Latent Space: The AI Engineer Podcast

Today's episode is with Paul Klein, founder of Browserbase. We talked about building browser infrastructure for AI agents, the future of agent authentication, and their open source framework Stagehand.

[00:00:00] Introductions

[00:04:46] AI-specific challenges in browser infrastructure

[00:07:05] Multimodality in AI-Powered Browsing

[00:12:26] Running headless browsers at scale

[00:18:46] Geolocation when proxying

[00:21:25] CAPTCHAs and Agent Auth

[00:28:21] Building “User take over” functionality

[00:33:43] Stagehand: AI web browsing framework

[00:38:58] OpenAI's Operator and computer use agents

[00:44:44] Surprising use cases of Browserbase

[00:47:18] Future of browser automation and market competition

[00:53:11] Being a solo founder

Transcript

Alessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.

swyx [00:00:12]: Hey, and today we are very blessed to have our friends, Paul Klein, for the fourth, the fourth, CEO of Browserbase. Welcome.

Paul [00:00:21]: Thanks guys. Yeah, I'm happy to be here. I've been lucky to know both of you for like a couple of years now, I think. So it's just like we're hanging out, you know, with three ginormous microphones in front of our face. It's totally normal hangout.

swyx [00:00:34]: Yeah. We've actually mentioned you on the podcast, I think, more often than any other Solaris tenant. Just because like you're one of the, you know, best performing, I think, LLM tool companies that have started up in the last couple of years.

Paul [00:00:50]: Yeah, I mean, it's been a whirlwind of a year, like Browserbase is actually pretty close to our first birthday. So we are one years old. And going from, you know, starting a company as a solo founder to... To, you know, having a team of 20 people, you know, a series A, but also being able to support hundreds of AI companies that are building AI applications that go out and automate the web. It's just been like, really cool. It's been happening a little too fast. I think like collectively as an AI industry, let's just take a week off together. I took my first vacation actually two weeks ago, and Operator came out on the first day, and then a week later, DeepSeat came out. And I'm like on vacation trying to chill. I'm like, we got to build with this stuff, right? So it's been a breakneck year. But I'm super happy to be here and like talk more about all the stuff we're seeing. And I'd love to hear kind of what you guys are excited about too, and share with it, you know?

swyx [00:01:39]: Where to start? So people, you've done a bunch of podcasts. I think I strongly recommend Jack Bridger's Scaling DevTools, as well as Turner Novak's The Peel. And, you know, I'm sure there's others. So you covered your Twilio story in the past, talked about StreamClub, you got acquired to Mux, and then you left to start Browserbase. So maybe we just start with what is Browserbase? Yeah.

Paul [00:02:02]: Browserbase is the web browser for your AI. We're building headless browser infrastructure, which are browsers that run in a server environment that's accessible to developers via APIs and SDKs. It's really hard to run a web browser in the cloud. You guys are probably running Chrome on your computers, and that's using a lot of resources, right? So if you want to run a web browser or thousands of web browsers, you can't just spin up a bunch of lambdas. You actually need to use a secure containerized environment. You have to scale it up and down. It's a stateful system. And that infrastructure is, like, super painful. And I know that firsthand, because at my last company, StreamClub, I was CTO, and I was building our own internal headless browser infrastructure. That's actually why we sold the company, is because Mux really wanted to buy our headless browser infrastructure that we'd built. And it's just a super hard problem. And I actually told my co-founders, I would never start another company unless it was a browser infrastructure company. And it turns out that's really necessary in the age of AI, when AI can actually go out and interact with websites, click on buttons, fill in forms. You need AI to do all of that work in an actual browser running somewhere on a server. And BrowserBase powers that.

swyx [00:03:08]: While you're talking about it, it occurred to me, not that you're going to be acquired or anything, but it occurred to me that it would be really funny if you became the Nikita Beer of headless browser companies. You just have one trick, ...

Did you know that adding a simple Code Interpreter took o3 from 9.2% to 32% on FrontierMath? The Latent Space crew is hosting a hack night Feb 11th in San Francisco focused on CodeGen use cases, co-hosted with E2B and Edge AGI; watch E2B’s new workshop and RSVP here!

We’re happy to announce that today’s guest Samuel Colvin will be teaching his very first Pydantic AI workshop at the newly announced AI Engineer NYC Workshops day on Feb 22! 25 tickets left.

If you’re a Python developer, it’s very likely that you’ve heard of Pydantic. Every month, it’s downloaded >300,000,000 times, making it one of the top 25 PyPi packages. OpenAI uses it in its SDK for structured outputs, it’s at the core of FastAPI, and if you’ve followed our AI Engineer Summit conference, Jason Liu of Instructor has given two great talks about it: “Pydantic is all you need” and “Pydantic is STILL all you need”.

Now, Samuel Colvin has raised $17M from Sequoia to turn Pydantic from an open source project to a full stack AI engineer platform with Logfire, their observability platform, and PydanticAI, their new agent framework.

Logfire: bringing OTEL to AI

OpenTelemetry recently merged Semantic Conventions for LLM workloads which provides standard definitions to track performance like gen_ai.server.time_per_output_token. In Sam’s view at least 80% of new apps being built today have some sort of LLM usage in them, and just like web observability platform got replaced by cloud-first ones in the 2010s, Logfire wants to do the same for AI-first apps.

If you’re interested in the technical details, Logfire migrated away from Clickhouse to Datafusion for their backend. We spent some time on the importance of picking open source tools you understand and that you can actually contribute to upstream, rather than the more popular ones; listen in ~43:19 for that part.

Agents are the killer app for graphs

Pydantic AI is their attempt at taking a lot of the learnings that LangChain and the other early LLM frameworks had, and putting Python best practices into it. At an API level, it’s very similar to the other libraries: you can call LLMs, create agents, do function calling, do evals, etc.

They define an “Agent” as a container with a system prompt, tools, structured result, and an LLM. Under the hood, each Agent is now a graph of function calls that can orchestrate multi-step LLM interactions. You can start simple, then move toward fully dynamic graph-based control flow if needed.

“We were compelled enough by graphs once we got them right that our agent implementation [...] is now actually a graph under the hood.”

Why Graphs?

More natural for complex or multi-step AI workflows.

Easy to visualize and debug with mermaid diagrams.

Potential for distributed runs, or “waiting days” between steps in certain flows.

In parallel, you see folks like Emil Eifrem of Neo4j talk about GraphRAG as another place where graphs fit really well in the AI stack, so it might be time for more people to take them seriously.

Full Video Episode

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Chapters

00:00:00 Introductions

00:00:24 Origins of Pydantic

Welcome to the almost 3k latent space explorers that joined us last month! We’re holding our first SF listener meetup with Practical AI next Monday; join us if you want to meet past guests and put faces to voices! All events are in /community.

Who among you regularly click the ubiquitous 👍 /👎 buttons in ChatGPT/Bard/etc?

Anyone? I don’t see any hands up.

OpenAI has told us how important reinforcement learning from human feedback (RLHF) is to creating the magic that is ChatGPT, but we know from our conversation with Databricks’ Mike Conover just how hard it is to get just 15,000 pieces of explicit, high quality human responses.

We are shockingly reliant on good human feedback. Andrej Karpathy’s recent keynote at Microsoft Build on the State of GPT demonstrated just how much of the training process relies on contractors to supply the millions of items of human feedback needed to make a ChatGPT-quality LLM (highlighted by us in red):

But the collection of good feedback is an incredibly messy problem. First of all, if you have contractors paid by the datapoint, they are incentivized to blast through as many as possible without much thought. So you hire more contractors and double, maybe triple, your costs. Ok, you say, lets recruit missionaries, not mercenaries. People should volunteer their data! Then you run into the same problem we and any consumer review platform run into - the vast majority of people send nothing at all, and those who do are disproportionately representing negative reactions. More subtle problems emerge when you try to capture subjective human responses - the reason that ChatGPT responses tend to be inhumanly verbose, is because humans have a well documented “longer = better” bias when classifying responses in a “laboratory setting”.

The fix for this, of course, is to get out of the lab and learn from real human behavior, not artificially constructed human feedback. You don’t see a thumbs up/down button in GitHub Copilot nor Codeium nor Codium. Instead, they work an implicit accept/reject event into the product workflow, such that you cannot help but to give feedback while you use the product. This way you hear from all your users, in their natural environments doing valuable tasks they are familiar with. The prototypal example in this is Midjourney, who unobtrusively collect 1 of 9 types of feedback from every user as part of their workflow, in exchange for much faster first draft image generations:

The best known public example of AI product telemetry is in the Copilot-Explorer writeup, which checks for the presence of generated code after 15-600 second intervals, which enables GitHub to claim that 40% of code is generated by Copilot.

This is fantastic and “obviously” the future of productized AI. Every AI application should figure out how to learn from all their real users, not some contractors in a foreign country. Most prompt engineers and prompt engineering tooling also tend to focus on pre-production prototyping, but could also benefit from A/B testing their prompts in the real world.

In short, AI may need Analytics more than Analytics needs AI.

Amplitude’s Month of AI

This is why Amplitude is going hard on AI - and why we recently spent a weekend talking to Jeffrey Wang, cofounder and chief architect at Amplitude, and Joe Reeve, head of AI, recording a li...

We are now launching our dedicated new YouTube and Twitter! Any help in amplifying our podcast would be greatly appreciated, and of course, tell your friends!

Notable followon discussions collected on Twitter, Reddit, Reddit, Reddit, HN, and HN. Please don’t obsess too much over the GPT4 discussion as it is mostly rumor; we spent much more time on tinybox/tinygrad on which George is the foremost authority!

We are excited to share the world’s first interview with George Hotz on the tiny corp!

If you don’t know George, he was the first person to unlock the iPhone, jailbreak the PS3, went on to start Comma.ai, and briefly “interned” at the Elon Musk-run Twitter.

Tinycorp is the company behind the deep learning framework tinygrad, as well as the recently announced tinybox, a new $15,000 “luxury AI computer” aimed at local model training and inference, aka your “personal compute cluster”:

738 FP16 TFLOPS

144 GB GPU RAM

5.76 TB/s RAM bandwidth

30 GB/s model load bandwidth (big llama loads in around 4 seconds)

AMD EPYC CPU

1600W (one 120V outlet)

Runs 65B FP16 LLaMA out of the box (using tinygrad, subject to software development risks)

(In the episode, we also talked about the future of the tinybox as the intelligence center of every home that will help run models, at-home robots, and more. Make sure to check the timestamps 👀 )

The tiny corp manifesto

There are three main theses to tinycorp:

If XLA/PrimTorch are CISC, tinygrad is RISC: CISC (Complex Instruction Set Computing) are more complex instruction sets where a single instruction can execute many low-level operations. RISC (Reduced Instruction Set Computing) are smaller, and only let you execute a single low-level operation per instruction, leading to faster and more efficient instruction execution. If you’ve used the Apple Silicon M1/M2, AMD Ryzen, or Raspberry Pi, you’ve used a RISC computer.

If you can’t write a fast ML framework for GPU, you can’t write one for your own chip: there are many “AI chips” companies out there, and they all started from taping the chip. Some of them like Cerebras are still building, while others like Graphcore seem to be struggling. But building chips with higher TFLOPS isn’t enough: “There’s a great chip already on the market. For $999, you get a 123 TFLOP card with 24 GB of 960 GB/s RAM. This is the best FLOPS per dollar today, and yet...nobody in ML uses it.”, referring to the AMD RX 7900 XTX. NVIDIA’s lead is not only thanks to high-performing cards, but also thanks to a great developer platform in CUDA. Starting with the chip development rather than the dev toolkit is much more cost-intensive, so tinycorp is starting by writing a framework for off-the-shelf hardware rather than taping their own chip.

Turing completeness considered harmful: Once you call in to Turing complete kernels, you can