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And very briefly, as I promised, I want to show you what it looks like if we restart everything.

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And now we go through and we put all on one worker as false, what does this mean exactly?

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So we are going to, of course, have the same message, the same hosted, uh, the same host that we

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start running on localhost at 551.

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And we use the same tools, the same instructions, we make the same agents.

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Our agent code isn't touched, but now it's going to be managed differently.

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We're now going to run this and we're going to be following this line right here.

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And this is actually going to create three different runtimes that I'm calling worker one, worker two,

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and worker and just worker for the third one.

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Uh, the judges one.

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And we're going to start each of these three, uh, gRPC worker runtimes.

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And then player one, we're going to register with worker one, player two with worker two.

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And the judge uh, with just the one that's just called worker.

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And so that is happening.

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And so what's the difference here.

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What we're saying is that instead of having, instead of having our three agents running in a remote

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worker on on our remote host, we now have three workers, and the three workers are each running one

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of our agents.

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So they're now in different runtimes.

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They're all on the same host.

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But you could imagine that this is now they're completely separate.

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This is something where things are interacting between runtimes.

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And so now we send the same go message.

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And presumably very similar messages are now crossing the ether.

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One agent is calling Gpt4 on many for the pros, one agent for the cons.

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And then as a result of that they will come back to the to the judge.

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And the judge has come I will we see pretty similar looking pros.

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The community and support is a new one.

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I think scalability is now put first the cons, um, limited customization that seems to slightly fly

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in the face of flexibility I guess our agents disagree.

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Uh, performance variability, cost considerations and ethical and content control.

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Okay.

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Uh, but nonetheless, the recommendation is to proceed.

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That is the view of even even when we distribute across multiple workers, it does still maintain that

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viewpoint.

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Uh, so there we go.

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That the example I wanted to show you, I know we've gone through it quickly, but it's just to get

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that sense that the powerful thing about this is that without changing your, your, your code with

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the same definitions of your classes, these can be running in different configurations and different

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kinds of runtimes.

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And basically, uh, the what Autogen is doing is it's handling message calling across process boundaries

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as transparently as if these were just simply classes with, with, uh, with methods calling each other

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directly in Python code, that abstraction is what it's doing.

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And when you think about a future when potentially there could be, uh, millions or maybe even Billions

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of agents interacting all over the place.

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What Microsoft is doing is putting their stake in the ground for this.

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This is a sort of playpen.

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This is a world where agents can live and interact.

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You just put your agent code within this wrapper, this agent wrapper, you make your versions of message,

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uh, the that that you declare the way that we have.

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And then your agents can interact with each other, no matter where they are in the world, and no matter

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what programming language they're written in.

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And with that, that brings us to the end of day four.

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It means we're getting on to day five.

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And in the spirit of the other things we've done this week, the project is going to be some something

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that's more a sort of an idea.

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It's more something to tease out some thinking, to give you some some insights.

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It's not as much of a kind of commercial banger, perhaps, as the prior week, which is really great,

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but I think it will intrigue you in new ways.

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And a lot of what we've been doing with Autogen is about stretching what you think might be possible,

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and that will be the plan for tomorrow.

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And I can't wait to show it to you.