Meeting Title: Uttam <> Brittany - Nimbly-Agenthub-Walkthrough Date: 2024-02-21 Meeting participants: Brittany Bond, Uttam Kumaran
WEBVTT
1 00:00:27.780 ⇒ 00:00:32.229 Brittany Bond: Hey! How’s it going?
2 00:00:32.479 ⇒ 00:00:40.350 Uttam Kumaran: How was your long weekend? Did you get a long weekend no, I worked on someone. I was talking to my dad. He was like.
3 00:00:40.380 ⇒ 00:00:56.220 Uttam Kumaran: Oh, yeah, it’s the. It’s a long weekend like, what are you gonna do? And I’m like, I don’t know. Do I get. I don’t think I get that like, yeah, no, I just work. I guess it was. This week has been pretty positive. And
4 00:00:56.560 ⇒ 00:01:01.910 Uttam Kumaran: I’m on boarding a couple of more people onto like my company some more.
5 00:01:02.230 ⇒ 00:01:10.160 Uttam Kumaran: So some things are coming off my plate. Which is good. I’m spending some more time on like writing and some content stuff.
6 00:01:10.380 ⇒ 00:01:19.000 Uttam Kumaran: so yeah, I’m excited to talk today. II wanted to get your feedback on like what you saw. And then even just have you drive, and we can kind of poke around at agent. Hub.
7 00:01:19.220 ⇒ 00:01:20.330 Brittany Bond: yeah.
8 00:01:20.670 ⇒ 00:01:23.899 Uttam Kumaran: Yeah. And kind of my, my thinking overall was
9 00:01:24.780 ⇒ 00:01:26.879 Uttam Kumaran: like on the spectrum of what’s
10 00:01:27.230 ⇒ 00:01:36.690 Uttam Kumaran: like the most technical, the least technical. But also what’s easy to iterate on this is middle it’s like a new platform. But I just follow
11 00:01:36.700 ⇒ 00:01:55.689 Uttam Kumaran: some of the folks that started that on Twitter. And I was like, Oh, this is a kind of a somewhere in the middle way of not being constrained by Chat Gp, but also not so technical that we can’t all work on it. And it’s somewhat of a ui, but at least you can piece things together. And then we could take that and build a version of that. So that was
12 00:01:55.940 ⇒ 00:01:56.790 Uttam Kumaran: thinking.
13 00:01:57.390 ⇒ 00:02:14.959 Brittany Bond: yeah, no. I. So being a non technical person, it take it took me 2 times to look at all the different links that you sent for me to understand what was what? And specifically the the prototype line where you like enter stuff. It took me a while to figure out that was the
14 00:02:15.250 ⇒ 00:02:24.670 Brittany Bond: products part of it. But you know I was glancing initially, and then I came back to. And it was. It was really obvious. And I really liked.
15 00:02:24.710 ⇒ 00:02:26.220 you know.
16 00:02:26.380 ⇒ 00:02:32.019 Brittany Bond: you know the flow in Agent Hub, and how you’re able to put those different inputs.
17 00:02:32.060 ⇒ 00:02:40.300 Brittany Bond: And it did feel approachable in terms of, you know, being able to iterate upon, and the prototype gave these, you know, some immediate
18 00:02:40.350 ⇒ 00:03:01.699 Brittany Bond: kind of reactions in terms of things I would want to see out of that. So all in all it was great. And sending loom videos is perfect. It was, yeah. We used loom a lot during our civil days. And so that was super helpful as well. Thank you for doing all that.
19 00:03:01.710 ⇒ 00:03:07.139 Brittany Bond: I think what I would love for you to do is
20 00:03:07.500 ⇒ 00:03:18.429 Brittany Bond: maybe walk me through the let’s see, not magic prompts. That was good. Not the prototype.
21 00:03:18.560 ⇒ 00:03:24.150 Brittany Bond: but like the build of the prototype.
22 00:03:24.410 ⇒ 00:03:33.019 Brittany Bond: Yeah. Input because I want to understand that flow a little bit better.
23 00:03:33.140 ⇒ 00:03:39.019 Brittany Bond: and also like where I can start iterating on it.
24 00:03:39.410 ⇒ 00:03:45.459 And I’ll I guess, just to explain. So my initial reaction to
25 00:03:45.690 ⇒ 00:04:10.729 Brittany Bond: to like the the case study it was coming up with was that it felt super random in terms of like the triggers and outcomes that it was identifying and none of them interrelated. And then also, I’m really struggling with the fact that there’s like no calculation in this in terms of square footage, and you know they want to reduce their footprint. But
26 00:04:11.410 ⇒ 00:04:38.839 Brittany Bond: by how much? Or you know, whatever it is. So what I’m realizing is that from a user perspective, we need to get more detail about what input they provide. But then also, we need to start figuring out, okay, how can we start doing calculations on the other side of this to come up with those outputs? And develop more realistic case studies. Does that make sense? And I’m just gonna
27 00:04:38.950 ⇒ 00:04:47.259 Uttam Kumaran: I’ll share, and I’ll just take some notes honestly, directly in here. So the one thing you mentioned is like needs
28 00:04:47.710 ⇒ 00:05:08.979 Uttam Kumaran: calculation. So so I’ll just walk through this. And then, basically, I was like, okay, let me replicate what has been done so far, then, the benefit of this is, we’re just gonna break each of these apart and get exactly the hopefully answers we need. Right now, I’ve I’ve basically replicated taking all these
29 00:05:08.980 ⇒ 00:05:17.830 Uttam Kumaran: different factors, taking in a company description that, like a user can input combining those and then
30 00:05:17.980 ⇒ 00:05:25.570 Uttam Kumaran: providing that with like a a prompt to say, Here’s what you are. Here’s all the background data, and then.
31 00:05:25.580 ⇒ 00:05:51.429 Uttam Kumaran: you know, produce this output. So a couple of things that I think we can immediately work on is one understanding what the outputs are. So, although there could be an output. That’s just a broad like case study for specific factors. We can require also produce a calculation right? So we can run that a few different prompts. And then some things are better served where
32 00:05:51.640 ⇒ 00:06:04.419 Uttam Kumaran: it’s like not deterministic meaning. It just will output anything. There’s other stuff where just want like a value or a couple of values. And that’s what we can identify. So can you elaborate? Maybe we just look at the current
33 00:06:04.660 ⇒ 00:06:16.629 Uttam Kumaran: prompts like which, what parts do you think W. Would rec. We would want to identify with? We want to like, answer with calculations as well, and drive towards that.
34 00:06:17.220 ⇒ 00:06:25.300 Brittany Bond: So. starting from the most basic cause, I’ve seen this in some of the case of the outcomes, it’s like addressing growth.
35 00:06:25.370 ⇒ 00:06:38.449 Brittany Bond: So you know. Say, a client is 25,000 square feet, and they’re growing by 10 people or 10 like, I want it to be able to come up with that outcome.
36 00:06:39.250 ⇒ 00:06:51.549 Brittany Bond: so that’s just like, okay, they were 200 square feet per person. They’re still 200 square feet per person. But they accommodated X percent more space
37 00:06:51.630 ⇒ 00:07:00.460 Brittany Bond: like that’s probably the most basic calculation I can think of. And then I’ll go ahead and let you write that.
38 00:07:00.570 ⇒ 00:07:01.759 Uttam Kumaran: Oh, that’s great.
39 00:07:02.410 ⇒ 00:07:07.409 Brittany Bond: And then the other factor is like, okay, they may have grown
40 00:07:07.470 ⇒ 00:07:21.260 Brittany Bond: by X percent from a headcount perspective, but they got more efficient with their square footage, right? So they were 200 square foot per person. Now they’re 1 75. But they
41 00:07:21.440 ⇒ 00:07:28.120 Brittany Bond: through their head count, or we’re able to accommodate their headcount by X more percent.
42 00:07:30.310 ⇒ 00:07:34.100 Uttam Kumaran: Okay, cool. So there’s some notion of efficiency. And then how does that factor?
43 00:07:34.260 ⇒ 00:07:39.279 Brittany Bond: Yeah? And then another one would be
44 00:07:39.550 ⇒ 00:07:54.630 Brittany Bond: part of their workforce went hybrid, right or remote. So they acquired less space, and 30 of their
45 00:07:55.110 ⇒ 00:07:59.989 Brittany Bond: employees became remote, which saved them 30% of their space.
46 00:08:06.140 ⇒ 00:08:07.100 Uttam Kumaran: Okay? Great.
47 00:08:08.360 ⇒ 00:08:10.450 Uttam Kumaran: So that’s a lot about
48 00:08:10.910 ⇒ 00:08:24.490 Uttam Kumaran: like this section, a lot about a couple of the factors that are related. So it’s almost like we want to understand which factors could affect
49 00:08:24.870 ⇒ 00:08:31.870 Uttam Kumaran: square. So there’s almost like square foot usage. There’s also the mix of like the build out
50 00:08:32.070 ⇒ 00:08:34.119 Brittany Bond: right. There’s also the growth
51 00:08:34.200 ⇒ 00:08:35.339 Uttam Kumaran: right? Right?
52 00:08:36.570 ⇒ 00:08:41.259 Brittany Bond: And so we would need to know from them the company.
53 00:08:41.740 ⇒ 00:08:44.910 Uttam Kumaran: What is our square footage today?
54 00:08:45.970 ⇒ 00:08:49.050 Brittany Bond: And what is their head count today?
55 00:08:53.430 ⇒ 00:08:56.499 Brittany Bond: And then. you know the
56 00:08:57.900 ⇒ 00:09:04.389 Brittany Bond: like. Maybe they have objectives like, we need to decrease by accident, or we need to add
57 00:09:05.170 ⇒ 00:09:11.479 Brittany Bond: X more people, or you know, and that is going to be hopefully, what ties together
58 00:09:11.490 ⇒ 00:09:15.470 Brittany Bond: the triggers and the outcomes? Right?
59 00:09:20.030 ⇒ 00:09:22.000 Uttam Kumaran: Yeah, exactly. Okay, great.
60 00:09:25.240 ⇒ 00:09:36.430 Uttam Kumaran: So I think a couple of things we can try is there’s there’s a lot here, I think, that are interrelated like a ton that’s interrelated. But some of it is actually.
61 00:09:37.040 ⇒ 00:09:42.220 Uttam Kumaran: I think we should funnel through to just get the metric outputs
62 00:09:42.430 ⇒ 00:09:49.249 Uttam Kumaran: like try to drive towards that scenario, we said. But in this case don’t get 3 case studies, almost get
63 00:09:49.340 ⇒ 00:09:52.530 Uttam Kumaran: 3 scenarios, and then potentially even build
64 00:09:52.610 ⇒ 00:09:56.289 Uttam Kumaran: the case study. Given that those those
65 00:09:56.410 ⇒ 00:09:59.650 Brittany Bond: quantitative outputs right?
66 00:10:00.920 ⇒ 00:10:06.509 Uttam Kumaran: Because that’s the one part that I think is much more fixed. And we want
67 00:10:06.580 ⇒ 00:10:07.890 Uttam Kumaran: that to
68 00:10:08.140 ⇒ 00:10:17.049 Uttam Kumaran: be like, okay, we have 3 scenarios. Here are the levers that affect. And then we can build a case. Study around the the artificial case study around
69 00:10:17.080 ⇒ 00:10:21.059 Uttam Kumaran: those and then have a couple of those. So I think that makes a lot of sense.
70 00:10:21.080 ⇒ 00:10:32.440 Uttam Kumaran: Again, it’ll we will have these factors, but some will go, and directly, you know, affect this, some will be about the story.
71 00:10:32.570 ⇒ 00:10:37.680 Brittany Bond: and so, okay. And so
72 00:10:38.840 ⇒ 00:10:45.509 Brittany Bond: II think we’ve talked about this before. I expect that when we eventually provide.
73 00:10:45.700 ⇒ 00:10:54.199 Brittany Bond: when the agent is providing people solutions, attendant solutions. there will be multiple scenarios they can choose from.
74 00:10:54.250 ⇒ 00:11:03.379 Brittany Bond: Because there’s no one. Right? Answer. Necessarily, there are multiple ways. You can achieve a 25 reduction.
75 00:11:04.120 ⇒ 00:11:04.800 Uttam Kumaran: Yeah.
76 00:11:09.990 ⇒ 00:11:16.120 Uttam Kumaran: so let’s expand the inputs to kind of take in a couple of actual specific
77 00:11:16.680 ⇒ 00:11:32.250 Uttam Kumaran: metrics like, what’s your current? Square foot like? What’s your current occupancy? What’s your current? Square foot? And then what are your goals. The goals will refine like what ends up working long term. And then, instead of now having just the case study.
78 00:11:32.280 ⇒ 00:11:34.940 Uttam Kumaran: we’ll drive it to produce 3
79 00:11:35.330 ⇒ 00:11:43.370 Uttam Kumaran: quantitative scenarios based on like the real key. Quantitative factors, not taking in any external data will maybe give it
80 00:11:43.450 ⇒ 00:11:47.050 Uttam Kumaran: some baseline understanding of like, what’s price per square foot.
81 00:11:47.180 ⇒ 00:11:53.320 Uttam Kumaran: and and try to give it enough information that maybe later we can source from the market.
82 00:11:53.800 ⇒ 00:12:03.800 Uttam Kumaran: Let’s try to modify this to do that. Basically what what I’m gonna do just to do that is kind of just add a couple of more inputs
83 00:12:03.900 ⇒ 00:12:19.299 Uttam Kumaran: and string this together. And I don’t know if you had a chance to kind of play around with the outputs on this. I didn’t. But I would love to see. Yeah. So basically, you can log in here. And then once you click into this kind of like builder view, if you just click this like user view here.
84 00:12:19.380 ⇒ 00:12:36.240 Uttam Kumaran: you’re gonna get it to open. And then, basically, I did, I did play with this, okay, yeah, you’re just given this. So at the moment I was just like, okay, let me replicate what exists today. And then, hopefully, what we’ll do is you’ll be given a couple of inputs
85 00:12:36.350 ⇒ 00:12:40.080 Brittany Bond: or maybe depending on how it’s all staged.
86 00:12:40.450 ⇒ 00:12:51.229 Brittany Bond: you know, we’ll get a little bit further. Yeah, maybe there’s a company description one. And then maybe there is like a objective section I can imagine. Yeah, exactly.
87 00:12:57.760 ⇒ 00:13:06.799 Uttam Kumaran: And then the the thing that’s nice about this is I think what we’ll do is once we get to a good point. We can try. And I’ll put like, multiple to Google Sheet
88 00:13:06.950 ⇒ 00:13:16.870 Uttam Kumaran: or Google, Doc, and then. again, the nice thing about this is this, we can replicate quite easily in python. So there’s not like.
89 00:13:17.080 ⇒ 00:13:18.490 Uttam Kumaran: because of how
90 00:13:18.570 ⇒ 00:13:35.789 Uttam Kumaran: like, it’s so fairly technical in the way they do combinations. But generally what they’re running. It makes sense how to port this over. So I think it’s a good place to to start things like that, too. So again, hopefully, we can get somewhere pretty far, just via this.
91 00:13:35.830 ⇒ 00:13:38.880 Brittany Bond: get those case studies out. And then
92 00:13:38.970 ⇒ 00:13:44.469 Uttam Kumaran: when, when maybe when we need to load data or other things, maybe we’ll reach a breaking point. But
93 00:13:45.430 ⇒ 00:13:46.220 Uttam Kumaran: yeah.
94 00:13:46.400 ⇒ 00:13:47.610 Brittany Bond: okay, great.
95 00:13:47.790 ⇒ 00:13:57.900 Brittany Bond: Yeah. So yeah, let’s, I mean, just continue to iterate on these inputs and whatnot the prompts, etc.
96 00:13:58.040 ⇒ 00:13:59.569 Brittany Bond: And you know.
97 00:13:59.990 ⇒ 00:14:05.450 Brittany Bond: see how close we can get to a good case study. I think that’s a good exercise for now.
98 00:14:05.520 ⇒ 00:14:09.270 Brittany Bond: Just to update you on the funding side. So
99 00:14:09.360 ⇒ 00:14:28.149 Brittany Bond: scott had had a couple of meetings with trust ventures. They’re Austin based. They’re the ones that have legal backgrounds. And so they work particularly in regulated spaces, looking for opportunities to kind of help. People navigate those regulated environments.
100 00:14:28.520 ⇒ 00:14:38.170 Brittany Bond: So they had already known about the real estate, you know, cases out there. That are potentially gonna change some of the brokers dynamics?
101 00:14:38.510 ⇒ 00:14:47.519 Brittany Bond: And we’re interested in us. And so Scott had, I think meetings about several of his projects that are going on
102 00:14:47.920 ⇒ 00:14:54.550 Brittany Bond: and then they wanted to learn more about this one, so I had a one on one with them
103 00:14:55.550 ⇒ 00:14:59.870 Brittany Bond: not last week, but the week before, I think on a Friday.
104 00:15:00.160 ⇒ 00:15:02.920 And that went really well.
105 00:15:03.060 ⇒ 00:15:06.120 Brittany Bond: Some of the things that they asked that were.
106 00:15:06.720 ⇒ 00:15:15.220 Yeah, kind of funny that we pull? Not funny. But we need to work through. Is there like, why are you only funding this for 6 months like, you know why? Such a short
107 00:15:15.760 ⇒ 00:15:16.700 Brittany Bond: time?
108 00:15:17.040 ⇒ 00:15:24.409 Brittany Bond: Because typically they’re writing like 2 to 5 million dollar checks, not like 250,000 like we asked for
109 00:15:24.540 ⇒ 00:15:30.209 Uttam Kumaran: and they said, We do have a fun that we do this smaller tech center. But you know, just like.
110 00:15:30.280 ⇒ 00:15:36.699 Brittany Bond: you know, does your 6 months have any buffer? Does that from a timeline or cost perspective? And I was like, No, no.
111 00:15:37.480 ⇒ 00:15:42.019 and I said, you know, generally just were focused on proving out
112 00:15:42.520 ⇒ 00:15:44.509 Brittany Bond: the ability to
113 00:15:45.950 ⇒ 00:15:58.060 Brittany Bond: train these agents through this approach. And so that’s why we have kind of a short term objective that would determine kind of where we go from there. So
114 00:15:58.110 ⇒ 00:16:20.300 Brittany Bond: then that was their first question. Like, why not do a year? So we looked at the operating budget to see. Okay, what would a year look like? What? How would our goals change if we did a year? Obviously we would need to show some revenue, traction and customers, and more of a significant product, all of those things. So we were working through that.
115 00:16:20.690 ⇒ 00:16:28.970 Brittany Bond: And then the other thing they said was like, we, you know, how can we specifically as lawyers, essentially help you?
116 00:16:29.250 ⇒ 00:16:35.599 Brittany Bond: You know, they wanna make sure that it’s a fit from the perspective that they can really add value.
117 00:16:35.680 ⇒ 00:16:46.950 Brittany Bond: So Scott and I talked about that, and we were like, well. we don’t know. Well, one, we need it closer to these cases. Better understand them. See what kind of real traction is there?
118 00:16:47.730 ⇒ 00:16:52.949 Brittany Bond: Excuse me. But also you know, we’re gonna have to get to a point where, like
119 00:16:53.040 ⇒ 00:16:57.990 Brittany Bond: do we decide to get our own brokerage license in order to do these deals?
120 00:16:58.120 ⇒ 00:17:10.540 Brittany Bond: How do we need a brokerage license, do we not, you know, kind of what are the legalities around that? So Scott is meeting with them again on Thursday?
121 00:17:10.940 ⇒ 00:17:23.700 Brittany Bond: And we’re gonna go from there and see what they say like, do they wanna fund a year or not? Yeah, exactly. And
122 00:17:23.800 ⇒ 00:17:27.039 Brittany Bond: also the person that introduced us to them.
123 00:17:27.410 ⇒ 00:17:31.349 Uttam Kumaran: Is Tom Ball from next coast ventures.
124 00:17:31.540 ⇒ 00:17:35.999 Brittany Bond: and he and Scott know each other. And so
125 00:17:36.310 ⇒ 00:17:47.019 Brittany Bond: Tom had referred him to these trust ventures. Guys and trust ventures also said, We typically like to go in with someone else. So Scott is trying to see if
126 00:17:47.260 ⇒ 00:18:05.429 Brittany Bond: next post would wanna go in with trust relationship. So lots of moving pieces. Obviously, I feel like these things can feel really positive sometimes, but then they can easily back out, too. So I don’t wanna get overly optimistic. But it was a really good conversation. So
127 00:18:05.440 ⇒ 00:18:15.669 Uttam Kumaran: I think it’s a good fit, because I think a lot of the questions in terms of actually like operationalizing this stuff regulations and the the revenue model.
128 00:18:16.290 ⇒ 00:18:20.279 Uttam Kumaran: And like again, just like legally, and just having, like
129 00:18:20.490 ⇒ 00:18:31.429 Uttam Kumaran: some sort of confidence that you’re not gonna get sued. Or this is seems to like it’s gonna go down around where conflicts with a lot of like status quo. I think it’s helpful.
130 00:18:31.880 ⇒ 00:18:33.610 Brittany Bond: Yeah, yeah, yeah.
131 00:18:33.660 ⇒ 00:18:35.320 So
132 00:18:36.300 ⇒ 00:18:40.489 Brittany Bond: I will update you after Thursday, when I hear from Scott.
133 00:18:40.740 ⇒ 00:18:48.959 Brittany Bond: If I can supplement anything like on the product side, or just need me to answer any questions, just feel free to
134 00:18:49.110 ⇒ 00:18:51.780 Brittany Bond: CC, me or Lubyen. Yeah.
135 00:18:52.060 ⇒ 00:18:53.799 Brittany Bond: I will for sure.
136 00:18:55.090 ⇒ 00:18:55.760 Brittany Bond: Okay.
137 00:18:56.060 ⇒ 00:19:02.930 Uttam Kumaran: okay, so I’m gonna make some of these updates. And then I’ll just keep shooting emails over as I get. Did you finally get the invite to the chat?
138 00:19:03.020 ⇒ 00:19:11.540 Brittany Bond: I didn’t. I didn’t have anything. Okay, let me show you.
139 00:19:11.550 ⇒ 00:19:17.550 Uttam Kumaran: I mean II set it up well, like I enabled it for
140 00:19:17.660 ⇒ 00:19:19.409 Uttam Kumaran: my domain. But
141 00:19:20.930 ⇒ 00:19:25.030 Uttam Kumaran: I’m on like chat, dot. This is just like chat.google.com.
142 00:19:29.230 ⇒ 00:19:31.489 Brittany Bond: See? I added, you here, but
143 00:19:31.700 ⇒ 00:19:35.149 still not pinging you. So I had to go to like
144 00:19:35.980 ⇒ 00:19:44.210 Brittany Bond: Google chat. It’s Google Chat sign in Google Workspace. Yes.
145 00:19:46.480 ⇒ 00:19:51.410 And there is like a section down here that says spaces rather than up here.
146 00:19:52.260 ⇒ 00:19:55.499 Uttam Kumaran: Yeah. So I don’t have that space. I wonder?
147 00:19:56.360 ⇒ 00:19:57.890 Uttam Kumaran: I wonder if it’s like
148 00:19:58.370 ⇒ 00:20:03.249 Uttam Kumaran: if you have a little the little down arrow there, can you add? Can you add somebody?
149 00:20:04.290 ⇒ 00:20:05.030 Brittany Bond: Yeah.
150 00:20:05.520 ⇒ 00:20:07.369 Brittany Bond: Oh, there you go! That’s
151 00:20:07.380 ⇒ 00:20:08.810 Brittany Bond: why it wasn’t working.
152 00:20:08.860 ⇒ 00:20:11.749 Uttam Kumaran: So maybe maybe you have to hit the plus ad.
153 00:20:17.300 ⇒ 00:20:18.660 Uttam Kumaran: Okay, let’s see.
154 00:20:18.960 ⇒ 00:20:20.380 Brittany Bond: Oh, I added, Ian.
155 00:20:20.910 ⇒ 00:20:25.410 Brittany Bond: or maybe I did. I don’t know. I might have added my husband instead of you.
156 00:20:25.780 ⇒ 00:20:27.220 Brittany Bond: No, he’s not on here.
157 00:20:29.110 ⇒ 00:20:31.630 Brittany Bond: Okay, says it was invited.
158 00:20:31.720 ⇒ 00:20:33.070 Uttam Kumaran: Let me refresh.
159 00:20:39.680 ⇒ 00:20:42.440 Uttam Kumaran: It’s maybe it’s in my email.
160 00:20:44.560 ⇒ 00:20:53.569 Brittany Bond: See? I don’t think it goes to email. I think you have to log into the chat to see the end invite. I could see the invite. Okay, well, let me try
161 00:20:55.150 ⇒ 00:20:56.380 Uttam Kumaran: and refresh
162 00:21:13.600 ⇒ 00:21:15.940 Uttam Kumaran: brown spaces.
163 00:21:24.050 ⇒ 00:21:25.520 Uttam Kumaran: Message request.
164 00:21:34.330 ⇒ 00:21:37.099 Uttam Kumaran: Okay, I’m in the right area.
165 00:21:44.400 ⇒ 00:21:45.870 Brittany Bond: Do you want me to try to?
166 00:21:45.920 ⇒ 00:21:49.530 Uttam Kumaran: Well, let me send you a note, and let’s see if it pops up for you.
167 00:21:51.010 ⇒ 00:21:56.379 Uttam Kumaran: You see anything on your end. Okay, so I’m I’m in the right place.
168 00:22:01.420 ⇒ 00:22:02.900 Brittany Bond: Let me try one more time.
169 00:22:14.200 ⇒ 00:22:17.980 Brittany Bond: Did you get an invite that time or see anything? Pop up.
170 00:22:18.360 ⇒ 00:22:19.910 Uttam Kumaran: see anything?
171 00:22:21.880 ⇒ 00:22:28.219 Uttam Kumaran: Wow! They really need to work on this. Huh? Let me. Oh, I like, I got it. Okay, there it is.
172 00:22:28.360 ⇒ 00:22:29.060 Brittany Bond: Okay.
173 00:22:29.470 ⇒ 00:22:31.490 Uttam Kumaran: Okay, join.
174 00:22:38.080 ⇒ 00:22:40.030 Uttam Kumaran: Okay, this is
175 00:22:41.150 ⇒ 00:22:44.319 Brittany Bond: great. I’m just gonna say, Hi, oh, yes, as you joined.
176 00:22:44.960 ⇒ 00:22:46.080 Uttam Kumaran: Okay, cool.
177 00:22:48.170 ⇒ 00:22:52.409 Uttam Kumaran: alright great. So maybe I’ll just start. I’ll just share updates
178 00:22:52.440 ⇒ 00:23:11.030 Brittany Bond: directly in here. If that’s easier. Yeah, that’s perfect. No more emails. Okay, great. Alright. Thank you so much for catching up this morning. And I’ll keep you updated. And we can just keep going back and forth on the chat now. Okay, alright, thanks. Bye.