Meeting Title: Friday Brainforge Demos & Retro Date: 2025-05-16 Meeting participants: Annie Yu, Luke Daque, Mustafa Raja, Uttam Kumaran, Amber Lin, Hannah Wang, Robert Tseng, Miguel De Veyra, Awaish Kumar, Caio Velasco
WEBVTT
1 00:02:02.940 ⇒ 00:02:05.599 Uttam Kumaran: Hello! Good morning! Good evening!
2 00:02:05.920 ⇒ 00:02:06.970 Mustafa Raja: Hello! How are you?
3 00:02:07.320 ⇒ 00:02:08.520 Uttam Kumaran: Hey! How are you?
4 00:02:09.000 ⇒ 00:02:09.659 Mustafa Raja: I’m good.
5 00:03:20.910 ⇒ 00:03:26.799 Annie Yu: Hello! Do you wanna make me like the host so I can do the breakout.
6 00:03:26.800 ⇒ 00:03:28.900 Uttam Kumaran: Thank you, happy to.
7 00:03:28.900 ⇒ 00:03:38.199 Annie Yu: So I don’t even know this is gonna be my like 1st ever attempt to start a breakout room. So Hannah will be my guide if anything.
8 00:03:38.400 ⇒ 00:03:40.319 Uttam Kumaran: Your host. It’s all yours.
9 00:03:41.180 ⇒ 00:03:42.440 Annie Yu: Awesome.
10 00:04:07.710 ⇒ 00:04:14.075 Uttam Kumaran: I have had a long week. I am so tired today.
11 00:04:15.330 ⇒ 00:04:17.910 Uttam Kumaran: I was kind of tired yesterday.
12 00:04:18.130 ⇒ 00:04:22.050 Uttam Kumaran: Well, I was tired on Wednesday, and then I was like, Okay, you have like 2 days like lock in.
13 00:04:22.590 ⇒ 00:04:26.759 Uttam Kumaran: I’m so thankful we don’t have like client meetings anymore on Friday that, like.
14 00:04:28.320 ⇒ 00:04:32.699 Uttam Kumaran: I don’t know amber like I’m like that was like one thing I wanted to do for a long time.
15 00:04:32.970 ⇒ 00:04:33.970 Amber Lin: That’s great!
16 00:04:33.970 ⇒ 00:04:38.960 Uttam Kumaran: Yeah, very thankful. Because it’s just like I just, it’s like.
17 00:04:40.300 ⇒ 00:04:44.339 Uttam Kumaran: I’m happy to mute clients. But it just takes a lot of energy.
18 00:04:44.990 ⇒ 00:04:50.870 Amber Lin: Yeah, and you can’t. You can’t not have energy. So that’s the.
19 00:04:50.870 ⇒ 00:04:53.999 Uttam Kumaran: Yeah, like, someone has to come to the room with some energy.
20 00:04:54.000 ⇒ 00:05:00.869 Amber Lin: I know if it’s our internal meeting, I’m like guys, I’m so dead. And then it’s okay. If I can’t tell clients that.
21 00:05:00.870 ⇒ 00:05:05.039 Uttam Kumaran: It’s also like Friday is good for, like reflection, like, we have this meeting.
22 00:05:05.170 ⇒ 00:05:09.709 Uttam Kumaran: You know, we have a I call a couple of people just think about how stuff went like
23 00:05:10.260 ⇒ 00:05:15.119 Uttam Kumaran: I’d rather it be like a little bit of a reflection on the week, you know.
24 00:05:17.830 ⇒ 00:05:20.829 Uttam Kumaran: Okay, let me ping some people to join.
25 00:05:47.140 ⇒ 00:05:51.560 Uttam Kumaran: Okay, I ping Robert Casey, and they go
26 00:05:53.800 ⇒ 00:05:55.470 Uttam Kumaran: here. We give it one more minute.
27 00:06:09.340 ⇒ 00:06:13.660 Annie Yu: Is that plant next to Finn like dying.
28 00:06:18.510 ⇒ 00:06:20.558 Uttam Kumaran: Not not really.
29 00:06:21.070 ⇒ 00:06:23.599 Annie Yu: Oh, okay. Oh, okay. Cause I thought that was.
30 00:06:23.600 ⇒ 00:06:24.189 Uttam Kumaran: It’s like.
31 00:06:24.190 ⇒ 00:06:25.460 Annie Yu: The other.
32 00:06:25.460 ⇒ 00:06:38.820 Uttam Kumaran: Kind of drooping, but like I watered it, it’s like, seems okay. But I don’t like that’s that plant came with the house. So that’s not even my plan like. So I it’s like, I don’t care about it. I care about this plant a lot, though.
33 00:06:39.400 ⇒ 00:06:42.599 Uttam Kumaran: way more. I care about this cent 10 times more than that one.
34 00:06:42.820 ⇒ 00:06:44.790 Annie Yu: It looks like it’s Hi Megan.
35 00:06:45.680 ⇒ 00:06:49.619 Uttam Kumaran: Yeah, this one is good, and and that’s like a pretty large.
36 00:06:50.000 ⇒ 00:06:54.530 Uttam Kumaran: And I like have it kind of go. I like this Viney plants.
37 00:06:54.790 ⇒ 00:07:01.669 Uttam Kumaran: and I have another like sort of vine, and I don’t know I like the Viney plants that just sort of like you can just trace across the wall and stuff. So
38 00:07:05.520 ⇒ 00:07:09.630 Uttam Kumaran: okay, cool. Let’s maybe. Let’s let’s get started.
39 00:07:10.910 ⇒ 00:07:11.800 Annie Yu: Yeah, sure.
40 00:07:11.800 ⇒ 00:07:17.980 Annie Yu: Sure. So I guess 1st thing 1st heard that we have a wait. Let me share my
41 00:07:19.360 ⇒ 00:07:23.069 Annie Yu: my deck. Can you see the slide?
42 00:07:27.590 ⇒ 00:07:28.559 Annie Yu: Can everyone see?
43 00:07:28.560 ⇒ 00:07:29.150 Uttam Kumaran: Yes.
44 00:07:29.150 ⇒ 00:07:43.180 Annie Yu: Okay, cool. And 1st thing first.st So her heard that we have a new friend who I haven’t met. So let’s give him like a warm welcome, and Mustafa, wanna say, Hi! Introduce yourself.
45 00:07:45.520 ⇒ 00:07:51.229 Mustafa Raja: Hello, everyone I am the new AI team member. It’s good to meet you all.
46 00:07:53.000 ⇒ 00:07:57.926 Uttam Kumaran: Do you wanna share a little bit about? Yeah, where? Where you are in the world? And
47 00:07:58.814 ⇒ 00:08:01.730 Uttam Kumaran: maybe tell us a little bit about your background.
48 00:08:02.930 ⇒ 00:08:12.820 Mustafa Raja: So so I’m from Pakistan. And I have a background in AI automations and AI engineering in Lama Index and in.
49 00:08:13.410 ⇒ 00:08:15.300 Miguel de Veyra: Hello! Team. Sorry for being late.
50 00:08:16.780 ⇒ 00:08:17.460 Uttam Kumaran: A.
51 00:08:18.010 ⇒ 00:08:19.539 Annie Yu: Love, Miguel.
52 00:08:21.500 ⇒ 00:08:27.221 Uttam Kumaran: Cool. I guess I don’t have any other icebreaker questions for Mustafa. But
53 00:08:27.880 ⇒ 00:08:32.260 Uttam Kumaran: Does anyone have any questions for him?
54 00:08:34.350 ⇒ 00:08:36.330 Luke Daque: What are your hobbies.
55 00:08:39.190 ⇒ 00:08:52.070 Mustafa Raja: it’s playing ticket. It’s a game played mostly in subcontinent. And then Uk, apart from that, I like chess. And AI.
56 00:08:56.200 ⇒ 00:08:58.360 Uttam Kumaran: Cricket is big these days.
57 00:08:58.710 ⇒ 00:08:59.829 Uttam Kumaran: That’s awesome.
58 00:08:59.830 ⇒ 00:09:01.710 Amber Lin: What is cricket?
59 00:09:02.399 ⇒ 00:09:07.849 Amber Lin: It’s it’s kind of like baseball, but it’s more spread out.
60 00:09:09.270 ⇒ 00:09:10.020 Amber Lin: I see.
61 00:09:10.020 ⇒ 00:09:16.420 Uttam Kumaran: Yeah, it’s kind of like baseball, except very different than baseball. It’s like, very. The rules are so much different.
62 00:09:16.830 ⇒ 00:09:18.555 Uttam Kumaran: Well, you’re hitting a ball.
63 00:09:18.900 ⇒ 00:09:21.290 Mustafa Raja: Yeah, yeah. A ball is thrown and you hit it.
64 00:09:27.550 ⇒ 00:09:28.440 Annie Yu: Nice.
65 00:09:28.760 ⇒ 00:09:31.720 Annie Yu: Okay? So that’s
66 00:09:31.950 ⇒ 00:09:56.049 Annie Yu: well, I did prepare an icebreaker. So let’s start with something real like, everybody eats right? So today’s question is, what’s your? Everything’s broken. But at least I have this comfort, food and pro tip. It doesn’t have to be like fancy. It can be something you probably wouldn’t serve to others like a spoon of peanut butter, or something like that. And
67 00:09:56.260 ⇒ 00:10:10.279 Annie Yu: it’s like, do you share if there’s like a perfect setting to eat it to like with the music blasting in the background. So yeah, I’m gonna do a breakout room, probably for this. Oh.
68 00:10:11.100 ⇒ 00:10:13.430 Annie Yu: how do I do.
69 00:10:13.430 ⇒ 00:10:18.799 Uttam Kumaran: If you hit hit us. If you hit escape, I think you can get out of the zoom, but I don’t know, Hannah. It’s probably.
70 00:10:19.730 ⇒ 00:10:23.959 Hannah Wang: It’s in the tab, like the participants chat.
71 00:10:23.960 ⇒ 00:10:26.730 Annie Yu: Do I have to stop, share.
72 00:10:26.730 ⇒ 00:10:31.239 Hannah Wang: No, you should find the tab should be floating around in one of your screens.
73 00:10:31.520 ⇒ 00:10:32.380 Annie Yu: Okay.
74 00:10:32.980 ⇒ 00:10:33.900 Hannah Wang: Somewhere.
75 00:10:34.160 ⇒ 00:10:35.700 Annie Yu: And then.
76 00:10:36.050 ⇒ 00:10:38.270 Hannah Wang: And there should be like a breakout room.
77 00:10:39.000 ⇒ 00:10:40.310 Hannah Wang: Okay, cool.
78 00:10:40.790 ⇒ 00:10:42.969 Annie Yu: How many people we have here? Okay.
79 00:10:42.970 ⇒ 00:10:43.680 Hannah Wang: Have 12.
80 00:10:43.680 ⇒ 00:10:47.459 Annie Yu: Can we do? 3 3 people in the room.
81 00:10:47.990 ⇒ 00:10:48.710 Hannah Wang: Okay.
82 00:10:49.150 ⇒ 00:10:56.110 Annie Yu: Cool assign automatically cool. Okay, I’ll see you guys in a bit.
83 00:10:57.980 ⇒ 00:10:58.720 Annie Yu: Yeah.
84 00:10:59.290 ⇒ 00:10:59.940 Hannah Wang: And then, if you.
85 00:11:00.480 ⇒ 00:11:01.550 Annie Yu: Timer.
86 00:11:02.310 ⇒ 00:11:07.570 Hannah Wang: No? Well, I don’t know if there’s a timer, but what I did was I gave like a 2 min warning.
87 00:11:07.730 ⇒ 00:11:13.069 Hannah Wang: so I just kept track of the time, and you can make an announcement like it should be
88 00:11:13.360 ⇒ 00:11:14.880 Hannah Wang: there somewhere.
89 00:11:16.350 ⇒ 00:11:18.810 Hannah Wang: If not, it’s okay. Just bring us back.
90 00:11:19.340 ⇒ 00:11:22.229 Annie Yu: Okay? And and am I? Am I saying this wrong?
91 00:11:22.230 ⇒ 00:11:23.863 Hannah Wang: Yeah, you stay alone here.
92 00:11:24.190 ⇒ 00:11:25.660 Annie Yu: Oh, okay. Okay.
93 00:11:25.660 ⇒ 00:11:27.059 Hannah Wang: Find people who come in.
94 00:11:27.678 ⇒ 00:11:29.109 Annie Yu: Got it. Got it.
95 00:11:29.110 ⇒ 00:11:29.800 Hannah Wang: Oh, yeah.
96 00:11:29.920 ⇒ 00:11:30.760 Hannah Wang: Alright.
97 00:11:30.760 ⇒ 00:11:31.520 Annie Yu: Thanks.
98 00:11:42.150 ⇒ 00:11:43.909 Annie Yu: Oh, wait! So we have a.
99 00:11:44.040 ⇒ 00:11:45.369 Miguel de Veyra: I think I got wrong.
100 00:11:47.030 ⇒ 00:11:50.060 Annie Yu: Oh, oh, really! Wait! You do.
101 00:11:50.890 ⇒ 00:11:53.499 Annie Yu: I do see you’re in room 2.
102 00:11:53.500 ⇒ 00:11:54.910 Miguel de Veyra: How do I go there again.
103 00:11:56.930 ⇒ 00:11:59.180 Annie Yu: Let me see move to.
104 00:12:00.340 ⇒ 00:12:02.500 Annie Yu: I’m gonna can you see it.
105 00:12:02.780 ⇒ 00:12:04.254 Miguel de Veyra: Join join room? One. Yeah.
106 00:12:04.550 ⇒ 00:12:10.909 Annie Yu: Zoom. Okay, I just got you moved back to Room 2. Okay, cool.
107 00:15:33.340 ⇒ 00:15:35.280 Annie Yu: Was that enough time? I’m not sure.
108 00:15:35.280 ⇒ 00:15:36.310 Uttam Kumaran: Yeah, yeah.
109 00:15:36.310 ⇒ 00:15:36.819 Annie Yu: Right.
110 00:15:37.620 ⇒ 00:15:39.549 Uttam Kumaran: That was great. That’s fine!
111 00:15:40.920 ⇒ 00:15:44.059 Amber Lin: That’s a pretty brief question. It was really nice.
112 00:15:45.544 ⇒ 00:15:49.975 Annie Yu: I’m like all for random and fun. So.
113 00:15:51.600 ⇒ 00:15:54.570 Uttam Kumaran: Are we are we gonna report out on like what we all said or.
114 00:15:55.560 ⇒ 00:15:59.090 Annie Yu: Sure. Oh, sorry about that.
115 00:15:59.090 ⇒ 00:16:04.709 Uttam Kumaran: Our our crew was very utilitarian. I’ll wait for everybody to come, but our crew was very like.
116 00:16:06.170 ⇒ 00:16:08.330 Uttam Kumaran: there’s a lot of what can I eat really fast?
117 00:16:11.110 ⇒ 00:16:12.670 Miguel de Veyra: We have the same background.
118 00:16:13.360 ⇒ 00:16:14.580 Annie Yu: Know, what.
119 00:16:15.710 ⇒ 00:16:18.470 Uttam Kumaran: Robert guns out. Let’s go.
120 00:16:18.980 ⇒ 00:16:19.790 Robert Tseng: No, no, it’s.
121 00:16:19.790 ⇒ 00:16:20.360 Uttam Kumaran: Without God.
122 00:16:21.650 ⇒ 00:16:22.650 Uttam Kumaran: Yeah, that’s.
123 00:16:23.043 ⇒ 00:16:25.009 Robert Tseng: I need to do it.
124 00:16:25.010 ⇒ 00:16:25.489 Uttam Kumaran: I know I.
125 00:16:25.490 ⇒ 00:16:25.810 Robert Tseng: Customer.
126 00:16:25.810 ⇒ 00:16:37.510 Uttam Kumaran: It’s Friday. It’s Friday, too, and I’m like, it’s so hot here. But I really just wanted to wear a hoodie, and I’ve been like going to things where I’m like dressed off. And I like, I don’t want to dress up
127 00:16:38.517 ⇒ 00:16:46.450 Uttam Kumaran: alright. We’re reporting out. Maybe I’ll go 1st for our team. So I I think consensus. We like pizza.
128 00:16:46.960 ⇒ 00:16:53.000 Uttam Kumaran: We like bread, bowls, soup, bread, bowls, tomato soup, sourdough bread, bowl.
129 00:16:53.230 ⇒ 00:16:56.126 Uttam Kumaran: bread. I think we’re a big fan of carbs, and then
130 00:16:57.392 ⇒ 00:17:00.710 Uttam Kumaran: just like instant noodle ramen. Yeah.
131 00:17:01.090 ⇒ 00:17:01.435 Annie Yu: Hmm.
132 00:17:03.220 ⇒ 00:17:03.850 Uttam Kumaran: Yeah.
133 00:17:04.720 ⇒ 00:17:07.609 Annie Yu: Sounds good. Anyone else want to share.
134 00:17:09.790 ⇒ 00:17:22.119 Robert Tseng: Our group was. I think I think Miguel’s is interesting. But you know, I guess Anna Ryan and I all talked about pretty much this noodles and rice different ethnic variations. But then Miguel had an interesting one.
135 00:17:23.980 ⇒ 00:17:24.719 Miguel de Veyra: Yes, you’ll.
136 00:17:24.720 ⇒ 00:17:25.799 Uttam Kumaran: What is amigo?
137 00:17:25.800 ⇒ 00:17:32.509 Miguel de Veyra: It’s yogurt with the I forgot what it’s called, but the Hershey’s thing that solidifies on contact with something gold.
138 00:17:33.710 ⇒ 00:17:35.000 Uttam Kumaran: What?
139 00:17:35.190 ⇒ 00:17:37.280 Robert Tseng: Like. Apparently it’s yogurt with chocolate.
140 00:17:37.650 ⇒ 00:17:38.139 Miguel de Veyra: Yeah, yeah.
141 00:17:38.140 ⇒ 00:17:39.660 Robert Tseng: Perry. I’ve not had it before.
142 00:17:39.660 ⇒ 00:17:40.260 Annie Yu: The same.
143 00:17:40.260 ⇒ 00:17:43.150 Uttam Kumaran: Wow! Is it the owner.
144 00:17:44.705 ⇒ 00:17:47.950 Miguel de Veyra: My girlfriend buys it so I’m not sure
145 00:17:48.100 ⇒ 00:17:50.189 Miguel de Veyra: it’s just. I have a stock in my rep.
146 00:17:50.650 ⇒ 00:17:53.049 Miguel de Veyra: It’s mango flavored, so it’s a mix of.
147 00:17:53.050 ⇒ 00:17:54.849 Amber Lin: All the favors in the world.
148 00:17:54.850 ⇒ 00:17:56.720 Uttam Kumaran: It’s like those little chobanies.
149 00:17:57.450 ⇒ 00:17:58.500 Miguel de Veyra: I think so. I can.
150 00:17:58.500 ⇒ 00:17:59.220 Uttam Kumaran: Funny little.
151 00:17:59.220 ⇒ 00:18:00.500 Miguel de Veyra: Imagely, yeah.
152 00:18:01.140 ⇒ 00:18:04.739 Uttam Kumaran: Alright interesting! That’s good for your gut health. By the way.
153 00:18:06.680 ⇒ 00:18:15.049 Amber Lin: You know. Once upon a time I had I was interested. Cause yogurt is dairy, and I was interested. How it would taste like coffee. I do not recommend.
154 00:18:17.190 ⇒ 00:18:18.040 Uttam Kumaran: Out of there.
155 00:18:18.040 ⇒ 00:18:24.440 Amber Lin: It was bitter and sour, and it reminded me that it came from my stomach and not from outside.
156 00:18:24.570 ⇒ 00:18:25.609 Amber Lin: so wouldn’t I.
157 00:18:25.610 ⇒ 00:18:26.090 Uttam Kumaran: Recommend.
158 00:18:26.090 ⇒ 00:18:27.809 Amber Lin: Experimenting that way.
159 00:18:28.586 ⇒ 00:18:28.953 Annie Yu: Yeah.
160 00:18:30.920 ⇒ 00:18:39.784 Annie Yu: Sounds good. Well, Hope, everyone enjoyed that, and I was sad. I had to like, stay here alone. I did not know that.
161 00:18:40.170 ⇒ 00:18:43.040 Uttam Kumaran: Yeah, well, okay. So when’s yours? What’s your comfort? Food?
162 00:18:43.260 ⇒ 00:18:53.710 Annie Yu: Mine is very particular and sad, too, so it’s like soggy fries, and it has to be from Mcdonald’s so like you, and you don’t always get those like they’re.
163 00:18:53.710 ⇒ 00:18:55.210 Uttam Kumaran: Like at the bottom of the bag.
164 00:18:55.210 ⇒ 00:18:56.336 Annie Yu: Yeah, yeah.
165 00:18:56.900 ⇒ 00:19:01.939 Robert Tseng: So you have to like. Tell them, please give me look like the oldest soggy fries you have.
166 00:19:04.340 ⇒ 00:19:09.689 Uttam Kumaran: They’re like, Oh, we got a fresh batch you’re like, no, no, no fresh batch like. Give me the old.
167 00:19:09.690 ⇒ 00:19:10.030 Robert Tseng: Yeah.
168 00:19:10.840 ⇒ 00:19:14.103 Uttam Kumaran: The one that you just threw out. Give me that one.
169 00:19:15.850 ⇒ 00:19:17.629 Uttam Kumaran: Yeah, maybe those are just soak.
170 00:19:17.630 ⇒ 00:19:18.869 Uttam Kumaran: It’s good. Yeah.
171 00:19:19.450 ⇒ 00:19:21.939 Uttam Kumaran: It’s just soaked with oil. It’s good. Yeah.
172 00:19:22.240 ⇒ 00:19:40.800 Annie Yu: Okay, thanks everyone. So today’s lecture, I’m gonna make this quick. But today is going to be a bit more light hearted. And but still something very close to my heart. So I’m gonna share some fun facts about my like literal favorite animal on the entire planet.
173 00:19:41.220 ⇒ 00:19:42.180 Robert Tseng: Oh!
174 00:19:42.180 ⇒ 00:19:43.890 Annie Yu: The amenities.
175 00:19:44.910 ⇒ 00:19:45.640 Annie Yu: So
176 00:19:46.870 ⇒ 00:20:14.089 Annie Yu: so it’s kind of annoying. So I’m a manatee lover, and whenever I mention manatees, people are like, Oh, I love sea lions. I love dolphins, but honestly, they are like not related to any marine mammals, but elephants are their closest relatives, and it kind of tracks, because they are both herbivorous, slow moving, and they even have toenails on their flippers here, if you can see
177 00:20:15.010 ⇒ 00:20:15.870 Uttam Kumaran: Wow!
178 00:20:16.380 ⇒ 00:20:30.380 Annie Yu: So, yeah, there are like nothing like dolphins or sea lions. So stop thinking that and then they also fun fact. They fart to control their buoyancy. So
179 00:20:30.730 ⇒ 00:20:42.699 Annie Yu: hard powered navigation is a real thing. Guys. They like when they need to dive, they release gas, and when when they want to stay afloat, they hold it in, and I
180 00:20:42.870 ⇒ 00:20:53.960 Annie Yu: so I go. I’m committed to going swimming with manatees every year in Florida. So when I do, I sometimes actually saw like bubbles coming out of them. And
181 00:20:54.370 ⇒ 00:21:03.770 Annie Yu: I’m telling you it’s not like not only not disgusting, but super adorable. So that’s 1 thing, and also amenities.
182 00:21:04.963 ⇒ 00:21:18.619 Annie Yu: They constantly grow new molars, so they spend up to about 8 HA day grazing on seagrass, so as their like front teeth wear down the newer teeth from the back, will
183 00:21:18.760 ⇒ 00:21:30.036 Annie Yu: move forward kind of like a conveyor belt, and then the fresh new teeth will grow in the back, and I think, to my knowledge, they are the only mammals with
184 00:21:30.850 ⇒ 00:21:35.950 Annie Yu: the only memos know to do this, and then
185 00:21:36.440 ⇒ 00:21:51.240 Annie Yu: finally, they have no like natural predators like, so they never evolved to be aggressive, not even sharks or orcas hunt them. So they are like, literally the the chillest
186 00:21:51.866 ⇒ 00:22:03.770 Annie Yu: but here’s like it gets kind of sad. So they are still kind of nearly endangered, because human activities, like both strikes, killed them
187 00:22:03.770 ⇒ 00:22:24.330 Annie Yu: and pollutions causing them to lose their food sources and starved. That’s why some places like Florida have pretty strict protection. Law like you can’t chase, feed or touch them. And I’ve been following a couple of like Seagrass restoration projects. So if you’re interested, there’s something out there.
188 00:22:24.330 ⇒ 00:22:40.561 Annie Yu: So yeah, that’s pretty much it. And final thought manatees are like literally the chillest and most peaceful creatures in the world. In my view. So hopefully we can all channel a little like manatee energy.
189 00:22:41.000 ⇒ 00:22:48.449 Uttam Kumaran: Where? What’s where? Where are they found in the world like is this? They’re just in like Pacific Ocean, or like, where are they?
190 00:22:48.790 ⇒ 00:23:06.629 Annie Yu: I think I’m not familiar with the rest of the world. But I know that in America I can see the most in Florida, but also when I was in. I think, Puerto Rico. There’s also a few down in like the Caribbean
191 00:23:06.790 ⇒ 00:23:15.400 Annie Yu: Sea area, but I I do believe I think people do sight see them in somewhere in Europe, too.
192 00:23:15.870 ⇒ 00:23:17.520 Annie Yu: So but.
193 00:23:17.520 ⇒ 00:23:20.577 Uttam Kumaran: They’re kind of in between Sea Lion and
194 00:23:21.570 ⇒ 00:23:24.820 Uttam Kumaran: walrus, right? Or like what’s what or like
195 00:23:25.561 ⇒ 00:23:28.988 Uttam Kumaran: Sea Lion, and there’s some there’s a what’s the other one?
196 00:23:29.700 ⇒ 00:23:31.980 Uttam Kumaran: they have those in sf like up here.
197 00:23:34.410 ⇒ 00:23:35.639 Annie Yu: What do you mean?
198 00:23:36.630 ⇒ 00:23:40.370 Uttam Kumaran: Like those big like Sea lion things.
199 00:23:41.860 ⇒ 00:23:43.240 Uttam Kumaran: What are they called.
200 00:23:44.230 ⇒ 00:23:45.219 Annie Yu: Like the base.
201 00:23:46.920 ⇒ 00:23:51.839 Uttam Kumaran: No, but what’s the bigger sea line? It’s not. It’s not like, or what’s what’s the smaller one.
202 00:23:52.400 ⇒ 00:23:53.220 Robert Tseng: Otter.
203 00:23:54.060 ⇒ 00:23:54.690 Uttam Kumaran: Is it, Otter?
204 00:23:55.070 ⇒ 00:23:58.979 Robert Tseng: Sea Otter. Yes, it’s like Sea Otter, and then Sea Lion is bigger.
205 00:24:00.320 ⇒ 00:24:01.549 Annie Yu: And there’s like Dugan.
206 00:24:02.940 ⇒ 00:24:04.220 Robert Tseng: Yeah. Duke, ons.
207 00:24:04.380 ⇒ 00:24:07.220 Luke Daque: We have that in the Philippines into gongs.
208 00:24:08.060 ⇒ 00:24:09.110 Annie Yu: Oh, really.
209 00:24:09.110 ⇒ 00:24:09.830 Miguel de Veyra: Those are Admins.
210 00:24:09.830 ⇒ 00:24:16.689 Annie Yu: They look kind of similar, think they are. They are actually the same families like.
211 00:24:16.690 ⇒ 00:24:18.179 Miguel de Veyra: Oh, they are animals!
212 00:24:19.310 ⇒ 00:24:21.349 Luke Daque: What do you mean? You don’t know.
213 00:24:21.350 ⇒ 00:24:24.169 Miguel de Veyra: My parents used to call people Dupo.
214 00:24:25.170 ⇒ 00:24:25.680 Annie Yu: Oh!
215 00:24:25.680 ⇒ 00:24:27.309 Miguel de Veyra: So I thought they were like, you know.
216 00:24:27.950 ⇒ 00:24:28.470 Luke Daque: Yeah, it’s.
217 00:24:28.470 ⇒ 00:24:29.300 Miguel de Veyra: As long.
218 00:24:29.720 ⇒ 00:24:30.620 Miguel de Veyra: Okay.
219 00:24:32.530 ⇒ 00:24:34.229 Uttam Kumaran: He is an elephant seal.
220 00:24:34.870 ⇒ 00:24:37.340 Robert Tseng: Can I add humanity? Fact
221 00:24:38.010 ⇒ 00:24:49.640 Robert Tseng: like their their lips are like tongs, so they can move each side independently. So like it helps them to like grab onto stuff. So it’s kinda interesting. I wish my lips could do that.
222 00:24:53.350 ⇒ 00:24:53.775 Annie Yu: And
223 00:24:54.200 ⇒ 00:24:57.809 Uttam Kumaran: What would you use that for? What would their use case be?
224 00:24:57.980 ⇒ 00:24:59.029 Robert Tseng: Alright, I just I just found.
225 00:24:59.030 ⇒ 00:24:59.530 Uttam Kumaran: Feature.
226 00:24:59.530 ⇒ 00:25:04.989 Robert Tseng: So like. Look, look it like here’s it like holding onto a rope with its lips.
227 00:25:06.230 ⇒ 00:25:08.000 Annie Yu: Yeah, they do that.
228 00:25:08.720 ⇒ 00:25:09.620 Uttam Kumaran: Oh, wow!
229 00:25:09.620 ⇒ 00:25:16.911 Annie Yu: They like like, see grasses everywhere. Right? I think that’s how they like. I don’t even know.
230 00:25:17.340 ⇒ 00:25:18.740 Uttam Kumaran: It’s like separated.
231 00:25:19.200 ⇒ 00:25:21.829 Robert Tseng: Yeah, yeah, it’s like a split lip, almost like.
232 00:25:21.830 ⇒ 00:25:22.800 Uttam Kumaran: Sideways.
233 00:25:23.130 ⇒ 00:25:23.460 Robert Tseng: Yeah.
234 00:25:23.460 ⇒ 00:25:25.529 Uttam Kumaran: Sits like sideways, detached.
235 00:25:27.450 ⇒ 00:25:28.420 Robert Tseng: Yeah.
236 00:25:28.420 ⇒ 00:25:28.990 Uttam Kumaran: Interesting.
237 00:25:28.990 ⇒ 00:25:35.520 Robert Tseng: Guess we would call it a cleft lip. And in human physiology, I guess.
238 00:25:38.090 ⇒ 00:25:40.781 Uttam Kumaran: See potato. I just noticed that.
239 00:25:41.230 ⇒ 00:25:59.199 Annie Yu: Yeah, there, that’s their like nicknames. And just sharing one more thing. I’m like getting carried away. But I because I live in Portland, Oregon. There’s no amenities, but I do collect like manatee crochets. So here
240 00:25:59.540 ⇒ 00:26:03.509 Annie Yu: you could see like I have, I think, 13 of them.
241 00:26:05.220 ⇒ 00:26:08.450 Annie Yu: And yeah, that’s how how much I love them!
242 00:26:09.910 ⇒ 00:26:13.179 Uttam Kumaran: Portland has probably some great wildlife on the coast there, right
243 00:26:14.300 ⇒ 00:26:17.690 Uttam Kumaran: Portland and Oregon. They have some great great wildlife.
244 00:26:18.600 ⇒ 00:26:19.200 Annie Yu: I think there’s.
245 00:26:19.200 ⇒ 00:26:20.100 Uttam Kumaran: First.st
246 00:26:20.100 ⇒ 00:26:23.289 Annie Yu: And well, people like go well watching.
247 00:26:23.290 ⇒ 00:26:24.170 Uttam Kumaran: Watching.
248 00:26:25.120 ⇒ 00:26:28.019 Amber Lin: Go. I have a question. What is your cat’s name?
249 00:26:32.851 ⇒ 00:26:36.610 Miguel de Veyra: Ragnar Ragnar, he’s eating something. I don’t understand what it is.
250 00:26:37.225 ⇒ 00:26:37.840 Amber Lin: True.
251 00:26:37.840 ⇒ 00:26:39.529 Miguel de Veyra: I need to get it out of his mouth.
252 00:26:40.020 ⇒ 00:26:42.820 Amber Lin: I know Ryan also has a cat.
253 00:26:43.600 ⇒ 00:26:47.279 Uttam Kumaran: My dog, yeah. Been just. He just passed out during that.
254 00:26:47.280 ⇒ 00:26:48.110 Miguel de Veyra: You remove the button.
255 00:26:55.400 ⇒ 00:27:02.100 Annie Yu: Okay, let’s move on to the weekly update from exact.
256 00:27:03.110 ⇒ 00:27:07.558 Uttam Kumaran: Yeah, do you want to go to the next slide? So I think we’ll run through this.
257 00:27:08.480 ⇒ 00:27:16.259 Uttam Kumaran: yeah. So we’re I feel like for for the clients that are active. I think we’re we’re in pretty good shape.
258 00:27:17.150 ⇒ 00:27:19.719 Uttam Kumaran: I think I don’t have much
259 00:27:19.970 ⇒ 00:27:34.109 Uttam Kumaran: particular. It’s a comment here. I saw really good things about Eden this week. So all of the active clients I feel pretty good on, I would say. Pool parts is continues to be the one I think we’re sort of like on and off on.
260 00:27:34.280 ⇒ 00:27:36.229 Uttam Kumaran: I mean, frankly, I think,
261 00:27:37.140 ⇒ 00:27:50.840 Uttam Kumaran: one of the things that we will learn as a business is not. All clients are good clients. Similarly like not all revenue is good revenue. So some clients where they sort of change requirements. Really often. They don’t know exactly what they want.
262 00:27:51.228 ⇒ 00:28:09.640 Uttam Kumaran: Maybe they’re not a fit for us, or maybe we have to find a way to work with them. That’s that’s less stressful on our end. So I think we’re gonna make some decisions on how best to work with pool parts. We’re doing some AI work for them, you know. But ultimately, look, we want to deliver solutions that work and that go the distance, and if clients don’t want to plan
263 00:28:09.850 ⇒ 00:28:10.790 Uttam Kumaran: ahead.
264 00:28:10.910 ⇒ 00:28:31.239 Uttam Kumaran: And for us, planning ahead means we get work done accurately. Then there’s a fundamental mismatch in in how we do engineering work. I’m not saying that they can. They may be able to get the same results while planning ahead. But for us, planning and requirements, gathering is a requirement for us to ensure that our solution is accurate.
265 00:28:31.370 ⇒ 00:28:34.150 Uttam Kumaran: And so we don’t compromise on that right? So
266 00:28:34.760 ⇒ 00:28:48.333 Uttam Kumaran: you know, we’re as we’re growing. We’re doing a better job of getting clients that align with that and you know, as clients stick with us for a long time. We hope that they sort of continue to grow with us. But we’ll sort of figure things out there.
267 00:28:49.120 ⇒ 00:28:52.339 Uttam Kumaran: any notes from anyone on like any active clients
268 00:28:56.990 ⇒ 00:28:58.539 Uttam Kumaran: cool. We can go to the next slide.
269 00:29:00.880 ⇒ 00:29:07.749 Uttam Kumaran: Yeah, this is a little bit of a denser slide, you know. I kind of. I’m working on like sort of a monthly Update deck.
270 00:29:08.387 ⇒ 00:29:16.702 Uttam Kumaran: That we’ll we’ll start to do. I think this is helpful just to get a sense of like good, bad, and money coming in the door.
271 00:29:17.160 ⇒ 00:29:22.423 Uttam Kumaran: So yeah, I feel like, probably the biggest call outs on this slide are,
272 00:29:23.460 ⇒ 00:29:29.447 Uttam Kumaran: you know, at this point we’ve it looks like we’re gonna churn out of Javi coffee, at least in the short term.
273 00:29:29.920 ⇒ 00:29:33.129 Uttam Kumaran: I I think there is a probably a lot of reasons
274 00:29:33.280 ⇒ 00:29:40.669 Uttam Kumaran: for that. Some in our control, some not in our control, either, I would say, the biggest takeaway for us.
275 00:29:41.234 ⇒ 00:30:00.146 Uttam Kumaran: You know, in in reflecting on on this was we didn’t move 2 things, one this was one of the clients when we were transitioning from like a lot of external contractors into having this team. That sort of got the brunt of that like transition pain.
276 00:30:00.730 ⇒ 00:30:08.679 Uttam Kumaran: which is, that’s on us. The second piece is, I don’t think we got 2 pieces we didn’t get. We didn’t get moved to the insights piece fast enough.
277 00:30:08.820 ⇒ 00:30:12.519 Uttam Kumaran: meaning, I think, we were doing a lot of insights. Then we spent a lot of time on modeling.
278 00:30:12.730 ⇒ 00:30:30.149 Uttam Kumaran: We had a stakeholder in Aman, that, you know, although it was very interested in the data, had no internal, he had no like cloud internally meaning like he wasn’t making decisions. And so we, I think we failed to identify the right people to support early on
279 00:30:30.670 ⇒ 00:30:34.960 Uttam Kumaran: and then, 3, rd yeah, I just think we we sort of
280 00:30:36.130 ⇒ 00:30:42.470 Uttam Kumaran: we’re moving. We were moving towards the insights piece towards the end. But I just think too little too late.
281 00:30:42.870 ⇒ 00:30:50.191 Uttam Kumaran: All of these learnings, though I don’t think there were anything that we’re we’ve not improved on for other clients. So
282 00:30:50.980 ⇒ 00:31:04.239 Uttam Kumaran: I don’t know. I feel more stoic in that like, look, it’s lost money and that’s that’s unfortunate. There’s still good chance to come back to us. But we we’re taking a lot of those learnings and adjusting how we’re interacting with all of the other clients.
283 00:31:04.470 ⇒ 00:31:15.470 Uttam Kumaran: So you know some things in life like this, you learn by experience or in the hard way. Thankfully. I think the experience for all of our clients are going to get better based on what we learn from them. So
284 00:31:17.050 ⇒ 00:31:19.021 Uttam Kumaran: that’s my feedback. There, I think.
285 00:31:19.600 ⇒ 00:31:37.070 Uttam Kumaran: terms of customers in the pipeline. We have a couple couple of clients that are about to sign verbal yeses, or sort of at the finish line. And then we’ve signed a couple of new partnerships. So so that’s kind of the high level there. Accomplishments.
286 00:31:37.170 ⇒ 00:31:58.030 Uttam Kumaran: Yeah, over the last, like 2, 3 weeks. Just went on like a binge on cutting expenses. On the software side. So you know, we we’re I. I think 1,500 is probably on the low end. Actually. You’ll be surprised at how expensive some stuff is. But this is like a housekeeping item that we just have to do over and over again.
287 00:31:58.412 ⇒ 00:32:05.229 Uttam Kumaran: So I’m really thankful that we were able to do that and get some money back. We’ve had some good awareness growth on linkedin.
288 00:32:05.754 ⇒ 00:32:14.510 Uttam Kumaran: Like we’re we’re doing a lot on there. I think we’re close to Ryan’s when when Ryan joined us on the marketing team.
289 00:32:14.760 ⇒ 00:32:22.088 Uttam Kumaran: You know he was very adamant that we try to post every day, and I think finally, we’re at the point where we’re we’re we’re doing that
290 00:32:22.730 ⇒ 00:32:35.950 Uttam Kumaran: And so that is a level of consistency that the algorithm is rewarding us for. And so we’re we’re starting to get more awareness there. Our follower growth rate, I don’t think, is as fast as it needs to be, but it is
291 00:32:36.150 ⇒ 00:32:41.609 Uttam Kumaran: the speed is growing meaning. The velocity is growing. We’re all data people. It just means like.
292 00:32:41.830 ⇒ 00:33:01.870 Uttam Kumaran: the curve is not like linear meaning. There is some acceleration. So that’s great. Like. I want to see us hit the 5,000 follower milestone on my account, and then subsequent accounts as fast as we can. You know we’re we’re hitting about 50 to 60 people a week.
293 00:33:02.323 ⇒ 00:33:19.190 Uttam Kumaran: And so you could do the math like, it’ll be a while till we hit that. So this needs to get towards 100 200 300 a week. For us to get there faster. We are manufacturing some of these by doing our own outbound connections which we’re getting a really great connection, except rate, like 20 to 25%
294 00:33:19.270 ⇒ 00:33:33.980 Uttam Kumaran: acceptance rate is is solid meaning. My profile looks good. The company profile looks like people trust us. And then 20% reply rate, which is really really good. So I feel good. There, our copy is good. We’re gonna it should just get better.
295 00:33:34.790 ⇒ 00:34:01.909 Uttam Kumaran: yeah, I think, one of the probably the the other big line item there, which is the 4th thing is finally, this week, I felt really that, like, I was able to spend 30 to 40% of my time on sales, whether that’s just like the couple of hours in the morning where I can call people or or send out, you know, emails or follow up. And then I I almost I think I went to one event almost one or 2 events almost every day this week.
296 00:34:02.393 ⇒ 00:34:25.010 Uttam Kumaran: Which does 2 things at a minimum. We get a Linkedin post out of it. And are the Linkedin posts that we tag other people in, or go to events perform way better than anything that we post on ourselves. And so I. My job is to build up a backlog of those for our marketing team to then take advantage of. We connected our 1st
297 00:34:25.199 ⇒ 00:34:34.719 Uttam Kumaran: like interview series as part of something that that we’re working on on the marketing team. Taking advantage again of having friends of Brain forge that we can interview
298 00:34:35.309 ⇒ 00:34:40.940 Uttam Kumaran: get timely and timeless content for socials that went really, really well.
299 00:34:42.060 ⇒ 00:35:03.940 Uttam Kumaran: And so I’m I’m a lot happier, I think, where we’re seeing this in the data. And I didn’t have. I didn’t sort of show some of these graphs, but I did some work in notion to to look at. The amount of leads that are getting into different lead stages over time and especially in the last month and a half. We’re seeing the charts all accelerate. Meaning
300 00:35:04.320 ⇒ 00:35:10.730 Uttam Kumaran: more leads are getting into the pipeline, and more of them are moving towards the end.
301 00:35:12.440 ⇒ 00:35:39.709 Uttam Kumaran: the numbers aren’t like these aren’t like thousands and thousands, but they are meaningful. And so for us, the number one thing is not only to stay linear, but to accelerate right. We don’t want to say cool. We’re just growing at this amount, and and we’re happy. How do we get the chart to continue to curve right like, how do we actually get acceleration? And so this is sort of where I go on the right side, which is like one. I don’t think we’re very data driven as a company. I think this is like.
302 00:35:39.820 ⇒ 00:35:51.479 Uttam Kumaran: I don’t think we’re like 6 months away. I think we’re probably one hardcore week from having like real data on how our business is growing. We’re data. People like this is not gonna be that bad but like
303 00:35:51.760 ⇒ 00:36:01.450 Uttam Kumaran: we’re we’re we should push there. Second is, sales is extremely manual. I mean, I don’t think we’re gonna get the benefit of more than 30 to 40% of time on sales every week.
304 00:36:02.044 ⇒ 00:36:09.429 Uttam Kumaran: So now those time need to be more efficient, need to be made more efficient, which is going to be done through a lot of automation
305 00:36:09.600 ⇒ 00:36:13.270 Uttam Kumaran: that hopefully, Mustafa will be working with us on
306 00:36:14.184 ⇒ 00:36:18.429 Uttam Kumaran: yeah, finance team is still sort of getting on boarded.
307 00:36:20.060 ⇒ 00:36:31.599 Uttam Kumaran: Just like need to completely hand that off. The partner work has been going well, and that we’ve been able to sign a lot of partners. I think we’re now need to move towards actually like pushing them to get us leads
308 00:36:32.186 ⇒ 00:36:58.569 Uttam Kumaran: and then the internal AI agents which I think we’ll share a little bit about, you know. I think it’s helpful for us to be honest about whether we’re seeing Roi from that internal work. And I don’t think we’re we’ve yet to see true, demonstrated Roi from the internal automations. Hopefully, this, like lights a fire under the AI team, meaning like this needs to basically happen for us to as a crew know that this is a good use of our time.
309 00:36:58.770 ⇒ 00:37:08.274 Uttam Kumaran: But I think, as as everyone here knows the vision and how this is going to impact everybody is there. So it’s purely on execution. Now.
310 00:37:09.030 ⇒ 00:37:13.979 Uttam Kumaran: I I just want to reiterate that like we. This is not.
311 00:37:14.180 ⇒ 00:37:25.380 Uttam Kumaran: This isn’t just doing AI, just because it’s the shiny new thing. These need to save us time and improve customer experience for us to be able to continue to fund these activities.
312 00:37:26.030 ⇒ 00:37:33.500 Uttam Kumaran: so we’re close. I think a lot has changed in the last 2 weeks in particular, but just wanna like, call that out
313 00:37:35.340 ⇒ 00:37:39.639 Uttam Kumaran: any thoughts on anything here?
314 00:37:39.760 ⇒ 00:37:42.920 Uttam Kumaran: Glad I could like, condense everything into one side.
315 00:37:49.140 ⇒ 00:37:50.020 Uttam Kumaran: Okay.
316 00:37:53.370 ⇒ 00:37:54.190 Uttam Kumaran: cool.
317 00:38:02.310 ⇒ 00:38:03.160 Annie Yu: So we.
318 00:38:03.160 ⇒ 00:38:04.399 Uttam Kumaran: Oh, wait, is this?
319 00:38:05.060 ⇒ 00:38:06.049 Uttam Kumaran: Yeah, go ahead.
320 00:38:08.920 ⇒ 00:38:15.049 Annie Yu: Talk through some data platform. And I think this was my bad actually ask them a lot to demo. But I think both of us
321 00:38:15.870 ⇒ 00:38:23.069 Annie Yu: out today. But, ohish! If you want to share something, if you you’re up for it.
322 00:38:24.574 ⇒ 00:38:27.809 Awaish Kumar: I can share the updates that we have done so far.
323 00:38:28.030 ⇒ 00:38:30.040 Awaish Kumar: And the data platform team
324 00:38:30.670 ⇒ 00:38:40.140 Awaish Kumar: so we have been targeting 2 streams. Mainly one is monitoring and observability and other one is building the documentation
325 00:38:42.309 ⇒ 00:38:56.579 Awaish Kumar: we have made progress on both the funds on the documentation side, like Kyle, any, and like the has been working on building the templates along with
326 00:38:58.070 ⇒ 00:39:04.709 Awaish Kumar: actually writing some docs. So we have shipped like the motion knowledge base. This talks where we have the
327 00:39:04.880 ⇒ 00:39:12.049 Awaish Kumar: Faqs. Or more business related context about the clients. They are ready for almost all the clients.
328 00:39:12.220 ⇒ 00:39:18.130 Awaish Kumar: And in the next week we are targeting to ship the spreadsheets as well
329 00:39:18.260 ⇒ 00:39:22.030 Awaish Kumar: for all on the right. Mo- mostly all the clients
330 00:39:22.150 ⇒ 00:39:32.129 Awaish Kumar: so like in in the next week. We will be kind of mostly done with documentation, which we can basically use for our AI agents as well.
331 00:39:32.850 ⇒ 00:39:35.969 Awaish Kumar: On the observability side. We we are like.
332 00:39:36.551 ⇒ 00:39:38.719 Awaish Kumar: we are. We are trying to.
333 00:39:40.770 ⇒ 00:39:48.209 Awaish Kumar: we have set up the Meta plan for 18, and we are trying to create a Poc for for one client and then scale it for others.
334 00:39:48.700 ⇒ 00:40:09.989 Awaish Kumar: So we have been facing few issues with like Pr comments. And, like the the Meta plane is not like sending impact report on Pr itself, but otherwise it’s set up. Monitors are set up. And now we are also have to like defining, maybe working on, define the policy of how we are going to act on those
335 00:40:10.928 ⇒ 00:40:14.370 Awaish Kumar: monitoring events. So, for example.
336 00:40:14.740 ⇒ 00:40:18.099 Awaish Kumar: we are going to get like, how we are going to get the alerts.
337 00:40:18.220 ⇒ 00:40:26.539 Awaish Kumar: what? What specific data sets for each client we are going to monitor. And then how are we going to process.
338 00:40:27.010 ⇒ 00:40:31.770 Awaish Kumar: If there is any alert like, who’s who will be the 1st one to resolve them.
339 00:40:32.500 ⇒ 00:40:39.629 Awaish Kumar: So this is after, like, like, we are targeting that in this week, and then the next week, maybe we have something to roll out for
340 00:40:40.100 ⇒ 00:40:41.810 Awaish Kumar: for other clients as well.
341 00:40:46.750 ⇒ 00:40:49.880 Uttam Kumaran: Well, I’m really glad I I want us to
342 00:40:50.800 ⇒ 00:41:08.020 Uttam Kumaran: quickly move from doing it for one client, and then having his client, and then sales will then take advantage of that and start showing it to people. So we’re gonna put that that new spreadsheet in front of folks. We’re gonna put all this work in front of prospects. So this is where it’s like none of the work we’re doing for clients is gonna go on unmarketed or unsold
343 00:41:08.512 ⇒ 00:41:11.839 Uttam Kumaran: so as sharp as their stuff can be. Internally, we’re gonna
344 00:41:11.970 ⇒ 00:41:15.030 Uttam Kumaran: do a lot more showing. So
345 00:41:15.240 ⇒ 00:41:18.449 Uttam Kumaran: I’m glad that you know this all moving forward. I wish.
346 00:41:22.590 ⇒ 00:41:23.590 Annie Yu: Awesome.
347 00:41:24.010 ⇒ 00:41:30.570 Annie Yu: And then next we have a also like a small update slash Demo
348 00:41:30.900 ⇒ 00:41:34.499 Annie Yu: from Madam or Luke. If wanna take over.
349 00:41:37.922 ⇒ 00:41:42.150 Luke Daque: Can you say? Oh, can you say it again? Is it the synthetic data.
350 00:41:42.370 ⇒ 00:41:43.330 Annie Yu: Yeah, yeah.
351 00:41:43.680 ⇒ 00:41:47.600 Annie Yu: And it can be a very scrappy. It’s nothing like super formal.
352 00:41:48.050 ⇒ 00:41:50.599 Luke Daque: Oh, yeah, sure. Let me.
353 00:41:52.280 ⇒ 00:41:55.270 Luke Daque: I guess I can share my screen so like
354 00:41:56.550 ⇒ 00:42:05.190 Luke Daque: everybody can see. Wait, let me open up the what we have.
355 00:42:13.990 ⇒ 00:42:18.739 Luke Daque: Okay, so let me share my screen.
356 00:42:22.350 ⇒ 00:42:25.599 Luke Daque: I try to make this quick. Can you see my screen.
357 00:42:26.600 ⇒ 00:42:27.110 Annie Yu: Yes.
358 00:42:27.400 ⇒ 00:42:28.190 Uttam Kumaran: Yes.
359 00:42:28.640 ⇒ 00:42:35.420 Luke Daque: So basically, for matter more, which is in a client of ours.
360 00:42:35.610 ⇒ 00:42:41.220 Luke Daque: One of the tasks that they wanted us to do was to create synthetic data.
361 00:42:42.076 ⇒ 00:42:44.560 Luke Daque: so that they can essentially
362 00:42:44.880 ⇒ 00:42:47.600 Luke Daque: test or see what the data would
363 00:42:47.750 ⇒ 00:42:56.200 Luke Daque: look like. Once they get the actual data from Microsoft. And what’s the other one? The the Sf.
364 00:42:56.570 ⇒ 00:42:57.680 Annie Yu: Success factors.
365 00:42:57.680 ⇒ 00:43:08.580 Luke Daque: Success factors. Yeah. So they they don’t have their data connected to bigquery yet. They’re using bigquery as the the warehouse. So they asked us to create
366 00:43:10.460 ⇒ 00:43:25.840 Luke Daque: synthetic data for it. Basically. So what we did, Annie and I had a pairing session for this one. So we utilize cursors AI to essentially do it for us.
367 00:43:27.320 ⇒ 00:43:30.685 Luke Daque: So what we did was just open up this
368 00:43:31.790 ⇒ 00:43:36.440 Luke Daque: just cursor chat here at the right, and then basically,
369 00:43:39.460 ⇒ 00:43:46.249 Luke Daque: well, 1st of all, we needed the context, right? So like what the data should look like. And they did provide us with
370 00:43:46.800 ⇒ 00:43:51.759 Luke Daque: my Google sheet like this would be one of the Api endpoints for
371 00:43:52.520 ⇒ 00:43:55.589 Luke Daque: Microsoft, like missed messages, for example. And
372 00:43:56.020 ⇒ 00:44:02.499 Luke Daque: so we just basically enrich this. This is Annie who created this by the way? So these would be the fields for
373 00:44:02.740 ⇒ 00:44:09.460 Luke Daque: this source table and the data types and like example values
374 00:44:09.650 ⇒ 00:44:16.949 Luke Daque: of this. And there’s a couple of like assumptions here, like nuances that we want to include in the
375 00:44:18.680 ⇒ 00:44:23.789 Luke Daque: synthetic data like the idea of the conversion should belong to
376 00:44:25.026 ⇒ 00:44:33.219 Luke Daque: could can be shared across multiple ids and and stuff like that. Right? So basically, we just had
377 00:44:33.680 ⇒ 00:44:34.770 Luke Daque: cursor.
378 00:44:35.650 ⇒ 00:44:40.220 Luke Daque: So basically, we we made a screenshot of this
379 00:44:40.630 ⇒ 00:44:43.830 Luke Daque: and put this as an input to cursor.
380 00:44:45.070 ⇒ 00:44:48.679 Luke Daque: And we asked it to create a python script for us.
381 00:44:49.630 ⇒ 00:45:01.769 Luke Daque: So create a python script that will generate synthetic data boost. And the requirements something like that.
382 00:45:05.170 ⇒ 00:45:14.070 Luke Daque: Yeah, so like, this could be improved like the prompt, essentially.
383 00:45:14.310 ⇒ 00:45:20.009 Luke Daque: But this is like how we started it, because we don’t really have the best knowledge on what
384 00:45:20.160 ⇒ 00:45:24.729 Luke Daque: prompts the use. But like, as you can see here, it’s already doing.
385 00:45:25.340 ⇒ 00:45:27.270 Luke Daque: it’s already creating a python script.
386 00:45:27.990 ⇒ 00:45:30.329 Luke Daque: like what the requirements it needs
387 00:45:32.730 ⇒ 00:45:38.249 Luke Daque: like faker, for example, for the synthetic data, pandas for like data frames and stuff.
388 00:45:39.206 ⇒ 00:45:43.349 Luke Daque: And what’s cool about cursor is, once you are connected to
389 00:45:43.680 ⇒ 00:45:50.569 Luke Daque: the repository, it will, if you, for example, if you apply this, or like accept this
390 00:45:53.691 ⇒ 00:46:04.159 Luke Daque: proposal that he had it will automatically create the file. The requirements file, as you can see here. Well, I already have it. So that’s why, like, it’s showing
391 00:46:04.947 ⇒ 00:46:06.739 Luke Daque: different diffs here.
392 00:46:06.860 ⇒ 00:46:09.630 Luke Daque: But yeah, we can essentially accept this.
393 00:46:09.840 ⇒ 00:46:19.629 Luke Daque: and it already creates the requirements text for us. And if we accept this python script, for example, it will already essentially create
394 00:46:19.970 ⇒ 00:46:25.650 Luke Daque: the generate email data dot pi.
395 00:46:26.450 ⇒ 00:46:33.950 Luke Daque: well, it, it added it here. Basically. So yeah, this is the Python script that it created. As you can see, it’s like already
396 00:46:34.680 ⇒ 00:46:39.380 Luke Daque: getting all the id field steps from the screenshot.
397 00:46:39.810 ⇒ 00:46:44.740 Luke Daque: So it has id conversion. Id index everything.
398 00:46:46.270 ⇒ 00:46:53.069 Luke Daque: Yeah, essentially, we can scrutinize the python script. If we know python, we can like double check.
399 00:46:53.330 ⇒ 00:46:54.840 Luke Daque: And yeah.
400 00:46:55.630 ⇒ 00:47:01.600 Luke Daque: once this is done, it also even shows you like what you need to do to install the requirements and like to run the script
401 00:47:01.800 ⇒ 00:47:07.400 Luke Daque: in the terminal. We can always, we can just do like install requirements first.st
402 00:47:08.020 ⇒ 00:47:11.670 Luke Daque: Or, yeah, you can do it from here. Actually, you can just click this, apply.
403 00:47:11.930 ⇒ 00:47:14.829 Luke Daque: It’s gonna essentially run that.
404 00:47:18.660 ⇒ 00:47:28.719 Luke Daque: And then here apply. Essentially, it’s gonna run via the the script, and it should, once it’s done.
405 00:47:30.260 ⇒ 00:47:39.049 Luke Daque: it’s going to create a Csv file. Well, we should have added this as like the part of the prompt to output a Csv file that looks like they’ve already did that.
406 00:47:39.190 ⇒ 00:47:44.280 Luke Daque: So it’s going to create this synthetic email.
407 00:47:46.860 ⇒ 00:47:54.539 Luke Daque: Yeah. So essentially, it’s gonna be. It’s gonna look something like this. Once it’s done. So this is a Csv file. We can open this in
408 00:47:55.060 ⇒ 00:48:01.380 Luke Daque: excel, for example, to to show what it looks like.
409 00:48:05.370 ⇒ 00:48:10.880 Uttam Kumaran: And Lou, can you also, when once you share this, can you talk about how long this would have taken you without using AI to do.
410 00:48:10.880 ⇒ 00:48:11.750 Luke Daque: This year.
411 00:48:11.750 ⇒ 00:48:15.300 Uttam Kumaran: Because we cause we did it. We we even tried it last year, right? Remember.
412 00:48:15.950 ⇒ 00:48:20.009 Luke Daque: Yeah, it would take us like, I don’t know. It’s gonna take. Several
413 00:48:20.290 ⇒ 00:48:23.429 Luke Daque: hours were just for one table, and like
414 00:48:23.970 ⇒ 00:48:27.111 Luke Daque: including even the assumptions that we have.
415 00:48:28.080 ⇒ 00:48:32.210 Luke Daque: it’s gonna be even more complicated, because, like we, we needed to
416 00:48:32.780 ⇒ 00:48:36.310 Luke Daque: make sure that the Ids here would be the same
417 00:48:36.450 ⇒ 00:48:45.110 Luke Daque: ids for the other tables that way we can join them together. And like all of these nuances, these would be very
418 00:48:45.300 ⇒ 00:48:52.350 Luke Daque: complicated to like, think through and create the python script yourself. And this, like, you just had to click
419 00:48:53.240 ⇒ 00:48:57.180 Luke Daque: a couple of buttons here, and it already generated that. And
420 00:48:57.380 ⇒ 00:48:59.993 Luke Daque: we we still did a couple of like
421 00:49:00.991 ⇒ 00:49:14.720 Luke Daque: revisions, right? Because, like initially, we did not, we forgot to add the assumptions and like it, just regenerated like unique ids, random ids and stuff like that, and like the tables don’t have the same ids.
422 00:49:14.840 ⇒ 00:49:15.760 Luke Daque: So
423 00:49:15.950 ⇒ 00:49:22.390 Luke Daque: yeah, we did a couple of iterations there, but that only took us like, I don’t know, like a few hours total
424 00:49:22.680 ⇒ 00:49:29.220 Luke Daque: compared to like if we did this manually, that would have taken us like weeks, months, even probably. Like.
425 00:49:30.340 ⇒ 00:49:34.260 Luke Daque: yeah, and like doing a lot of research, what? What we can use to?
426 00:49:36.770 ⇒ 00:49:44.539 Luke Daque: yeah, to generate these synthetic data. And like the python script here is very well made. It already, even has like.
427 00:49:45.990 ⇒ 00:49:49.149 Luke Daque: comments like, what this does and stuff like that.
428 00:49:51.520 ⇒ 00:49:56.139 Luke Daque: So yeah, that’s what essentially happened. Basically, yeah.
429 00:49:56.780 ⇒ 00:50:11.490 Annie Yu: Thank you so much, Luke. And then but I do want to add, I think it’s amazing how this makes it so quick. But also we realize, at least in my view, I don’t know about you, Luke. I think this is like AI is obviously gonna be evolving. But I feel like it’s not
430 00:50:11.740 ⇒ 00:50:21.450 Annie Yu: smart enough yet. So like looks say, like we had to go back to see. Okay, if let’s say, like an email thread, if one person’s replying to
431 00:50:21.790 ⇒ 00:50:28.080 Annie Yu: like the 1st email in this thread. The you mean, like the recipient to be. That
432 00:50:28.200 ⇒ 00:50:55.869 Annie Yu: should be that like 1st person who sent out their email like we had to like cross check a lot of that logic. And also like, if it’s on an online meeting, there should be like a meeting provider like zoom teams, but it just doesn’t know those logics. So we had to like fine tune it on like a very granular granular level. So, I think. But that’s where, like our efforts were different.
433 00:50:57.600 ⇒ 00:51:06.590 Annie Yu: then, like, if we didn’t have AI, we had to not only double check those logic, but also like writing those scripts, so.
434 00:51:06.590 ⇒ 00:51:08.149 Luke Daque: The logic itself. Right?
435 00:51:08.150 ⇒ 00:51:09.220 Luke Daque: Yes, script.
436 00:51:09.772 ⇒ 00:51:14.099 Luke Daque: But yeah. So the challenge, the only challenge here, I guess, would be
437 00:51:14.630 ⇒ 00:51:23.130 Luke Daque: the the prompt itself, where you can give it as much context as you want like, especially with the nuances like that, like
438 00:51:23.750 ⇒ 00:51:31.660 Luke Daque: the from, should be the same email with the of the recipient and stuff like that. So, yeah.
439 00:51:35.520 ⇒ 00:51:38.219 Annie Yu: Hey? That’s amazing. Thank you, Luke.
440 00:51:38.510 ⇒ 00:51:42.885 Annie Yu: I’m gonna seal the screen back, and then we can power through.
441 00:51:43.360 ⇒ 00:51:47.340 Annie Yu: Let me see, where’s my slide?
442 00:51:50.230 ⇒ 00:51:56.709 Hannah Wang: It’s in another window, so I think you could just like Swipe with 3 fingers to the right.
443 00:51:57.210 ⇒ 00:51:58.562 Hannah Wang: If you have that.
444 00:51:59.500 ⇒ 00:52:00.880 Annie Yu: I think it was.
445 00:52:00.880 ⇒ 00:52:01.290 Hannah Wang: Top.
446 00:52:01.290 ⇒ 00:52:02.179 Annie Yu: Oh, she’s doing fine.
447 00:52:02.180 ⇒ 00:52:03.309 Hannah Wang: To go from.
448 00:52:04.630 ⇒ 00:52:05.440 Annie Yu: Okay.
449 00:52:09.960 ⇒ 00:52:11.500 Hannah Wang: The tech support.
450 00:52:12.710 ⇒ 00:52:14.250 Hannah Wang: It support.
451 00:52:14.250 ⇒ 00:52:16.309 Annie Yu: Okay, cool. Good.
452 00:52:16.490 ⇒ 00:52:18.529 Annie Yu: Is this it? Okay, cool.
453 00:52:18.710 ⇒ 00:52:20.040 Annie Yu: All right.
454 00:52:21.725 ⇒ 00:52:26.629 Annie Yu: And then utam. Do you have any updates on stretch possibilities?
455 00:52:28.759 ⇒ 00:52:39.929 Uttam Kumaran: Yes. So I just want to continue to highlight this every week. We don’t have to spend too much time here, but the only addition here is and is going to be helping out on a lot of marketing analytics.
456 00:52:40.431 ⇒ 00:52:56.400 Uttam Kumaran: Which is really great. So I’m very excited to work with her on sort of taking some of this stuff up. We continue to have basically anyone who wants to help on Linkedin, speaking at conferences, basically anything that’s like.
457 00:52:56.912 ⇒ 00:53:03.889 Uttam Kumaran: client perspective client facing, we, we could use a lot of help on in case anyone is interested about that.
458 00:53:06.040 ⇒ 00:53:10.060 Uttam Kumaran: And yeah, I I don’t don’t have much else to say here. So.
459 00:53:11.390 ⇒ 00:53:12.220 Annie Yu: Nice.
460 00:53:12.920 ⇒ 00:53:18.560 Annie Yu: Okay, let’s move on next. Any shout outs this week.
461 00:53:20.168 ⇒ 00:53:23.010 Awaish Kumar: Yeah, like, I will go.
462 00:53:23.530 ⇒ 00:53:28.542 Awaish Kumar: 1st of all, I would like to shout out data platform team like,
463 00:53:29.640 ⇒ 00:53:39.125 Awaish Kumar: we have made good progress. So I like really good work from Kyle and any other side that
464 00:53:40.090 ⇒ 00:53:44.700 Awaish Kumar: icons on the poc on.
465 00:53:44.860 ⇒ 00:53:49.230 Awaish Kumar: Oh, no, we are somebody’s tool apart from that.
466 00:53:49.741 ⇒ 00:54:01.189 Awaish Kumar: Luke has been done has done a great job. Meta plane. So yeah, good good work everybody. And apart from that, I would love to shout out to the
467 00:54:01.480 ⇒ 00:54:03.630 Awaish Kumar: the I team as well. So on
468 00:54:04.283 ⇒ 00:54:11.550 Awaish Kumar: in terms of engagement and the collaboration. They’ve been really great anything I need from them like
469 00:54:11.810 ⇒ 00:54:15.830 Awaish Kumar: they’re available quickly accessible. So yeah.
470 00:54:16.861 ⇒ 00:54:21.430 Awaish Kumar: so yeah, good. Like shout out to Mick and Casey, as well.
471 00:54:26.370 ⇒ 00:54:29.090 Uttam Kumaran: Hannah, do we have any any of them? From the automation.
472 00:54:29.800 ⇒ 00:54:31.200 Caio Velasco: I have a chantel.
473 00:54:31.960 ⇒ 00:54:37.579 Uttam Kumaran: Oh, yeah, yeah, go ahead. Go ahead, dude. You actually glowing dude also, like.
474 00:54:37.960 ⇒ 00:54:39.110 Caio Velasco: It’s because of the weekend.
475 00:54:41.350 ⇒ 00:54:42.170 Annie Yu: You know bye.
476 00:54:42.170 ⇒ 00:54:43.450 Caio Velasco: For the year.
477 00:54:43.650 ⇒ 00:54:44.409 Annie Yu: It’s me and.
478 00:54:44.410 ⇒ 00:54:50.669 Uttam Kumaran: That’s the Europe glow. That’s the Europe glow dude. Because here we have all these pesticides and the food and stuff like that, like.
479 00:54:50.900 ⇒ 00:54:52.409 Uttam Kumaran: you guys don’t have that problem.
480 00:54:52.410 ⇒ 00:54:52.750 Caio Velasco: Then it.
481 00:54:56.090 ⇒ 00:54:58.256 Caio Velasco: I can assure it’s delight.
482 00:55:01.360 ⇒ 00:55:09.220 Caio Velasco: So okay, so 2 or 3 shout outs, first, st I wish, because I think
483 00:55:09.400 ⇒ 00:55:23.019 Caio Velasco: we needed help in the data platform to move things forward, that I think he has been helping us a lot and pushing and like being very pragmatic with what we have to do, and I really appreciate that. So that definitely like helpful
484 00:55:23.493 ⇒ 00:55:36.610 Caio Velasco: then also to you, because I think in the beginning you were not like, very maybe this is not gonna work. People won’t like it. And I think we are making progress. I think I mean you. You brought it up. And I think that’s important.
485 00:55:37.160 ⇒ 00:55:38.910 Caio Velasco: And
486 00:55:40.450 ⇒ 00:55:47.680 Caio Velasco: what else? And then, yeah, to the AI team as well. It’s been helpful to see like that. They were interacting with us and helping.
487 00:55:47.850 ⇒ 00:55:56.239 Caio Velasco: I understand, like how how the bot works or the agent works, or like the guidelines. I think we made good pro progress. It’s been quite helpful.
488 00:56:00.320 ⇒ 00:56:01.160 Caio Velasco: Okay.
489 00:56:04.352 ⇒ 00:56:11.430 Hannah Wang: From the automation. It’s just the ones that you put Tom for Demo A and Casey.
490 00:56:13.290 ⇒ 00:56:16.640 Uttam Kumaran: Do you wanna share those? Or alright? I don’t have them in front of me, but.
491 00:56:16.850 ⇒ 00:56:23.169 Hannah Wang: Oh, yeah, reason for shouting out Demo. A was helping out on urban stems. Renewal calls.
492 00:56:23.430 ⇒ 00:56:30.279 Hannah Wang: and then for Casey for rocking. Otr. I don’t know what that means, but good job.
493 00:56:30.563 ⇒ 00:56:33.116 Uttam Kumaran: Off. The record is one of our clients. But
494 00:56:34.220 ⇒ 00:56:35.444 Uttam Kumaran: And then do you mind
495 00:56:35.830 ⇒ 00:56:40.132 Uttam Kumaran: Or maybe, Annie, do you mind just sharing with everybody how to do the
496 00:56:40.740 ⇒ 00:56:42.919 Uttam Kumaran: do! The shout outs in slack.
497 00:56:43.210 ⇒ 00:56:46.549 Uttam Kumaran: and if you don’t know how to do it, we could all do it together right now.
498 00:56:46.550 ⇒ 00:56:48.960 Annie Yu: No, but we. We can do that.
499 00:56:49.180 ⇒ 00:56:49.610 Uttam Kumaran: Okay.
500 00:56:50.290 ⇒ 00:56:52.410 Uttam Kumaran: It’s in the team channel, right? Hannah.
501 00:56:52.620 ⇒ 00:56:53.250 Hannah Wang: Yeah.
502 00:56:53.870 ⇒ 00:56:54.480 Uttam Kumaran: Okay.
503 00:56:54.890 ⇒ 00:56:57.390 Annie Yu: Is it somewhere here? Is it this one.
504 00:56:57.970 ⇒ 00:57:04.549 Hannah Wang: So go to workflows at the top. Yeah. So there’s 2. There’s a shout out one, and then there’s a question one.
505 00:57:04.820 ⇒ 00:57:07.059 Hannah Wang: So you just submit it.
506 00:57:08.850 ⇒ 00:57:14.490 Uttam Kumaran: Shout out to Hannah for becoming a slack app developer we know to Casey and Casey and Hannah.
507 00:57:15.560 ⇒ 00:57:20.109 Annie Yu: Do I add? Wait, so I have to know their or.
508 00:57:20.390 ⇒ 00:57:20.720 Uttam Kumaran: You do.
509 00:57:20.720 ⇒ 00:57:21.110 Hannah Wang: 30.
510 00:57:21.110 ⇒ 00:57:24.458 Uttam Kumaran: You know, you have to know their name. Yeah.
511 00:57:25.410 ⇒ 00:57:26.569 Uttam Kumaran: Oh, yeah.
512 00:57:27.930 ⇒ 00:57:29.729 Annie Yu: And setting up.
513 00:57:29.730 ⇒ 00:57:30.575 Uttam Kumaran: Me!
514 00:57:31.420 ⇒ 00:57:40.650 Annie Yu: Setting up. What’s this like? Workflow workflows in this channel?
515 00:57:44.080 ⇒ 00:57:49.509 Hannah Wang: Yeah. So it goes to like a Google sheet that I can share with everyone but.
516 00:57:49.510 ⇒ 00:57:50.190 Annie Yu: Oh, okay.
517 00:57:50.190 ⇒ 00:57:55.609 Hannah Wang: You manually do that, I think in the message. If you type, slash, shout out.
518 00:57:55.940 ⇒ 00:57:57.309 Hannah Wang: You just like, type it?
519 00:57:58.953 ⇒ 00:58:04.960 Hannah Wang: Yeah, it should show up there, so it’s easier. I’ll have to click around.
520 00:58:07.760 ⇒ 00:58:08.920 Annie Yu: Nice.
521 00:58:11.300 ⇒ 00:58:14.728 Annie Yu: Sorry I’m doing this live time.
522 00:58:15.300 ⇒ 00:58:16.179 Uttam Kumaran: It does not.
523 00:58:16.720 ⇒ 00:58:18.100 Awaish Kumar: Posted in slack channel.
524 00:58:20.090 ⇒ 00:58:21.069 Annie Yu: What’s that?
525 00:58:21.300 ⇒ 00:58:22.809 Uttam Kumaran: It. No, it goes to Hannah.
526 00:58:22.990 ⇒ 00:58:24.118 Awaish Kumar: Directly goes to the.
527 00:58:24.400 ⇒ 00:58:26.660 Hannah Wang: Oh, it goes to like a good question.
528 00:58:27.340 ⇒ 00:58:28.050 Uttam Kumaran: Yeah.
529 00:58:28.050 ⇒ 00:58:28.680 Hannah Wang: Yeah.
530 00:58:30.610 ⇒ 00:58:39.359 Hannah Wang: So the goal is that whoever’s facilitating it, they can pull up that sheet. And then, just like, read out the shout outs.
531 00:58:39.993 ⇒ 00:58:42.009 Hannah Wang: I created like a
532 00:58:42.460 ⇒ 00:58:59.419 Hannah Wang: notion, Doc, for how to facilitate the meetings after I realized I was just copy pasting messages. So I’ll share that out. Once I reach out to you if you want to run the meeting, so watch out for that in the future. But I created documentation. So it should be easier.
533 00:59:01.870 ⇒ 00:59:02.680 Annie Yu: Cool.
534 00:59:03.540 ⇒ 00:59:06.910 Annie Yu: Okay, is that it? I think that’s about it.
535 00:59:08.280 ⇒ 00:59:19.819 Annie Yu: That’s pretty much it. And and I think I guess we still have 2 min. I do. Wanna shout out to Awaii and the Kyle. This is also, for, like the top platform, I think.
536 00:59:20.100 ⇒ 00:59:49.929 Annie Yu: echoing what Kyle said, like always just the one like holding us accountable. But I think he’s also like giving really reasonable timeline, considering everyone’s workload so it’s pretty great. We are making good progress. And Kyle, like really a lot of the credits, go to him, I wanna say and he’s not like on every project. But he’s like getting like different, like business context, and also like the data like
537 00:59:50.230 ⇒ 00:59:56.400 Annie Yu: flow from, like all the projects that he’s not necessarily on. And he’s like.
538 00:59:56.540 ⇒ 01:00:04.890 Annie Yu: I know, he’s like, probably short on time, but he’s like still powering through. And so, thanks so much awaitian Kyle.
539 01:00:07.650 ⇒ 01:00:08.410 Caio Velasco: You’re welcome.
540 01:00:11.110 ⇒ 01:00:21.150 Uttam Kumaran: Cool guys awesome. I think we we spent 2 weeks not showing any marketing stuff just because I think we were showing marketing stuff every week. So next week I think we’ll do a good like.
541 01:00:21.440 ⇒ 01:00:23.719 Uttam Kumaran: we’ll have a lot of flashy stuff to show.
542 01:00:23.970 ⇒ 01:00:27.329 Uttam Kumaran: And maybe some Youtube videos. So
543 01:00:27.870 ⇒ 01:00:37.836 Uttam Kumaran: and we’re our shorts are getting watched. So I don’t know. I don’t know. People are listening to whatever dumb stuff I have to say. So.
544 01:00:38.220 ⇒ 01:00:40.510 Robert Tseng: That was me. I watched you. I watched you a thousand times.
545 01:00:41.111 ⇒ 01:00:45.448 Uttam Kumaran: Thank you. Yeah. Please just leave it before you go to sleep.
546 01:00:45.810 ⇒ 01:00:48.220 Robert Tseng: Yeah, I just had it like running overnight.
547 01:00:55.390 ⇒ 01:00:59.680 Uttam Kumaran: Okay, thank you. Everyone appreciate it. Have a great weekend.
548 01:01:00.555 ⇒ 01:01:01.400 Hannah Wang: Everyone and.
549 01:01:01.400 ⇒ 01:01:03.059 Caio Velasco: Thank you. Thank you. Branding.
550 01:01:03.060 ⇒ 01:01:03.460 Annie Yu: Okay.