WEBVTT

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Now, one thing we're expecting is we're expecting it to have an error initially because we haven't

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yet properly configured that that that chat input.

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Uh, but still we should be ready pretty much to at least see that happening.

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So let's go back into N810.

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Here we are.

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And make sure we we come here and we press Execute workflow so that this is up and running and waiting

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for us.

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We expect this will throw an error.

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Now we'll come back into 11 labs and we're going to come back to our agent, which is now equipped with

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this tool.

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And we're going to give it a try.

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Uh, let's uh let's see, uh, we're going to press preview.

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Up it comes and we're now going to call our agent.

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Here we go.

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Hello.

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How can I help you today?

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Hello.

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I'd like to know how many, uh, shares of spy I have in my portfolio.

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I'm sorry.

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I was unable to retrieve the number of spy shares you hold.

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Is there anything else I can help you with regarding your equity portfolio.

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Okay, let's stop it there.

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Let's go over to to Nan and see what happened.

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There was indeed so.

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So first up.

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First up.

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Wow.

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It did call.

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We weren't even watching it.

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And it came in and it called the webhook.

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And then something went wrong.

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And we know exactly what went wrong.

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What went wrong is that we were thinking it was like a chat trigger node and it wasn't.

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It was this stuff here.

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So what we really want to do is get this question here.

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Body question how many shares of Spy do I have in my portfolio?

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Do you see the way it's filled in the field that we described in 11 labs?

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Remember we described that the body of the question needed needed the body of the of the post, of the

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of the webhook needed to contain a field called question with a question.

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So we need this input to point to there.

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So we simply select define below just like before.

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And now we can just drag and drop from here.

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You can probably now already fill in that.

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That is going to be the JSON is the expression is going to be JSON dot body dot question.

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We want the body of this and we want to collect the question.

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And there is the question okay.

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And now we go back here again.

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And now we press execute Workflow again.

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And we will try this one more time okay.

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Over here we go.

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And now we're going to press preview.

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And let's try this.

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Hello.

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How can I help you today.

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Hello.

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How many shares of spy do I have in my portfolio?

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Let me check your portfolio for you.

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You have two shares of Spy in your portfolio.

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That's great.

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Thank you.

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So there you have it.

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I feel the need to thank it.

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Uh, success wasn't that cool.

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And, of course, what you saw, even though there still is.

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The latency isn't perfect and there's more that can be done there, but it's pretty impressive and certainly

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compared to the prior approach.

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And whilst I called this more complicated, it was actually pretty simple.

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We've got this thing running fast and it was super cool.

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And if you go over here you can see everything ran lots of ticks.

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Of course, I hardly need to tell you that this webhook was called by 11 labs and everything ran successfully.

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So let's talk through the round trip of what actually happened as a way to kind of revise what this

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is all about, because it was quite simple to build this, but there's a lot of moving parts behind

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the scenes.

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The complexity is in what actually happened.

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We start our journey in 11 labs where we set up an agent.

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Just saying, you're a helpful assistant and using Russell as our agent.

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And and then we equipped Russell with a tool.

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And that tool was there's some tools that we could have used like an end conversation tool, a skip

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turn tool.

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We didn't use that.

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We set up a new tool, which was a webhook tool, which means this is a tool that's able to call out

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to the internet, to a webhook that you give it.

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And here's how we set it up.

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We gave it a webhook.

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We gave it one that we'd taken from N810.

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We gave it a webhook.

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That means it can submit a request to N810 to do some processing, and we use a webhook.

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We use the method of post, which means that we can post a body of JSON to to this endpoint.

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Um, and look, I didn't save after I changed this, I don't think.

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Let me just move.

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This should have been a bit longer or we risk some sort of timeout.

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Always remember to press the save button here.

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It's easy to miss that.

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Uh, you probably saw me doing that and were asking me.

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Uh, okay, so we do that, we call this post.

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And in the description we can specify to the LM, we need you to provide some information and that will

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be sent in the post message in the body of it.

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And we said, hey, we want you to fill in a field called question.

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We want it to be the specific question about the users equity portfolio that the specialist should answer.

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And so our LLM did that.

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It populated that question and that got sent to Nan.

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And that's where we pick up the tail in and end the webhook gets called.

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This is the same URL that gets called that then passes it to the AI agent.

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It doesn't look for some chat information.

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No, it looks for JSON, which is exactly where the question comes in right there.

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That then goes to Gemini in the usual way.

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It also uses a tool.

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The tool it uses is one to look up in Google Sheets.

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And then that goes into a respond to webhook, which just takes all of this JSON and sends it back.

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Back to Russell, who is waiting for us over here and comes back as this tool.

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And Russell.

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The agent then responds, wow, take a breath.

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There's a lot of moving parts, even though it looks so simple and the result is a lower latency, faster,

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more responsive approach, which has the combination of the best functionality from both 11 labs and

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from Nln.

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And if you try this yourself, you probably noticed that if you try to ask a follow on question, then

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you fail miserably.

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And that's because we're using the test version, which only executes workflows once at a time.

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Surely it's time for us to publish the production version.

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To do that, we want to grab this production webhook URL.

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We come here.

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We'll save it and we'll press the publish button.

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So this gets published out there.

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How exciting.

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Workflow is published.

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Got it.

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And now we want to come back to 11 labs.

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We want to come into our tool.

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We want to come into this tool.

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We need to go to this URL and change it for the production URL.

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Don't forget to press save and you always forget to press save.

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There we go.

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I didn't, and now it's time for us to test our production agent.

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And of course we should start by publishing it here as well.

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We also want to publish Live our agent on on this side to uh, so that is, uh, and we'll do a copy

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shareable link.

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Uh, so we have now published both we've published and and live and we've published 11 labs live.

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And it's time for us to try this out.

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I can open a new tab, I can paste in the shareable link.

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This is my published AI agent Russell on the internet.

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Let's give it a try.

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Hello.

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How can I help you today?

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Hello.

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I'm interested in how many shares of spy I have in my portfolio.

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Let me quickly check your portfolio for the number of spy shares you hold.

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Do you currently have two shares of Spy in your portfolio?

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Is there anything else I can help you with regarding your investments?

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Yeah.

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What what other stocks do I have in my portfolio apart from Spy?

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Let me check what other stocks you have in your portfolio.

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Besides Spy, your portfolio includes B and D, BMD, which is the Vanguard Total Bond Market ETF,

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the iShares Core Allocation ETF, and the iShares Core Aggressive Allocation ETF.

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Do you have any questions about these holdings?

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No thanks.

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There we have it.

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Well, everything ran nicely.

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That was us running in production with multiple questions between 11 labs and nt.m.

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The end to end flow.

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The second approach working perfectly with both 11 labs and deployed to production.

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Well, you remember I told you that today was going to be both challenging and satisfying?

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I think I delivered on both.

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It was a bit.

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It was a little bit fiddly from time to time.

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Uh, especially when we were configuring that, that request.

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Uh, again, don't worry if you if that was too much, we're going to do more of that.

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And also remember ChatGPT is so good with that kind of thing.

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This this is boilerplate stuff.

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So if you want help configuring a webhook, a tool like that, you can just describe it.

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You'll get step by step instructions so easily.

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Most important is that you get the gist of what we're doing.

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The basic building blocks.

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And essentially we've discovered that the best way to integrate 11 labs with N810 in order to build

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an interactive voice agent with relatively low latency.

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It's still not perfect, but it's much better.

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The right way to do it is to have 11 labs be the boss.

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Use the voice agents in 11 labs, give it a tool, and let it use that tool to call into our business

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logic running in Nan, which is what Nan is so, so great at.

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Okay.

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And that concludes week two.

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Day two integrating 11 labs and N810.

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That's that's a wrap.

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And that means that you are now at the 47% point about to cross the halfway mark.

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Tomorrow is going to be a whole new topic.

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Can't wait to see you then.
