision was to release so that we can study safety in the wild because when you think about safety and you know i keep talking about the word safety the reason anthropic exists as a lab is safety this is the reason it was founded this is the reason it exists if you ask anyone at anthropic why they chose it it's because of safety and so if you think about model safety you know there's different layers at which to think about it there's kind of alignment and mechanistic interpretability this is at the model layer then there's evals and this is kind of like a it's kind of putting the model in a petri dish and synthetically studying it in this way. [28:15](https://realalpha.app/v/julbw1JuAz0/?t=1695) And then you can study it in the wild and you can see how it actually behaves. You can see how users talk about it. You can see like, what are the risks in the wild? And you actually learn a lot this way. And by doing this, we've been able to make the model much safer. So in hindsight, it was totally the right decision. It's amusing to hear about it from your perspective because from the outside, what I saw and what a lot of engineers saw is like, oh, Antroflik released cloud code. oh wow this you know for the first release with uh i i believe it was with sonnet 4 release was what did it come out with sonnet 4 originally or sonnet 4.5 i think it was it was for that was the general availability in february but i think it was research preview before that yeah but when it came out my interpretation was like, oh, this thing can write code pretty well. [29:03](https://realalpha.app/v/julbw1JuAz0/?t=1743) And over time, it became a lot more capable. So from our perspective, it was like this really capable coding tool that we just started to adopt and use and use for all sorts of increasingly productive parts. And it has become, I believe, one of the fastest growing developer tools. And I'm always surprised to hear the story that it actually comes from research and the goal to understand how people use the model. Because, on the other hand, like, some startups have been trying to build developer tools deliberately to get adoption, and yet this research tool is getting a lot more adoption. I mean, this is, you know, Anthropic, we're a research lab, we're a safety lab, and, you know, product is this kind of thing tacked onto the side. [29:43](https://realalpha.app/v/julbw1JuAz0/?t=1783) Product exists so that we can serve research better and so we can make the model safer. And this is kind of how we think about everything. There was this, there's also this funny moment early on when we had this launch review and we were deciding whether to launch it. I remember this moment because we were in the room, I think there was like, there was Mike Krieger, there was Dario, there were some other folks in the room and we were deciding what should we do. We were looking at the internal adoption chart, which was just vertical. Like since we released it, it was just insane. It was, you know, like nowadays. [30:12](https://realalpha.app/v/julbw1JuAz0/?t=1812) It was 100%, right? Just 100%. Like nowadays, everyone at, every technical employee at Anthropic uses quad code every day is pretty much a hundred percent for non-technical employees it's also like it's actually getting quite close to a hundred percent it's it's increasing very quickly like you know like half the sales team uses quad code um and i think that's increasing it's just it's crazy dario had this question about like how how did it grow this fast are you like forcing people to use it and i was like no we offer this tool people vote with their feet and you know just like let people use the tool that they prefer yeah and they chose it you don't seem like the person who's exactly forcing people to use your tool yeah yeah i mean the way we did it we just we launched a thing and then we just like listened to the users and we talked to people we saw how they use it we followed up we made it better and yeah i mean now now we're at the point where quad code writes i think something like 80 percent of the code and anthropic on average and you know it writes all my code for sure yeah and this started for you it started the first time you mentioned i think it was in November when it started to write all of your code. When did that switch come and what [31:17](https://realalpha.app/v/julbw1JuAz0/?t=1877) happened to made you trust it to write your code or how much you trusted, how much you reviewed that code, for example? So the switch was instant when we started using Opus 4.5. This was before it came out. You know, we were dogfooting it for a little bit and it was just right away. It's such a more capable model. I just found that I didn't have to open my IDE anymore. I just uninstalled my id because because i just didn't need it at that point i actually did that like a month later because i i just didn't even realize that i wasn't using it anymore yeah a lot of us had similar experiences once opus 4.5 was out in the public and especially over the winter break i had a similar experience i just realized that this thing it actually writes if i'm being honest with myself as good code as i would have written in the stack that i'm very familiar with and my code base my side projects where I know it and just a lot better than what I could for code base that I'm not as familiar or technologies I'm not as familiar with. Yeah I'll be honest it writes better code than I do. I don't want to go there I still like to keep my pride but probably true. Yeah yeah I realized this because also in December I was traveling a little bit I was like on a I was on [32:24](https://realalpha.app/v/julbw1JuAz0/?t=1944) a coding vacation. We were talking about this before but I went to Europe we were just in a different time zone kind of nomading around and it was so fun because I was just coding all day every day which is my favorite thing to do and uh i wrote maybe you know like 10 20 pull requests every day something like that opus 4.5 and quad code wrote 100 of every single one i didn't edit a single line manually and i realized uh at the end of that month opus introduced maybe two bugs whereas if i'd written that by hand that would have been you know like 20 bucks or something like that can we talk about your development workflow you have written threads about I'll be right back. [32:59](https://realalpha.app/v/julbw1JuAz0/?t=1979) This, which is awesome, it's on social media, on threads, and on X, but can you tell us how you use today Cloud Code in terms of, you know, parallelism, and tips and tricks that you and the team have kind of learned and share across the team? Yeah, I mean, look, there's no one right way to use Cloud Code, so I can share some tips and things, but I think the wrong conclusion to draw would be to just copy these and use it. the way we build cloud code is we build it to be hackable because we know every engineer's workflow is different there's no one way to do things there's no two engineers that have the same workflow it's just every every engineer is the same with workstation setup right like keyboards monitor placement all that ever has it differently yeah it's like we're like crafts people right like you choose you choose your tools like we care deeply about it so there's no one right way to do it so for me the way that i do it generally is i have five terminal tabs Each one of them has a checkout of their repository. [33:55](https://realalpha.app/v/julbw1JuAz0/?t=2035) So it's five parallel checkouts. And usually I'll kind of round robin and start quad code in each one. Almost every time I start in plan mode. So that's like shift tab twice in the terminal. And I also overflow as I run out of tabs because there's only so many terminal tabs. I used to use web a lot for this, like quad.ai slash code. That's the place that I overflow to. Nowadays, I actually use the desktop app. It's more convenient. it. So quad code, you know, it's been in our desktop app for, you know, for many months. It's just the code tab in the, in the quad app. And I actually really like it because it has built in a work tree support. So that's existed for a while. And that's quite nice for parallelism. So you have multiple, you don't need multiple checkouts. You just have one and then we automatically set up get work trees for you. So you get this kind of environment isolation. The reason I do that is I actually just really hate fiddling with get work trees on the command line, because it's kind of fiddly, like you need to know the cd. [34:50](https://realalpha.app/v/julbw1JuAz0/?t=2090) And the git work tree, for those who are not as familiar with it, it's when you can check out, instead of having a separate local folder, it's almost like checks out a separate branch, right? And then you can work on it separately, but not have the conflicts only at like merge time. That's right. Imagine that you have a folder, but you have maybe like, git makes five copies of that folder in a way that's very cheap and kind of easy to throw away, so you get this kind of isolation. it can work in parallel and the quads don't interfere. Yeah, so you now have support for this, which I think you recently added like native support, but like for your workflow, you just stuck with the old one of checking out all the separate folders, right? [35:29](https://realalpha.app/v/julbw1JuAz0/?t=2129) Yeah, exactly. I actually find that over time, I'm using the desktop app more and more for this just because I don't need these separate checkouts and I just have a bunch of quads running in parallel and I don't have to think about it. The other surprise hit is the iOS app for me. Every day I start, like I wake up and I just start a few agents on my phone. Oh, the native one, yeah. The native one, yeah. It's just like, it's the quad app, it's the code tab in the quad app, and it's the same exact quad code. Except it runs in the cloud, right? It runs in the cloud. Yeah, so you have to kind of configure the environment. [35:57](https://realalpha.app/v/julbw1JuAz0/?t=2157) Luckily, our environment's pretty simple, so, you know, and we just use hooks for it. So you just use the session start hook and configure it. This is kind of one of the benefits of making quad code really hackable, is it's very easy to do this kind of configuration. And this is something, honestly, I would never have predicted, because you know like i i code on a computer if you told me six months ago i'd be writing i don't know a third i haven't told the data maybe like a third half something like this of my code on a phone that's crazy but that's that's what i'm doing today and you're using parallel agents at what point did you start using them and how has it changed your work because one thing that i noticed on myself i don't really use that many parallel agents i may be like two at a time but i'm someone who well i i like to be in charge and especially with claude claude is is a tool that you can follow it along it tells you what it's doing it you can also have for example learn mode which this was shipped a lot earlier where you can actually follow along it gives you tasks i i feel that like Staying in one tab and following along, the model is pretty fast as well. I can kind of keep in touch. I'm assuming at some point you must have done this, but then what happened when you changed to parallel? And do you feel you're losing any control or it doesn't really matter that much? [37:13](https://realalpha.app/v/julbw1JuAz0/?t=2233) Yeah, I think there's kind of like two modes to think about or kind of like two kind of workflows to think about. So when you're new to a code base, learn mode is awesome. Highly recommend it. For people that are onboarding to the QuadCode team, people that onboard to Anthropic, the thing that we recommend is, so you do, for people that haven't tried it, you do slash config in QuadCode, you pick the output style and you can do learn or explanatory. We usually recommend explanatory because that tends to be better for new code bases that you kind of haven't been in before. For me, once you're familiar with the code base, you just want to be productive, right? [37:48](https://realalpha.app/v/julbw1JuAz0/?t=2268) Like you just want to ship as much as you can and you want to kind of be effective doing that. So the world really switches. I don't really go deep into tasks anymore. I start a quad in plan mode. I'll have it kick something off. With Opus 4.5, I think it got there. With 4.6, it just really, really does it. [38:05](https://realalpha.app/v/julbw1JuAz0/?t=2285) **Boris Cherny:** Once there is a good plan, it will one-shot the implementation almost every time. [38:09](https://realalpha.app/v/julbw1JuAz0/?t=2289) So the most important thing is to go back and forth a little bit to get the plan right. So what I do is I start one, I enter plan mode, I give it a prompt. As it's chugging along, I'll go to my second tap and I'll start the second quad, also in plan mode, get it chugging along, then go to the third tap, go to the fourth one. Then maybe I'll go back to the first one when I get notified that it's done. And then I'll kind of... Do you have notifications on or do you turn them off? I actually operate in both modes. Sometimes I do like, you know, focus mode on the Mac. So I just have it off. But also, sometimes I use the system notifications. [38:41](https://realalpha.app/v/julbw1JuAz0/?t=2321) And you're very, very productive with PRs. I mean, I think it was very visible. Even around the holiday breaks on social media, you actually were responding to, I think someone reported a bug or a feature request. I'm not sure which one it was. And then an hour or two later, it was done because you did it. You've also talked about like number of pull requests you've done on a day, not to like show up, but just as context. What does a pull request typically involve in terms of complexity? Are these like, are some super trivial or some actually like larger pieces of work as well? Yeah, each one varies a lot. [39:17](https://realalpha.app/v/julbw1JuAz0/?t=2357) Sometimes it's a few lines, sometimes it's a few hundred or a few thousand lines. They're all just very, very different. It's changed so much. Like back when I was at Instagram, I think I was one of the top two, maybe top three most productive engineers at Instagram just by volume of code written. Oh, wow. So I've always, you know, for me, I've always just coded a lot. like this is a coding is like a way that I can express myself. And it's just like it's a way that my brain thinks also. And so now I just get to do it. But I think with quad code, the kind of code that you write, if you are very productive, it tends to be even it's just the number of PR sort of undersells what's happening. Because I think people that used to be very productive in the old days before AI assistants, a lot of the code maybe was like code migrations or something like this so like people that ship you know 20 30 prs every day a lot of it was like pretty you know like a one-liner or kind of migrating a to b or whatever nowadays i ship you know 20 30 prs every day but every pr is just completely different some of them are thousands of lines some of them are hundreds some of them are dozens of them are one-liners it's none of these are kind of code [40:22](https://realalpha.app/v/julbw1JuAz0/?t=2422) migrations because actually claude just does those and i don't need to be part of that shipping this much code or this much more productive the obvious question that comes up for any i guess software professional as well the review what the way teams used to work and i'm not sure if instagram did this but a lot of other companies that this is you make a pull request you put it up there there's a mandatory human reviewer at google there's actually two because there's one on code quality as well how has this workflow changed how does the hot code team think about code review and how has it changed over time yeah i'll start by thinking i'll start by talking about how code review used to work for me so the way that i used to do it is uh every time i i also used to be one of the most prolific coder viewers oh okay so both yeah yeah right or is it code reviewers that's actually and that's one of the benefits of being in a different time zone like i'm not super human i just didn't have any meetings and the the way that i approach code review is every time that i would have to comment about something i would drop it in a spreadsheet and i would like describe the issue so let's say you know like someone named a [41:23](https://realalpha.app/v/julbw1JuAz0/?t=2483) parameter you know in a function badly i would like put that in a spreadsheet if someone did some bad react pattern or something, I would put that in a spreadsheet. And then over time, [41:30](https://realalpha.app/v/julbw1JuAz0/?t=2490) **Boris Cherny:** I would just kind of tally up the spreadsheet. And anytime that a particular row had more than three or four instances, I would write a lint rule for it. So just automate it with kind of [41:39](https://realalpha.app/v/julbw1JuAz0/?t=2499) analysis. And so that's what it used to look like. For me, I've always tried to automate myself away because there's just so many things to do. And this is one of our superpowers as engineers is we're able to automate all of the tedious work. There's very few other fields where you're able to do this thing. This is a thing uniquely that we're able to do. And this is a thing that I've just always enjoyed because it gives me more free time and I get to do the work I actually enjoy. And so today, the way this looks is a little different, but it mirrors this a little bit. So when CloudCode writes code, it generally, it will run tests locally. [42:13](https://realalpha.app/v/julbw1JuAz0/?t=2533) And this is something Cloud just often decides to do when it's relevant or it'll write new tests. So you kind of do this kind of verification. When we make changes to CloudCode, Cloud will also test itself. so it'll launch itself kind of in a sub process it'll verify itself and it will test itself end to end this is for the look your internal cloud code implementation so you have like this test suite so they can test itself yeah that's right that's right but it'll literally launch itself just in a bash process and kind of just see like hey do i still work okay so it'll do this and this is something that we just didn't code in like it just with opus 4.5 especially it just started spontaneously doing this it just wants to kind of check so so we do this and then we also around Claude-P. So this is the Claude agent SDK in CI. So every pull request at Anthropic is code reviewed by Claude code. And that actually catches maybe like 80% of bugs, something like this. And it's the first round of kind of code review. Claude will automatically address some of these. Some of them it'll leave to a human because it's not sure what to do. There's always an engineer that does the second passive code review. And, you know, there always has to be a [43:18](https://realalpha.app/v/julbw1JuAz0/?t=2598) person in the loop approving the change so on on the team before anything goes into production if you will an engineer does look at it yes as you're thinking of code review would you do this for every type of project or this is specifically because you now know that this actually has real world impact people depend on it you know there's a lot of users let me put it the other way around like can you see places where you would just not have an engineer review uh code what situations would that be in i think it depends how how it's used yeah i'd agree with that like you know if you're building some personal side project like you can just yolo straight to main you know like even even before ai you would have not reviewed you just trust yourself or you know just ship to production or cessation to production and do some changes that kind of stuff right exactly exactly um the very first versions of quad code that were internal like you know i committed straight to main but then you know as soon as you have users and you know for anthropic our main customer base is enterprises. This is what we care about the most. For us, for safety reasons, security is really important. Privacy is important. These are all related. It's also [44:21](https://realalpha.app/v/julbw1JuAz0/?t=2661) very important for our customers. And so because this is an enterprise product, it has to be secure. It has to be, we have to make sure that it meets a certain bar. So we definitely use a lot of automation, but at least for now, there has to be a human in the loop just to make sure. One thing that is just known about LLMs is they're non-deterministic. And by putting LLM as a reviewer, Claude, doing a review, it will give good feedback. But how would you deal with the fact that you can't be sure if it's always giving the feedback, you cannot be sure that even if it's capable of catching an issue, that it will necessarily catch that. [44:59](https://realalpha.app/v/julbw1JuAz0/?t=2699) Are you doing anything in this loop to do deterministic thing? For example, linting is very deterministic, as you will very well know. Like, have you thought of marrying some of these ideas or are using, for example, are using linters on the code base where you found no need for it? Yeah, absolutely. Absolutely. Yeah. This is just, yeah. Yeah. We have type checkers. We have linters. We run the build. Cloud is actually so good at writing lint rules. So actually what I do now, I used to tally stuff up in the spreadsheet. Now what I do is when a coworker puts up a pull request and I'm like, this is lintable, I'll just be at Claude, please write a lint roll for this in that PR on their PR. [45:32](https://realalpha.app/v/julbw1JuAz0/?t=2732) And we have, you know, you just run like slash, I think it's like setup GitHub or something like this. You can do this in Claude code and it'll install the GitHub app, which then makes it so you can tag at Claude on any pull request, any issue. I use this every single day. So very, very useful. So you want these deterministic steps. Also, though, there are there are ways to get Claude to be a little bit more deterministic. So, for example, you can do best event, you can have it do multiple passes. And this is actually quite easy to do. So, you know, for example, the code review skill that we use internally, it's open source and it's available in the cloud code repo. [46:09](https://realalpha.app/v/julbw1JuAz0/?t=2769) And so all we do is, you know, we launch parallel agents to do stuff and then we launch parallel deduping agents to check for false positives. But essentially best event, the way you implement it is all you say is cloud start three agents to do this. And that's it. Wors just talked about building that enterprise infrastructure layer, the auth, the permissions, the security, that has to all work before you can ship to real customers. This makes it a great time to speak about our seasoned sponsor, WorkOS. If you're building any SaaS, especially the AI product one, then authentication, permissions, security, and enterprise identity can quietly turn into a long-term investment. [46:45](https://realalpha.app/v/julbw1JuAz0/?t=2805) SAML edge cases, directory sync, audit logs, and all the things enterprise customers expect. It's a lot of work to build these mission-critical parts and then some more to maintain them. But you don't have to. WorkOS provides these building blocks as infrastructure so your team can stay focused on what actually makes your product unique. That's why companies like Anthropic, OpenAI, and Cursor already run on WorkOS. Great engineers know what not to build. If identity is one of those things for you, visit WorkOS.com. And with this, let's get back to building Cloud Code with Boris. How does CloudCode work in terms of architecture? [47:21](https://realalpha.app/v/julbw1JuAz0/?t=2841) So as an engineer, how can I imagine it's set up? We covered some of this in the deep dive, and I think you told me that you had some pretty complex ideas when you started and you just simplified a lot of it. Yeah, yeah. It's very simple. Like, you know, there's not much to it. There's like, there's a core query loop. There's a few tools that it uses. We delete these tools all the time. We add new tools all the time. We're just always experimenting with it. So there's kind of this core kind of agent part of it. Then there's the TUI part of it. And then there's actually a ton of different pieces around security and making sure that everything that Quadco does is safe and that there's a human in the loop for when it happens. [48:02](https://realalpha.app/v/julbw1JuAz0/?t=2882) And by safety, do you mean as a user when it's doing stuff on my computer or also as entropic monitoring use cases that could be deemed unsafe? Yeah, there's kind of a couple versions of this. Safety, there's just many, many layers. And for things like safety and security, there's no one perfect answer. So, you know, it's always a Swiss cheese model. You just need a bunch of layers. And with enough layers, the probability of catching anything goes up. And so you just have to kind of count the number of nines and that probability and pick the threshold that you want. And so for something like prompt injection, for example, we do this generally at three different layers. [48:37](https://realalpha.app/v/julbw1JuAz0/?t=2917) So let's think about something like WebFetch. So quad fetches a URL and it reads the contents of that web page and then it does something in quad code. So one of the risks for something like this is prompt injection. Maybe there's an instruction on that website to be like, hey, quad, delete all the folders or something like that. So we think about this in a number of ways. The most basic way is it's an alignment problem. And so... Opus 4.6 is the most aligned model we've ever released because we've taught the model how to be more resistant to prompt injection. And so you can read about this on the model card and I think it was part of the release. [49:11](https://realalpha.app/v/julbw1JuAz0/?t=2951) The second part is that we have classifiers at runtime where if there is a request that seems to be prompt injected, we block it. And we just make the model try again. And then the third layer is for something like WebFetch, we actually summarize the results in using a subagent. and then we return that summary back to the main agent. So again, this kind of reduces the probability of prompt injection. And so you can kind of see how this isn't just one mechanism. It's a layer. And by having a bunch of these different layers, it just reduces the probability a lot. One interesting technical choice that you also mentioned is using RAG or not RAG retrieval augmented generation. [49:48](https://realalpha.app/v/julbw1JuAz0/?t=2988) And you mentioned how in the earlier version of Cloud Code, you use a local vector database to to get some to speed up search and you layer through this away can you talk about how this one because this was another example where i guess did the model get better yeah i mean this is one of those things where we try so many different things we try so many different tools and just statistically most of them we throw away even something like the spinner in quad code i think it's gone through like a hundred iterations i want to say oh just the spinner and you know out of those we've landed maybe like 10 or 20 in production and like 80 of them i probably just threw away because it didn't feel good enough so just statistically almost all the code we write we throw away because it's just so easy to write this code and try stuff and see what feels good so for something like rag we tried a bunch of different approaches early on so the first one was rag for retrieval because i think this i was just like reading up like how people were doing retrieval and it seemed like all the papers were talking about rag and so the way i did it was it was like a local vector database i think it was like written in typescript and [50:51](https://realalpha.app/v/julbw1JuAz0/?t=3051) you just looked on the user machine uh and then i was using some like embedding uh model that was in the cloud to compute the embeddings before storing it um and that worked like pretty good but there's a lot of issues with rag um so for example i was finding that the code drifted out of sync like if i make a local function it's not yet indexed and so rag isn't going to find it there's also this question of like how exactly is the index permissioned so who can access it I can access it. But then how do we like encode that in kind of permission policies? How do we make sure no one else can access it? [51:24](https://realalpha.app/v/julbw1JuAz0/?t=3084) How do we make sure that like if there's a rogue IT person within the company, they can't access someone else's data? This is really, really important that we think about this. And so we just decided like it was sort of working, but it also has a lot of downsides. And so we tried a bunch of other stuff. One of them was just using the model to kind of index everything recursively. That was kind of a cool idea. There was another version where we just tried Glob and Grep. We tried a bunch of different stuff. [51:51](https://realalpha.app/v/julbw1JuAz0/?t=3111) **Boris Cherny:** It turned out that agentic search just outperformed everything. [51:55](https://realalpha.app/v/julbw1JuAz0/?t=3115) And when I say agentic search, it's a fancy word for Glob and Grep. That's all it is. Nice. So the model both got good enough and you realize that they can use these tools pretty efficiently. Yeah. And this was partially inspired, honestly, by my experience at Instagram. Because at Instagram, click to definition didn't work. Because the dev stack was just borked like half the time. And I think now it's better. And so what engineers learned to do instead is, let's say you're looking for the definition of the function foo. Instead of click to definition, what you would do is you would use the global index, which is quite good at meta, and then you would search for foo per opening parentheses. [52:32](https://realalpha.app/v/julbw1JuAz0/?t=3152) And this worked pretty well. And it's funny because this works for the model pretty well too. Interesting how one idea from one area can come to the other. One of the more advanced parts of cloud code that we also previously talked about is the permission system. Can you talk about what was complex about it? And also you recently opened the open source sandboxing, right? Permissioning is really complex. There's, like everything else that has to do with security, it's a Swiss cheese model. There are a number of classifiers that run to make sure the command is safe. And there's also static analysis that we do to make sure the command is safe. [53:14](https://realalpha.app/v/julbw1JuAz0/?t=3194) As a user, you can also allow list particular patterns that you know to be safe. So for example, some standard Unix utilities, we pre-allow because we know they're read-only because we know they can't expel tree data or anything like this. so we just won't prompt you for permission but actually quite you tools fall into this category because even something like the find command there's actually a way to execute arbitrary code as part of that command because there's there's like system flags that you can use for this or even something like the said command there's ways to use this so there's just like all this like arcania about these various unix utilities where it's actually not as safe as you think and so we want to be by default fairly conservative about what we allow by default As a user, though, you can configure an allow list. [53:58](https://realalpha.app/v/julbw1JuAz0/?t=3238) So you can say, for example, like these patterns are allowed, these patterns are not allowed. And so we let you define that. And we also check this allow list to make sure that it's safe. Yeah. And then you have this like neat permission system where every time you run a command that needs permission, you can decide to run it once or run it for either the session or whatever it makes sense or just globally allowed going forward, right? That's right. This is a funny artifact. this was actually in the very very first version of plot code this is the way permissions worked this is the very first release this was like september 2024 the first internal release i remember at the time we weren't sure whether agentic safety could be even be solved and so there was actually a lot of pushback internally from safety teams because they were like okay like you can't just run let the model run bash commands like that's unsafe so like what do you do like this is not a solvable problem so like we can't launch this i i brainstormed with ben man and ben was he started the labs team he's one of the founders at anthropic um he's actually he's the the person that hired me to anthropic we just came up with permission prompts as the way to do this you put [55:02](https://realalpha.app/v/julbw1JuAz0/?t=3302) the if you're not sure just ask the human and then they can decide no i want to ask you about how software engineering is done in general in terms of antroffic and one of the first questions which is a i guess a more formal one but or from the outside is titles or lack of them everyone at Antroffic has the same title member of technical staff why did this happen and what does this result in this kind of like everyone there are basically no titles right except for one I think it's kind of an acknowledgement that everyone just is figuring stuff out and if you kind of squint and look at the work people are doing it's all quite similar and it's it's kind of quite generalist and if you talk to the average software engineer they might not just be doing coding, they might also be doing a little design. They might also be talking to users. [55:53](https://realalpha.app/v/julbw1JuAz0/?t=3353) They might be writing their own product requirements. They might be writing software and also, you know, doing research. They might be writing product code and also infrastructure code. At Anthropic, there's a lot of generalists. This is also, you know, from my background, this is one of the reasons that I gravitated towards it. And I think member of technical staff just kind of encodes this in the way that people talk to each other, even if they don't know each other without this title the default would have been i see your name on slack and under your name it's a software engineer and then i'm like well okay i guess you're like you're the coding person and so i'm not going to ask you like product questions but when everyone's title is member of technical staff by default you assume everyone does everything and so it kind of inverts this this relationship between people even if you don't know each other well yet in in a way it's kind of this like optimism built into the built into the structure um i think it's also a glimpse of the future because i i think this is where software engineering is going i think this is where every discipline is going is more of this generalist model it definitely feels like it in [56:54](https://realalpha.app/v/julbw1JuAz0/?t=3414) software engineering and i've heard this funny uh comment by Mark Andreessen, how we said that there's this Mexican standoff happening in the tech world where the designers are saying that they're actually now doing like PM and engineering work. The engineers are saying that we're doing design and like everyone thinks they're doing the work of the others and they're kind of standing there like I'm doing your work as well. But the reality is everyone's role is expanding most of it thanks to AI because it makes it easier for an engineer to do product work or for a product person to engineer work and so on. [57:26](https://realalpha.app/v/julbw1JuAz0/?t=3446) So it's what you've said. I remember back in June or July of last year, I walked into the office and there's a row of data scientists that's right next to the quad code team, at least at the time. And I walked in and our data scientist for the quad code team had quad code up on his monitor and he was using it. And I was like, this is interesting because you're a data scientist. Did you like, why are you using a terminal? Like you didn't have Node.js installed because we depended on Node.js back then. I was like, are you are you dogfooding it? Like, are you just like trying to like figure out how this thing works or something? He's like, no, no, I'm like, I'm using it to run queries. He was just like using it to run SQL. And it has like little like ASCII visualizations in the terminal. And then the next week, the entire row of data scientists had quad code running on their computers. And this expanded. And so if you look at the team today, on the quad code team, everyone codes. [58:20](https://realalpha.app/v/julbw1JuAz0/?t=3500) the engineers code, our engineering manager codes, designers code, data scientist code, our finance guy codes, everyone on the team codes. And I think part of it is quad code just makes it so easy. So you don't really have to understand the code base. You can just like dive in and kind of make small changes quite easily. But I think another thing is people are able to use quad code to do their jobs more whether it's you know financial forecasts or you know data science or whatever and by doing this it's actually quite an easy crossover to just use it to write a little bit of code also so it's just a way to dip your toe in the water one other interesting thing about how you work is cat who was talking about she is i guess you the title is the same but people might gravitate for a role a little bit more i understand she's a little bit more on a product role but you said that prds are just not really written inside entropic and prd's product requirement document it's a well-known artifact across big tech and increasingly over larger startups where you write a spec and the idea is that you write down your thoughts people align you send it over and now you know what to build but apparently you're not doing much of this or [59:28](https://realalpha.app/v/julbw1JuAz0/?t=3568) at all some of this i think is because entropic is still you know it's still a startup so you don't actually have to align with that many people usually you can just kind of talk about or do it in Slack or whatever. But yeah, also part of it is, you know, like Kat used to be an engineering manager. She's extremely technical. And I think this is the way that, you know, our product team thinks about it too, is, you know, better just send a PR. You're doing a lot of prototyping instead. So like that's also something where when we talked about how you were building Cloud Code early on, you were showing actually, you had a whole thread about the number. [1:00:01](https://realalpha.app/v/julbw1JuAz0/?t=3601) I think you did like 15 or 20 prototypes for the to-do list and all of them interactive, working. And what surprised me compared to my past experience and you said that, well, you did this in like a day and a half, all 20, tried it out, got a feeling for it, which incomprehensible for me, it would have taken a week or two weeks and people would have not done 20. They would have done three. So like, are you seeing this? Is there an increase in prototyping and building and showing instead of writing things? Yeah, absolutely. I mean, on our team, the culture is we don't really write stuff. We just, we show. [1:00:33](https://realalpha.app/v/julbw1JuAz0/?t=3633) It's a little hard to reflect back on the time before, because I think now just prototyping everything is so baked into the way that we build. Just everything is prototyped multiple times. Like, you know, we launched agent teams earlier this week. This is our implementation of Swarms. It's very exciting because it just lets Claude do more work for longer, more autonomously. You have a bunch of different uncorrelated context windows. And you have this. kind of communication between agents, they can just do more. This is something that Daisy and Suzanne and other folks on the team and Karen, they prototyped this for months. And they tried all in all probably hundreds of versions of this before they got a user experience that felt really good. It was just really, really hard to get right. There's just no way we could have shipped this if we started with, you know, like static mocks in Figma, or if we started with a PRD or something like this it's a thing that you have to build and you have to feel and you have to see how it feels and to me one of the takeaways even from there was like we probably should prototype more and just be more daring or just release your priors of how long it took to build a prototype [1:01:39](https://realalpha.app/v/julbw1JuAz0/?t=3699) or who needed to build back then it was always an engineer that needed to build but it's probably not true anymore yeah that's right i mean we're in this world right now also where we just we don't know what the right answer is you know like i i think back in the old way of building you the cost of building was high. And so you had to actually spend a lot of effort to aim very carefully before you take your shot. Because after you take your shot, it's very hard to course correct. You can only take so few shots. But now it's changed. The cost of building is very low, but also we don't know where we're aiming. So we just have to like, we have to try and we have to see what feels good. [1:02:12](https://realalpha.app/v/julbw1JuAz0/?t=3732) And it's just very, very exploratory. And I think also a big part of it is humility, where, you know, personally, I'm wrong. Like half the time, I'd say like most of my ideas are bad, At least half of them are bad. And I don't know which half until I try it. And I get feedback from others as well sometimes. That's right. It's like I have to try it myself and then I have to see what others think because, you know, my intuition does not always match others. When you were showing these prototypes of just how the tasks were built, you were telling me that you build the prototypes and then your process was always you first like looked at it, you tried it out, you got a feel for it. [1:02:47](https://realalpha.app/v/julbw1JuAz0/?t=3767) And then for the ones that you felt were good, you showed it to others. and sometimes they give you feedback like nah this doesn't work and then sometimes when it felt good then you share it even broader so i feel like you know like it's a mix right where like sometimes you can decide already and then sometimes you get feedback and then eventually some good ideas come out of it yeah and there's a lot of examples of this like uh we we launched this kind of condensed view for file reads and file search just because the the model is just so agentic now like i felt like half the screen is these like file reads and i actually don't care like you know you write a thing. I don't really care what it is. And so we condensed this down to make the output a little bit more readable. I really liked it after probably 30 prototypes or something like this. It took so much effort to make that feel really good and clean. We rolled it out to employees at Anthropic for about a month and we had everyone dog fooded and I fixed another probably dozen bugs, dozen tweaks based on all this feedback. We launched it externally and, you know, almost all users liked it, but there were a few users that didn't because they want more expanded output. And so on the [1:03:47](https://realalpha.app/v/julbw1JuAz0/?t=3827) githubishi i was just going back and forth with people to be like you know like what don't you like and people give a lot of feedback i shipped another version then some people liked it some people didn't and so i iterated again and kind of made it good and it's actually i think almost there where people can configure it the way that they want but still the default is really good but this is just the process you know we we get it right some of the time we have to learn from our users we want to hear from people so we can get it right do you use ticketing systems for your work where you know where you capture like all right here's the work i want to or do you just pretty much do the work as as it comes in so at anthropic we leave it up to teams on the quadco team we leave it up to every person uh different people use uh use this differently for example i don't use a ticketing system some people like to use asana or notes or something like this one of the coolest things that i saw this was maybe like three months ago or something we launch plugins and the way we launched that is daisy for a weekend she had a very early version of swarms and she let the swarm run and she told that your job is to build plugins you have to come [1:04:49](https://realalpha.app/v/julbw1JuAz0/?t=3889) up with a spec then you have to make a asana board and split up into tasks and then all the different agents have to build it and she set up a container and she set up a clod in. Dangerous mode. And she let it run for the entire weekend. It spawned a couple hundred agents. They made a hundred tests on the Asana board, and then they implemented it. And that's pretty much the version of plugins that we shipped. These kind of coordination systems, they used to be for humans, but I think nowadays it's just as much for models. let's let's talk about cloud co-work uh it's one of the very impressive things about us it looks great so i tried it out it's inside cloud you have the co-work tab there and you can i feel it's a lot more visual way of running agents interacting with them one of the surprising things i just heard that it was built in 10 days can can you take us through like what it took to build it and what does it actually mean was it from the idea or like from the decision of of building it and how big was the team building it? [1:05:44](https://realalpha.app/v/julbw1JuAz0/?t=3944) The team was really small. It was just a few people. For a long time, we felt that there is some product to be built for non-engineers. The reason we felt this is for a long time, people that were using quad code are non-engineers. And so, you know, in the product world, when you see latent demand, you see people jumping through hoops to use a product that was not designed for them. That's a really good sign it's time to build another product that is built just for them. There's all these people on Twitter that there's this one guy that was using QuadCode to like monitor his tomato plants. I just, I love this. [1:06:18](https://realalpha.app/v/julbw1JuAz0/?t=3978) It was like, he had like a webcam set up and the quad was like, oh my God, I'm so happy that our plant is budding. And because it had like a webcam and just like every day it was like monitoring it. And it was so happy that the tomatoes were growing. There was someone that was using QuadCode to, you know, recover photos off of a corrupted hard drive. And it was like his wedding photos. Wow. You know, like I said, our entire finance team at Anthropic uses QuadCode. Our sales team uses QuadCode. So there's just all these people that are non-engineers that were using it. And at that point, QuadCode, it's available in a lot of form factors, right? [1:06:49](https://realalpha.app/v/julbw1JuAz0/?t=4009) Like we started in a terminal, then we expanded and we added support for IDEs. So we have extensions for, you know, every VS Code-based IDE, every JetBrains-based IDE. There's also iOS and Android apps. There's the desktop app. There's web. So then there's like Slack and GitHub apps. So we kind of expanded to all these places to make QuadCode easier for engineers. But ultimately, none of these are built still for non-engineers. And so CloudCode evolved a lot, but it still felt like there's a there's kind of a gap and there's a product that could make this even easier for people. And so for the last couple of months, the team was kind of hacking around and just saying, like, what is the right product? [1:07:29](https://realalpha.app/v/julbw1JuAz0/?t=4049) And at some point, someone came up with this idea of like, what if we just take CloudCode, add some guardrails? So, for example, co-work ships with a virtual machine. This is one of the many ways that we make sure it's really safe, especially for non-technical users that don't want to read like bash commands to figure out what it what it's doing and they were hacking on this i think it was something like 10 days until end or something it was just fully built with quad code uh and then we shipped it and can you give us a sense of like the complexity behind an app like this and if we can walk through like what parts needed to be built because from the outside it's a little bit hard to tell like is this just a nice ui wrapper that's you know like i don't know like a few hundred lines of code i'm just being obviously i'm i'm provocative here or behind the scenes it's actually a really complex piece of software and the reason i ask is like uber is a great example where people look at the app it looks really simple i work there and i know it's it's really really complex because you don't see a lot of the complexity there's a lot of regional things there's a lot of back-end things that are all [1:08:28](https://realalpha.app/v/julbw1JuAz0/?t=4108) hidden so from just from looking at it cloud co-work it's hard to tell how much of this is is additional business logic that needed to be carefully thought out versus it's actually just a nice little thin wrapper on top of the the model in some places i think there's less complexity than you would think in some places there's more complexity so on the product side it's quite simple um because it's just the quad desktop app so you know you download the quad app it's well it's a single desktop app it has a tab for co-work it has a tab for code it has a tab for chat so it is just one app and we're able to inherit a lot of that product logic there's some UI rendering code under the hood, you know, it's just the same quad code running as the same quad [1:09:03](https://realalpha.app/v/julbw1JuAz0/?t=4143) **Boris Cherny:** agent SDK that powers quad code. A lot of the complexity actually is about safety because we know, like I said, we know the user is non-technical. And so we just want to make sure they have a good [1:09:13](https://realalpha.app/v/julbw1JuAz0/?t=4153) experience. And so, for example, if someone launches the app and then, you know, like they delete a bunch of family photos, that's really not good. And so we wanted to make sure that we protect against this. So you can't accidentally do that. And so that's where a lot of the guard rails came from. So there's a bunch of classifiers running on the back end. This is for safety and again, extra mitigations for things like prompt injection and, you know, risks like this around security. On the front end, there's an entire virtual machine that we ship. There's a bunch of operating system level integrations to make sure people don't accidentally delete things. [1:09:46](https://realalpha.app/v/julbw1JuAz0/?t=4186) So just around safety, there's a lot there. And then we also had to rethink the permission system because we inherit the permission system from quad code. But also for co-work, actually a big part of the value is not just running locally, but it's using all of your tools the way that quad code uses it. But the thing is for non-technical users, your tools aren't really available as CLIs. Some of them are available for MCP. Many of them are available in a browser. And so co-work is really, really good when you pair it with a Chrome extension. And this is the way that I usually use it. So, you know, for example, I use it every week to do project management for the team. [1:10:20](https://realalpha.app/v/julbw1JuAz0/?t=4220) we have like we have a spreadsheet that tracks kind of at a really high level what everyone's working on and this is kind of my personal way of project managing you know other people like i said use asana other people use notes or whatever for my own test i don't use anything but kind of for the team overall i have the spreadsheet and i have co-work kind of check in and i i just ask co-work every week hey can you look at the rows for any status that has not been filled out can you just ping the engineer on slack and so it'll open one tab in chrome for the spreadsheet it'll open another tab with slack and then it'll just start messaging engineers in slack and it just one shots it there's like one engineer's name for some reason it can't autocomplete um but everyone everything else it just gets and so this is actually like from a safety point of view we also thought pretty deeply about this chrome extension how this works and how the permissioning model should interact with this local permissioning model so there's also a bunch of code to kind of make sure that that's that feels smooth and what's the tech side behind this i assume a lot of will similar to the the cloud app but is it is it electron typescript those kind of things or [1:11:21](https://realalpha.app/v/julbw1JuAz0/?t=4281) something else yeah just electron and typescript actually some of the people working on it are early electron folks so uh felix who's uh you know the creator of of co-work he was a really early engineer on electron and he helped build it oh amazing and co-work launched mac os only uh what was the reason for both for choosing this platform first and for now only choosing this platform yeah so windows coming soon um i think probably by the time this podcast comes out we will have windows support uh we just wanted to start early and start learning you know like everything we do at anthropic it's kind of like the way that i told my own story the one of the things i like about anthropic is it just really really matches the way that people here think about it you know back to this point where like we don't have high certainty about the things that we build and our intuition is often wrong and so we just have to like learn from users and figure out what people actually want and just spend a lot of time listening to people and understanding the feedback deeply. This is the way that we build a product. And so we always launch a little bit before it's ready. We did this for QuadCode. When we launched QuadCode initially, it didn't even [1:12:27](https://realalpha.app/v/julbw1JuAz0/?t=4347) support Windows also. It didn't support, you know, like a lot of different stacks. And then over the coming weeks, we added support for every stack. Now QuadCode supports every single stack, you know, like Windows, whatever weird Linux destroyer you use, macOS, we support everything. And so for co-work also we just wanted to launch early we wanted to start with mac as that was just the starting point but um yeah it's it's going to support everything what one thing you mentioned is is getting feedback i'm curious both for cloud code and for cloud co-work how do you go about things like observability monitoring when you're rolling out do you use any feature flags and i'm more interested in like did you build custom tools for this or did you decide to use certain vendors because especially for observability i'm sure that this is this is both important but it also sounds like pretty high scale in terms of the number of users that we can derive or it's this will not be a small operation yeah there's there's some off-the-shelf vendors that we use there's some custom code that we use so um it's actually it's mix of both. There's nothing too surprising about it. There's one thing about Anthropic that's kind [1:13:29](https://realalpha.app/v/julbw1JuAz0/?t=4409) of interesting is because we're an enterprise company and we care a lot about privacy and security, we can't see people's data. And so, you know, like if someone reports a bug, like I actually can't pull up your logs to kind of see what's going on. A lot of work goes into kind of figuring out how to log events and things like this in a privacy preserving way. This is just very important to the way that we operate. For Cowork, what kind of learnings have you had so far it's it's been out for i think a few weeks now did you see something unexpected uh are you shaping the product based on feedback that you're getting yeah uh every day the team is landing so many fixes the most surprising thing is just how much people are loving it to be honest when quadcode first came out it actually wasn't an overnight hit this is something people think it was but it was sort of a slow takeoff at the beginning and i think the first big inflection was in May, when we released Opus 4 and Sonnet 4, that's when it really clicked. And that's when our growth became exponential. But at the beginning, it was sort of a research preview. [1:14:27](https://realalpha.app/v/julbw1JuAz0/?t=4467) People didn't really know how to use it. Some people got it immediately, but most people didn't. It took a little while. For CoWork, it's a much steeper growth trajectory than QuadCode was at the beginning. So it's just been an instant hit. And that's actually been very surprising. I didn't really expect that. One of your new releases, which came out just very recently, it was I think yesterday or the day before when we're recording this podcast was agent teams and as I understand the idea with what agent teams, agent swarms instead of single agent you can have a lead agent and it can delegate to its different teammates how do you start experimenting with this and how did you decide to ship it now? [1:15:06](https://realalpha.app/v/julbw1JuAz0/?t=4506) We're always doing experiments right there's all sorts of ways to get more mileage out of quad code. One way you can do it is by extending context. Another way is auto-compacting context. So it's essentially infinite context. And that's what we have right now. Another way is using sub-agents. So you have multiple agents kind of working together. There's just like a lot of different approaches to get a little bit more mileage out of the context window. There's this one idea called uncorrelated context windows. That's what we call it. And the idea is you have multiple context windows, but they essentially start fresh. So they don't know about each other. [1:15:43](https://realalpha.app/v/julbw1JuAz0/?t=4543) And so an example of this is like a correlated context window is if you have one, if you have the model and it does a task and then you have it just do a second task in that same context window. And in this case, the second task knows about the first one because it's in the same window. But for something like a subagent, it's uncorrelated because the main agent prompts the subagent, but the subagent's context window is fresh. Besides that prompt, it doesn't know what's in the parent context window. And you can see this actually a little bit in, for example, like subagents versus skills. because when you run a skill, you know, or a slash command, it sees the parent context window versus for a subagent, it doesn't. [1:16:18](https://realalpha.app/v/julbw1JuAz0/?t=4578) So it's uncorrelated. There's some cases where you want that context. There's some cases when you don't. [1:16:24](https://realalpha.app/v/julbw1JuAz0/?t=4584) **Boris Cherny:** And there's this kind of interesting thing where uncorrelated context windows and just throwing more context at the problem and throwing more tokens at it when the windows are uncorrelated gives you better results. [1:16:35](https://realalpha.app/v/julbw1JuAz0/?t=4595) It's actually a form of test time compute to do this. And for something like Teams, we've been experimenting with this for a while, I think since maybe like October or September or something like this. And it really just felt like with Opus 4.6, it clicked where the model figured out really how to use this. And sometimes you see these kind of cute exchanges where the agents are talking to each other and they're like discussing something. It's very cool to see. It's very like humanistic in a way. But there's other times where you just get very good results. And so we had a bunch of internal evaluations, for example, where we have quad build something very, very complex, something more complex than what a single quad would build. [1:17:12](https://realalpha.app/v/julbw1JuAz0/?t=4632) And we saw the results just really, really improve with Opus 4.6 with Teams. And that's why we felt it's the right time to release it. We also wanted to be careful. And the reason you have to opt into it, the reason it's a research preview is it uses a ton of tokens because it's just a bunch of quads that are running. Not everyone wants this all the time. So just excited to see how people use it and, you know, to hear the feedback. It's something you want for fairly complex tasks. You don't probably want this for every task. The main clod decides the roles for the subclods. We don't have a kind of a regimented way to do this. [1:17:45](https://realalpha.app/v/julbw1JuAz0/?t=4665) It's context specific. I wouldn't say there's one right way to do it. I think actually a lot of the magic of this comes out of this idea of uncorrelated context windows. It's less about the specific configuration of the agents. But, you know, it's something that people should experiment with. I don't think there's a one size fits all. Have you seen use cases, even in, even, I know it's still research, but have you seen use cases where it could look, it looks promising, this approach, the swarm approach? Well, you know, like I said before, plugins were fully built with swarms. There's a bunch of other features since that were built in this way. [1:18:15](https://realalpha.app/v/julbw1JuAz0/?t=4695) So yeah, I think for anything where you see a single quad struggling, swarms can help. It's an interesting tool to look at. talking about change and in general with under carpet that you had a really interesting exchange back in december where when he posted that he's never felt as much behind as as a programmer as he is now because of the progress with ai and then you share the story about how you started debug a memory leak the old-fashioned way and then claude just one shot at it i think it was a reflection of like how everyone is feeling that things are changing so fast and in the in the holiday break, I started to feel that things have ever really shifted. How did you, I guess, come to terms with this or start to embrace this change? This is something I really struggle with. [1:19:01](https://realalpha.app/v/julbw1JuAz0/?t=4741) The model is improving so quickly that the ideas that worked with the old model might not work with the new model. The things that didn't work with the new model might work or with the old model might work with the new model. And it's weird because there's just not a lot of other technologies like this. So I just don't really have a lot of experience to draw on to figure out how I should approach this. And it's been this new skill that I've had to learn. In a way, it's like you just always have to bring this beginner mindset. Honestly, I'm using the word humility a lot, but you always just have to bring this kind of intellectual humility because just all of these ideas that were bad before are now good and the inverse. I think that's honestly it. It's something i constantly have to remind myself about and back in the it's funny back in the old world when someone tries an idea again and we've tried it in the past and it didn't work usually the feedback is like why are you doing this again yeah yeah this should learn this i mean we used to call a bit of a gatekeeping but it was somewhat valid where i know with architecture someone came and said like why don't we do microservice and someone said we tried it [1:20:09](https://realalpha.app/v/julbw1JuAz0/?t=4809) and it didn't work and if you tried it a year or two or three years ago it was kind of valid right Right. Because not much has changed. Yeah, that's right. That's right. And something like Microsoft is funny because it's like every 10 years it goes in and out of style. But yeah, now it's I think the first time ever where it's actually not crazy to just try the same idea every few months because the model improves and it just works. And I actually see this with engineers on the team, like people that are newer to the team, people that are newer to engineering sometimes do things in a better way than I do. [1:20:39](https://realalpha.app/v/julbw1JuAz0/?t=4839) And I just have to, like, look at them, and I have to learn, and I have to adjust my expectations. You know, like, an example of this is, you know, when we release features, sometimes I'll, like, screenshot myself using them on, you know, on X or on threads or whatever just to kind of talk about it. But recently, Tariq, our, you know, our DevRel guy, he actually codes a lot. He's amazing. and he just started automating this so he's having like quad code generate its own videos for for its launches and he just started doing this and you know this is something like i thought would be you know maybe it's possible it's not something i would have tried because i wouldn't have thought the model was ready but he just he just did it and it just kind of worked one thing that i i felt like just a bit like odd about and i think a lot of developers can relate is i've come to terms with this starting from opus 4.5 the and and also similar models like i think gpt 5.2 gave me similar vibes as well the models have been just really good at writing code and i i realized that i don't think i will hand write the code when i'm get i want i want to get stuff done if i actually want to you know get the pleasure of writing i can still do it but one thing i [1:21:46](https://realalpha.app/v/julbw1JuAz0/?t=4906) reflected on is it's just been so much effort to get good at coding i i remember when i when i was learning when i started from like kind of hacking around to go into university to learning c and c plus plus and it was just bloody hard and actually you know going through my first few jobs where i started to become better at it became better at debugging and there's a point where like a lot of my identity was tied to being good at coding that's how we used to get jobs or higher paying jobs when i was an engineering manager when we designed the interview loop at uber we we had talk with managers of what we need to screen for and we would talk like well what do developers do most of their time about 50% of the time they code therefore we placed about 50% of the signal was all about coding so there was a lot of things tied into coding because it is just hard I think we all know that it takes grit it takes some level of intelligence to get good at it and there's a sense of loss of like well I think it's great on one end that the model can do it but it feels that something really quickly got taken away that I don't think I personally thought it would happen this quickly and i'm i think a lot of other people are feeling like some people move on a bit easier [1:22:55](https://realalpha.app/v/julbw1JuAz0/?t=4975) but there's definitely the sense of of grief how did you think about it because again you're you're an example of you you wrote so much code at at facebook also outside of it i know it was just a tool of doing it but not many people could do what you did and now the models can also work as good as you have, or if not better. That's the challenge. Yeah, I think it's something that used to be a thing that we do as software engineers. It's becoming a thing that everyone is able to do. There was a moment, you know, like when I started coding, it was a very practical thing and it was a way to get things done. [1:23:30](https://realalpha.app/v/julbw1JuAz0/?t=5010) And at some point I just fell in love with the art of coding and like languages and kind of the tools themselves. And at some point I kind of fell down this rabbit hole. I wrote a book about, you know, know a programming language type script you were the first ever type script uh book at with o'reilly yeah yeah yeah that's right um it was it was funny actually there was this like there's this amazing moment for me in my little town in japan i went to the bookstore and i found that book translated in japanese no in this tiny town that was just like the coolest moment and then i actually realized i don't remember type script at all because i was only writing python for a couple years at that point yeah and like at some point i started the the first the the biggest type script meetup in the world. That was in SF. And I got to meet kind of a lot of my heroes. There was like Chris Colwell, who wrote like general theory of reactivity. There was Ryan Dahl, the guy that made Node. One of the first times that I went really deep into this community and just the language itself and the tools themselves. And for something like TypeScript, there's this beauty in the type system because Heilsberg is just like, he's just brilliant. Like the idea of like [1:24:39](https://realalpha.app/v/julbw1JuAz0/?t=5079) conditional types and just like anything can be a literal type and there's these very deep ideas that even the most hardcore functional languages do not have like even in something like Haskell like it doesn't go this far and Anders just took it and he pushed it much further than than it had been pushed and you know like Good job. Pamer and a bunch of other folks kind of explored a lot of these ideas and thought of this. And I think for them, it was also very practical, right? Because they had these large on-type JavaScript code bases. How do you gradually migrate it to something typed? And you have to come up with these very beautiful ideas to do this. [1:25:13](https://realalpha.app/v/julbw1JuAz0/?t=5113) For me, Scala was another kind of rabbit hole that I fell into in kind of like this functional programming world. And still, when I write code and when the model writes code, I always think in the types first. That's what matters is what is the type signature? that matters more than the code itself and getting that right so there is this beauty to it there's a there's an art to it for sure but in the end it's a practical thing and in the end this is a thing that we use to to build things and you know it's a means it's a means to an end it's not an it's not an end to itself i i think one metaphor i have for kind of the this moment in time that we're in is the the printing press in you know like the the 1400s or whatever because at that moment it was actually quite similar right like there was a group of scribes that you know knew how to write and it it was as i understand of course we never lived it but as i imagine it was it was a hard process to learn you need to run you need to get the equipment you probably needed some sponsorship or being selected yeah practicing because you needed to produce the same thing over and over again and few people could do that and i assume it was either high prestige or highly paid [1:26:21](https://realalpha.app/v/julbw1JuAz0/?t=5181) or who knows let's assume it was but then the printed press came along yeah yeah and at least in europe like you have to like a lord or a king or something had to had to employ you and then you had to go through you know years of training and there was this class of scribes that knew how to write they were employed by someone like this often the king themselves like or you know the queen it was it was not literate so it was this very very niche skill and it was like less than one percent of the population was literate in Europe, you know, back then. And then the printing crust came out. And what happened? So the cost of printed material went down something like 100x [1:26:58](https://realalpha.app/v/julbw1JuAz0/?t=5218) **Boris Cherny:** over the next, I think, 30 years, 50 years or something. The quantity of printed materials went up like 10,000x in the next 50, 100 years. This was the first effect. Literacy, it took a [1:27:09](https://realalpha.app/v/julbw1JuAz0/?t=5229) little while for it to catch up. So I think global literacy, it went up to something like 70%, but That took like another 200 years, 300 years, because learning to read is just very hard. Learning to write is hard. It takes a lot of effort. It takes education system. It takes, you know, infrastructure to have paper and ink and the free time to do this instead of working on a farm. So it kind of it took early stage of industrialization to actually get there. But but I think this effect of making it so this thing that was locked away in ivory tower and now it's accessible to everyone. This is just, you know, like none of the things around us would exist today without this. [1:27:44](https://realalpha.app/v/julbw1JuAz0/?t=5264) Like if we weren't literate, if the people that built, you know, this microphone weren't literate, it would have just been very hard to have a modern economy. None of these things would exist. And I just kind of think about back then, if people had to predict what would happen when the printing press came out, no one would have predicted that the microphone would become a thing. So I just feel like this is the best analog for the moment that we're in right now. And it's interesting that you say that some of the kings were illiterate who are employing the scribes because if we're being honest with ourselves, we have business owners who know what they want to build and they are employing software engineers because they themselves cannot write code. [1:28:28](https://realalpha.app/v/julbw1JuAz0/?t=5308) And I think we like to mock the CEOs who are coming there, coming to the team. They might even have a drawn prototype or whiteboard and saying this should be easy. But of course, they don't understand how difficult it is. But there seems to be a bit of analogy where there's a person who wants what they want. But until now, they needed to hire a software, a specialist who can build that. And there's always that disconnect between the idea and the person. And just like with the printing press, like what would happen if they could actually express and like the king could actually read or write their own letters, they wouldn't need that middleman. [1:29:02](https://realalpha.app/v/julbw1JuAz0/?t=5342) And things become more efficient. I mean, of course, for the scribe, it's not the best news necessarily, but I mean, smart scribes can also do, so someone needs to write the books, run the press, et cetera. Yeah, exactly. And if you think about what happened to the scribes, they ceased to become scribes, but now there's a category of writers and authors. These people now exist. And the reason they exist is because the market for literature just expanded a ton. and i guess also if we think about like back then a scribe's work was read by a few people and with the printing press and author there's a lot more authors and some of them are not really read but some of them have wider reach than than they could imagine there's new careers that that exists because of that yeah i love the analogy and the most exciting thing for me is it's just so impossible to say today what will happen after this happens and after this transition happens just you know the the economy as we know it would not have existed without it so what's next like what what is the thing that we can't even predict today that will exist because anyone can do this well we cannot predict but i think we can look at what is working right [1:30:15](https://realalpha.app/v/julbw1JuAz0/?t=5415) now if you look around in your environment may that be the team across centrophic who are software engineers or builders or members of technical staff however we call them who to you are stand out what are they doing what skills have they built up and and how have they changed the way they they work it's hard to name individuals because honestly this is just the strongest these are the strongest people i've ever worked with in my career there's all sorts of different archetypes there's some people that are really amazing prototypers um so take something from zero to point five just you know figure out like what are some cool ideas what is the technology and walk. There's other people that are amazing at finding product market fit. So kind of 0.5 to one or maybe 0 to one. There's other people that span different disciplines. And I'm just seeing more and more of these people. Like I said, like people that span product engineering and infrastructure engineering or, you know, product and design or design and engineering. I think I'm just seeing a lot more of these of these hybrids. What's a belief that changed from last year to this year something that you know like you either believed or or a conviction that you [1:31:22](https://realalpha.app/v/julbw1JuAz0/?t=5482) have that you've either revised or completed through away i think one thing i wasn't sure about is how big a problem is safety to be totally honest um i joined anthropic because like i said i read a lot of sci-fi and i kind of i know how bad this thing can go if it goes bad it wasn't something i was sure about um but seeing it from the inside and then seeing how So the new risks that have arisen in the last year, it just makes me much, much more worried about it. So I think it was kind of an important thing for me. Now it's just the most important thing for me is how do we make sure this thing goes well? [1:31:58](https://realalpha.app/v/julbw1JuAz0/?t=5518) I think it's safe to say you were a really great software engineer even before all the AI things started. And you seem to be a very productive engineer, of course, part of a team as well, but also individually. Julie, what are some skills of like, you know, before being a software engineer that are still as valuable or maybe even more valuable than before? And what are ones that are maybe just not as much and they're best left behind? Probably, OK, so the stuff that's left behind is best left behind is maybe like very strong opinions about like code style and languages and things like this. Like I can't wait to get past like these endless language debates and framework debates and all the stuff because the model can just like, you know, use whatever language and framework. [1:32:40](https://realalpha.app/v/julbw1JuAz0/?t=5560) And if you don't like it, it can just rewrite it for you. So it just doesn't matter anymore. I think something that still matters a lot today is thing is it's being methodical and hypothesis driven. This matters both in product design in this world where everything is being disrupted and we need to figure out what to build next. And this is something everyone. is thinking about. But it also matters for engineering day to day, you know, like something like debugging. You just have to be very methodical about it. And the model can do this and it can help a lot. But I think still we're in this transition point where you still need to have the skill. I don't know if you're still going to need to have it in six months. Other skills that I think are more valuable are being curious and being open to doing things beyond your swim lane. [1:33:26](https://realalpha.app/v/julbw1JuAz0/?t=5606) so you know if you're working on engineering but you really understand the business side you can just build really awesome products and i think the next you know billion dollar product you know like after quad code whatever the next startup is that you know becomes the next trillion dollar startup it might just be like one person that has some cool idea and their brain just is able to think across you know engineering and product and business or you know like design and finance and something else like it's people are going to become more and more multi-discipline and this will become more and more rewarded so in in some ways i think this will be the year of the generalist i think the other skill that's actually been rewarded of it is uh having a short attention span i was being rewarded now oh yeah it's uh you know like people you know like teenagers are using you know like like tiktok and and all this stuff and i think in some ways it's kind of dangerous for society um because like you want people that can think deeply and can contemplate ideas and aren't just moving on to the next idea very quick but in some ways i think this year is kind of the year that is going to reward it's like the year of adhd because the [1:34:36](https://realalpha.app/v/julbw1JuAz0/?t=5676) work for me has become jumping between quads it's become managing quads and so it's not so much about deep work it's about how good am i about context switching and you know jumping across multiple different contexts very quickly could i add that from what i understand what all you said But maybe we could add one thing, which is adaptability, because you're saying, of course, that ADHD and you can jump across. But of course, earlier, you were very good at focusing deeply on one thing as well. And what strikes me about you, and maybe this is true for other people as well, you're just kind of very open to adapting your working style and seeing what works well for this stage, especially when things are changing. [1:35:15](https://realalpha.app/v/julbw1JuAz0/?t=5715) I think the one certain thing we can be sure is whenever the next model comes out, it'll change again. And you need to be curious and open to adapting how you work, right? Yeah. And as closing, what's a book or books that you would recommend? I've gone down a Xi Xin Liu rabbit hole. So he's the three-body problem guy, but he actually has like a lot of other really great books. I really love his short stories. He has a couple books of short stories. I'm a big fan. For people that are new to sci-fi and you want like a little bit like harder sci-fi, I really love Accelerando by Strauss. This is a book I would totally recommend. [1:35:47](https://realalpha.app/v/julbw1JuAz0/?t=5747) It's like essentially the product roadmap for the next 50 years. It starts with Takeoff kind of starting to happen and kind of AI singularity. And then it ends up with like these kind of like group lobster consciousnesses orbiting Jupiter. And it's just like amazing. And the thing that I think it really captures is just the pace, this like quickening, quickening, quickening pace of how this feels. It really matches the feeling right now. And then on the technical side, I would strongly recommend functional programming in Scala, even if language choice just doesn't matter as much anymore. I think there is this art to functional programming That just teaches you how to code better. [1:36:25](https://realalpha.app/v/julbw1JuAz0/?t=5785) And it'll just teach you how to think in types. If you read this book, I think what's really important is to do the exercises also. And I've gone through and I've done all of them probably like three times over. And it's just amazing. It really just like knocks this idea of functional types into your head. And it's just a thing you can't stop thinking about. Boris, thank you so much. This was awesome. Yeah. Thanks, Gergay. This was a really interesting conversation. And the thing that I keep coming back to is to Boris's printing press analogy. The idea that medieval scribes were this tiny elite who could write, employed by kings. [1:36:59](https://realalpha.app/v/julbw1JuAz0/?t=5819) who themselves were often illiterate and that we soft rangers might be in a similar position today. We are the scribes. We spent years mastering this craft and now the printing press is arriving. But what Boris told me is that the scribes did not disappear. They became writers and authors and the entire market for written work expanded beyond anything anyone could have predicted. I do find this hopeful and also appreciate that Boris didn't sugarcoat it. The other thing that struck with me is just how differently the Cloud Code team built software. No PRDs, no mandatory ticketing system designers and data scientists and finance people all writing code and building dozens or hundreds of prototypes before shipping a feature and boris is shipping 20 to 30 pull requests a day without editing a single line by hand and there are different verification systems in place clock code reviewing its code automated lint rules best of end passes and human code review if you've enjoyed this podcast please do subscribe on your favorite podcast platform and on youtube a special thank you if you also leave a rating on the show thanks and see you on the next one.