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Okay, let's unpack this. Today we are diving deep into something pretty exciting in

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web tech.

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It's about controlling web browsers using real artificial intelligence. We're

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looking at this

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fascinating open source project. You might have seen it on GitHub called Magnitude.

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It's a vision

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first browser agent. Now, if you're someone who's, you know, constantly battling

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fragile web

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automation, maybe you're trying to scrape data or run integration tests or just

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automate some

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repetitive clicking, well, you're definitely going to want to listen in. Our

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mission today is really

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to understand how a tool like this can actually see and understand a web page well

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enough to

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handle complex tasks reliably. We want to get why this vision first thing is

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apparently so much

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better. And importantly, we want to make sure that even if you're sort of new to

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this, you get a

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clear idea of how you could start using this kind of power. But before we really

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get into the nuts

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and bolts, the architecture and all that, let's just take a moment to thank our

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supporter.

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This deep dive is made possible by SafeServer. SafeServer is all about hosting

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software and

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helping out with digital transformation. So if you're thinking about hosting

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solutions,

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especially for cutting edge stuff like these browser agents, check them out.

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You can find out more at www.safeserver.de. Right, so back to the magnitude. The

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core promise here,

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it sounds almost too good to be true. Using just natural language, plain English to

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control a

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browser and have it actually work reliably even as the site changes underneath.

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That really is the

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core of it, yeah. And that reliability promise, it comes directly from how it's

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built, its whole

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philosophy. Moving beyond just the code. Just for context, right? A browser agent

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is basically

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software that does web tasks for you. Think of it like your digital assistant for

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the web.

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People use them for all sorts, like running really complex tests from start to

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finish,

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or maybe connecting to online services that don't have a proper API to talk to each

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other.

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Okay. And anyone who's tried, say, web scraping or running tests with the older

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tools, maybe

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Selenium or something similar, they know the pain points. It all depends on the

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website's

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code structure, right? The DOM. But tell me, why is relying on that DOM structure

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such a recipe for,

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well, headaches? This is really problem number one we need to tackle.

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Yeah. It really boils down to just one word. Riddleness. Traditional agents, they

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look at

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that hidden structure, the code, and they try to click on things or type into boxes

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by finding

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their specific name or ID in that den. They're basically drawing numbered boxes

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around things

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based on the underlying HTML. You can't see the boxes, but that's how the agent

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finds things.

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But here's the problem. Modern websites are incredibly dynamic. They change all the

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time.

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A developer might run an A-B test, shift things around, update a tiny bit of code,

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and bam,

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the automation breaks instantly. It just doesn't generalize well because it's

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totally dependent

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on those hidden code details, not on what the user actually sees and interacts with.

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So the automation script only works if the website is basically frozen in time,

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which, let's be honest, never happens. Precisely. Magnitude just completely

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sidesteps that whole dependency. It uses a vision AI, think of it like artificial

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eyes,

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to actually see and understand the layout, the interface, just like you or I would.

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It doesn't

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really care what the code is doing underneath. That is a massive shift. So we're

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not looking at

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element IDs or class names anymore. We're looking at the actual pixels, the visual

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arrangement on the screen. How does that work technically? How does it make it more

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robust?

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Well, the architecture is centered around what's called a visually grounded LLM.

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That's a large language model, an AI that's been specifically trained to connect

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language commands

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like click the checkout button with visual input from the screen. And here's the

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absolute key

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detail. Instead of trying to find some fragile code ID for that button, the LLM

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tells the system

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where to click using precise pixel coordinates X and Y on the screen. So the agent

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sees the thing

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that looks like a checkout button in the right context, and it directs the mouse

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click right to

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that spot on the screen. The code behind it could change completely, but as long as

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the button looks

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like a button and is where you'd expect it, the action works. Okay, got it. So if

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the button's

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ID changes from, I don't know, button one toe to three to button ADC, the old way

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breaks. But

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magnitude just sees the button shape and text and clicks in the right place.

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Exactly. And if

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you think bigger picture for a second, because this whole approach relies purely on

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what's visually

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on the screen, it's kind of inherently future-proof, isn't it? I mean, you could

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potentially use this

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same idea for automating tasks inside desktop apps, or even controlling things

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inside a virtual

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machine where there's no DOM at all. Wow. Yeah, the potential beyond just web

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browsers is huge.

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Okay, let's get practical. So the architecture is the brain that sees. What about

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the arms and

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light? How does it actually do things? The source material breaks it down into four

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key capabilities,

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or pillars. Yeah, it's a really nice modular design, good for developers because

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things

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are clearly separated. Okay, pillar one is navigate, the little compass icon. Right,

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that's the high-level planner. It uses that visual understanding to figure out the

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steps

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needed to get from A to B based on your natural language goal. It understands the

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journey,

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so to speak. Pillar two, interact, the mouse pointer icon. This is the action bit,

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right? Making

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the precise clicks, typing things in, even complex stuff like dragging and dropping.

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Exactly, that's

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the execution layer. Does the clicking, the typing, moving the mouse precisely. And

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pillar three,

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extract, the magnifying glass. This sounds crucial for anyone needing data. It's

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about pulling

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structured info out of that visual mess. Yeah, intelligently grabbing the useful

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bits of structured

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data from the page. And finally, pillar four, verify, the check mark. This sounds

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like it's

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for testing, making sure things actually worked. That's right. It integrates a test

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runner with,

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and this is cool, powerful visual assertions. So you can automate a process and

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then use the

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same vision AI to check if the visual result is what you expected. Like, did that

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green success

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message actually appear? Okay, let's look at how flexible this is. The examples

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given show it can

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handle really broad goals, but also very specific fiddly actions. Like, for a high-level

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goal,

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you could just say something like await agent dot act, create a task, data, title,

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use magnitude,

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description, and magnitude just figures it out. Finds the fields, clicks the button.

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Pretty much,

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yes. It interprets create a task in the context of the screen and plans the

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necessary navigation

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and interaction steps itself. But then if you need super fine control, it seems it

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can handle

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that too. Like the example, await agent dot act, drag use magnitude to the top of

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the in progress

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column. That's not just finding an element, that's understanding spatial stuff,

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right? Columns,

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positions. Exactly. That drag and drop based purely on visual understanding and

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natural language

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is pretty powerful. It really shows the level of comprehension.

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It does feel a bit like magic. Now let's dig into that extract pillar a bit more.

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The source implies it's more than just grabbing raw text. How does it get

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structured data?

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Ah, yes. This is where it really shines for serious use cases, moving beyond basic

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scraping.

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It guarantees structure by matching the content it finds visually against a predefined

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Zod schema

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you provide. Okay, Zod schema. For listeners maybe not deep into TypeScript

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development,

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can you break that down? Sounds a bit technical. Sure, absolutely. Think of the Zod

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schema as just

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a strict blueprint or maybe a contract for the data you want. You tell Magnitude

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exactly what

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pieces of information you're looking for, say a title, a date, a price, and

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importantly what

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format they should be in, like text, number, date. This forces the data pulled from

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the website to

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come out perfectly structured, predictable, ready to plug straight into another

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system or database.

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No messy cleanup needed. And what's really interesting, sometimes even insightful,

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is that the agent can use this schema for more than just retrieving what's already

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there.

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The example given is defining a field in the schema called difficulty, expecting a

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number

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from one to five, so magnitude is told. Extract the tasks title and description,

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which are on the

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page, and also rate the tasks difficulty from one to five based on what you read

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according to the

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schema. It's interpreting the content and adding new structured insight. That's

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incredible. Not just

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pulling data, but categorizing or interpreting it based on a template. That really

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does sound

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like what you'd need for complex, reliable flows. Which brings us neatly to problem

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number two,

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why typical agents often fail in real-world production scenarios. Yes, exactly. The

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second

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big weakness of traditional automation, besides brittleness, is often a lack of

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real control and

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predictability. Many agents, especially some simpler ones you see, kind of follow

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this opaque

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loop. You give it a high-level prompt, it uses some tools, and it just tries until

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it thinks it's done.

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That might look impressive in a quick demo video, right? But what happens when a

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real website throws

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up an unexpected pop-up, or loads slowly, or hits you with a cappy THA? Those

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simple demo agents often

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just fall over unpredictably. They lack the fine-grained control needed for

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business critical

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stuff. So, Magnitude tackles this by focusing on controllability and repeatability.

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How does that

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actually feel for the person writing the automation script? It gives the developer

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choices, flexible

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levels of abstraction. If you're feeling confident about a simple step, sure, give

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the agent a high

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level task like, complete the checkout, let it figure it out. But crucially, if you

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need rock

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solid reliability for a tricky part, you can break it down. You can tell it

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precisely. Okay, first,

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fill in field A with this text. Now, wait until you visually see that the text is

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confirmed.

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Then, click the next step button, which should be around these coordinates. Oh, and

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if you see an

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error message pop up, then try doing action Z instead. Ah, okay. So, you're not

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just throwing

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a command into a black box and hoping for the best. You can guide it step by step

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when needed,

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handle errors predictably. Exactly right. That detailed control is essential for

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building

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automation you can actually trust in a production system. You can build in proper

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error handling,

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logging, auditing, all the things you need for serious applications. And for true

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repeatability,

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thinking especially about automated testing, the source mentioned something about

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deterministic

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runs via caching. That sounds important. Oh, hugely important. It's noted as in

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progress,

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but that's potentially a game changer for test suites. If you run the same test a

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thousand times,

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you need the exact same result a thousand times, assuming the website hasn't

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changed.

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A native caching system would essentially stabilize certain visual interpretations

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or navigation choices the AI makes, ensuring that for a given input and a website

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state,

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the outcome is perfectly predictable, truly deterministic. We should also touch on

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performance. This isn't just a cool idea, right? It's been benchmarked. It has,

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yeah. Magnitude

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performs very well. It's considered state of the art, actually. It scored an

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impressive 94% on the

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Web Voyager benchmark. And Web Voyager isn't trivial. It tests agents across a

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really wide

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range of complex, real-world web tasks. Getting a score that high is a strong

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signal that this

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approach is robust. Okay, but there's a catch, isn't there? A technical requirement.

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Being vision

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first means it needs serious AI muscle behind it. It won't run on my laptop's basic

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CPU.

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That's correct. Since it fundamentally relies on seeing and interpreting the screen

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visually,

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it needs a large, powerful, visually grounded model to do that interpretation.

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The documentation specifically recommends using Cloud Sonnet 4 for the best results

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right now.

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Seems like it gives the highest quality visual understanding. However, it's also

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compatible with

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open models, specifically mentioning Quinn 2.5VL72B. But yes, you need access to

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one of these

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quite sophisticated visual AI engines. That's what's doing the actual seeing.

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Right. That makes sense. So for our listeners who are thinking,

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okay, this sounds amazing. I want to try it. Maybe someone just starting out.

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What's the easiest way to dip their toes in? They've actually made the getting

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started

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process really smooth. To just create your first basic automation script, there's a

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simple command

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npx create magnitude app. That one command sets up a new project, handles the

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configuration,

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and crucially, it drops in a working example script right away. So beginners get

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something

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tangible they can run and tinker with immediately. That's great. And what about

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developers who

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already have, say, a web app and want to use magnitude for testing it? Maybe use

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those visual

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assertions. Yeah. If you're integrating it into an existing project, primarily for

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testing,

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the commands are slightly different. It's ntm isave dev magnitude test to install

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it as a

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development dependency, followed by npx magnitude init. That init command sets up

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the necessary

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configuration files so you can start writing those reliable vision-based tests

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pretty much

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straight away. Okay. We have covered a lot of ground. We've seen how magnitude aims

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to

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solve those two huge problems in browser automation. First, that brittleness issue

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by swapping numbered boxes for pixel coordinates and actual vision. And second,

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that lack of

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production-ready reliability by focusing on fine-grained controllability and

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ensuring

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structured output using things like Zod schemas. Yeah, the whole vision-first open-source

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approach

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really does feel like it could change the game for how we think about reliable

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automation and

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maybe even system integration. So here's the final thought to leave you with. If a

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tool can reliably

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see, understand, and interact with any visual interface, web page, desktop, app,

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whatever,

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just using plain language, and it doesn't really care about the messy code

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underneath,

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what does that imply for the future? Does it maybe reduce the need for complex

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custom-built

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APIs for getting systems to talk to each other? If you can just tell an agent to

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use the interface

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like a human would, that's definitely something worth mulling over. Thank you so

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much for joining

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us on this deep dive. Remember, this show is supported by Safe Server. Safe Server

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supports

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00:13:27,040 --> 00:13:30,670
your digital transformation needs and handles software hosting perfect for

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00:13:30,670 --> 00:13:31,440
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technologies like Magnitude. Find out more at www.safeserver.de