Cline: The Collaborative AI Coder

Hey, everyone. Welcome to the Leading Space Podcast. This is Alessio, partner and CTO at Decibel, and I'm joined by my co-host, Wix, founder of Small AI.

Welcome, welcome, and today we're in the studio with a nice, uh, two guests from Cline, Pash and Saoud.

That's right.

Yes. You nailed it.

Yeah. Let's go.

I think that Cline has a decent fan base, but not everyone has heard of it. Maybe we should just get, like, an upfront, like what is Cline maybe from you, and then, like, you can modify that as well.

Yeah. Cline's an open source coding agent. It's a VS Code extension right now, but it's coming to JetBrains and Neovim and the CLI. You give Cline a task, and it just goes off and does it. It can take over your terminal, your editor, your browser, connect to all sorts of MCP services, and essentially take over your entire developer workflow, and it becomes this point of contact for you to get your entire job done, essentially.

Beautiful. Uh, Pash, what would you modify, or what's another way to look at Cline that you think is also valuable?

Yeah. I think Cline is the kinda infrastructure l- layer for agents, for all open source agents, people building on top of this, like, agentic infrastructure. Cline is a fully modular system. That's the way we envision it, and we're trying to make it more modularized so that you can build any agents on top of it.

Yeah.

So with the CLI and with the SDK that we're rolling out, you're gonna be able to build fully agentic systems for anything, not just coding.

Oh, okay. That, that is a different, uh, perspective on Cline that I had. So, okay, let's, let's talk about coding first, and then we'll talk about-

Yeah

... the, the broader stuff. You also are similar to Aider, I don't know who comes first, in that you use the Plan and Act paradigm quite a bit. I'm not sure how well-known this is. Like, to me, I'm relatively up to speed on it.

But again, like, maybe you guys wanna explain, like, why different models for different things.

Yeah. I'm gonna take the cred for coming up with Plan + Act first.

Okay.

And then we-- Cline was the first to sort of come up with this concept of having two modes for the developer to engage with. Uh, so just in, like, talking to our users and seeing how they use Cline, where it was really only an input field, we, we found a lot of them starting off working with the agent, coming up with a markdown file where they ask the agent to put together some kind of architecture plan for the work that they want the agent to go on to, to do.

And so they-- we would find that, that people just came up with this workflow for themselves just organically. And so we thought about how we might translate that into the product, so it's a little more intuitive for new users who don't have to kinda pick up that pattern for themselves, uh, and can kind of direct and, and put in guardrails for the agent to adhere to these different modes whenever the user switches between them.

So, for example, in Plan mode, the agent's directed to be more exploratory, read more files, get, uh, sort of understanding, fill up its context with any sort of relevant information to come up with a plan of attack for whatever the task is the user wants to accomplish.

And then when they switch to Act mode, that's when the agent gets this directive to look at the plan and start executing on it, running commands, editing files. And it just makes working with agents a little bit easier, especially with something like Cline, where a lot of the times people's engagement with it is mostly in the Plan mode, where there's a lot of back and forth, there's a lot of extracting context from the developer, you know, asking questions, you know, "What do you want the theme to look like?

What pages do you want on the website?" Just trying to extract any sort of information that the user might not have put into their initial prompt. Once the user feels like, "Okay, I'm ready to let the agent go off and work on this," they switch to Act mode, check Auto Approve, and just kick their feet up and, you know, get coffee or whatever and let the agent get the job done.

So yeah, most of the engagement happens in the Plan mode, and then Act mode, they kinda just have a peripheral vision into what's going on, mostly to course correct whenever it goes in the wrong direction. But for the most part, they can just rely on the model to get it done.

And was this the first shape of the product, or did you get to the Plan + Act iteratively? And maybe was this the first idea of the company itself, or were you exploring other stuff-

It was a lot of... Especially in the early days of Cline, it was a lot of experimenting and talking to our users and seeing what kinda workflows came up that they found that were useful for them and, and translating them into the product.

So Plan and Act was really a byproduct of just talking to people on our Discord, just asking them what would be useful to them, what, what kind of prompt shortcuts we could add into the UI. I mean, that's really all Plan and Act mode is.

It's, is essentially a shortcut for the user to save them the trouble of having to type out, you know, "I want you to ask me questions and put together a plan," um, the way that you might have to in, you know, some of the other tools.

You'd have to, like, be explicit about, "I want you to come up with a plan before, you know, acting on it or editing files." Incorporating that into the UI just saves the user the trouble of having to type that out themselves.

But you started right away as a coding product, and then this was part of, "Okay, how do we get better UX?" basically.

Exactly.

Yeah. What was the model evaluation at the time? So I'm sure part of, like, the, "We need Plan and Act" is, like, maybe the models are not able to do it end to end. When you started working on that paradigm, what were the model limitations?

What were the best models? And then how has that evolved over time?

Yeah. When I first started working on Cline, this was I think ten days after Claude 3.5 Sonic came out. I was reading Anthropic's model card addendum, and there was this section about agentic coding and how it was so much better at this step-by-step accomplishing tasks.

And they talked about running this internal test where they let the model run in this loop where it could call tools. Um, and it was obvious to me that, okay, they have some version, and they have some application internally that's really different from how the o- you know, the other things at the time were, things like Copilot and Cursor and Aider.

They didn't do this sort of, like, step-by-step reasoning and accomplishing tasks. They were more suited for the Q&A and, and one-shot prompting paradigm. Uh, at the time, I think it was, uh, June 2024, Anthropic was doing a Build with Claude hackathon.

So I thought, "Okay, this is a really cool new capability that none of the models have really been capable of doing before." And, uh, I think being able to create something from the ground up and take advantage of kind of like the nuances of how much the model's improved in that point in time.

So for example, Claude 3.5 was also really good at this test called needle in a haystack, where if it has a lot of context in its context window, for example, you know, 90% of its 200K context window is filled up, it's really good at picking out granular details in that context.

Whereas before Claude 3.5, it'd really pay a lot more attention to whatever was at the beginning or the end of the context. So just taking advantage of kind of the nuances of it being better at understanding longer context and it being better at task by task, uh, sorry, step-by-step accomplishing tasks and building a product from the ground up just kind of let me create something that just felt a little bit different than anything else that was around at the time.

And some of the core principles in, in building the first version of the product was just keep it really simple. Just let the developer feel like they can kind of use it however they want. So make it as general as possible and kind of let them come up with whatever workflows, you know, works well for them.

People use it for all sorts of things outside of coding. Our product marketing guy, Nick Baumann, he uses it to connect to, you know, a Reddit MCP server, scrape content, connect it to an ex-MCP server and, and post tweets essentially.

Even though it's an, a VS Code extension and a coding agent, MCP kind of lets it function as this everything agent where it can connect to, you know, whatever services and things like that. And that's really a, a side effect of, of having very general prompts just in the product and not sort of limiting it to just coding tasks.

I was at a conference in Amsterdam, and I built my whole presentation, my whole slide deck using this library. It's like a JavaScript library called SlideDev. And I just asked Cline like, "Hey, like, here's, like, my style guidelines."

I wrote, like, a big Cline rules document explaining, like, how I wanna style the presentation in SlideDev. I told Cline, like, the agenda. I kinda recorded using this other app called Limitless, like transcribed my voice into text about, like, my thoughts, just like stream of consciousness about what I was gonna talk about for this conference for my talk, and Cline just went in and built the whole, the whole deck for me.

So, you know, Cline really can do anything.

In, in JavaScript.

In JavaScript, yeah.

Yeah.

No problem.

So it's, it's kind of a coding use case.

It was kind of a coding use case, but then making a presentation out of it. But it can also, like, run scripts, like do, like, data analysis for you and then put that into a deck.

Okay.

You know?

Okay.

Kind of combine things.

Yeah.

Yeah.

And being, being a VS Code extension is, is kind of this, like... It gives you these interesting capabilities where you have access to the user's OS, you have access to the user's terminal, and, you know, you can read and edit files.

Being an extension, it reduces a lot of the onboarding friction for a lot of developers where they don't have to, you know, install a whole new application or have to, you know, go through whatever internal jumping through hoops to try to get something approved to, to use within their organizations.

So the marketplace gave us a ton of really great distribution and is sort of like the perfect conduit for something that needs access to files on your desktop or to be able to run things on your terminal, to be able to edit code and to take advantage of VS Code's really nice UI and show you, like, diff views, for example, before and after it makes changes to, to files.

Weren't you tempted to fork VS Code, though? I mean, you know, you could be sitting on three billion dollars right now.

Well, no, I, I actually, like, pity anybody that has to fork VS Code because- ... Microsoft makes it, like, notoriously difficult to maintain these forks. So a lot of resources, um, and efforts go into just maintaining, keeping your fork up to date with all the updates that VS Code is making.

I see.

Um, but-

Is that 'cause they, they have a private repo and they just sync it? There's no, like-

Exactly. Exactly.

Yeah.

And there's-

It's one of those kinds of open source projects.

Right.

Yeah.

And VS Code's moving so quickly where I'm sure they run into all sorts of issues, not just in, you know, things like merge conflicts, but also in the back end. They're always making improvements and changes to, for example, their VS Marketplace API.

To have to, like, reverse engineer that and figure out kind of how to make sure that your users don't run into issues using things like that is, I'm sure, like, a huge headache for anybody that has to maintain a VS Code fork.

And it also, you know, being an extension also gives us a lot more distribution. It's not that you have to use us or somebody else. You can use Cline in Cursor or in Windsurf or in VS Code, and I think Cline complements all these things really well in that, you know, we get the opportunity to kind of figure out and, and work really closely with our users to figure out what the best agentic experience is.

Whereas, you know, Cursor and Windsurf and, and Copilot have to think about the entire developer experience, the inline code edits, the Q&A, sort of all the other bells and whistles that go into writing code. We get to just focus on what I think is the future of programming, which is this agentic paradigm.

Um, and as the models get better, people are gonna find themselves using natural language, working with an agent more and more and less being in the weeds and editing code and tab autocomplete. Yeah, just like imagine how many, like, resources you would have to spend maintaining a fork of VS Code where w- we can just kinda stay focused on the core agentic loop, optimizing for different Model families as they come out supporting them.

You know, there's so much work that goes into all this that maintaining a fork on the side would just be such a massive distraction for us that I don't think it's really worth it.

I feel like when you talk, I hear this distinction between we wanna be the best thing for the future of programming, and then also this is also great for non-programming. Is this something that is being recent for you, where, like, you're seeing more and more people use the MCP servers especially to do less technical thing, and that's an interesting area?

Or do you feel like programming is still, like, the highest kinda, like, economic value thing to be selling today? I'm curious if you can share more.

In terms of economic value, I-- programming is definitely the highest cost to benefit for language models right now. And I think, you know, we're seeing a lot of, you know, model labs recognize that. OpenAI, Anthropic are taking coding a lot more seriously than I think they did a year ago.

What we've seen is while, yes, like MC-- the MCP ecosystem is growing, and a lot of people are using it for things outside of programming, the majority use case is mostly developer work. There was an article on Hacker News a couple weeks ago about how a developer deployed a buggy Cloudflare worker and used a Sentry MCP server to pull a stack trace and ask Cline to sort of fix the bug using the stack trace information, connect to a GitHub MCP server to close the issue and deploy the fix to Cloudflare all right within Cline using natural language, never having to leave VS Code, and it sort of interacts with all these services that otherwise the developer would have had to have the cognitive overload of having to, you know, figure out for himself and leave

his developer environment to, to essentially do what the agent could have done just all in the background just using natural language. So I, I think that's kinda, like, where things are headed, is the application layer being connected to sort of all the different services that you might have had to interact with before manually, and it being this sort of single point of contact for you to interact with using natural language.

And you being less and less in the code and more and more a high-level understanding of what the agent's doing and being able to course correct. I think that's another part of what's important to us and what's allowed us to kind of cut through the noise in this, like, incredibly noisy space is- ...

I think a lot of, a lot of people have really grand ideas for, you know, where things are heading, but we've been really maniacal about what's useful to people today. And a large part of that is understanding sort of the, the limitations of these models, what they're not so good at, and giving enough insight into those sorts of things to the, the end developer so that they know how to course correct, they know how to give feedback when things don't go right.

So, for example, Cline is really good about, you know, giving you a lot of insight into the prompts going into the model, into when there's an error, why the error happened, into the tools that the model's calling. We try to give as much insight into what exactly the model is doing at each step in accomplishing a task, so when things don't go wrong or it starts to go off in the wrong direction, you can, you know, give it feedback and course correct.

And I think the course correcting part is so incredibly important in, in getting work done, I think much more quickly than if you were to kind of give a sort of a background agent work, you come back a couple hours later, and it's just, like, totally wrong, and it, it didn't do anything that you expected it to do, and you kinda have to retry a couple times before it gets it right.

I think the Sentry example is great because I feel like in a way the MCPs are, like, cannibalizing the products themselves. Like, I started using the Sentry MCP, and then Sentry released Here, which is, like, their issue resolution agent, and it was free at the start.

So I turned it on in Sentry. I was using it. It's great. And then they started charging money for it, and I'm like, "I can use the MCP for free-

Yeah

... put the data in my coding agent, and it's gonna fix the issue for free and send it back." I'm curious to see, especially in coding, where you can kinda have this closed loop, where, okay, are these MCPs gonna become the paid AI offering so that then you can plug it in, and is Cline gonna have kinda like a MCP subscription where, like-

Totally

... you're kinda-

Yeah

... fractionalizing all these costs? Um-

Yeah

... to me, today it feels like it doesn't make a lot of sense the way they're structured. Um-

Well, yeah, we, we were like, uh... Very early on we-- like, we've been bullish on MCP from the very beginning, and, um-

Were you a launch partner?

A funny story about MCP, I think-

Sorry to interrupt.

Yeah, no worries.

I, I think, uh, when Anthropic first launched MCP and, and they made this big announcement about, you know, this new protocol that they've been working on and open sourcing it, nobody really understood what it meant, and it took me some time really digging into their documentation about how it works and why this is important.

I think they, they kind of took this bet on the open source community contributing to an ecosystem in order for it to really take off. And so I wanted to try to help with that effort as much as possible.

So for a long time, most of Cline's system prompt was: How does MCP work? 'Cause it, it was so new at the time that, you know, the models didn't know anything about it. And how to make MCP servers.

So, like, if the developer wanted to, you know, make something like that, it'd be really good at it. And I'd like to think that, you know, Cline had something to do with how much the MCP ecosystem has grown since then, and just getting developers more insight and, and sort of awareness about how it works under the hood, which I think is incredibly important in, in using it, let alone just developing these things.

And so, yeah, when, when we l-launched MCP in Cline, I remember our, our Discord users just trying to wrap their heads around it, and in seeing clients build MCP servers from the ground up, they were like, "Okay." They started to connect the dots.

"This is how it works under the hood. This is why it's useful. This is how agents connect to these tools and services and these APIs and, uh, sort of saved me a lot of the trouble of having to do this sort of stuff myself."

Those were, like, the early days of, of MCP when people were still trying to wrap their heads around it.

Yeah.

And there was, like, a big problem with discoverability. So back in, like, February, we launched the MCP marketplace, where you could actually go through and have, like, this one-click install process where Cline would actually go through looking at a README in-- that's, like, linked to a GitHub, install the whole MCP server from scratch, and just get it running immediately.

And that was, like- I think around that time, that's when MCP really started taking off with, like, the launch of the marketplace, where people were able to discover MCPs, contribute to the MCP marketplace. We've listed over, like, a hundred fifty MCP servers since then, and, um, like, the top MCPs in our marketplace have over, you know, hundreds of thousands of downloads, people using them.

And, you know, there's, like, really notable examples where you mention, uh, like, how are people-- like, it's, like, kind of eating existing products. But at the same time, we're starting to see, like, this ecosystem evolve where people are monetizing MCPs.

Like, a, a notable example of this is Twentyfirstdev Magic MCP server, where it injects some taste into this coding agent, into the LLM, where they have this library of beautiful components, and they just inject relevant examples so that Cline can go in and implement beautiful UIs.

And the way they monetize that was, like, an, a standard API key. So we're starting to see developers really, like, um, take MCPs, build them in, have distribution platforms like the MCP marketplace in Cline, and monetize their whole business around that.

So now it's, like, almost like you're selling tools to agents-

Yeah

... which is a really interesting topic.

And you can do that because you're in VS Code, so you have the terminal, so you can do NPX, run-

Yes

... the different servers. Have you thought about doing remote MCP hosting, or do you feel like that's not something you should take over?

Yeah, we haven't really hosted any ourselves. We think that's-- We, we're looking into it. I think it's, uh, it's all very nascent right now, the, the remote MCPs. But we're definitely interested in, in supporting remote MCPs and, and listing them on our marketplace.

And a-another part, I think, with sort of local MCP servers and remote MCPs is most of the remote MCPs are only useful to connect to different APIs. But that's only a, you know, that's only a small use case for MCPs.

A lot of MCPs help you connect to different applications on your computer. For example, there's, like, a, a Unity MCP server that helps you create, you know, 3D objects within-- right from within VS Code. There's, uh, an Ableton MCP server, so you can, like, make songs using something like Cline or, uh, whatever else uses MCPs.

We won't see a world where these MCP servers are only hosted remotely. There will always be some mix of local MCP servers and remote MCP servers. I think the remote MCP servers do make, uh, the, uh, installation process a little bit easier with s- you know, with something like an OAuth flow and, and just authenticating a little bit, not as painful as having to manage API keys yourself.

But for the most part, I think the MCP's ecosystem is, is, is really in its earlier days. We're still trying to figure out this good balance of security, but also convenience for the end developer so that it's not a, a pain to have to set these things up.

And I think we're still in this very much experimental phase about how useful it is to people. And I think now that it is seeing this level of market fit and, and people are coming out with, you know, these sorts of, like, articles and workflows about how it's totally changing their jobs, I think there's gonna be a lot more of a resources and efforts that go into the ecosystem and just building out the protocol, which I think there's a lot on Anthropic's roadmap, and I, I think the community in general just has a l- a lot of ideas.

And our marketplace, in particular, has, has given us insights into some ways that we could improve it, things that, you know, developers have asked for, uh, from it, that where we're kind of thinking about how do we-- you know, what does the MCP marketplace of the future look like?

And for us, that's-- it's, it's gonna be a combination of, you know, well, there's a lot of our users are very s- security conscious, and there's a lot of ways that MCP s- uh, you know, servers can be pretty dangerous to use if you don't trust the end developer of these things.

And so we're trying to figure out, you know, what does a, a future look like where you can-- where you have some level of confidence in the MCP servers you're installing? Uh, I think right now it's just, it's too early, and there's a lot of trust in the community that I don't think a lot of, you know, enterprise developers or organizations are, are quite willing to do yet.

So that's something that's top of mind for us.

There's an interesting tension between the Anthropic and the community here. You basically kind of have a model regi-- uh, MCP registry internally, right? Honestly, I think you should expose it. I was looking for it on your website, and you don't have it.

Like, the only way to access it is to install Cline. But there's others like, uh, Smithery and all the other guys, right? But then Anthropic has also said they'll launch a model registry at some point or MCP registry at some point.

Some point.

If Anthropic l-launched the official one, would they just, just win by default, right? W- Because, like, would you just, would you just use them?

I think so. I think the, I think the entire ecosystem will just converge around whatever they do. They just have such good distribution-

Yeah

... and, and they're, yeah. They, I mean, they're-

They came up with it.

Yeah, exactly.

Cool. And then I, I wanted to, uh, I noticed that you had some, like, really downloaded MCPs. I was going by most installs. I'm just gonna read it off. You can stop me anytime to, uh, comment on them.

So top is FileSystem MCP. Makes sense. Browser Tools from Agent Desk AI. Don't know what that is. Sequential Thinking, that, that one came out with the original MCP release. Context Seven? I don't know that one.

That's a, that's a big one.

What? What, what is it?

Um, thou- uh, Context Seven kinda helps you pull in documentation from anywhere, and it has, like, this big index of all of the popular libraries and documentation for them.

Okay.

And you can-- your agent can kind of submit, like, a natural language query and search for any documentation.

It just says everyone's docs.

Yes. Yeah.

Uh, and but apparently Upstash did that, which is-

Mm-hmm

... also unusual because Upstash is just normally Redis. Git Tools, that one came out originally. Fetch, Browser Use. Browser Use, I imagine, competes with Browser Tools, right? I guess. And then below that-

Yeah, there's some competition

... Playwright. Playwright, right?

Yeah.

Like, so there's a lot of, like, let's automate the browser, and let's, let's do stuff, I assume for debugging. Firecrawl, Puppeteer, uh, Figma. Then here's one for you, Perplexity Research. Is that yours?

Um, well, yeah. I forked that one-

Okay

... and, and listed it. But yeah, that's, you know, that's another very popular one where you can research anything with Perplexity.

Right. So people wanna, people wanna emulate the, uh, automate the browser. I'm just trying to learn lessons from what people are doing, right? Uh, they wanna automate the browser. They wanna access Git and FileSystem. They wanna access docs and search.

Anything else that you think, like, i- is notable?

There's all kinds of stuff where it's like, you know, there's like the, the Slack MCP where you can send, you know. That's actually one workflow that I have set up where you can, like, automate repetitive tasks in Cline.

So I tell Cline, like, "Okay, pull down this PR, uh, use the GH command line tool," which I already have installed using the terminal to pull the PR, get the description of the PR, the discussion on it, and get the full diff as like a, like a single command, non-interactive command.

Pull in all that context, read the files around the diff, review it, ask a question like, "Hey, do you want me to approve this or not with this comment?" And if I say yes, approve it, and then send a message in Slack to my team using the Slack MCP, for example.

Oh, use it to write?

Yes.

I would only use it to read. But, yeah.

Yeah, no, it's, you know, people-- Like, I love it, you know. I love being able to just, like, send an automated message- -in Slack or whatever. Um, you can also, like, set it up, like set up your workflow however you want, where it's like, "Okay, Cline, please ask me before doing anything, you know.

Just make sure you're asking me to, like, approve before you send a message," or something like that.

Yeah. Okay, just, just to close out MCP side. Anything else interesting going on in MCP universe that we should talk about? MCP Auth was recently ratified.

I think monetization is a big question-

Okay

... right now for the MCP ecosystem.

Yeah.

Um, we've been talking a lot with Stripe. They're very bullish on MCP, um, and they're trying to figure out, like, a monetization layer for it. But it's all so early that it's kinda hard to really even envision where it's gonna go.

Let me just put up a straw man-

Mm-hmm

... and then you can tell me what's wrong with it. Like, how is this different from API monetization, right? Like, you sign up here, make an account, I give you a token back, and then you use the token, we charge you against your usage.

No, like, uh, like I think that's how it is right now. That's how, like, the, the Magic, uh, MCP, the 21 Dev guys did it. But we're kind of envisioning a world where agents can pay themselves for these MCP tools that they're using and pay for each tool call, and you can't deal with like a million different API keys from different products and, like, signing up for all this.

There needs to be, like, a unified kinda payment layer. Some people talk about, like, stable coins, how, like, those are coming out now, that agents can natively use those. Stripe is-- they're considering this, like, abstraction around the MCP protocol for payments.

But like I said, it's kinda hard to really tell where it's gonna-- how that's gonna manifest.

Yeah. I would say, like we-- I, I covered when they launched their agent toolkit, um, last year, a few months ago. It seemed like that was enough. Like, it-- you didn't seem to need stable coins except for the fact that they take, like, thirty cents every transaction.

Yeah.

Have you seen people use the X 402 thing by Coinbase to make... It's basically like the-- you can do a HTTP request that includes payment in it.

What?

Yeah, yeah. It's, uh, it's been around forever, the 402 error that's, like, payment not accepted or something, right? So yeah, we've seen some people talking about that, like, more, like, natively building that in. But yeah, nothing-

People online don't really-

Yeah, no one's really doing that right now.

Anything you're seeing on... Like, are people, like, m-making MCP startups that are interesting?

Mostly around rehosting local ones-

Yeah

... and do remote, and then basically do instead of setting up ten MCPs, you have, like, a canonical URL that you put in all of your tools and then expose all the tools from all the servers.

Yeah.

There's, like, mcp.run, some of these tools.

Yeah.

Wow.

But I think it kinda has the same issues of how do you incentivize people to make better MCPs-

Mm-hmm.

You know, and charge people for it.

And will it be mostly first party or will it be third party?

Yeah, exactly.

Like your Perplexity MCP was the forte. What was wrong with the Perplexity one?

With MCPs and installing them locally on your device, there's always a massive risk associated with that, and when an MCP is created by someone that we have no idea who they are, at any point they might, you know, update the GitHub to, like, introduce some kind of malicious stuff.

So even if you, like, verified it when you were listing it-

Okay

... you might change it. So I ended up having to fork a few of those to make sure that we lock that version down.

Oh, okay. So this is just-

Yeah

... like you're just forking it so that you, you don't change it-

Yes

... without, without-

Exactly

... this. Interesting. These are all the problems of a, of a registry, right? Like-

Right

... that you need to, uh, ensure security and all that. Cool. I'm happy to move on. I, I would say, like, the last thing that's kind of curious is like, if Anthropic hasn't co- hadn't come along and made MCP, what would have happened?

What's the alternative history? Like, like, would you have come up with MCP?

So we saw some of, uh, our competitors who have been kind of working on their own version of plug-and-play tools into these agents. They kind of had to natively create these tools and integrations themselves-

Yeah

... directly into their product. And so I think anybody in the space would have had to just do the laborious work of having to recreate these tools and integrations for, uh... So I think Anthropic just saved us all a lot of trouble and tapped into the power of open source and community-driven development and allowed, you know, individual contributors to make an MCP for anything people could think of, um, and really take advantage of people's imagination in a way that I think is, like, necessary right now for us to really tap into full potential of, of this sort of thing, so.

We've had, I think, a dozen episodes with different coding products. Um-

Yeah. And this-- By the way, this, this episode came directly out after he tweeted about Claude-

Our Claude episode

... the Call Code episode.

Mm-hmm.

Where they were sitting right where you're sitting.

Thanks for sharing it, Claude.

I'm a rag, yeah.

Um, can you give people maybe the matrix of the market of, you know, you have, like, fully agentic, no IDE. You have agentic plus IDE, which is kinda yours. You have IDE with some co-piloting. How should people think about the different tools and what you guys are best at, or maybe what you don't think you're best at?

I think what we're best at and, like, our ethos since the beginning is just meet the developers where they're at today. I think there is a little bit of insight and hand-holding these models need right now, and the IDE is sort of the perfect conduit for something like that.

You can see the edits it's making. You can see the commands that it's running. You can see the tools that it's calling. It gives you the perfect UX for you to have the level of insight and control and be able to course-correct the way that you need to to work with the limitations of these models today.

But I, I think it's pretty obvious that as the models get better, you'll be doing less and less than that-- less and less of that and more and more of the initial planning and prompting and sort of have the trust and confidence that, you know, the model will be able to get the job done pretty much exactly how, how you want it to.

I think there will always be a little bit of a gap in that these models will never be able to read our minds, so we'll, we'll-- there, there will have to be a little bit of, you know, making sure that you give it the most comprehensive and, uh, sort of like all the details of what you want from it.

So if you're a lazy prompter, you can expect a ton of friction and, and back and forth before you really get what you want. But I think we're, we're all learning for ourselves as we work with these things kind of the, the right way to prompt these things and the-- to be explicit about what it is that we want and kind of how they hallucinate the gaps that they might need to fill to, to get to the end result and how we might wanna avoid something like that, so.

What's interesting about Claude Code is there isn't really a lot of insight into what the agent's doing. It kinda gives you this, like, checklist of what it's doing at-- holistically at a, at a high level. I don't think that really would've worked well if the models weren't good enough to actually produce work that people were generally happy with.

We're kind of there, and I think the space has to catch up to, okay, maybe people don't need as much insight into these sorts of things anymore, and they, they are okay with letting an agent kind of get the job done.

And really, all you need to see is sort of the end result and tweak it a little bit before it's, it's really perfect. And I think there is gonna be different tools for different jobs. I think something like totally autonomous agent that you don't have a lot of insight into is great for maybe scaffolding new projects.

But for kind of the serious, more complex sorts of things where, you know, you do need a certain level of insight or you do need to kind of have, like, more engagement, you might wanna use something that does give you some more insight.

So I think, I think these sorts of tools complement each other. So for example, writing tests or spinning off 10 agents to try to fix the same bug, you know, might be useful for a tool that doesn't require too much engagement from you.

Whereas something that requires a little bit more creativity or imagination or extracting context from your brain requires a little bit more of a insight into what the model's doing and a back and forth that-

Yeah

... I think Cline is a little better suited for.

So let's say you have, like, visibility into what the agent is doing. That's, like, one axis, and then another is autonomy, like how, how automated it is. And we have a category of companies that are focusing more on the use case of people that don't even wanna look at code, which is like, you know, the Lovables, the Repl.its, where it's like you go in, you build an app, and you might not even be technical, and you're just happy with the result.

And then you have kind of stuff that's kind of like a hybrid, where it's, you know, for engineers. It's built for engineers, but you don't really have a lot of visibility into what's going on under the hood. This is, like, for, like, the vibe coders, where they're, you know, fully, you know, letting, letting the AI take the wheel and building stuff very rapidly.

And lots of open source fans and, you know, people that are hobbyists enjoy coding in this, in this manner, and it is really fun. And then you get to, like, serious engineering teams where they can't really give everything over to the AI, at least not yet, and they need to have high visibility into what's going on at every step of the way and make sure that they actually understand what's happening with their code.

You're kind of handing off your production code base to this non-deterministic system and then hoping that you catch it in review if anything goes wrong. Whereas personally, the way I use, um, AI, the way I use Cline is I like to be there every step of the way and kind of guide it in the right direction.

So I know every step of the way, like, as every file is being edited, I approve every single thing and make sure that things are going in the right direction, and I have a good understanding as things are being developed, where it's going.

So, like, this kinda hybrid workflow really works for me personally. But you know, sometimes if I wanna go full YOLO mode, I go ahead and just auto-approve everything and just step out for a cup of coffee and then come back and, you know, review the work.

My issue with this, as an engineer myself, is that we all wanna believe that we work on the complex things. Um, how, how have you guys seen the line of complex change over time? I mean, if we sat down having this discussion 12 months ago, complex was, like, much easier than today for the models.

Do you feel like that's evolving quickly enough that, like, you know, in 18 months, it's like you should probably just do full agent tech for, like, 75% of work, 80% of work, or do you feel like it's not moving as quickly as you thought?

I think, I think what was complex a couple years ago is totally different to what is complex today. Now, I think what we need to be more intentional about are the architectural decisions we make really early on and how the model kind of builds on top of that.

If you have kind of a clear direction of where things are headed and what you want, you kind of have a good idea to-- about how you might wanna, like, lay the foundation for the code base that you're producing.

And I think what we might have considered complex a few years ago, algorithmic, you know, challenges, that's pretty trivial for models today and stuff that we don't really necessarily have to think too much about anymore. We kind of give it, you know, a certain expectation or unit test about what we want, and, and it kinda goes off and puts together the, you know, the perfect solution.

So I think- There's a lot more thought that has to go into tasteful architectural decisions that really comes down to you having experience with what works and what doesn't work, having a clear idea for the direction of where you wanna take the project, and sort of your vision for the code base.

Those are all decisions that I think is, is, is hard to rely on a, a model for because of its limited context and its, you know, its inability to kind of see your vision for things and really have a good understanding of, of, uh, what you're trying to accomplish without you, you know, putting together a massive prompt of, of, you know, everything that you want from it.

I think what we were... You know, what we spent most of our time working on a couple years ago has, has totally changed and, and I think for the better. I think architectural decisions are a lot more fun to think about than-

Yeah

... putting together algorithms.

It kind of frees up the senior software engineers to think more architecturally, and then once they, they, they have a really good understanding of what's, what the current state of the repository is, what the current state of the architecture is, and when they're introducing something new, they're really thinking at an architectural level, and they articulate that to Cline, and that's also, there's, like, some skill involved there.

Um, and some of that can be mitigated with, like, asking follow-up questions, being proactive about clarifying things on the agent side, but ultimately, you need to articulate th- this new architecture to the agent, and then the agent can go down and, and down into the mines and implement everything for you.

And it is more fun working that way. Like, personally, like, I, I find it a lot more engaging to just think on a more architectural level. And for junior engineers, um, it's a really good paradigm to learn about the code base.

It's kinda like having a senior engineer in your back pocket, where you're asking Cline, like, "Hey, can you explain the re- the repository for me? If I wanted to implement something like this, what files would I look at?

How does this work?" It's great for that as well.

If we're moving on from competition-

Okay

... I have one last question on competition.

Yes. Yeah.

So there's Twitter beef with RoCode. I just wanna know what the backstory is. Because you tweeted yesterday somebody asked RoCode to add Gemini CLI support, and then you guys responded, "Just copy it from us again." And they said, "Thank you.

We'll make sure to give credit."

Is it a real beef?

No, no.

Is it a friendly beef? Or it's the-

Uh, I think we're all just having fun on the timeline. Um, there is, there is a lot of forks, uh, that-

There's like 6,000 forks now.

Yeah, there's like, if you search Cline on the-

What?

... on the VS Code marketplace, it's like the, the entire page is just, like, forks of Cline. And, um, there's, like, even forks of forks that, you know, came out and raised, like, a whole bunch of money and, and it's-

What?

Yeah, it's, it's, it's crazy.

The top three apps on, the top three apps on OpenRouter are all Cline-

Cline forks?

... and then Cline fork, Cline fork. Yeah, it's funny.

Yeah, billions of tokens getting sent through, like, all these forks.

Yeah.

Um, there's, like, there's, like, fork wars and 10,000 forks, and all you need is a knife, you know? Um, so... Uh, no, it's, it's exciting. Uh, I think they're all really cool people. We got people in Europe forking us.

We got people in China making, like, a little fork of us. I think Samsung, uh, recently came out with, like, a... Was it a Wall Street Journal article where they're using Cline, but they're using, like, their own little-

Right

... fork of Cline that's kind of isolated. You know, we, we encourage it.

Do you have any regrets about being open source or-

Not at all. I think Cline started off as this, like, really good foundation for what a coding agent looks like, and people just had a lot of their own really interesting ideas and spinoffs and concepts about, you know, what they thought, you know, they, that they wanted to build on top of it was.

And just being able to see that and see the excitement around just in the space in general has just been, I think, inspirational and has helped us kind of glean insights into what works and what doesn't work and incorporate that into our own product.

And for the most part, I think for, you know, the Samsungs and all the organizations where there is a lot of friction in, in being able to use software like this on their, on their code bases, it reduces that barrier to entry, which I think is, like, incredibly important when you wanna get your feet wet with this whole new agentic coding paradigm that's gonna completely upend the way that we've written software for, for decades.

So in the grand scheme of things, that's... I, I think it's a net positive for the world and for the space, and so no regrets.

In, in a lot of ways, like, you know, it's us and the forks, we were kind of there originally when we were, like, the only ones with this, like, philosophy of keeping things simple, keeping things down to, like, the model, letting the model do everything, not cutting on, not trying to make money off of inference, going context heavy, reading files into context very aggressively.

And kind of going back to Claude Code, I was actually, like... It was really nice to see that they, they came out and they validated our, our whole philosophy of, like, keeping things as simple as possible. And that kinda goes in with, like, the whole RAG thing, which is, like, RAG was this early thing in, like, 2022.

You started getting these vector database companies. Context windows were very small. This was, like, a way of... People called it, like, "Oh, you can give your AI infinite memory." It's not really that, but that was, like, the marketing that was sold to the venture backers that were, like, investing in all these companies, and it became this narrative that really stuck around.

And, like, even now, like, we, we get, like, potential, like, you know, enterprise perspective. Like, they're, they're going through, like, the procurement process, and they're... It's almost like they're going through, like, a checklist asking, like, "Hey, do you guys do, like, indexing, like, of the code base and doing RAG?"

And I'm like, "Well, why? Like, why are you... Like, why do you wanna do this?" Um, I think Boris said it, said it very well on, on this exact podcast where we tried RAG, and it doesn't really work very well, especially for coding.

It's like, the way RAG works is you have to, like, chunk all these files across your entire repository and, like, chop them up into small little pieces and then throw them into this hyperdimensional vector space and then pull out these random chunks when you're searching for relevant code snippets.

And it's like, fundamentally it's, like, so schizo and, like, I think it actually distracts the model- ... and you get worse performance than just doing what, like, a senior software engineer does when they first-- they're introduced to a new repository, where it's like you look at the folder structure, you look through the files.

"Oh, this file imports from this other file. Let's go take a look at that." And you kind of agentically explore the repository. That's like We found that works so much better. And there's like similar things where it's like, like the simplicity always wins, like this bitter lesson where Fast Apply is another example.

So Cursor came out with this Fast Apply, like they call the Instant Apply back in July of 2024, where the idea was models at the time were not very good at editing files. And the way editing files works in kind of the context of an agent is you have a search block and then a replace block where you have to like match the search block exactly to what you're trying to replace, and then a replace block just swaps that out.

And at the time, models were not very good. It was like, I forget, like GPT they were using under the hood at the time wasn't very good at formulating these search blocks perfectly, and it would fail oftentimes. So they came up with this clever workaround to fine-tune this Fast Apply model where they let these frontier models at the time, they let them be vague, they let them output those like lazy code snippets that we're all very familiar with, where it's like rest of the file here, like rest of the imports here, and then fed that into this fine-tuned Fast Apply model that was like probably like a Qwen 7B or something quantized, very small, dinky little model.

And they, they fed this lazy code snippet into this smaller model, and the smaller model we fine-tuned to output the entire file with the code changes applied. And that, you know, the one of the founders of Aider said this really well in like very early GitHub discussions where he said like, "Well, now instead of worrying about one model messing things up, now you have to worry about two models messing things up."

And what's worse is the other model that you're giving, that you're handing your production code to, this like Fast Apply model, it's like s- it's a tiny model. Its reasoning is not very good. Its maximum output tokens, y- you know, there, there might be eight thousand tokens, sixteen thousand tokens.

Now they're training like thirty-two thousand tokens maybe. And a lot of the coding files, like we have a file in our repository that's like forty-two thousand tokens long, and that's longer than the maximum token output length of one of these smaller Fast Apply models.

So what do you do then? Then you have to build workarounds around that. Then you have to build all this infrastructure to like pass things off, and then it's making mistakes. It's like very subtle mistakes too, where it's like it looks like it's working, but it's not actually what the original frontier model suggested, and it's like slightly different.

And it introduces like all of these subtle bugs into your code. And what we're starting to see is like as AI gets better, the application layer is reducing. You're, you're not gonna need all these clever workarounds. You're not gonna have to maintain these systems.

So it's really liberating to not be bogged down with RAG or with Fast Apply and just focus on this like core agentic loop and, and maximizing diff edit failures. Like in our own internal benchmarks, Claude Sonnet 4 recently hit a sub five percent or like around actually four percent diff edit failure rate.

At the, like when Fast Apply came out, that was way higher. That was like in the twenties and the thirties. Now we're down to four percent, right? And in six months, right?

How does it go to zero?

Well, it's going to zero, like as we speak. It's going to zero every day, you know. And I, I was actually talking with, uh, the founders of some of these companies that do Fast Apply. They were trying to kind of work with us.

They, their whole bread and butter is, uh, fine-tuning these Fast Apply models and, you know, like Relays and Worf. And I had a, like a very candid conversation with these guys where I was like, "Well, there's a window of time where Fast Apply was relevant.

Cursor started this window of time back in July. How much time do you think we have left until they're no longer relevant? Do you think it's an infinite time window?" They're like, "No, it's definitely finite. Like this, this era of Fast Apply models is definitely coming to an end."

And I was like, "Well, how long do you guys think?" They were like, "Maybe three months, maybe less." So I still think there's some cases where RAG is useful. You know, if you have a lot of human read-readable documents, a large knowledge base of documents where you don't really care about, like inherent logic within them, like sure, index it, chunk it, do retrieval on it, or Fast Apply is like, maybe if your organization, you're forced into using like a very small model that's not very good at search and replace, like a DeepSeek or something, you know, maybe use a Fast Apply model.

I think RAG and Fast Apply were these just tools in a toolkit for when models weren't the greatest at large context or search and replace diff editing. Um, but now they are extra ingredients that could make things go wrong that you just don't need anymore.

There was an interesting article from Cognition Labs about, you know, multi-agent orchestration and-

Oh, getting right into it.

Yeah.

It's like you're on, you're on autopilot for us. It's like that's cool. Yeah. So I mean, they, they- It's a great article, by the way.

The... Yeah, it's a great art- They, they talked about how, you know, when you start working with different models, different agents, there's a lot that gets lost in the details. And, you know, the devil are in the details.

That's, those are the most important things, and making sure that it doesn't, you don't have the agents who are like running in loops and running to the same issues again and, and it have sort of like all the, the right context.

And, and so I think being close to the model, throwing all the context you need at it, not taking these cost-optimized approach to pulling in relevant context, using something like RAG or a cheaper model to apply edits to a file.

I think ultimately, yes, it's more expensive asking, you know, a model like Claude Sonnet to do s-sort of all these sorts of things, to grip a, an entire code base and to, you know, fill up its entire context.

But you kinda get what you pay for. And that, I think that's been another benefit of, of being open source, is that our developers, they can peek under the kimono. They can see, you know, where their requests are being sent, what prompts are going into these things, and that creates a certain level of trust where, you know, when they spend ten, twenty, hundred dollars a day, they know kind of where their data is being sent, what model it's being sent to, what prompts are going into these things.

And so they get comfortable with the idea of spending that much money, get the job done.

Yeah. It's like not making money off of inference. I think the, the incentives are so They're so relevant in this discussion because, you know, if you're incentivized to, you know, if you're charging, you know, $20 per month and you're trying to make money on that, you're gonna be offloading all kinds of important work to smaller models-

Mm-hmm

... or optimizing for cost with RAG, like retrieval with RAG, not reading en- the entire file, but maybe reading like a small snippet of it. Whereas if you're not making money off inference and you're just going direct, you know, uh, users can bring their own API keys, well, then all of a sudden you're, you're not incentivized to cut down-

Right

... on cost. You're actually incentivized just to build the best possible agent. And we're, we're starting to see this trend of the whole industry is moving in that direction, right? You're starting to see like, um, everyone open up to pay-as-you-go models or pay directly for inference, and I think that is the future.

What's the client pricing business model?

Right now, it's Bring-Your-Own-API-Key. Essentially, just, uh, whatever pre-commitment you might have to whatever inference provider, whatever model you think works best for your type of work, you just plug in your Anthropic or OpenAI or OpenRouter, whatever it is, API key into Cline, and it connects directly to whatever model you select.

And I think that level of transparency, that level of we're building the best product, we're not focused on sort of capturing margin on, you know, the price obfuscation and clever tricks and model orchestration to, you know, keep costs low for us and optimize for higher profits.

I think that's put us in this like unique position to really push these models to their full potential. And, uh, and I, I think that's shown. You know, I think that's, that's... You get what you pay for. Throw a task in Cline and, and it gets expensive, but, um-

That's the cost of intelligence, right?

It's the cost of intelligence, yeah.

Yeah.

So yeah, the, the business model right now is, is, uh, you, you get to choose kinda where-- It's open source, you can fork it, you can choose where your da-data gets, and you can choose who you wanna pay.

A lot of organizations we've talked to get some, you know, a certain level of volume-based discounts with, with these providers, and so they can, they can take advantage of that through Cline, which is helpful because Cline can get pretty expensive and, uh, yeah.

Wait, so, I, I mean, I'm, I'm still not hearing how you make money. Like-

Why?

... you said you don't... Huh?

Why?

Why make money?

Yeah.

Uh, 'cause you have to pay your salaries.

No, that, that's the, that's the-- A lot of people ask us that-

Yeah

... and I always just throw the why at them. But it's, um, the, the-

You sound like the Partyful guys.

Yeah.

Partyful is like...

The, the real answer is enterprise. So, um-

Which, uh, we can say because you're, we're, you know, we release this when you launch it. Yeah.

Yeah. So you wanna talk about enterprise?

Yeah. I think being open source and Bring-Your-Own-API-Key has given us a lot of easy adoption in these organizations where things like data privacy and control and security are top of mind, and it's hard to commit to sending their code in plain text to God knows what servers, training their data to do-- training their data on models that might, you know, output their IP to random users.

I think there-- people are a lot more conscious about where their data's get-getting sent and what's being used to it. And so it's given us this opportunity to say, "Okay, nothing passes through our own servers. You have total control over the entire application, where your data gets sent."

And that's given organizations that, you know, we've been talking to over the course of the last couple of months, this sort of like easy adoption, and I think this opportunity for us to, to work more closely with them and say, "You know, what are all the things that we can do to help with adoption in the rest of your organization?"

Essentially, how can we pour gasoline on sort of the evangelism that, you know, people have for Cline in these organizations and spread the usage of, of agentic coding, I think at an enterprise level.

Well, yeah, what's, what's crazy is, um, so we, we had-- We open sourced Cline, people really liked it, developers were using it within their organizations. Their organizations were kind of like reluctantly okay with it because they saw like we're open source, and we're not sending our da-their data anywhere.

They could use their existing API keys. And then we launched, like on our website, like a contact form for enterprise. Like, if you're interested in an enterprise offering, hit us up, and we had no real enterprise product at the time.

And it turned out like we just got this massive influx of big enterprises reaching out to us. And, you know, we had a Fortune 5 company come up to us, and they were like, "Hey, um, we have hundreds of engineers using Cline within our organization, and this is a massive problem for us.

This is like a fire that we need to put out because we have no idea what API keys they're using, how much they're spending, where they're sending their data. Please, just like let us give you money to make an enterprise product."

So the product kind of just evolved out of that, right?

Right. Right. I mean, it's, it really just comes down to more of listening to our users. So right after we put out this page, we just had a lot of demand for sort of like the table stake enterprise features, the security, uh, guardrails and governance and insights that sort of like the admins in these organizations need to, to reliably use something like Cline.

Yeah, we've gotten a lot of people wanting us to sort of give them two things: invoices, just to help with like all the budgeting and spending the, you know, thousands of dollars a month-

All the Europeans.

Yeah, just... The other thing which I thought was a little bit surprising was some level of insight into the benefit that Cline's providing them, so it could be hours saved or lines of code written. 'Cause it allows these sort of like AI forward drivers for adopting these sorts of tools in these organizations to take that as a proof point and go to the rest of their teams and say, "This is how much Cline's helping me.

You need to start adopting this, so we can keep up with the rest of the industry."

This is for like internal champions to prove their ROI?

Exactly.

Okay.

Use as sort of evidence for this, you know, to justify the spend-

Yeah

... but also to promote the product in these organizations.

We can do this afterwards-

Okay

... but we would like to talk to those and actually feature some of them, what they're saying to their bosses. Uh, on the podcast so that we can get a sense. 'Cause, like, oftentimes we hear-- we only talk to founders and builders of, like, the dev tool, but, like, not the end consumer.

Yeah.

And actually, we, we wanna hear from them, right? Like, about how they're thinking about it, what they need. Could be kinda, kinda cool. One thing I wanted to ask, uh, to double-click on is the relationship between OpenRouter and then, like, your, your, your enterprise offering, right?

So, uh, my understanding is currently everything runs through OpenRouter.

Not everything. So you can bring API keys to OpenAI, Anthropic, Bedrock-

And then you have a direct connection there if, if I understand the main-

The, the user has a direct connection there.

Yeah, correct.

Right?

But, uh, everything else would run through OpenRouter. And so basically, the enterprise version of Cline would be you have your own OpenRouter that you would provide visibility and control to, uh, that enterprise.

Uh, yeah. K- like, that's for, like, the self-hosted-

Yeah

... option, right? Like, there's a lot of enterprises where they're okay with not self-hosting, but as long as they're using their own Bedrock API keys and stuff like that, whereas the ones that are really interested in, like, self-hosting or, like, that wanna be able to manage their teams, there would be, like, this internal router-

Yeah

... going on.

The curious thing here is, like, what if, what if model costs just go to zero? Like, Gemini Code just comes out, and it's like, "Yeah, guys, it's free."

Well, yeah. No, they-

That'd be great for us

... they came out with... Yeah-

Yeah

... that'd be great for us. So our, our thesis is inference is not the business.

You would just never make money on inference, right?

We w- yeah. We wanna give the end user total transparency into price, into... Which I think is, like, incredibly important to, you know, even get comfortable with the idea of spending as much money as you do. I think the, the price obfuscation in this space has given developers this reluctance to opt into usage-based t- plans.

And, and we're seeing a lot of people kind of converge on this concept of, okay, maybe have, like, a, a base plan just to, to use the product, but sort of get out of the way of the inference and, um, respect the end developer enough to give them the level of insight into not just the cost, but the models being used and, uh, give them more confidence in spending however much it takes to get the work done.

I think there, you know, there, you know, you can use tricks like RAG and Fast Apply and things like that to keep costs low. But for the most part, there's enough ROI on, on, uh, coding agents where, you know, people are willing to spend-

Yeah

... money to, to get the job done.

And for a truly, like, good coding agent, the ROI is almost hard to even calculate because there's so many things that I would've never even bothered doing. But then I, now I have Cline, and I could just, like, do this weird experiment or do this side project or, you know, fix this random bug that I would've never even thought about.

So, like, how do you measure that?

Yeah.

Right?

One variant of this problem, I-- we're about to move on to context engineering and memory and all the other stuff. One variant of this I wanted to touch on a little bit was just, uh, background agents and multi-agents.

So the instantiations of this now, I would say are background agents is it would be Codex, for example, like, spinning up, you know, one PR per minute and, uh... or Devin or Cognition. So would you ever go there?

That's one concrete question I can ask you. Like, would there be Cline on the server, whatever. And then the other version is still on the laptop, but more sort of parallel agents, like, kinda the, the Kanban is, is currently very hyped right now.

People are making, like, Kanban interfaces for Cursor and also for, uh, Cloud Code. Just anything, like, in a parallel or background-

Yeah

... side of things.

So we're releasing a CLI version of Cline. And using the CLI version of Cline, it's fully modular, so you can ask Cline to run the CLI to spin up more clients. Or you could run Cline in some kinda cloud process in a, in a GitHub action, whatever you want.

So the, the CLI is really the form factor for-

Okay

... these kind of fully autonomous agents. And it's also nice to be able to tap into an existing client CLI running on your computer and be able to, like, take over and steer it in the right direction. So that's also possible.

Um, but what do you think, Saoud?

I don't think it's an either/or. I think all these different modalities complement each other really well. So the Codex, the Devins, Cursor's background agent, I think they all sort of accomplish the same thing. They-- If we were to come out with our own version of it, I'd say that it, it would be the foundation for how other developers could build on top of it.

So Nick's older brother, Andre, he's sort of thinking ten years ahead, and it always kind of blows my mind a little bit about some of, some of the ideas that he has about where the space is going. But we recently had a discussion about building this open source framework for coding agents for any sort of platform, building the SDK and the tool necessary to bring Cline to, you know, Chrome as an extension, to the CLI, to JetBrains, to Jupyter Notebooks-

Mm

... to your smart car, whatever it is. But to build the-

Your fridge.

Your fridge, exactly. To, to put to-

The microwave maybe.

Yeah, exa-- I mean, this is what we saw kind of like with sort of the, you know, the six thousand forks, you know, on top of Cline is we sort of, like, put together this foundation for how this community of developers...

We sort of put together this foundation that this community of developers could, like, build on top of and sort of take advantage of, you know, their experiments and imagination and their creativity about where the space is headed. And I think looking forward, building an open source foundation and the building blocks for how we bring something like Cline to things that go outside the scope of software development or, or, you know, VS Code extension, I think that'll open up the door to things that, you know, ultimately complement each other really well, but it'll never be sort of this, like, either/or thing.

I think background agents are good for certain kinds of work, and parallel Kanban multi-agents might be good for when you wanna experiment and iterate on, you know, five different versions of, you know, how a landing page might look.

And then something like a, a back and forth with a single agent like Cline works really well for when you wanna, you know, pull context and put together a really complicated plan for a really complex task. And I think all these different tools will ultimately end up complementing each other, and people will kinda develop a taste and an understanding for what works best for what kind of work.

But I, I think something, just looking ten years ahead, we at the very least wanna sort of be at the frontier of- Providing sort of the building blocks for what the next thing is after background agents or, um, you know, multi agents.

I was gonna go into context engineering, kind of like topic du jour. I think that, uh, this is kinda similar-ish in a thread to RAG and how RAG is a mind virus, which I love, by the way, that, the way that you phrased it.

Yeah, you, you, you have, you have in your docs context management. You also have a section on memory bank, which is kinda cool. I think, uh, ev- a lot of people are trying to figure out memory. Let's just, let's start at the high level, and then we'll go into memory later.

Uh, what, you know, what does context engineering mean to you?

Context engineering mean to me?

Means prompt engineering.

Yeah. Right, like, I mean, so I think, like, there is a lot of art to like-

Yeah

... what goes in there. Y- y- I think that really is, like, the 80/20 of building a really good agent is, like, figuring out what goes into the context and, and like, you know, I think interplay between MCP and your system client, like, you know, recommended prompts, I think is what is, is ultimately making a good agent.

Yeah. I, I think, uh, context management is like one part of it is what you load into context. The other part of it is how do you clean things up when you're reaching the context window, right? Uh, how do you curate that whole life cycle from zero to maximum, uh, context window?

And the way that I think about it is there's so many options on the table, and there's so many risks to misdirecting the agents or distracting the agents. There's ideas about, you know, RAG or other kinds of forms of retrieval.

That's, that's one idea. There's the agentic exploration. That's another idea that we found works much better, and it seems like the trend is generally for loading things into context. It's giving the model the tools that it can use to pull things into context, letting the model decide what exactly to pull into context, as well as some hints along the way, kind of like a, like a, a map of what's going on, um, like ASTs, uh, abstract syntax trees, potentially what tabs they have open in VS Code.

That was actually in our internal kind of benchmarking that turned out to work very, very well. It's almost like it's reading your mind when you have, like, a few tabs open.

It stresses me out because, like-

Yeah

... sometimes then I'm like, I have, like, unrelated tabs open-

Yeah.

... and I have to go close them before I kick off a thing.

Yeah. I, I wouldn't think too much about it, especially when you're using Cline. Cline does a pretty good job of just navigating that.

Okay.

Um, but I, I definitely there are edge cases, right? There's edge cases for everything, and it's kind of like, okay, what's, like, the majority use case is, like, you know, when are you starting a brand-new task and you don't have a single tab open that's relevant to it?

Obviously, in the CLI, you might... you, you don't have that little indicator, so there you have to think outside the box for that. So that's, like, for reading things into context. And then for context management is when you're approaching the full capacity of the context window is how do you condense that?

And we've played around with this kind of naive truncation very early on, where we just, like, throw out the first half of the conversation.

That's common.

And it-- there is problems with that obviously because it's like, kind of like you're halfway through a book and you j- you, you're like, you start reading halfway through, right? You don't know anything that happened beforehand. And we like to think a lot about, like, narrative integrity is, like, every task in Cline is kinda like a story.

It might be a boring story where it's, like, this lonely coding agent that's just, you know, determined to help you solve, you know, whatever it is, like, the chal- like the, the big thing that the protagonist needs to overcome is, like, the resolution of the task, right?

But how do we maintain that narrative integrity where every step of the way the agent can kind of predict the next token, which is like predict the next part of the story to reach that conclusion. So we played around with things like cleaning up, uh, duplicate file reads.

That works pretty well. But ultimately, this is another case where it's like, well, what if you just give the model... like, what if you just ask the model, "Like, like what do you think belongs in context?" Another form of this is summarization, which is like, "Hey, summarize all the relevant details, and then we'll swap that in."

And that works really, really well.

Yeah. Uh, double-clicking on the AST mention, that's very verbose. When do you use that?

Right now, it's a tool. The way that it works is when Cline wants-- when Cline's doing sort of the agentic exploration of trying to pull in relevant context, and it wants to sort of get an idea of what's going on in a certain directory, for example, there's a tool that lets it pull in all the sort of language from a directory.

So it could be the names of classes, the names of functions, and that gives it some idea of, okay, here's what's going on in this, in this folder. And if it's, if it seems relevant to whatever the task is trying to accomplish is, then it sort of like zooms in and starts to actually read those entire files into context.

So it's, it's essentially a way to help it kind of figure out how to navigate through large code bases.

Yeah. We, we've seen some companies working on, it's like an interesting idea. It's like an AST, but it's also a knowledge graph, and you can run these discrete deterministic almost like actions on this knowledge graph, where you could say, like, "Hey, find me all the functions that-- find me all the functions of the code base, then find me all the functions that aren't being used and delete all of them."

And the agent can kind of reason in this almost like SQL-like language working with this knowledge graph to do these kinds of global operations. Like right now, if you ask a coding agent to go through and remove all unused functions or do, like, some kind of large refactoring work, in some cases it might work, but very oftentimes it's just gonna struggle a lot, burn a lot of tokens, and fail ultimately.

Whereas with these kinds of tools, it can actually operate on the entire repository with these kinds of query, like s- short little query statements. I think there is a lot of potential in something like this, where it's like the next level beyond the AST and it's, uh, like a language for querying this, this kind of knowledge graph.

But like we've seen with, with, like, the Claude 4 release is these frontier model shops, they tend to train on their own application layer, and you might come up with, like, a very clever tool that in theory would work re- work really well, but then it doesn't work well with Claude 4 because Claude 4 is trained to grep, right?

So that's another interesting phenomenon where it's like you're, you're expecting these, uh, frontier models to become more generalized over time, but instead they're becoming more specialized, and you have to, like, support these different model families.

Just to wrap on the memory side, memory is almost the artifact of summarization. So you summarize the context, and then you kinda extract some sides. Any interesting learnings from there? Like, things that are maybe not as intuitive, especially for code.

I think people grasp the, like, memory about humans, but, like, what are memories about code bases and things look like?

I think memories right now, for the large part, are mostly useless. I think- ...the kinds, the kinds of memories that you might want the coding agent to hold onto are, you know, specific quirks about how, you know, your team works in the project or certain rules like only use, like, camel case, for example.

It's better to place those sorts of things in, like, a general sort of, like, guideline or rules file, for example. But I, I found that this idea of, like, asking the agent, at least coding agents, to, like, hold on to certain memories about the project or, like, how you work or, or things like that are, are...

You mostly have to, like, force it to store those things into memory and, and, uh, and I don't think people-- they don't wanna have to think about those sorts of things. So it's something we're, we're thinking about is, is how can we hold on to the tribal knowledge that these agents learn along the way that people aren't documenting or putting into rules files without the user having to go out of their way to sort of force them to store these things into a memory database, for example.

Those are, like, kinda, like, workspace rules or tribal knowledge, like general patterns that you use as, as a team. Um, but then there's, like in our-- We ran this, like, internal experiment where we built this to-do list tool where it was only one tool where you could just write the to-do, and every time you could, like, rewrite the to-do from scratch.

And we would passively, as part of every, like, not every message, but, like, every once in a while, we would pass in this context of what the latest state of this to-do list is. And we found that that actually keeps the agent on track after multiple rounds of context summarization and, and compaction.

And it could all of a sudden build, like, an entire complex kind of task from scratch over, you know, ten x the to-- the, the context window length. And in internal testing, this was, like, very, very promising. So we're trying to flesh that out, and I think something like that, we, we had earlier versions of the, the memory bank, which actually, um, our, like Nick, uh, Nick Baumann, our, our marketing guy, came up with this memory bank concept where it was, like, this Cline rules where he would tell Cline, like: "Hey, whenever you're working, have the scratch pad of what you're working on."

And this is, like, a more built-in way of doing that. And I think that also might be very, very, very helpful for the agents to just have, like, a little scratch pad of, like: "Hey, what have I done so far?

What's left?" Specific @file mentions, like what kind of code we're working on, general context, and passing that off between sessions. Yeah.

Any thought on Claude MD versus Agents MD versus Agent MD? I built an open source tool called Agents nine two seven, like the XKCD that just copy pastes it across all the different file names, so all of them have access to it.

Do you think there should be a single file? Like, there's also, like, the IDE rules versus the agent rules.

Yeah.

There's kinda, like, a lot of issues right now.

I actually think it's fine that each of these different tools have their own specific instructions because I find myself using a cursor rules and a Cline rules separately when I want Cline the agent to-- I want him to work, you know, a certain way that's different than how I might want, you know, cursor to interact with my code base.

So I think each tool is specific to the kind of work that I do, and I have different instructions for how I want these things to operate. So I think I, I've seen, like, a lot of people complain about it, and I get that it can make code bases look a little bit ugly, but for me, it's been, like, incredibly helpful for them to be separated.

I noticed that you said him. Does, does Cline have an, like, a gender?

Cline's a he, him. Yeah.

Okay.

Yeah.

Does he have a whole backstory-

Yeah

...personality?

No. Cline-- So Cline is a play on CLI-

Yeah

...and editor.

'Cause it used to be Claude Dev, and now it's Cline.

Yeah. I feel like Cline kind of stands out in the space for having-- for being a little more humanized than something like, you know, a cursor agent or a copilot or a cascade. Uh, and I think-

Well, there's Devin, which is a real name, you know.

Well, yeah.

And Claude is a real name, I guess.

Claude's a real name.

Yeah. Um, yes, I, I've been, I've been in-- I think we've all been intentional about just sort of humanizing it 'cause it, at least in working with, kind of gives you more confidence in it and that I could, like, lean on it a little bit more.

There is, there is kind of a, of a trust-building with it, I think, with an agent, and the humanizing aspect of it, I think, has been helpful to me personally and-

This goes back to, like, the narrative integrity. It's just... It's actually really important, I think, to anthropomorphize agents in general, uh, because everything they do is like a little story, and without having a distinct kind of identity, you get worse results.

And when you're developing these agents, that's kinda how we need to think about them, right? We need to think that we're, like, crafting these stories. We're almost like Hollywood directors, right? We're, we're putting all the right pieces in place for the story to unfold.

And yeah, having an identity around that is really, really important. And Cline, you know, he's a cool little guy.

Yeah.

He's, uh, you know, he's-

Just a chill guy. He's a chill guy.

He's just... He's a chill guy. He's writing codes.

Right?

He's helping us out. You know, he's always, like, happy to help. Or if you tell him to not be happy, he can be very grumpy. You know. So it's great.

Awesome. Uh, I know you're hiring. You are-- You have-- You're twenty people now. You are aiming to a hundred. You have a beautiful new office. What's your best pitch for working at Cline?

A lot of our hiring right now is, um, so far it's been just friends of friends, people in our network, people that we've worked with before that we-we trusted and that we know can, can show up for, like, this incredibly hard thing that we're, we're working on.

And there's a lot of challenges ahead, and, and I think the problem space is probably the most exciting thing to be working on right now. Engineers in general love working on things that make their own lives easier, and so I couldn't imagine working on something more exciting than, than a coding agent.

And, and, you know, it's a little biased, but I think a large part of it is it's, it's an exciting problem space. Um, we're looking for really motivated people that wanna work on challenges like figuring out kinda like what the next ten years looks like and building kind of the foundation for, you know, what comes next after background agents or multi-agents and, and really help in sort of defining how all this shapes up.

We have this like really excited community of, of users and developers. I think being open source has also created a lot of goodwill with us, where a lot of the feedback we get is, like, incredibly constructive and helpful in shaping our roadmap and, and the product that we're building.

And working with a community like that is like one of the most fulfilling things ever. Right now we're, we're kind of, uh, um, in between offices, but, you know, doing things like go-karting and kayaking and things like that.

So it's, it's a lot of hard work, but, you know, we, we make sure to, to have fun along the way, so.

Yeah. No, like Cline is a, it's a unique company because it, it really does feel like we're all just, like, friends building something cool. And we work really, really hard, and the space is, it's not just competitive, it's like hyper competitive.

There's, like, capital is flowing into all, every single possible competitors. We have forks of forks, like I said, raising tens of millions of dollars, and we're growing very rapidly. We're at twenty people now. We're aiming to be at a hundred people by the end of the year.

And being open source, it has its own challenges. It's like people, we, we do all this research, we do all this benchmarking work to make sure our diff editing algorithm is robust the way we're working with these models to optimize for the lowest possible diff edit failures.

And then we open source that, and then we post it on Twitter, and someone's like, "Oh, thanks so much for open sourcing that. I'm gonna go and, like, raise a bunch of money with, like, our own product with it."

But the way that I see it is like, this is, you know, let them copy. We're the leaders in the space. We're, we're kind of showing the way for the entire industry, and being an eng-engineer and, and building all this stuff is super exciting.

So working with all these people is just amazing.

Okay, awesome. Thank you guys for coming on.

Yeah.

All right. Thank you so much.

Thank you. This was so much fun.