Meeting Title: Zoom Meeting Date: 2025-02-14 Meeting participants: Luke Daque, Nicolas Sucari, Uttam Kumaran, Ryan Brosas, Payas Parab, Bo Yoon, Robert Tseng, Hannah Wang, Casie Aviles, Sahana Asokan
WEBVTT
1 00:00:24.500 ⇒ 00:00:25.439 Ryan Brosas: Hey, guys.
2 00:00:27.310 ⇒ 00:00:29.000 Nicolas Sucari: Hey, Ryan, how are you?
3 00:00:30.010 ⇒ 00:00:31.870 Ryan Brosas: Doing fine. How about you?
4 00:00:32.680 ⇒ 00:00:33.530 Nicolas Sucari: All good.
5 00:00:35.050 ⇒ 00:00:35.900 Ryan Brosas: Nice.
6 00:00:52.620 ⇒ 00:00:53.324 Payas Parab: What’s up? Guys?
7 00:00:55.760 ⇒ 00:00:56.590 Nicolas Sucari: Hey, guys.
8 00:00:58.480 ⇒ 00:01:00.970 Luke Daque: Hello! How’s everyone going.
9 00:01:03.880 ⇒ 00:01:04.730 Nicolas Sucari: All good.
10 00:01:13.140 ⇒ 00:01:14.769 Nicolas Sucari: It’s too early, right. Paas.
11 00:01:16.430 ⇒ 00:01:21.640 Payas Parab: TV. It’s only it’s actually 9, am. I’m just. I’m just really tired. Just a long week.
12 00:01:21.910 ⇒ 00:01:24.329 Nicolas Sucari: But you you were early today, right.
13 00:01:24.330 ⇒ 00:01:24.710 Payas Parab: Yeah, yeah.
14 00:01:24.961 ⇒ 00:01:25.969 Nicolas Sucari: Or something like that.
15 00:01:25.970 ⇒ 00:01:26.760 Payas Parab: Yeah, yeah.
16 00:01:26.760 ⇒ 00:01:27.310 Nicolas Sucari: Yeah.
17 00:01:28.910 ⇒ 00:01:29.520 Payas Parab: Yeah.
18 00:01:29.520 ⇒ 00:01:30.490 Nicolas Sucari: So difficult.
19 00:01:31.390 ⇒ 00:01:41.869 Payas Parab: We’re doing some icebreakers today. That’ll be fun. I like, I feel like, I only know the people that like are in our little Pod nico, like Nico Ryan. And like the rest, I’m just like, Oh, hey, guys! So it’ll be fun to.
20 00:01:42.280 ⇒ 00:01:44.160 Luke Daque: The data team basically.
21 00:01:44.630 ⇒ 00:01:45.880 Payas Parab: The data team.
22 00:01:46.620 ⇒ 00:01:49.439 Payas Parab: Ryan, Marianne Casey, how are you guys doing.
23 00:01:50.680 ⇒ 00:01:51.980 Ryan Brosas: Doing fine.
24 00:01:52.640 ⇒ 00:01:57.780 Payas Parab: Yeah, oh, nice. I like your headset, Ryan.
25 00:01:59.890 ⇒ 00:02:00.440 Payas Parab: It’s a cool one.
26 00:02:00.440 ⇒ 00:02:01.220 Luke Daque: What is that?
27 00:02:02.590 ⇒ 00:02:07.890 Luke Daque: Ryan’s been always updating the articles Channel? So.
28 00:02:07.890 ⇒ 00:02:20.760 Payas Parab: Oh, really, okay, not gonna lie. And there’s a lot in there that I muted it. There’s just a lot in there like I can’t keep up. But you guys are into it. There’s a lot you guys are like, always looking at papers and random libraries and stuff like that right.
29 00:02:21.190 ⇒ 00:02:29.179 Ryan Brosas: Yeah, it’s kind of we need just needed to react to it. It’s kind of in the marketing
30 00:02:30.040 ⇒ 00:02:30.774 Ryan Brosas: strategy.
31 00:02:31.970 ⇒ 00:02:34.470 Ryan Brosas: I need to be on the trends always.
32 00:02:42.660 ⇒ 00:02:43.760 Bo Yoon: Morning guys.
33 00:02:44.990 ⇒ 00:02:45.350 Payas Parab: Somebody.
34 00:02:46.160 ⇒ 00:02:47.460 Luke Daque: Good morning!
35 00:02:48.440 ⇒ 00:02:48.810 Nicolas Sucari: I go.
36 00:02:48.810 ⇒ 00:02:50.120 Luke Daque: Valentine’s day, everyone.
37 00:02:50.120 ⇒ 00:02:50.799 Payas Parab: Yeah. Happy. Val.
38 00:02:51.270 ⇒ 00:02:51.740 Bo Yoon: Right.
39 00:02:52.880 ⇒ 00:02:58.327 Payas Parab: If anyone needs to make any last minute runs to the flower shop. Get on that right after this call.
40 00:02:58.600 ⇒ 00:03:03.290 Bo Yoon: Oh, where which one are you going to? Are you? Are you going to Flower District by any chance.
41 00:03:03.290 ⇒ 00:03:13.940 Payas Parab: I I it’s okay. I realized, I realize on like Wednesday. And I actually in my another work channel, I put like Psa. Valentine’s day is this Friday, because I like did totally forgot until Wednesday, and it was a.
42 00:03:13.940 ⇒ 00:03:14.670 Bo Yoon: Oh, my God!
43 00:03:14.670 ⇒ 00:03:18.506 Payas Parab: Reservations on a Valentine’s day on a Friday are rough.
44 00:03:19.359 ⇒ 00:03:20.299 Bo Yoon: Oh, yeah.
45 00:03:23.800 ⇒ 00:03:27.169 Robert Tseng: Ice. Your hair every time like looks different.
46 00:03:27.170 ⇒ 00:03:30.589 Payas Parab: Dude. I’m just. I’m just changing it up, man, just trying to.
47 00:03:32.050 ⇒ 00:03:34.400 Robert Tseng: This one looks more like a like
48 00:03:34.630 ⇒ 00:03:39.540 Robert Tseng: I don’t know like like Goku, or something like an anime character.
49 00:03:40.900 ⇒ 00:03:43.850 Robert Tseng: It’s like you cannot believe that it’s real like, it’s just like.
50 00:03:43.850 ⇒ 00:03:44.960 Payas Parab: It’s like, it’s like.
51 00:03:45.520 ⇒ 00:03:46.200 Robert Tseng: Thank you.
52 00:03:46.200 ⇒ 00:03:48.240 Luke Daque: Saiyan super, Saiyan.
53 00:03:56.620 ⇒ 00:03:59.740 Payas Parab: How are you, Robert? Rocking the data hat, as always.
54 00:04:00.090 ⇒ 00:04:01.201 Robert Tseng: Oh, yeah, this is
55 00:04:01.480 ⇒ 00:04:02.185 Payas Parab: Data.
56 00:04:03.840 ⇒ 00:04:09.773 Robert Tseng: With one of those days where you just roll out of bed and then get started. And it’s just yeah.
57 00:04:11.754 ⇒ 00:04:19.555 Robert Tseng: Kinda I start off the week. Well, then, by Friday I’m usually like almost empty in the gas tank, and I just
58 00:04:20.560 ⇒ 00:04:22.882 Robert Tseng: I I try less and less.
59 00:04:30.810 ⇒ 00:04:37.404 Robert Tseng: Well, I mean the other. I guess we told my other guys will probably jump in a bit late, so we can kind of kick it off.
60 00:04:37.740 ⇒ 00:04:50.879 Robert Tseng: yeah. Sorry for the late outline, I think. We’re still getting used to this like Monday Friday meeting cadence. I think probably going forward. Utam will probably run the Monday ones. And then I’m gonna run the Friday ones.
61 00:04:51.590 ⇒ 00:05:10.799 Robert Tseng: and yeah, we’ll probably just give you more than 1 h heads up to update slides. Next time I will try to push a midweek update like on a Wednesday. So you guys can get in and get an early start. But yeah, I mean, you know, we’ll just start off with an icebreaker, as always, Hannah volunteers. So thank you, Hannah, for
62 00:05:11.350 ⇒ 00:05:18.250 Robert Tseng: or for that and actually, I guess I don’t know if you volunteered this time. I just asked you anything.
63 00:05:18.690 ⇒ 00:05:21.479 Hannah Wang: I know both times you volunteered me so.
64 00:05:21.480 ⇒ 00:05:22.050 Robert Tseng: Oh!
65 00:05:22.050 ⇒ 00:05:27.260 Hannah Wang: But now I know now I know for the future weeks on Fridays I’ll lead a icebreaker.
66 00:05:27.740 ⇒ 00:05:28.140 Robert Tseng: Okay.
67 00:05:29.650 ⇒ 00:05:31.059 Hannah Wang: Yeah, we can keep it
68 00:05:31.290 ⇒ 00:05:35.534 Hannah Wang: short on the shorter side. Since we’re already 5 min in
69 00:05:36.450 ⇒ 00:05:44.900 Hannah Wang: okay, maybe this week, we can. You guys have to just grab a random item from your desk and explain why it’s there.
70 00:05:51.090 ⇒ 00:05:52.499 Payas Parab: Anyone want to go first? st
71 00:05:52.500 ⇒ 00:05:52.819 Payas Parab: I can.
72 00:05:52.820 ⇒ 00:05:53.240 Hannah Wang: Okay.
73 00:05:53.240 ⇒ 00:05:57.383 Payas Parab: I like, I like these. These are really fun. I my random thing is
74 00:05:57.690 ⇒ 00:06:04.420 Payas Parab: I’ve got my my Chinese textbook here because I’ve been taking Chinese lessons on the weekend sorry it’s blurred.
75 00:06:04.450 ⇒ 00:06:26.089 Payas Parab: but I’ve wanted to learn Chinese. After working at Tiktok I joined a lot of meetings, and they gave me a translator. And I was like, I’m just gonna start learning Chinese. So this is my, I’m on intermediate level. So I’m like able to do some basic conversation able to do like some basic like, I finished my level one. I’m excited. And I’m actually going with the the Chinese school I’m part of is
76 00:06:26.090 ⇒ 00:06:37.389 Payas Parab: sponsoring me to go to Shanghai and Beijing to meet with, like their Chinese school counterparts that are learning English in China in November. So that’s the fun. Fun fact from my desk.
77 00:06:38.130 ⇒ 00:06:40.275 Luke Daque: That’s absolutely amazing.
78 00:06:41.830 ⇒ 00:06:45.240 Robert Tseng: Hannah, you should practice Chinese with highest. Hannah’s been learning Chinese, too.
79 00:06:45.240 ⇒ 00:06:45.830 Payas Parab: You’ve been learning.
80 00:06:45.830 ⇒ 00:07:01.739 Hannah Wang: I know. I know, I literally plateaued like, I can only conversate around food. So yeah, I I mean, I was like, super motivated to learn Chinese. Because one of my good friends like her, she had a she has a toddler, and he’s also learning
81 00:07:01.910 ⇒ 00:07:07.720 Hannah Wang: like mandarin, because they’re Chinese, and I was like oh, I felt motivated to like talk to him.
82 00:07:07.720 ⇒ 00:07:10.230 Payas Parab: Hey? We can get back into it. We can do it. Let’s do it. We can do it.
83 00:07:10.230 ⇒ 00:07:22.250 Hannah Wang: Oh, maybe. Yeah, it’s so hard to learn. But yeah, I mean, my husband also speaks Chinese, but he doesn’t practice with me. So I need another, another buddy.
84 00:07:22.510 ⇒ 00:07:25.320 Payas Parab: Woman yi chien.
85 00:07:25.560 ⇒ 00:07:28.389 Hannah Wang: Okay, it’s too early for that. My brain.
86 00:07:29.174 ⇒ 00:07:29.640 Robert Tseng: We.
87 00:07:29.640 ⇒ 00:07:32.820 Hannah Wang: Something, something that’s all I got.
88 00:07:34.200 ⇒ 00:07:35.350 Hannah Wang: Oh, man.
89 00:07:36.150 ⇒ 00:07:36.960 Robert Tseng: Cool.
90 00:07:39.790 ⇒ 00:07:40.370 Robert Tseng: So is that.
91 00:07:40.370 ⇒ 00:07:42.180 Hannah Wang: Anyone want to go next? Yeah.
92 00:07:43.580 ⇒ 00:07:48.130 Luke Daque: I guess I could go next, because it’s also related to Chinese, but
93 00:07:48.320 ⇒ 00:07:51.500 Luke Daque: not like learning the language. This one is a
94 00:07:51.670 ⇒ 00:07:57.380 Luke Daque: I don’t see, it’s a new pack of table tennis balls, cause I’m
95 00:07:57.600 ⇒ 00:08:04.730 Luke Daque: into 10 table tennis now. I’m like playing table tennis. So I just bought some balls.
96 00:08:06.400 ⇒ 00:08:07.670 Robert Tseng: You keep them at your desk.
97 00:08:08.370 ⇒ 00:08:11.810 Luke Daque: Well, they’re they’re they just arrived. So it’s still in my desktop.
98 00:08:11.810 ⇒ 00:08:13.420 Robert Tseng: Oh, it should have to be there!
99 00:08:13.650 ⇒ 00:08:15.599 Luke Daque: Just happened to be here. So yeah.
100 00:08:19.310 ⇒ 00:08:20.590 Nicolas Sucari: I can go. Okay.
101 00:08:20.590 ⇒ 00:08:21.640 Robert Tseng: Oh, go ahead!
102 00:08:22.340 ⇒ 00:08:25.649 Nicolas Sucari: I have. This is like a small
103 00:08:26.220 ⇒ 00:08:35.559 Nicolas Sucari: like decoration that I have. That is a replica from like a cup of a soccer. Liberta Lores, like the tournament that we play here in South America.
104 00:08:35.960 ⇒ 00:08:38.839 Nicolas Sucari: I really like it. I have it here as a decoration.
105 00:08:39.288 ⇒ 00:08:48.760 Nicolas Sucari: I don’t know. So it’s nice to have like small decorations here at the desk that you can look at, and it’s really good. It’s like an official replica that a friend of mine
106 00:08:48.870 ⇒ 00:08:51.449 Nicolas Sucari: gifted. And it’s really nice.
107 00:08:51.680 ⇒ 00:08:55.959 Nicolas Sucari: The the actual cap is really big. This one is small. So it’s really nice to have here.
108 00:08:57.260 ⇒ 00:08:58.270 Robert Tseng: Wow!
109 00:09:01.260 ⇒ 00:09:08.580 Robert Tseng: I’ll I can go. So this is gonna be, I have a bunch of earplugs
110 00:09:09.510 ⇒ 00:09:15.099 Robert Tseng: so very multi purpose very sensitive to sound. And so when my
111 00:09:15.400 ⇒ 00:09:19.130 Robert Tseng: the neighbor upstairs is like their dog is yapping, I’ll put them in.
112 00:09:19.290 ⇒ 00:09:38.919 Robert Tseng: or if there’s construction outside, because it’s the city. I’ll put them in, and then in meetings I’m always fidgeting with them, and I I do this thing where I rip them apart so they don’t last very long. It’s not good, it’s not sustainable. I know it’s not good for the environment, I I but it’s it’s a habit I like fidget with things, but I don’t like fidget spinners because they’re too like
113 00:09:39.270 ⇒ 00:09:43.890 Robert Tseng: I don’t like dropping them, but these are foam, so if I drop them, if they don’t make any sound. So
114 00:09:44.860 ⇒ 00:09:45.570 Robert Tseng: yeah.
115 00:09:46.200 ⇒ 00:09:50.799 Hannah Wang: Okay, mine’s related to that. I have a stress ball. So maybe you can go into these.
116 00:09:52.010 ⇒ 00:09:54.890 Robert Tseng: Yes, that’s probably what I need.
117 00:09:55.740 ⇒ 00:09:57.100 Hannah Wang: I mean, it’s not like that.
118 00:09:57.100 ⇒ 00:09:57.959 Robert Tseng: In your ear.
119 00:09:57.960 ⇒ 00:10:02.850 Hannah Wang: Why? But why would you want to put anything in your okay? Well.
120 00:10:03.340 ⇒ 00:10:05.300 Robert Tseng: Part, too, you know.
121 00:10:05.300 ⇒ 00:10:11.140 Hannah Wang: Oh, then you put the earplugs away when you’re done using it. So you don’t fidget with it and get this.
122 00:10:11.400 ⇒ 00:10:11.930 Hannah Wang: Okay.
123 00:10:11.930 ⇒ 00:10:13.320 Robert Tseng: That’s true. I could do that.
124 00:10:13.560 ⇒ 00:10:27.839 Hannah Wang: Yeah, I I used to have a bad habit of like, if I’m just not typing and just using my cursor or my mouse, I would like touch my face like I had a bad habit of doing that. So I tried. I don’t have the habit anymore. I don’t know why I have this still. But
125 00:10:28.030 ⇒ 00:10:35.350 Hannah Wang: this was for my left hand when I’m not typing to not touch my face. So that’s why this is here.
126 00:10:36.160 ⇒ 00:10:36.820 Robert Tseng: Nice.
127 00:10:43.400 ⇒ 00:10:45.850 Hannah Wang: Yeah, maybe one more. Or we can get started.
128 00:10:46.256 ⇒ 00:10:51.543 Robert Tseng: Kind of blocked off 10 to 15 min. So that’s why we’re good.
129 00:10:52.410 ⇒ 00:10:59.339 Bo Yoon: Okay, I’ll go next. It’s really random. But this, an apple watch stripe
130 00:10:59.530 ⇒ 00:11:00.940 Bo Yoon: got it from our friend.
131 00:11:01.450 ⇒ 00:11:03.066 Robert Tseng: Just this wrap.
132 00:11:05.440 ⇒ 00:11:06.370 Bo Yoon: Yeah.
133 00:11:11.500 ⇒ 00:11:14.230 Payas Parab: Do you have an apple watch, or do you just have the strap? That’s it.
134 00:11:14.230 ⇒ 00:11:15.730 Bo Yoon: Oh, yeah, I do. I do. I do.
135 00:11:19.460 ⇒ 00:11:22.249 Payas Parab: Like one day I’ll get the apple watch, and I’ll have.
136 00:11:23.370 ⇒ 00:11:24.739 Bo Yoon: That would be weird.
137 00:11:31.410 ⇒ 00:11:32.870 Robert Tseng: Maybe, like one or 2 more.
138 00:11:40.070 ⇒ 00:11:49.269 Sahana Asokan: I can go. I don’t really like keeping anything at my desk. I like it to be really like empty. So the only thing I have is like my base with my like
139 00:11:49.540 ⇒ 00:11:51.159 Sahana Asokan: pence. That’s it.
140 00:11:53.250 ⇒ 00:11:53.880 Robert Tseng: Wow!
141 00:11:56.280 ⇒ 00:11:58.990 Hannah Wang: That’s a really cool vase. I’ve never
142 00:11:59.120 ⇒ 00:12:01.740 Hannah Wang: seen a face like that. Yeah.
143 00:12:01.940 ⇒ 00:12:08.270 Sahana Asokan: Yeah, I have, like a, I really like interior design. So I have a bunch of little cool like knickknacks.
144 00:12:09.950 ⇒ 00:12:16.840 Hannah Wang: Yeah, cause last week you mentioned like you were moving. So you had magazine or the photo that you showed was like magazine
145 00:12:17.490 ⇒ 00:12:20.540 Hannah Wang: table, coffee books, or it was something like that.
146 00:12:20.540 ⇒ 00:12:25.039 Sahana Asokan: There is a coffee table book, so my coffee table is coming in next week.
147 00:12:25.555 ⇒ 00:12:33.460 Sahana Asokan: But for some reason the books took priority. But yeah, it’ll, it’ll look good. Once the table comes.
148 00:12:33.460 ⇒ 00:12:34.730 Hannah Wang: Yeah. Yes.
149 00:12:38.330 ⇒ 00:12:42.799 Robert Tseng: Cool any last takers, otherwise we can jump into it.
150 00:12:43.717 ⇒ 00:12:45.552 Ryan Brosas: I think I can share
151 00:12:46.720 ⇒ 00:12:59.659 Ryan Brosas: Here is my thing. A pen, I guess. Oh, yeah, I like to run right sometimes. So I’d like it to be on my desk. And yeah, I think that’s.
152 00:13:01.780 ⇒ 00:13:05.700 Robert Tseng: Wow, but you keep it in a case. So it’s like a really important pen to you.
153 00:13:06.210 ⇒ 00:13:09.270 Ryan Brosas: Yeah, and it’s expensive. Also.
154 00:13:09.970 ⇒ 00:13:12.190 Robert Tseng: Okay. Okay, cool.
155 00:13:15.836 ⇒ 00:13:17.859 Robert Tseng: Right? Analogy board. Yeah.
156 00:13:17.860 ⇒ 00:13:26.519 Payas Parab: That my roommate used to call used to call ipad. He used to call my, or used to analog ipad. So now there’s an analog keyboard.
157 00:13:27.430 ⇒ 00:13:28.340 Robert Tseng: Nice.
158 00:13:29.040 ⇒ 00:13:35.320 Robert Tseng: Alright. Well, let’s jump. Let’s jump into it. I’ll share my screen this time. So
159 00:13:35.480 ⇒ 00:13:49.740 Robert Tseng: yeah, for those of you that weren’t here on Monday. So we’re kind of having trying to get into this meeting cadence now, where on Mondays we kind of just like talk through like kind of some planning for the week kind of tied to the okrs that we kind of presented on
160 00:13:50.155 ⇒ 00:14:02.729 Robert Tseng: and then on Friday is more like a retro like, how did we do? And like any updates kind of against those initiatives that we kind of talk through. And then, we have a segment, where I will kind of talk about
161 00:14:03.410 ⇒ 00:14:24.080 Robert Tseng: from like our perspective, what we’ve been working on for for the team. And then we’ll we’ll end with like a demo. And so this time I know it was a bit late notice, but we were thinking that the AI team could give a demo, so I don’t think we we saw was very specific about what to demo, so I guess
162 00:14:24.440 ⇒ 00:14:33.249 Robert Tseng: since Casey, you’re the representative here. If you have a you could be thinking about it all you would like to show by the time we get to the end of this meeting.
163 00:14:34.410 ⇒ 00:14:35.230 Casie Aviles: Okay. Sure.
164 00:14:35.860 ⇒ 00:14:52.189 Robert Tseng: Yeah, thanks. So by the yeah, I guess China, we’ve we kind of keep going over this these mission values. And maybe this time we’ll kind of skip the the shout out just because I want. I think we have a lot of content to go through, and we’ll be a bit more organized next week.
165 00:14:52.724 ⇒ 00:14:56.360 Robert Tseng: But yeah, as far as, like our main objectives.
166 00:14:57.180 ⇒ 00:15:21.740 Robert Tseng: We have a revenue target. We have some object. We have some. We have an objective to kind of drive our service delivery and then I guess it’s just like performance measurement for our brand and content team hopefully tied to revenue. So I would say that we’re. I mean, this is not very. It’s not much detail here, but I think overall. We’ve made progress towards all 3.
167 00:15:22.286 ⇒ 00:15:23.179 Robert Tseng: And so
168 00:15:23.390 ⇒ 00:15:39.219 Robert Tseng: I think a few folks are on the call. We’ll each speak to kind of the objective that they’re kind of working towards and regarding revenue, I’ll I’ll kind of speak, for on the revenue side these are kind of the initiatives and updates that I shared at the beginning of the week.
169 00:15:39.647 ⇒ 00:15:46.629 Robert Tseng: And then I guess I’ll just kind of rehash or and add on to what progress has been made this week.
170 00:15:47.280 ⇒ 00:16:13.739 Robert Tseng: So I think just to recap one of our clients kind of we we were able to bump them up to a bigger contracts. And I think there’s there’s still more room to grow, and I’m hoping that in the next week we’ll be able to push to a higher target. That’s what. Edit. Same with Javi. I think now that we are a lot more integrated with this team and the the works been picking up, I do think that there is room for that conversation to kind of bump this contract size up as well.
171 00:16:14.173 ⇒ 00:16:29.200 Robert Tseng: With one of our clients that we just wrapped up this week. Our helper. We’re kind of pending them to renew hopefully, like starting next week, but I will be following up with them. We sent out a new proposal for a new client that’s going out
172 00:16:29.200 ⇒ 00:16:48.250 Robert Tseng: today and then on the data side, and then also on the AI side. We also send a proposal out this week. So yeah, overall, it’s been a great week. We we have 2 verbal agreements to start new clients. So yeah, I think. This is this, the the deals, the deals keep coming.
173 00:16:48.950 ⇒ 00:17:05.520 Robert Tseng: And then in the pipeline side one of my targets was to get build up our pipeline back to like 20 active leads. I was not able to successfully do that this week. I’ve only got us up to 12. So that means I’m having 12 active conversations with leads.
174 00:17:06.222 ⇒ 00:17:17.509 Robert Tseng: Yeah, where I’ve already hopped on a call with them where we’ve had some exchange, talking about like the service offering that we have, and we’re working towards, like building a proposal for them.
175 00:17:18.769 ⇒ 00:17:31.559 Robert Tseng: And then we’ve already launched a few more outbound campaigns. So I’ve been monitoring the progress there. We’ll probably make a couple of adjustments and turn off to these campaigns based on what results I’ve been seeing this week.
176 00:17:32.035 ⇒ 00:17:37.730 Robert Tseng: But yeah, that’s just a ongoing effort for us to keep reaching out to new customers.
177 00:17:38.150 ⇒ 00:18:07.240 Robert Tseng: And then, Pius, I know you’re highlighted here. I’m sorry, Bro. We didn’t really connect on on our partnership stuff. But Pius did invite the team to a real demo yesterday, which I heard was really awesome. And yeah, I just think, thank you for the push. And just to keep giving, like letting us kind of build relationships with our vendor partners. And also, you know, and I are continuing to explore new partnerships with other agencies that we can kind of work with as well. So
178 00:18:08.240 ⇒ 00:18:28.519 Robert Tseng: the last piece here is, yeah on the legal services side had a really great conversation with prospective clients, their personal injury law firm, based here in New York decent volume, like 200 clients a month, and they’re really interested in the AI capabilities we have. So working on a proposal or a demo to put in front of them in the next week.
179 00:18:28.520 ⇒ 00:18:50.130 Robert Tseng: and then we’ll probably talk to A, the the head of the firm in a couple of weeks to see if we can can work work something out there so excited that this bet that we’re making into this into this industry. Is kind of leading to some productive conversations, and we’ll hope that we can get the deal here.
180 00:18:51.613 ⇒ 00:19:02.786 Robert Tseng: I think, since Utam is not here yet. Oh, he is here. Okay, maybe the next part is we’ll just kinda give a quick, exact update.
181 00:19:04.100 ⇒ 00:19:05.470 Robert Tseng: I can kind of just
182 00:19:05.640 ⇒ 00:19:19.789 Robert Tseng: continue on. And then every time we can jump in. But basically, with our accelerator pixel. This week we were working on like a 5 slide like capabilities deck. This is more than 5 slides. But yeah, you can just click through and look at it.
183 00:19:20.226 ⇒ 00:19:45.210 Robert Tseng: Really, the credit credit to the design team for putting this together. This ugly slide is just a work in progress. But just a way, just kind of just broadcasting. We’re trying to find ways to better showcase the case studies that we have. And so I think this is definitely one part of our deck that maybe is a bit that we that we need to. We have more work on. I know there’s other designs out there
184 00:19:45.210 ⇒ 00:19:58.399 Robert Tseng: case studies that we put together. But I think, there was this format of just kind of really focusing on the outcomes that we’ve achieved for clients. That, I think we could really learn from and adapting it to our our deck.
185 00:19:58.550 ⇒ 00:20:18.669 Robert Tseng: and then also just kind of like a implementation kind of like. So people know, like, what does it look like to work with us? What are the different stages? So that’s kind of one, just another way that we’re learning to tell the story of Brainforge in a more concise way. And hopefully, we get to a place where we can start
186 00:20:18.920 ⇒ 00:20:28.530 Robert Tseng: shooting off this deck to to leads before we even jump on a call with them. So I think that’s kind of the goal of the this exercise. For for us.
187 00:20:30.820 ⇒ 00:20:31.570 Robert Tseng: Okay,
188 00:20:33.380 ⇒ 00:20:46.251 Robert Tseng: maybe I’ll just pause there any kind of questions on, like any other like general company like updates, or like thoughts on, on, like what we shared there. I know it wasn’t really that. Nothing, nothing groundbreaking but
189 00:20:46.590 ⇒ 00:20:50.800 Uttam Kumaran: I think maybe one thing I’ll add, so we are.
190 00:20:50.900 ⇒ 00:20:53.140 Uttam Kumaran: We’re starting to build more
191 00:20:53.680 ⇒ 00:21:01.779 Uttam Kumaran: like resiliency and redundancy on the data side. I think. We’re now 2 folks on the Ae. Team.
192 00:21:02.469 ⇒ 00:21:22.920 Uttam Kumaran: Aisha and Luke, and we’re also now have Pia Sahana and Bo on the analyst side which has been really great. We have plans to bring on 2 more people on the data side, one more on the Ae side and most likely one more on the analyst side. Again. I think the goal now is moving from
193 00:21:23.320 ⇒ 00:21:24.240 Uttam Kumaran: like.
194 00:21:24.530 ⇒ 00:21:32.829 Uttam Kumaran: just see what happens every week to guaranteeing that work can get done. And there is redundancy right like beyond, just
195 00:21:33.030 ⇒ 00:21:45.229 Uttam Kumaran: like stuff falling through the caps like people will go out of office. People are gonna shift around on clients. So we wanna make sure that we have enough support on across all clients. And we finally have a little bit of
196 00:21:45.380 ⇒ 00:22:11.790 Uttam Kumaran: financial and like brain bandwidth to go, you know, execute on those. So that’ll be really great. I’m super excited to have everybody on the data team like this is awesome. We’re gonna that’ll sort of allow me. I’m in particular to sort of look back at all of the work that’s going through to all clients and sort of poke in on the things, and what we can do as on a platform side, to improve the way we ship and everything from dashboards to SQL. Code.
197 00:22:12.148 ⇒ 00:22:24.469 Uttam Kumaran: And you know, helping on the sort of larger picture items that don’t really fall into one client, but can definitely help everything. So I’ll kind of shift to do do a lot more stuff there and super excited
198 00:22:24.840 ⇒ 00:22:35.590 Uttam Kumaran: for that. So I think hopefully, on the data side, I mean it’s been huge, having a wish as well. And Bo, I know you’re ramping up as well. The pool part stuff went really really well this week, so super excited.
199 00:22:38.620 ⇒ 00:22:54.630 Robert Tseng: Cool. Yeah, I mean, pretty a lot of overlap with this kind of next. Okay, are the or the objective of delightful service delivery. So I think we’ve gone talked about a lot of the updates here already. I guess we did. We want to say anything else about the others, or is that kind of the
200 00:22:55.190 ⇒ 00:22:55.720 Robert Tseng: yeah?
201 00:22:55.720 ⇒ 00:22:59.710 Uttam Kumaran: I think this, I think. This is it? Yeah, I mean, I think we’ll
202 00:22:59.830 ⇒ 00:23:02.819 Uttam Kumaran: on the 1st item, we will talk about
203 00:23:03.240 ⇒ 00:23:06.590 Uttam Kumaran: how the AI team is helping to solve this
204 00:23:07.001 ⇒ 00:23:12.738 Uttam Kumaran: and the demos also the 3rd thing. So I guess I won’t take too much of their thunder.
205 00:23:13.310 ⇒ 00:23:16.689 Uttam Kumaran: And then, yeah, I think we’re working on the second item as well. So yep.
206 00:23:17.840 ⇒ 00:23:43.930 Robert Tseng: Yeah, I think if I’ll just jump in here and say, I have been actively interviewing folks. So yeah, I think I I talked to a couple of really good candidates today. On yeah, just on on the on the engineering side. And I think we talked to additional pm, as well. So yeah, I think we’re we’re gonna keep adding to this team and probably expect to see new faces kind of in the next week or 2.
207 00:23:44.340 ⇒ 00:23:45.120 Robert Tseng: yeah.
208 00:23:45.910 ⇒ 00:23:57.549 Robert Tseng: Alright, let’s jump to the lot. This this last objective here. Of yeah. Just the driving attribute revenue for branded content. Maybe I guess, is.
209 00:23:58.230 ⇒ 00:24:02.129 Uttam Kumaran: Yeah, if you go to the next slide, Robert, I think the updates are there.
210 00:24:02.130 ⇒ 00:24:02.640 Robert Tseng: There you go!
211 00:24:03.460 ⇒ 00:24:26.799 Hannah Wang: Yeah. So just to give a quick update on this. The pricing page is what we tried to focus on pushing out this week. It’s not out yet. But Heleem has implemented something on the staging website. So I think now that we have copy I’ll update the design with it, and it should be good to go out today, if possible, if not Monday. Next week.
212 00:24:27.253 ⇒ 00:24:55.130 Hannah Wang: And then for, like the one pagers and other assets. As you can tell from the previous slides where the design team is currently working on designing with consistent branding across all the assets. So I think once the capabilities deck is done, that’s kind of like the main source of all the other assets that we’ll have, and those can trickle into other one pagers and things that are still a work in progress, and or assets that we want to create in the future.
213 00:24:55.220 ⇒ 00:25:12.460 Hannah Wang: and then for the content side. I still haven’t like ramped up to that yet. I need to start taking lead on that but so I have no updates for those. So that is kind of related to like the newsletter and the lead generation. I talked to Ryan
214 00:25:12.460 ⇒ 00:25:28.609 Hannah Wang: and he said that right now the Newsletter has been halted because everyone’s just busy. There’s no time. But hopefully I can come into the picture and kind of get that initiative started as well as the lead generation initiative. So those are kind of the dates from me.
215 00:25:30.890 ⇒ 00:25:31.255 Robert Tseng: Cool.
216 00:25:33.660 ⇒ 00:25:40.485 Robert Tseng: yeah, I guess with the remaining time, did we, Casey? You feel like you’re ready to, I guess. Jump into the demo, I guess.
217 00:25:43.270 ⇒ 00:25:43.865 Robert Tseng: yeah.
218 00:25:45.660 ⇒ 00:25:50.450 Casie Aviles: So. Yeah, one of the things that I wanted to them is for the
219 00:25:50.770 ⇒ 00:25:55.160 Casie Aviles: so one of the initiatives that we have is for the
220 00:25:55.975 ⇒ 00:26:00.239 Casie Aviles: Junior. Pm, right? So we’re so we created this ticket here. Agent.
221 00:26:01.390 ⇒ 00:26:03.280 Casie Aviles: Yeah, let me share my screen.
222 00:26:06.850 ⇒ 00:26:13.539 Casie Aviles: Yep, so yeah. So here’s an example, like interaction that we have. So
223 00:26:14.527 ⇒ 00:26:17.380 Casie Aviles: we had Nico try out our ticket here agent.
224 00:26:17.790 ⇒ 00:26:24.109 Casie Aviles: And yeah, so basically, what would happen is you would give it up this input prompt like.
225 00:26:24.360 ⇒ 00:26:29.979 Casie Aviles: can you create a task for yeah, for this? And then the agent would
226 00:26:30.681 ⇒ 00:26:37.670 Casie Aviles: list it down like this and then ask for additional details if needed, and then
227 00:26:37.810 ⇒ 00:26:41.040 Casie Aviles: it would be created, and it would send a link to notion.
228 00:26:41.790 ⇒ 00:26:42.640 Casie Aviles: So
229 00:26:43.581 ⇒ 00:26:51.410 Casie Aviles: yeah, the created tickets would look like this. And although there, there are still a lot of like feedback. And Pico has been giving great feedback
230 00:26:51.550 ⇒ 00:26:56.779 Casie Aviles: on the agent. So yeah, like.
231 00:26:57.220 ⇒ 00:27:01.979 Casie Aviles: we just want to keep iterating and improving, based on the feedback that we get.
232 00:27:03.324 ⇒ 00:27:05.749 Casie Aviles: To show you how it works.
233 00:27:07.540 ⇒ 00:27:12.859 Casie Aviles: So another way is, we also thought of, you know, connecting this to the
234 00:27:13.070 ⇒ 00:27:19.289 Casie Aviles: Zoom summarizer agents that we have so we could ideally call it target this way. And then we send the
235 00:27:20.020 ⇒ 00:27:30.130 Casie Aviles: Transcript, or like this summary that we have, and then it would send a reply like this, so.
236 00:27:31.360 ⇒ 00:27:33.270 Uttam Kumaran: See, there’s oh, yeah. Okay. Sorry.
237 00:27:44.140 ⇒ 00:27:46.580 Casie Aviles: So, yeah, this is how it would work.
238 00:27:47.200 ⇒ 00:27:48.430 Casie Aviles: Yeah, so
239 00:27:48.910 ⇒ 00:27:57.340 Casie Aviles: basically, we just want to kind of, you know, Streamline, the process of creating these tickets and automate them and save some time. So
240 00:27:59.690 ⇒ 00:28:05.639 Casie Aviles: yeah, so yeah, basically, that’s how it works.
241 00:28:07.940 ⇒ 00:28:12.310 Uttam Kumaran: So everybody. I would encourage you if you’re creating tickets in notion
242 00:28:12.410 ⇒ 00:28:16.700 Uttam Kumaran: to give this a shot and give the team feedback if it doesn’t work
243 00:28:17.300 ⇒ 00:28:29.230 Uttam Kumaran: ideally. Our barometer is like, I think, ticket creation and notion is like really horrible and so we’re gonna hopefully be able to migrate most ticket creation to using this agent.
244 00:28:29.581 ⇒ 00:28:36.769 Uttam Kumaran: The way we’ll enforce quality is that the agent will sort of ask you if it needs more information like, if you don’t say who it’s assigned to, it’ll say like.
245 00:28:37.170 ⇒ 00:28:49.939 Uttam Kumaran: who who do you want to assign it to, or like? You need to give us a deadline and stuff like that much better process than sort of going into notion and creating this additionally, like we may long term not use notion for edge tickets.
246 00:28:50.747 ⇒ 00:28:55.640 Uttam Kumaran: But like, it doesn’t matter, because the the AI agent will just plug in to whatever structured
247 00:28:56.180 ⇒ 00:28:59.340 Uttam Kumaran: post requests we need to, anyway. So yeah.
248 00:28:59.650 ⇒ 00:29:02.304 Uttam Kumaran: so if you’re making tickets. Try to use this, please.
249 00:29:02.930 ⇒ 00:29:05.389 Nicolas Sucari: Yeah. I mean, he’s holy.
250 00:29:05.390 ⇒ 00:29:08.558 Robert Tseng: A lot of tickets. I’m gonna use it.
251 00:29:10.370 ⇒ 00:29:10.930 Nicolas Sucari: You see that
252 00:29:12.470 ⇒ 00:29:28.190 Nicolas Sucari: it’s really good. You just need to. We’re, we’re just yeah giving you some feedback to the guys. But it’s working really fine, like the ticket gets created. We just need to review some of the properties and see how like we can improve that automatically from slack so that we don’t need to go manually there. But it’s working really good. Yeah.
253 00:29:32.330 ⇒ 00:29:33.080 Robert Tseng: Cool.
254 00:29:34.330 ⇒ 00:29:47.630 Robert Tseng: Well, I mean, that’s pretty much all we had in terms of content for this meeting. Yeah, I would say like things to adjustments that we’re we’re still like, kind of working through. Yeah, hopefully, we’ll give the teams a bit more time to kind of
255 00:29:47.830 ⇒ 00:29:51.127 Robert Tseng: add, add content to their decks next week.
256 00:29:51.720 ⇒ 00:29:56.589 Robert Tseng: but yeah, I think. If there’s and then maybe Utam you and I like.
257 00:29:56.900 ⇒ 00:30:03.110 Robert Tseng: yeah, it’s great that we keep sharing things from what we’re working on in Pixel, but also, I think, be good to give the the team like
258 00:30:03.480 ⇒ 00:30:24.310 Robert Tseng: a look like a I don’t know like a month into the future. You know, I feel like, yeah, we we could. We could probably start adding that to our update as well. Obviously, things are changing, and we’re always making pivots in the middle of the week, but just at least giving the team some like clarity on like what’s coming for the next month at least, I feel like we could do.
259 00:30:24.790 ⇒ 00:30:26.540 Uttam Kumaran: Yeah, I totally agree. I think.
260 00:30:26.913 ⇒ 00:30:53.439 Uttam Kumaran: we haven’t done it because I really haven’t been able to think like that far in some time. And I think finally, we are getting some breathing room to be able to do that. And we want to show you guys that there is like a long list of stuff that we’re gonna work on, and sort of what we’re thinking about for the rest of the year. The Okrs were our 1st stab at doing that for this quarter. But again, it helps understand, like our velocity and what we can achieve throughout the year.
261 00:30:54.027 ⇒ 00:30:56.529 Uttam Kumaran: So yeah, I think we should put something together for sure.
262 00:30:57.000 ⇒ 00:31:03.689 Uttam Kumaran: Oh, yeah, I guess I I asked Casey to do one more demo. We worked on some.
263 00:31:03.880 ⇒ 00:31:09.620 Uttam Kumaran: We worked on something around slack message analytics with clients.
264 00:31:09.860 ⇒ 00:31:13.719 Uttam Kumaran: And it’s nice because the AI team is like in Snowflake.
265 00:31:13.950 ⇒ 00:31:20.979 Uttam Kumaran: And I’m like, welcome to the other part of the business, and it’s been cool to see that, like
266 00:31:21.310 ⇒ 00:31:27.769 Uttam Kumaran: I don’t know. This is sort of like a random idea I had. That’s like, and it’s it’s like, totally possible. And and
267 00:31:28.150 ⇒ 00:31:33.239 Uttam Kumaran: should be really, really cool to start. Yeah. So Casey, go ahead.
268 00:31:35.532 ⇒ 00:31:38.347 Casie Aviles: Yeah. So yeah, basically, Utam asked me to like
269 00:31:39.210 ⇒ 00:31:47.019 Casie Aviles: So when I mean, it’s also part of the initiatives where we want to as part of the delightful service delivery, where we want to measure the quantity and
270 00:31:47.280 ⇒ 00:31:51.849 Casie Aviles: quality of the messages that we send to the client. So
271 00:31:52.683 ⇒ 00:31:56.136 Casie Aviles: what we did for that is, we basically created
272 00:31:56.710 ⇒ 00:32:00.259 Casie Aviles: a brain forge bot. So so it
273 00:32:00.540 ⇒ 00:32:09.569 Casie Aviles: so ideally. What what Tom would do is, or, yeah, he will add it to like the client channels, and then this bot would be able to pull in like
274 00:32:10.980 ⇒ 00:32:16.360 Casie Aviles: messages from the team, and for that I just
275 00:32:16.550 ⇒ 00:32:20.189 Casie Aviles: connected it with some, you know, some scripts and python scripts.
276 00:32:21.650 ⇒ 00:32:23.890 Casie Aviles: What that does is it also
277 00:32:24.130 ⇒ 00:32:28.510 Casie Aviles: throws those messages to a table here on Snowflake. So
278 00:32:29.230 ⇒ 00:32:31.739 Casie Aviles: yeah, for example, this is how it would look like.
279 00:32:34.600 ⇒ 00:32:41.870 Casie Aviles: yeah, so and then after that, if you know, if there are no messages so we started with quantity first, st
280 00:32:42.560 ⇒ 00:32:44.670 Casie Aviles: and then there are no message of
281 00:32:45.120 ⇒ 00:32:52.449 Casie Aviles: or like. If there are messages there would be this alert sent. Let me show so something like this. So this was for
282 00:32:52.910 ⇒ 00:32:53.750 Casie Aviles: yesterday.
283 00:32:55.630 ⇒ 00:33:02.609 Casie Aviles: yeah. So it it takes a look at the rows from Snowflake. And it would send an alert. And yeah.
284 00:33:02.800 ⇒ 00:33:06.097 Casie Aviles: basically, that’s how it works at the moment.
285 00:33:07.040 ⇒ 00:33:12.830 Casie Aviles: yeah. And we also want to add this to mark clients for more clients. And
286 00:33:12.970 ⇒ 00:33:15.250 Casie Aviles: the next thing is also also, you know.
287 00:33:15.360 ⇒ 00:33:22.079 Casie Aviles: come up with like the quality. How do we like? What are? What’s our, and how do we determine
288 00:33:22.230 ⇒ 00:33:28.379 Casie Aviles: what is good quality message to the sent to the client. So we also want to work with the data team with that. So
289 00:33:28.720 ⇒ 00:33:30.920 Casie Aviles: yeah, that’s I guess that’s pretty much it.
290 00:33:32.830 ⇒ 00:33:34.589 Luke Daque: It’s absolutely amazing.
291 00:33:35.820 ⇒ 00:33:39.229 Uttam Kumaran: Yeah, it’s cool. And it’s like both both sides. I feel like it’s really cool.
292 00:33:39.630 ⇒ 00:33:50.090 Luke Daque: Like, yeah, it’s like difficult to track the messages in slack like you need to go to threads, especially those that are in the threads. So if you have this in Snowflake, and maybe even have
293 00:33:50.700 ⇒ 00:33:55.474 Luke Daque: a dashboard for this or something, then we can just go into that dashboard, see all the messages
294 00:33:56.160 ⇒ 00:33:59.159 Luke Daque: related to the client. So yeah, this is pretty cool.
295 00:34:02.070 ⇒ 00:34:18.889 Uttam Kumaran: Yeah, I think there’s a ton of use case, I mean, not only like, Hey, we’re we didn’t get back to a message, I mean at minimum, where our goal is to just make sure we send something every day. But looking at all the messages we did send, do we miss something that we should have got to. And then the last piece is the summary like, if we have all the messages, basically.
296 00:34:19.040 ⇒ 00:34:24.559 Uttam Kumaran: and we’ll start also handling email, we basically have all points of communication with client
297 00:34:24.770 ⇒ 00:34:28.500 Uttam Kumaran: digitally, we should be able to assist with actually putting together the
298 00:34:28.947 ⇒ 00:34:33.660 Uttam Kumaran: and we have notion we should be able to assist with putting together the weekly summaries a lot easier.
299 00:34:34.346 ⇒ 00:34:40.643 Uttam Kumaran: And yeah, I mean again, ideally, we just sort of prove that like we’re communicating with clients. And that’s growing over time.
300 00:34:41.210 ⇒ 00:34:50.230 Uttam Kumaran: This is sort of how far I thought about this idea, like, I think it could go multiple different ways. So if anyone has ideas as we’re sending this to the team
301 00:34:50.350 ⇒ 00:34:51.587 Uttam Kumaran: feel free like,
302 00:34:52.150 ⇒ 00:34:56.999 Uttam Kumaran: yeah, I feel like, I haven’t really thought too far ahead on like what we could do with this data.
303 00:34:57.580 ⇒ 00:35:02.270 Uttam Kumaran: But even the brain Forge Bot now could start to handle things.
304 00:35:03.750 ⇒ 00:35:07.430 Uttam Kumaran: I don’t know. I think there’s a bunch of different ways we can go. So it’s pretty cool.
305 00:35:17.930 ⇒ 00:35:21.167 Robert Tseng: Oh, well, I mean, I guess. Yeah.
306 00:35:22.570 ⇒ 00:35:32.040 Robert Tseng: I feel like we don’t. We don’t have to stay on till the end. But if yeah, we can. But yeah, I think that’s all we had for today.
307 00:35:32.590 ⇒ 00:35:50.070 Robert Tseng: Yeah, good work. Good work overall this week. I feel like we’re much more on top of things and like not playing catch up. So I do feel like the momentum has definitely shifted. Hopefully. I mean, people are smiling on this meeting and stuff so looks like. We’re not as we’re not as burned out.
308 00:35:50.070 ⇒ 00:35:53.379 Uttam Kumaran: I smile, and I smile you on every meeting. I feel like
309 00:35:54.170 ⇒ 00:35:55.790 Uttam Kumaran: I’m just happy to be here.
310 00:35:56.060 ⇒ 00:35:59.114 Uttam Kumaran: I’m just happy to get the invite to the meeting.
311 00:36:00.640 ⇒ 00:36:06.331 Robert Tseng: Yeah. And it was. It was sweet to see a Nico, a new time meeting in person. So hopefully, we get to do more of this.
312 00:36:07.260 ⇒ 00:36:07.970 Robert Tseng: wait.
313 00:36:08.330 ⇒ 00:36:16.118 Uttam Kumaran: I know I’ve been. I literally I’ve been on a plane, for like the last I don’t know, and I saw I feel like I saw Nico like an hour ago.
314 00:36:17.300 ⇒ 00:36:22.090 Uttam Kumaran: I literally landed and like hopped on the client. Call for ABC. And then I’m here.
315 00:36:22.380 ⇒ 00:36:25.529 Uttam Kumaran: It’s a i feel like I’ve just time traveled. But yeah.
316 00:36:26.600 ⇒ 00:36:31.279 Uttam Kumaran: And then, yeah, I got it pie. So let me know when the la thing is I I wanna I wanna attend.
317 00:36:31.280 ⇒ 00:36:34.109 Payas Parab: Will you? Will you actually come? We we like legit like kick.
318 00:36:34.110 ⇒ 00:36:37.980 Uttam Kumaran: Yeah, it can’t be this week. It can’t be this. It can’t be like this next week, though.
319 00:36:37.980 ⇒ 00:36:44.619 Payas Parab: Next 2 weeks or so. So I think in March we’re actually planning. But like Robert, and who, Tom, you guys actually want to roll through. You can craft.
320 00:36:44.620 ⇒ 00:36:46.449 Uttam Kumaran: Yes, I would love to try.
321 00:36:46.450 ⇒ 00:36:47.300 Payas Parab: Right, sure.
322 00:36:47.300 ⇒ 00:36:48.000 Uttam Kumaran: Yeah, please.
323 00:36:48.620 ⇒ 00:36:49.620 Robert Tseng: Yeah, okay, yeah.
324 00:36:49.620 ⇒ 00:36:50.300 Payas Parab: If this.
325 00:36:50.490 ⇒ 00:36:57.509 Robert Tseng: When it was I was coming to La once a quarter. And yeah, I mean, I would like to. I would like to do that if you like.
326 00:36:57.510 ⇒ 00:37:00.050 Uttam Kumaran: I can sleep on your kitchen floor. Yeah.
327 00:37:00.270 ⇒ 00:37:05.240 Payas Parab: Open invite to anyone. Seriously. I mean, it’s a it’s like, yeah, we we can do in la
328 00:37:05.350 ⇒ 00:37:07.140 Payas Parab: la team, meet up.
329 00:37:07.680 ⇒ 00:37:15.139 Uttam Kumaran: Like if you give me like, if you give me like a month in advance, but not like 3 months in advance. That’s a sweet spot, because I don’t know what.
330 00:37:15.140 ⇒ 00:37:16.300 Payas Parab: Anyway, between Bowen.
331 00:37:16.300 ⇒ 00:37:19.760 Uttam Kumaran: I don’t know what’s like. 5. I don’t know what’s 3 months later
332 00:37:20.230 ⇒ 00:37:24.699 Uttam Kumaran: I do know what’s like next 2 weeks, so you can. There’s there’s the good good slot right there.
333 00:37:25.085 ⇒ 00:37:25.470 Payas Parab: Right.
334 00:37:25.690 ⇒ 00:37:32.529 Uttam Kumaran: And I need to go. I need to go home also and and see my my parents, so I would just go to the Bay after. So that’d be really convenient.
335 00:37:32.810 ⇒ 00:37:33.400 Payas Parab: Cool.
336 00:37:34.770 ⇒ 00:37:39.430 Uttam Kumaran: Cool, awesome guys. Okay.
337 00:37:39.640 ⇒ 00:37:44.790 Uttam Kumaran: cool. Well, we’ll talk again on Monday. I know I have some meetings with some more people later today. So if not, talk on slack.
338 00:37:45.620 ⇒ 00:37:46.390 Robert Tseng: All right.
339 00:37:46.510 ⇒ 00:37:48.290 Robert Tseng: Thanks. Everyone have a good weekend.
340 00:37:49.200 ⇒ 00:37:49.950 Nicolas Sucari: Bye-bye.
341 00:37:49.950 ⇒ 00:37:50.870 Hannah Wang: Bye, thanks.