# Warp Launches Oz for Cloud Agent Orchestration

**Podcast:** Dev Interrupted
**Published:** 2026-02-13

## Transcript

Welcome to Dev Interrupted.
I'm your host, Andrew Ziggler.
And I'm your host, Ben Lloyd Pearson.
And joining us for this week's news is a good friend of the podcast, Zach Lloyd, the founder and CEO of Warp.
And Zach, it's really great to have you back.
For our listeners, if you haven't checked out Zach's episode from last year, we strongly encourage you to go back and give it a listen.
Thanks for having me back on.
I'm glad I glad I earned a repeat invite.
Oh, of course.
I mean, this will be first of many more, I'm sure.
Uh because we know when you back when you came on uh the pod and we talked about warp, which for those who aren't familiar is the agentic terminal.
Like it was a really net new thing for me.
And we talked for about everything under the sun about a developers and why they would adopt it.
And today we have a whole bunch of news in store uh about and developments in our industry around orchestration, things that we've been covering week after week here on Dev Interrupted because agentic orchestration is coming in for everything in engineering right now.
And the greatest thing is that right now, you know, Warp, you've just released a new product on this as well called Oz.
And we're gonna talk about that a little later in our news roundup.
But first, we do have to cover some of the highs and lows of this week in tech and the things that we all lived through as we evolve into the agentic developers of tomorrow.
And the first thing we're gonna talk about is GitHub, maybe struggling to keep up this week.
Because if you were like me or many other developers who have an orchestrator at this point, you might have rolled out of the weekend doing about a thousand commits.
So when it came Monday morning and you and all your co-workers were turning on these token machines, something dramatic very happened, very much happened to GitHub.
And uh if you were like me, you probably uh already know that GitHub was down.
So Ben, you know, what did you think of one of the one of the largest code forges in the world having this tumultuous start on Monday?
And what does it mean for us?
Yeah, I mean it it really points out how like as we become more dependent on agentic systems, like the infrastructure that runs all of this stuff is like more important than ever because like like if Claude goes down, then there's like significant portions of my job at this point that I'm like basically incapable of doing.
Like I could do it, but like the manual effort it takes for me to replicate what Claude would have replicated for me is just like it just doesn't make sense, like we'll just wait for it to come back up, you know?
Right.
But yeah, it's it's uh yeah, it's it's pretty crazy just how how impactful when you're when you're using AI to accelerate everything, how how big the gap feels when your infrastructure goes down.
Yeah.
What about you, Zach?
Did you did y'all feel it over there when GitHub wasn't responding Monday morning?
Yeah, this this was the day before we were doing our biggest product launch of the year, and all of a sudden we couldn't, you know, we couldn't see diffs, we couldn't make commits, it was uh like horrendous timing for us.
And it was we were just trying to find ways to work around it.
And like we were coming up with creative things, like we have a code review feature in Warp, so you can do it like locally, and so we were just trying to find ways where you can bypass GitHub, but it was just horrendous timing for us.
And I do think it's like I don't know if you all know like what the root cause, if if they've talked about it at all, but the I would assume that they are starting to strain because the amount of AI-generated code and GitHub activity has to be going through the roof right now.
Yeah, absolutely.
It's skyrocketing.
It's like code agents like Cloud Code are committing about four percent, five percent of all commits on GitHub right now.
And there's actually this chart that I saw on Monday, I'm sure we can include it in the show notes of the commits uh going up and up and up and getting steeper, and then right around the beginning of February, it just skyrockets and suddenly you have slope on slope growth.
And I think that's a moment where a lot of folks started to really understand orchestration and the power of running multiple agents at once and then then being able to scale to do so.
So GitHub, I think just maybe wasn't ready for that kind of wave.
I don't know if we've gotten an official kind of like breakdown on what happened, but um, maybe if you were like some uh folks too, like at one point my agents recommended like should we spit up our own Git forge?
Like, is GitHub gonna really be in the way of people?
I mean, uh, yeah, we had I mean we had Jeffrey Huntley on here, like basically just describing how he's building his own version of basically everything because why not?
So I mean, why not build these backup plans at the very least that allow you to to deal to circumnavigate the issues, you know?
But uh but yeah, I have to imagine if like if your autoscaling is built around like a month, a standard like Monday to Friday work week for like humans, like agents are like just nonstop working, like over the weekends and and you know, there's probably a pulse when everyone shows up Monday and like goes tells our agents to do a bunch of work.
You know, I just wonder how much that is impacting like GitHub right now and probably other companies too.
Yeah.
Maybe they're vibe coding some of their infrastructure also and that's leading props.
Yeah, I don't know.
Maybe it's eating itself.
It's it is I think harder to build super reliable software with with these coding agents right now.
Maybe it's eating itself.
Uh I have heard chatter also of like, is it time for a new piece of infrastructure at that layer of the stack that is more agent native?
I don't totally know what that what that means, but it's definitely an interesting thing to think about because the sort of all of the paradigms that we built for people do not necessarily translate perfect to agent first development.
Like have you guys thought about this?
Like what what what would the evolution of this look like?
You know, I've thought about this too.
About there's a lot of things right now about how we code that we've put there as crutches for us as humans to be able to code.
And with things like agents, it challenges us to like how much of that can we rip away?
Why does the agent have to write in a language that I can that's human readable to me?
How can we get closer to a more machine and deterministic a language?
Why does uh why do I need an interactive shell in these kinds of ways?
Like, what can the agent do with all these pipes and tubes in the background that can fundamentally change how data is is used?
I think I honestly think about how day uh agents have transformed software and it's more like TCP or IP, right?
It's like now you can put tokens in and you can pipe output and pipe execution somewhere else.
And it's just a different way of building.
I I think a lot of layers will go away, get replaced.
I think it's interesting to study what will happen.
I think this might be a a great way to take a dark segue into our next story on the AI Vampire this latest article from from Steve Yeah someone that we're both great fans of Andrew.
And I know this one really resonated with you so so walk us through what what's going on with this article.
Yeah so we read everything Steve Yage reads uh or writes on this um when we cover it here especially recently and he his most recent missive here is but called the AI Vampire and it's a reflective reflection of himself um about his experience working with Gastown and the like really rapid culture that is wrapped around it.
Steve is someone who's spoken verbally, you know, very much out before about his uh experiences with burnout at different companies.
And so this is really his reflection on how Gas Town accelerates developer work towards burnout and about how working in this unrealistic way, perhaps that's unsustainable is what he was reflecting upon.
And you know, first off, it it was really interesting to get into his head about some of his own guilt but interest around Gas Town.
There's so much conflict, I think, with in with him in this article.
But the biggest thing that stood out to me was like his own reflections on like what it means for everyone else.
Because like you know, Steve is a really seasoned engineer.
He's 30 plus years of engineering engineering experience at every level of the organization.
And he's really reflecting on the realities of junior engineers and mid-level engineers picking this up and then accelerating, and what does it mean for them in their careers and their ability to make money within our economic system?
So really great article, uh, really interesting reflection.
I it reads like someone who's been held up right against the fire.
And I think there's a lot of wisdom in it.
Ben, what did uh you think about it?
So so yeah, and I I think this ties in really well to the points that you brought up, Zach, on more agent forward tooling or tooling that is built for the agent space.
You know, because I really think we're in this like awkward transition period where you have this like small group of of people who have figured out how to like 10x significant portions of their work.
You know, they're not maybe 10x overall, but at times they are operating at that speed relative to where they used to be.
But they're still surrounded by all these like organizations, these processes, tooling and systems that weren't designed for like this scale of things.
And I've I've been thinking a lot about how we're gonna continue to extend uh agent orchestrators, which is why I'm really glad we have what we'll get to warp's launch here in a minute.
Uh, because I think it ties into this really well.
But I I have this sort of mental model that's starting to emerge where like, you know, everyone we've been covering in this space so far, like Ye Gaye, Jeffrey Huntley, Jeffrey Emmanuel.
They've all built these like single-purpose personal orchestrators that is like their own personal mental model for how like these orchestrators should work for them.
And uh, you know, we're missing the layers that connect those personal orchestrators to other things within their team or organization.
Uh and I almost wonder if like the future is like layers of orchestration for this where you have like the personal orchestrators that connect through team orchestrators that connect through like organization and company orchestrators.
Like this is like I feel like this is thinking way off in the future, but with how fast things are moving, I I really have no idea anymore.
But yeah, so Zach, I'm curious like how how you felt about this article as someone who's working really heavily in this space.
Yeah, I mean to to the orchestrator point first.
So my thought on that is that we just need the right primitives and then you can build like Gas Town, I don't know if you all have used it, it's cool.
It's a very opinionated like orchestration system with like poll cats and the mare and all like yeah we've kind of we covered the very the very colorful metaphors here on the Andrew has his own metaphors that are wonderful.
Yeah yeah the metaphors I it's been a roller coaster.
So my feeling on that versus like you want to do like Ralph Wiggum or you want to do cloud code teams is like I don't really know.
And I think there's a whole bunch of organizational systems that work for humans.
And I'm not convinced there's going to be like a one size fits all thing for for agents.
But what I do believe, and like what we've tried to do, like the the future that I see is that you you're gonna need primitives, and the primitives are like you need agents that can kind of like run off your laptop.
You need that then that to be programmatic, like API driven.
They need some way of like passing messages.
So there, I just think there's all you what we want to build right now is just like the primitives and let people organize these kind of like agent teams or agent organizations on top of them.
So that's that's the approach of warp.
Uh and then for the like the the AI vampire thing, the the quote that stuck out to me from that was like as an engineer, you you know, if you if you get really competent at using these coding agent tools, you you can 10x your development, but you don't get paid 10 times more for doing that.
But it's like, and so it's just like all of a sudden we all expect to do 10 times the work for like you know what we were doing before.
And then he's like, well, or you could spend uh and and if you do 10x the work and you don't get paid 10 times more, the company captures all that value.
Whereas if you just you know use the agent and you only work one hour a day, but you have your same output as before, then you capture all of the output for yourself.
And and but he also makes the point like no company's gonna allow that.
So it does, it's just like it's like, what is this?
How does this change the expectations of engineers?
I thought was an interesting thing.
And like as someone who is constantly running these agents, I do feel the pressure to ship more.
Like even during this podcast, like, you know, I I have an agent running.
I've been working on this thing in the background where it's like I want it to go all the time.
And so you, you know, you you feel like you're wasting time if you're not like multi-threading these agents.
And uh I think that's like a lot of pressure for an already kind of like for engineers who are already under a lot of pressure, especially for like the junior engineers because like there's also this danger if you're if you're early in your career and you're using these agents, it's very easy to like create a lot of like fury around using them, but actually not have a lot of productivity gain from them, like to like have them do a lot of stuff that can't actually be shipped.
And so I think I I I find that like one of the more frustrating things where it's like I don't actually know if I'm like gaining from these by having them work all the time.
I feel like it's straining my attention span a bunch to like manage all of them.
So I don't know, it's it's a weird state that we're in right now.
It's also gonna change extremely quickly.
We're not it's not gonna be in this state for very long.
Yeah, it's gonna constantly be evolving, and the way that what you're touching on it it covers what Steve said so well, but also too, it it covers an article that we we touched on recently about dark flow and about how like sometimes vibe coding is like or your agentic coding is like a slot machine, whereas like you're just like putting in attention and tokens and hoping you get the output you want, and then you're like putting all of these like extra coutre mall to try to get there, and so it's it was that that article when we covered it, you know, I felt like I had a bit of a pessimistic view because it ultimately does really revolve around the person using it and how they use it, and and ultimately, like, like what you said, I I feel the same pressure all the time to convert like the tokens available to me into like output and execution.
And like I have a lot of tokens available to me, so that's a lot of pressure, right?
And I think a lot of engineers feel the same way, yeah.
So I want to jump into our next uh story just real quick before we get to Oz.
And this next one is about uh leaning more on the research side of things.
Uh, we're we're in touch with AI2, is a research lab around um AI and AI research, and they they send us really cool developments from their lab all the time.
This most recent one is from Tim Detmers talking about open coding agents and how they were able to uh train them on top of specialized code bases to get really, really first in class model performance from open weight models that were then heavily uh trained on top of a target code base.
And this is a really interesting development because as part of the research, you know, their research team discovers that this problem ultimately broke down to like three or four critical failures uh that once they were able to address in like a systematic way, resulted in this open weight models exceeding the capabilities of like a foundation model teacher, like a claude, a claude or a codex.
This is interesting, and the implications of this are powerful for people that are trying to use AI that's tailored to their code bases.
Right now, there's a bit of a brownfield problem with AI and agentic development, like what you're saying, Zach, of like, you know, did they make code that you could ship?
That's a really different question of like, did they make code that like, oh, they could use or like save some time for them?
Like the stakes are so different there.
And so the idea of being able to fine-tune these agents that are from open weight models that are highly specialized on a very target code base.
Um, I think that opens a lot of doors for how people can build on top of their own kinds of models.
Yeah, I think uh LLM efficiency gains for LLM models are really like an underappreciated focus area right now.
Like uh Zach, you brought up uh being able to run stuff like on your own hardware, I think is like a really an unexplored area within uh a lot of I mean it's not totally unexplored, but it's not very matured yet.
Like, you know, most of us to today, we just kind of pick like this is why we spend so much tokens.
We we just kind of pick whatever our favorite model is and we just send everything via API over to that, you know.
Yeah.
But I I feel like a lot of when when we make this more efficient, a lot of the tasks will be able to be handled by uh local models in particular.
And yeah, this is this it's really detailed research into to how to train agents to be more knowledgeable about private code bases where there aren't a lot of like general purpose lessons that you can apply to them.
And that's I I'm hearing frequently this is a big problem for a lot of organizations.
So it's definitely something that we need to solve.
Yeah, that that was one thought I had when I read it.
So warp is built on like a million lines of custom Rust code, which I don't know if I would have done that decision again.
Uh it it there's there's there's good parts to it.
It's tough though, because the um no, I think it's actually great from like that's too real product quality a product quality perspective, but from a like uh the agents don't know our UI framework.
And so they will often make mistakes because they'll assume it works like some other thing like React or whatever.
It doesn't work like that.
It works in our own custom way.
So from a like user of a model like this I think you would it definitely piqued my interest.
And then from a founder in the coding agent space where we're you know we we offer Claude and Codex and Gemini and some open source models in our app that can create more competition or more options for our users there is great.
So you know local LMs awesome I have a uh concern that like Claude and Codecs are going to be a sort of oligopoly where it's like you you you know people building on top of them don't have much choice.
So I really want I really want a bunch of choice there.
So I I love developments like this.
Yeah really well said I I'm totally agree with you there.
Uh and you know with we we we love covering the competition between frontier models it's it's yeah very fun to watch how hard they're working for our attention.
I mean they're neck and neck right now, inference right now costs what it costs, but one day it's gonna cost something very different.
And I imagine it'll be a lot more expensive.
So I just am intrigued to see what happens with like competitors and the ability to even use those own models in your own machine is interesting capability.
I think it's good to scale down while we're all scaling up, is how I'll frame it, because we're all trying to get to like those really big heights, and you can't if you're like lugging all of this baggage from yesterday.
The the only way it'll get more expensive, in my opinion, is is if there is like uh anti and anti-competitive nature to it.
Otherwise, at any given level of intelligence, like the actual cost per token goes down.
It's only if certain companies have market power here that this will stay super expensive.
And then my my hope, or I think even my prediction is that uh for coding in particular, you're not gonna need to be at the frontier for that much longer in order to get good coding performance.
And so I think that will also open the market more, which is what I again I'm I'm very biased here, but like that is really what I want is like a market where people who are building this space are competing on the quality of the product, not the cost of the tokens, which I think is a little bit what's happening right now.
Yeah, absolutely.
You know, I'm excited to see kind of how these things evolve because the things that we take for granted and we use every day, they're continuing to change and new things are coming into our into our view that we can now finally see because of the things that we've been building yesterday.
And so I I want to get to the topic of the day, uh, which is your new release, Oz, the orchestration platform for cloud agents.
And I just want to open it up to you and maybe tell us a little bit about Oz, what it is and and where the idea came from.
Yeah.
So Oz, which we we launched earlier this week, is a like you said, it's a platform for launching and orchestrating cloud agents.
The uh sort of problem that it's trying to solve is is getting agents off of individual developers' laptops.
And the reason that's becoming a problem is there's a few things.
So one, if you're someone who is now running like three or four agents locally, you'll start to find that you're gonna run out of CPU or memory or disk, and it's gonna slow it down your computer, and that you're gonna want to multi-thread more.
And so Oz makes it very, very easy to do that.
From a more like sort of like enterprise or business perspective, what Oz is trying to do is make it easy for companies that want to really go all in on agents beyond just like giving individual developers agents as a developer tool, but like deploy agents across the whole company.
If you want to do that, you want an easy way of getting those agents into the cloud.
So you want things like sandboxing.
Uh, you want to be able to see what all the agents are doing as they're working.
Like right now, there's no visibility.
Every individual engineer is like just running these on their laptop.
You want to be able to secure them.
You wanna be able to get an audit trail.
You want these agents to be able to integrate into your developers' workflows.
So Oz is just trying to make that really easy and build the primitives, kind of almost like Vercell or Superbase, but for spinning up cloud agents.
And so yeah, that's that's what we uh that's what we launched this week.
Yeah, that's what it makes me think of.
It's like there's so much value in that being like the percell of where agents get deployed.
And I think everything that you've addressed, and like what we need, I really feel that.
As somebody who's like does um agentic orchestration to get a lot of my job done and I write a lot of code with it, like I did have to move to the cloud to support my throughput because they would bring my laptop to its knees, and honestly, it became a little like if someone walked by and install my screen, it became like concerning.
So it just was better to move it all to a uh somewhere else.
So now I literally do all of my coding like through SSH to like you know, just something that's sitting out in the middle of America somewhere, and I just hope there's no tornadoes later this year.
And so honestly, from there, I'm thinking like, and that's the and when I set it up, I was like, and this will be the last time I ever do this because either I will use this long enough to where it'll build the next one for me, and I'm not even gonna have to think about it, or someone else is going to figure out why I had to go and rent a VPS in order to get this to work, and they're gonna set this up in a way where I could do that, right?
And so I I I love this, just want to say from the beginning because I see all the value as someone who builds agents and I share them with coworkers constantly.
The whole idea of like, okay, now I have to like rewrite it and make it like re-loop it to get it up as a deployed state, and I need to do some serverless function on Vercel now.
And then like, how do I even know it's working?
Like, there's so I uh I really love the value of Oz.
I'm really excited to check it out.
Cool.
Yeah, the the model that we have is less like you rent a dev box in the cloud, and it's much more like a like a lambda model for agents.
So just in the same way that locally in Warp, you might fire off an agent to do something, or you might fire off cloud code.
Uh, you can just be like, okay, I want to fire this up, but I want it to run in the cloud.
So that's like the simplest use case.
But the other cool use cases that having these things in the cloud enables is like it's more like automations.
So for instance, like we have uh an agent that anytime we update our code, looks at our documentation and sees if it needs to be updated, or having an agent that writes our weekly change log, or we have an agent that's running pretty much constantly that's looking for patterns of fraud and abuse, uh, because we we have like a free AI tier to get people to try it.
And so thinking in terms of automations, and then even thinking in terms of like if you're building apps, where can you put agents?
And so, you know, we we have an internal app that we built where I uh like I built this thing, it's like it lets you triage GitHub issues, and what you know, you you can run an agent to dedupe the issue, you can run an agent to fix the issue, and that's all powered by by Oz.
And so it's all API driven and CLI driven.
It's all like a program first approach to um to launching these things.
I love it.
It's addressing a major need.
I feel it.
Like Ben Ben feels this too, because like I'm gonna remote stuff that he wants to use, and he's gonna be able to do that.
I mean, we were just talking about automatic change logs like this week, literally.
So yeah.
Yeah, yes, it's super easy.
So in the the way that we approach this was through using skills.
So, like, you know, the the skill standard, we basically one simple way of thinking of automations is you can just put like a skill on a timer and run it in the cloud.
Simple as that.
So you know, you just have to give it access to your, you know, build a Docker environment for it, and like then you have a skill that's like automatically making updates to your changelog and that kind of thing.
Yeah, skills are amazing.
And so the idea that you're using these same basic AI, you know, agentic principles underneath to scale and build this foundation.
It's I I think that's how this really uh the infrastructure that will stick around uh will come into being because it acknowledges that like we have to build with these just new primitives, these new exact starting points, right?
Uh and I I think the like the really cool thing that stands out to me about Oz is the ability to distribute and share your gains from AI in a more healthy way.
Going back to what we were talking about with like the 100x or 10xtra output, and but you don't get paid 10x or 100x more, and then you burn out, and then like you know, is that fair to even your co-workers?
Like, what's the value system of that?
Instead, this actually challenges and invites those folks that are getting the most of those benefits to find a way to distribute it more broadly to other people and other teams, because now there's no excuse for why you can't build that agentic thing that they need in finance for the last, you know, four months or whatever, right?
Correct.
So the, yeah, one of the things that we we did in Oz is that every every time an agent runs, no matter how you run it, if you run it through our CLI or through our API, you could run it through a web app, it's you can it's shareable.
And it's like a team construct, it's not an individual construct and it's behind ACL.
So you could be like, okay, I want these other engineers on my team to be able to sort of step in, see what this agent is doing.
You could have multiple people in there at once, actually, who are guiding and steering the agent, which is pretty cool.
You get all the aid, like whatever the agent does, whatever it produces lives on in the cloud.
And so I think this is the basis of uh like an agent memory across an organization.
Whereas right now, again, it's all just like local in your terminal session, which is not it's impossible to build on if that's the primitive you have.
Whereas if every uh agent conversation is is a sort of cloud synced object, then I can do something and my coworker on the team can continue from that state.
And it's it's like a pretty magical thing.
So, like I said, we're trying to build these primitives for what we imagine like people and companies that are building real software are gonna want to be able to do this at scale.
From your perspective, I'm sure you see even more places where this will go, and you're like, oh, I can see what people would build on top of so I was I we were just watching uh we just had our stand up and saw a demo of of what one of our like partners built.
And it was it was amazing because he built this thing where he's letting users of his app build the app, meaning like when the user of the app is like uh I wish that the like they could just submit a feature request in the app and and then Oz builds it, and it almost has the whole flow of users directly building the app that they're using, which I was just like, oh, that is such a cool creative use case.
So cool.
Yeah, I I've seen I've seen some some apps that are starting to do that, and it's yeah, it's really profound to see the next iteration of this.
Yeah, yeah, it's super fun.
Yeah, Asia orchestration is on like everyone's mind right now.
So I feel like this is very timely.
I love new products coming out to support this type of stuff.
We'll we'll link to in the show notes to the article.
There's some really cool examples of what Oz has been used for.
So our readers or listeners definitely need to go check it out.
Cool.
Yeah.
Any other last words you want to leave uh leave on that note about about Oz and why people should go check it out?
No, I mean, just we we we would love feedback.
I really want to see people build cool stuff on it.
Like that was that was the when's when's the hackathon?
When's the hackathon?
So we are we are we are doing we are working on that actually.
We're gonna do a hackathon.
I'd love to just see cool demos.
If you start to get creative with what you can do once you have these sort of programmatic cloud agents, it's like you can do such cool shit with it.
So uh that's it.
It's a it's at Oz.dev or warp.dev slash oz.
Either we'll get you there and you can try it out.
Amazing.
Well, we're gonna share those links, and I'm gonna be back.
I'm gonna be back in your inbox about that hackathon.
Yeah, make sure you invite Andrew.
He's gotta be cool.
All right, cool.
Amazing.
Okay, great.
Well, you know, uh huge thanks to Zach for joining us and giving us uh first look at Oz and for joining us on our news journey this week because it's been a pretty wild one.
Uh we'll have lots of show notes links where people can go and check out Zach and uh what he's building uh you know Oz over there at Warp, um, as well as check out his episode here on Dev Interrupted because remember, he was a past guest here, and his episode about Warp is really amazing.
Uh you can see the trajectory of how all this stuff is evolving by listening to Zach then and Zach now.
Um, and so definitely be sure to be tuning in.
And remember, if you're only listening to me and Ben and our guests here on the podcast, then you're only getting half the story.
Uh so be sure to subscribe to Dev Interrupted on Substack or on LinkedIn.
Uh, we drop a full newsletter with each of these and has a lot of links to uh further articles and things you can learn.
So uh be sure to check it out and continue the conversation there.
And um thanks, y'all, for tuning in.
Any exciting uh weekend plans on your ends.
I'm flirting with the idea of going skiing.
We live near a ski area.
We're supposed to get some snow this weekend.
I hope I can do it.
Ben loves to ski.
I'm with you on that.
Right now, I'm just trying to not be sick.
So nice time we get to work on.
Yeah.
Well, it's super sunny here in LA, so there's no skiing, but I'll have to check some picks, and y'all have a good rest of your weekend.
You too.
Thanks for having me.
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Our AI reviews every PR the moment it's created, catching bugs, security gaps, and performance issues before humans get involved.
It even writes the PR description automatically.
Your reviewers spend less time on first-pass problems and more time on architecture and business logic.
Break the bottleneck.
See how Linear B accelerates your workflow.